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Moriakov N, Peters J, Mann R, Karssemeijer N, van Dijck J, Broeders M, Teuwen J. Improving lesion volume measurements on digital mammograms. Med Image Anal 2024; 97:103269. [PMID: 39024973 DOI: 10.1016/j.media.2024.103269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 06/23/2024] [Accepted: 07/08/2024] [Indexed: 07/20/2024]
Abstract
Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 - 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 - 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 - 0.87] for consistency and 0.78 [95%CI 0.66 - 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. The algorithm may play a role in lesion characterization and breast cancer prognostication on mammograms.
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Affiliation(s)
- Nikita Moriakov
- Department of Radiation Oncology, Netherlands Cancer Institute, The Netherlands; Department of Medical Imaging, Radboud University Medical Center, The Netherlands; Institute for Informatics, University of Amsterdam, The Netherlands.
| | - Jim Peters
- Department for Health Evidence, Radboud University Medical Center, The Netherlands
| | - Ritse Mann
- Department of Medical Imaging, Radboud University Medical Center, The Netherlands
| | - Nico Karssemeijer
- Department of Medical Imaging, Radboud University Medical Center, The Netherlands
| | - Jos van Dijck
- Department for Health Evidence, Radboud University Medical Center, The Netherlands
| | - Mireille Broeders
- Department for Health Evidence, Radboud University Medical Center, The Netherlands
| | - Jonas Teuwen
- Department of Radiation Oncology, Netherlands Cancer Institute, The Netherlands; Department of Medical Imaging, Radboud University Medical Center, The Netherlands
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Sun J, Liu Y, Xi Y, Coatrieux G, Coatrieux JL, Ji X, Jiang L, Chen Y. Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke. Med Image Anal 2024; 97:103250. [PMID: 39096842 DOI: 10.1016/j.media.2024.103250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/05/2024] [Accepted: 06/21/2024] [Indexed: 08/05/2024]
Abstract
Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is crucial, which supports tissue plasminogen activator (tPA) thrombolysis decision-making. Deep learning methods demonstrate superiority in TSS classification. However, they often overfit task-irrelevant features due to insufficient paired labeled data, resulting in poor generalization. We observed that unpaired data are readily available and inherently carry task-relevant cues, but are less often considered and explored. Based on this, in this paper, we propose to fully excavate the potential of unpaired unlabeled data and use them to facilitate the downstream AIS analysis task. We first analyze the utility of features at the varied grain and propose a multi-grained contrastive learning (MGCL) framework to learn task-related prior representations from both coarse-grained and fine-grained levels. The former can learn global prior representations to enhance the location ability for the ischemic lesions and perceive the healthy surroundings, while the latter can learn local prior representations to enhance the perception ability for semantic relation between the ischemic lesion and other health regions. To better transfer and utilize the learned task-related representation, we designed a novel multi-task framework to simultaneously achieve ischemic lesion segmentation and TSS classification with limited labeled data. In addition, a multi-modal region-related feature fusion module is proposed to enable the feature correlation and synergy between multi-modal deep image features for more accurate TSS decision-making. Extensive experiments on the large-scale multi-center MRI dataset demonstrate the superiority of the proposed framework. Therefore, it is promising that it helps better stroke evaluation and treatment decision-making.
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Affiliation(s)
- Jiarui Sun
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
| | - Yuhao Liu
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
| | - Yan Xi
- Jiangsu First-Imaging Medical Equipment Co., Ltd., Nanjing 210009, China
| | | | - Jean-Louis Coatrieux
- Laboratoire Traitement du Signal et de l'Image, Université de Rennes 1, F-35000 Rennes, France; Centre de Recherche en Information Biomédicale Sino-francais, 35042 Rennes, France
| | - Xu Ji
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China.
| | - Liang Jiang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China.
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 210096, China
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Zhou Z, Yin P, Liu Y, Hu J, Qian X, Chen G, Hu C, Dai Y. Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning. Med Biol Eng Comput 2024; 62:2669-2686. [PMID: 38658497 DOI: 10.1007/s11517-024-03092-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel automatic method to predict registration uncertainty based on multi-category features and supervised learning. Three types of features, including deformation field statistical features, deformation field physiologically realistic features, and image similarity features, are introduced and calculated to train the random forest regressor for local registration uncertain prediction. Deformation field statistical features represent the numerical stability of registration optimization, which are correlated to the uncertainty of deformation fields; deformation field physiologically realistic features represent the biomechanical properties of organ motions, which mathematically reflect the physiological reality of deformation; image similarity features reflect the similarity between the warped image and fixed image. The multi-category features comprehensively reflect the registration uncertainty. The strategy of spatial adaptive random perturbations is also introduced to accurately simulate spatial distribution of registration uncertainty, which makes deformation field statistical features more discriminative to the uncertainty of deformation fields. Experiments were conducted on three publicly available thoracic CT image datasets. Seventeen randomly selected image pairs are used to train the random forest model, and 9 image pairs are used to evaluate the prediction model. The quantitative experiments on lung CT images show that the proposed method outperforms the baseline method for uncertain prediction of classical iterative optimization-based registration and deep learning-based registration with different registration qualities. The proposed method achieves good performance for registration uncertain prediction, which has great potential in improving the accuracy of registration uncertain prediction.
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Affiliation(s)
- Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Pengfei Yin
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yuhang Liu
- School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Jisu Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xusheng Qian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Guangqiang Chen
- The Second Affiliated Hospital of Soochow University, Suzhou, 215163, China
| | - Chunhong Hu
- The First Affiliated Hospital of Soochow University, Suzhou, 215163, China.
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215163, China.
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
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Liu W, Wang W, Guo M, Zhang H. Tumor habitat and peritumoral region evolution-based imaging features to assess risk categorization of thymomas. Clin Radiol 2024; 79:e1117-e1125. [PMID: 38862335 DOI: 10.1016/j.crad.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
AIM To develop an aggregate model that integrated clinical data, habitat characteristics, and intratumoral and peritumoral features to assess the risk categorization of thymomas. MATERIALS AND METHODS We retrospectively analyzed 140 thymoma patients (70 low-risk and 70 high-risk), including pathological data. The patients were randomly divided into training cohort (n = 114) and test cohort (n = 26). The k-means clustering was utilized to partition the primary tumor into habitats based on intratumoral radiomic features, 6 distinct habitats were identified. By expanding the region of interest (ROI) mask, 2 peritumoral regions were obtained. Finally, 7 clinical characteristics, 3 habitat values, 20 radiomic features were utilized to develop an aggregated model, to predict the risk of thymoma. Shapley additive explanations (SHAP) interpretation was used for features importance ranking. The accuracy and area under curve (AUC) were used to analyze the performance of the models. RESULTS The aggregated model, which utilized the XGBoost classifier, demonstrated the best performance with an AUC of 0.811 and an accuracy of 0.769. In comparison, the radiomic model produced an AUC of 0.654 and an accuracy of 0.692. Additionally, the Intratumoral + peritumoral model exhibited an AUC of 0.728 and an accuracy of 0.769. CONCLUSION Our study establishes a novel tool to predict the risk of thymoma with a good performance. If prospectively validated, the model may refine thymoma patient selection for risk-adaptative therapy and improve prognosis.
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Affiliation(s)
- W Liu
- School of Health Management, China Medical University, Shenyang City, Liaoning Province, PR China.
| | - W Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang City, Liaoning Province, PR China.
| | - M Guo
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang City, Liaoning Province, PR China.
| | - H Zhang
- Department of Radiology, Liaoning Cancer Hospital and Institute, Shenyang City, Liaoning Province, PR China.
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Podobnik G, Ibragimov B, Tappeiner E, Lee C, Kim JS, Mesbah Z, Modzelewski R, Ma Y, Yang F, Rudecki M, Wodziński M, Peterlin P, Strojan P, Vrtovec T. HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge. Radiother Oncol 2024; 198:110410. [PMID: 38917883 DOI: 10.1016/j.radonc.2024.110410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/12/2024] [Accepted: 06/15/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND AND PURPOSE To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge. MATERIALS AND METHODS The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test. RESULTS While 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities. CONCLUSION This challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general.
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Affiliation(s)
- Gašper Podobnik
- University of Ljubljana, Faculty Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia.
| | - Bulat Ibragimov
- University of Ljubljana, Faculty Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia; University of Copenhagen, Department of Computer Science, Universitetsparken 1, Copenhagen 2100, Denmark
| | - Elias Tappeiner
- UMIT Tirol - Private University for Health Sciences and Health Technology, Eduard-Wallnöfer-Zentrum 1, Hall in Tirol 6060, Austria
| | - Chanwoong Lee
- Yonsei University, College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea; Yonsei Cancer Center, Department of RadiationOncology, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul 03722, South Korea
| | - Jin Sung Kim
- Yonsei University, College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea; Yonsei Cancer Center, Department of RadiationOncology, 50-1 Yonsei-Ro, Seodaemun-gu, Seoul 03722, South Korea; Oncosoft Inc, 37 Myeongmul-gil, Seodaemun-gu, Seoul 03722, South Korea
| | - Zacharia Mesbah
- Henri Becquerel Cancer Center, 1 Rue d'Amiens, Rouen 76000, France; Siemens Healthineers, 6 Rue du Général Audran, CS20146, Courbevoie 92412, France
| | - Romain Modzelewski
- Henri Becquerel Cancer Center, 1 Rue d'Amiens, Rouen 76000, France; Litis UR 4108, 684 Av. de l'Université, Saint- Étienne-du-Rouvray 76800, France
| | - Yihao Ma
- Guizhou Medical University, School of Biology & Engineering, 9FW8+2P3, Ankang Avenue, Gui'an New Area, Guiyang, Guizhou Province 561113, China
| | - Fan Yang
- Guizhou Medical University, School of Biology & Engineering, 9FW8+2P3, Ankang Avenue, Gui'an New Area, Guiyang, Guizhou Province 561113, China
| | - Mikołaj Rudecki
- AGH University of Kraków, Department of Measurement and Electronicsal, Mickiewicza 30, Kraków 30-059, Poland
| | - Marek Wodziński
- AGH University of Kraków, Department of Measurement and Electronicsal, Mickiewicza 30, Kraków 30-059, Poland; University of Applied Sciences Western Switzerland, Information Systems Institute, Rue de la Plaine 2, Sierre 3960, Switzerland
| | - Primož Peterlin
- Institute of Oncology, Ljubljana, Zaloška cesta 2, Ljubljana 1000, Slovenia
| | - Primož Strojan
- Institute of Oncology, Ljubljana, Zaloška cesta 2, Ljubljana 1000, Slovenia
| | - Tomaž Vrtovec
- University of Ljubljana, Faculty Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
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Guo R, Fan Y, Liu B, Qian X, Dai J, Si D, Wang Y, Wang A, Dong G, Jin Y, Xiao J, Ding H, Tang X. MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence. Magn Reson Med 2024; 92:1064-1078. [PMID: 38726772 DOI: 10.1002/mrm.30124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/09/2024] [Accepted: 04/03/2024] [Indexed: 06/27/2024]
Abstract
PURPOSE This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. METHODS MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. RESULTS In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. CONCLUSION MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.
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Affiliation(s)
- Rui Guo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Bowei Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Xiaofeng Qian
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Jiahuan Dai
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Dongyue Si
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yuanyuan Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Ancong Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Guozhao Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yongsheng Jin
- Department of Infectious Diseases, The Affiliated Hospital of Yan'an University, Shaanxi, China
| | - Jingjing Xiao
- Bio-Med Informatics Research Center and Clinical Research Center, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Haiyan Ding
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
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7
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Kantola V, Karjalainen J, Jaakola T, Leskinen HPP, Nissi MJ, Casula V, Nieminen MT. Anisotropy of T 2 and T 1ρ relaxation time in articular cartilage at 3 T. Magn Reson Med 2024; 92:1177-1188. [PMID: 38558167 DOI: 10.1002/mrm.30096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE The anisotropy of R2 and R1ρ relaxation rates in articular cartilage contains information about the collagenous structure of the tissue. Here we determine and study the anisotropic and isotropic components of T2 and T1ρ relaxation parameters in articular cartilage with a clinical 3T MRI device. Furthermore, a visual representation of the topographical variation in anisotropy is given via anisotropy mapping. METHODS Eight bovine stifle joints were imaged at 22 orientations with respect to the main magnetic field using T2, continuous-wave (CW) T1ρ, and adiabatic T1ρ mapping sequences. Relaxation rates were separated into isotropic and anisotropic relaxation components using a previously established relaxation anisotropy model. Pixel-wise anisotropy values were determined from the relaxation-time maps using Michelson contrast. RESULTS The relaxation rates obtained from the samples displayed notable variation depending on the sample orientation, magnetization preparation, and cartilage layer. R2 demonstrated significant anisotropy, whereas CW-R1ρ (300 Hz) and CW-R1ρ (500 Hz) displayed a low degree of anisotropy. Adiabatic R1ρ was largely isotropic. In the deep cartilage regions, relaxation rates were generally faster and more anisotropic than in the cartilage closer to the tissue surface. The isotropic relaxation rate components were found to have similar values regardless of measurement sequence. CONCLUSIONS The fitted relaxation model for T2 and T1ρ demonstrated varying amounts anisotropy, depending on magnetization preparation, and studied the articular cartilage layer. Anisotropy mapping of full joints showed varying amounts of anisotropy depending on the quantitative MRI parameter and topographical location, and in the case of T2, showed systematic changes in anisotropy across cartilage depth.
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Affiliation(s)
- Ville Kantola
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu University Hospital, Oulu, Finland
| | - Jouni Karjalainen
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu University Hospital, Oulu, Finland
| | - Tomi Jaakola
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - Henri P P Leskinen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Mikko J Nissi
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Victor Casula
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu University Hospital, Oulu, Finland
| | - Miika T Nieminen
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu University Hospital, Oulu, Finland
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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8
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Greselin M, Lu PJ, Melie-Garcia L, Ocampo-Pineda M, Galbusera R, Cagol A, Weigel M, de Oliveira Siebenborn N, Ruberte E, Benkert P, Müller S, Finkener S, Vehoff J, Disanto G, Findling O, Chan A, Salmen A, Pot C, Bridel C, Zecca C, Derfuss T, Lieb JM, Diepers M, Wagner F, Vargas MI, Pasquier RD, Lalive PH, Pravatà E, Weber J, Gobbi C, Leppert D, Kim OCH, Cattin PC, Hoepner R, Roth P, Kappos L, Kuhle J, Granziera C. Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort. Bioengineering (Basel) 2024; 11:858. [PMID: 39199815 PMCID: PMC11351944 DOI: 10.3390/bioengineering11080858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 08/09/2024] [Accepted: 08/20/2024] [Indexed: 09/01/2024] Open
Abstract
The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support.
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Affiliation(s)
- Martina Greselin
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Department of Health Sciences, University of Genova, 16132 Genova, Italy
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, 4031 Basel, Switzerland
| | - Nina de Oliveira Siebenborn
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Medical Image Analysis Center (MIAC), 4051 Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Medical Image Analysis Center (MIAC), 4051 Basel, Switzerland
| | - Pascal Benkert
- Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Stefanie Müller
- Department of Neurology, Cantonal Hospital St. Gallen, 9000 St. Gallen, Switzerland
| | - Sebastian Finkener
- Department of Neurology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
| | - Jochen Vehoff
- Department of Neurology, Cantonal Hospital St. Gallen, 9000 St. Gallen, Switzerland
| | - Giulio Disanto
- Neurology Department, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland
| | - Oliver Findling
- Department of Neurology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
| | - Andrew Chan
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Anke Salmen
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Caroline Pot
- Service of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, 1005 Lausanne, Switzerland
| | - Claire Bridel
- Division of Neurology, Department of Clinical Neurosciences, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - Chiara Zecca
- Neurology Department, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland
- Faculty of biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland
| | - Tobias Derfuss
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Johanna M. Lieb
- Division of Diagnostic and Interventional Neuroradiology, Clinic for Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, 4031 Basel, Switzerland;
| | - Michael Diepers
- Department of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
| | - Franca Wagner
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Maria I. Vargas
- Department of Radiology, Faculty of Medicine, Geneva University Hospital, 1205 Geneva, Switzerland
| | - Renaud Du Pasquier
- Service of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, 1005 Lausanne, Switzerland
| | - Patrice H. Lalive
- Division of Neurology, Department of Clinical Neurosciences, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - Emanuele Pravatà
- Faculty of biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland
- Department of Neuroradiology, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland
| | - Johannes Weber
- Department of Radiology, Cantonal Hospital St. Gallen, 9000 St. Gallen, Switzerland
| | - Claudio Gobbi
- Neurology Department, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland
- Faculty of biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland
| | - David Leppert
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Olaf Chan-Hi Kim
- Department of Radiology, Cantonal Hospital St. Gallen, 9000 St. Gallen, Switzerland
| | - Philippe C. Cattin
- Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland;
| | - Robert Hoepner
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Patrick Roth
- Department of Neurology, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland; (M.G.); (R.G.); (A.C.); (E.R.)
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland;
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
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Tong MW, Yu HJ, Sjaastad Andreassen MM, Loubrie S, Rodríguez-Soto AE, Seibert TM, Rakow-Penner R, Dale AM. Longitudinal registration of T 1-weighted breast MRI: A registration algorithm (FLIRE) and clinical application. Magn Reson Imaging 2024; 113:110222. [PMID: 39181479 DOI: 10.1016/j.mri.2024.110222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 04/05/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
PURPOSE MRI is commonly used to aid breast cancer diagnosis and treatment evaluation. For patients with breast cancer, neoadjuvant chemotherapy aims to reduce the tumor size and extent of surgery necessary. The current clinical standard to measure breast tumor response on MRI uses the longest tumor diameter. Radiologists also account for other tissue properties including tumor contrast or pharmacokinetics in their assessment. Accurate longitudinal image registration of breast tissue is critical to properly compare response to treatment at different timepoints. METHODS In this study, a deformable Fast Longitudinal Image Registration (FLIRE) algorithm was optimized for breast tissue. FLIRE was then compared to the publicly available software packages with high accuracy (DRAMMS) and fast runtime (Elastix). Patients included in the study received longitudinal T1-weighted MRI without fat saturation at two to six timepoints as part of asymptomatic screening (n = 27) or throughout neoadjuvant chemotherapy treatment (n = 32). T1-weighted images were registered to the first timepoint with each algorithm. RESULTS Alignment and runtime performance were compared using two-way repeated measure ANOVAs (P < 0.05). Across all patients, Pearson's correlation coefficient across the entire image volume was slightly higher with statistical significance and had less variance for FLIRE (0.98 ± 0.01 stdev) compared to DRAMMS (0.97 ± 0.03 stdev) and Elastix (0.95 ± 0.03 stdev). Additionally, FLIRE runtime (10.0 mins) was 9.0 times faster than DRAMMS (89.6 mins) and 1.5 times faster than Elastix (14.5 mins) on a Linux workstation. CONCLUSION FLIRE demonstrates promise for time-sensitive clinical applications due to its accuracy, robustness across patients and timepoints, and speed.
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Affiliation(s)
- Michelle W Tong
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA.
| | - Hon J Yu
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | | | - Stephane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Ana E Rodríguez-Soto
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tyler M Seibert
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Radiation Medicine, University of California San Diego, La Jolla, CA, USA
| | - Rebecca Rakow-Penner
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
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10
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Jogi SP, Peng Q, Jafari R, Otazo R, Wu C. Novel spin-lock time sampling strategies for improved reproducibility in quantitative T1ρ mapping. NMR IN BIOMEDICINE 2024:e5244. [PMID: 39152756 DOI: 10.1002/nbm.5244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/12/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024]
Abstract
This study aimed to optimize the sampling of spin-lock times (TSLs) in quantitative T1ρ mapping for improved reproducibility. Two new TSL sampling schemes were proposed: (i) reproducibility-guided random sampling (RRS) and (ii) reproducibility-guided optimal sampling (ROS). They were compared to the existing linear sampling (LS) and precision-guided sampling (PS) schemes for T1ρ reproducibility through numerical simulations, phantom experiments, and volunteer studies. Each study evaluated the four sampling schemes with three commonly used T1ρ preparations based on composite and balanced spin-locking. Additionally, the phantom and volunteer studies investigated the impact of B0 and B1 field inhomogeneities on T1ρ reproducibility, respectively. The reproducibility was assessed using the coefficient of variation (CoV) by repeating the T1ρ measurements eight times for phantom experiments and five times for volunteer studies. Numerical simulations resulted in lower mean CoVs for the proposed RRS (1.74%) and ROS (0.68%) compared to LS (2.93%) and PS (3.68%). In the phantom study, the mean CoVs were also lower for RRS (2.7%) and ROS (2.6%) compared to LS (4.1%) and PS (3.1%). Furthermore, the mean CoVs of the proposed RRS and ROS were statistically lower (P < 0.001) compared to existing LS and PS schemes at a B1 offset of 20%. In the volunteer study, consistently lower mean CoVs were observed in bilateral thigh muscles for RRS (9.3%) and ROS (9.2%) compared to LS (10.9%) and PS (10.2%), and the difference was more prominent at B0 offsets higher than 50 Hz. The proposed sampling schemes improve the reproducibility of quantitative T1ρ mapping by optimizing the selection of TSLs. This improvement is especially beneficial for longitudinal studies that track and monitor disease progression and treatment response.
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Affiliation(s)
- Sandeep Panwar Jogi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Qi Peng
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
| | - Ramin Jafari
- Philips Healthcare, MR Clinical Science, Cambridge, Massachusetts, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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11
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Lo J, Du K, Lee D, Zeng C, Athertya JS, Silva ML, Flechner R, Bydder GM, Ma Y. Multicompartment imaging of the brain using a comprehensive MR imaging protocol. Neuroimage 2024; 298:120800. [PMID: 39159704 DOI: 10.1016/j.neuroimage.2024.120800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 07/25/2024] [Accepted: 08/16/2024] [Indexed: 08/21/2024] Open
Abstract
In this study, we describe a comprehensive 3D magnetic resonance imaging (MRI) protocol designed to assess major tissue and fluid components in the brain. The protocol comprises four different sequences: 1) magnetization transfer prepared Cones (MT-Cones) for two-pool MT modeling to quantify macromolecular content; 2) short-TR adiabatic inversion-recovery prepared Cones (STAIR-Cones) for myelin water imaging; 3) proton-density weighted Cones (PDw-Cones) for total water imaging; and 4) highly T2 weighted Cones (T2w-Cones) for free water imaging. By integrating these techniques, we successfully mapped key brain components-namely macromolecules, myelin water, intra/extracellular water, and free water-in ten healthy volunteers and five patients with multiple sclerosis (MS) using a 3T clinical scanner. Brain macromolecular proton fraction (MMPF), myelin water proton fraction (MWPF), intra/extracellular water proton fraction (IEWPF), and free water proton fraction (FWPF) values were generated in white matter (WM), grey matter (GM), and MS lesions. Excellent repeatability of the protocol was demonstrated with high intra-class correlation coefficient (ICC) values. In MS patients, the MMPF and MWPF values of the lesions and normal-appearing WM (NAWM) were significantly lower than those in normal WM (NWM) in healthy volunteers. Moreover, we observed significantly higher FWPF values in MS lesions compared to those in NWM and NAWM regions. This study demonstrates the capability of our technique to volumetrically map major brain components. The technique may have particular value in providing a comprehensive assessment of neuroinflammatory and neurodegenerative diseases of the brain.
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Affiliation(s)
- James Lo
- Department of Radiology, University of California, San Diego, CA, USA; Department of Bioengineering, University of California, San Diego, CA, USA
| | - Kevin Du
- Department of Radiology, University of California, San Diego, CA, USA
| | - David Lee
- Department of Radiology, University of California, San Diego, CA, USA
| | - Chun Zeng
- Department of Radiology, University of California, San Diego, CA, USA
| | - Jiyo S Athertya
- Department of Radiology, University of California, San Diego, CA, USA
| | - Melissa Lou Silva
- Department of Radiology, University of California, San Diego, CA, USA
| | - Reese Flechner
- Department of Radiology, University of California, San Diego, CA, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Graeme M Bydder
- Department of Radiology, University of California, San Diego, CA, USA
| | - Yajun Ma
- Department of Radiology, University of California, San Diego, CA, USA.
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12
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Kerckhaert CEM, de Jong HWAM, Meddens MBM, van Rooij R, Smits MLJ, Rakvongthai Y, Dietze MMA. Subtraction of single-photon emission computed tomography (SPECT) in radioembolization: a comparison of four methods. EJNMMI Phys 2024; 11:72. [PMID: 39143361 PMCID: PMC11324633 DOI: 10.1186/s40658-024-00675-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 08/05/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Subtraction of single-photon emission computed tomography (SPECT) images has a number of clinical applications in e.g. foci localization in ictal/inter-ictal SPECT and defect detection in rest/stress cardiac SPECT. In this work, we investigated the technical performance of SPECT subtraction for the purpose of quantifying the effect of a vasoconstricting drug (angiotensin-II, or AT2) on the Tc-99m-MAA liver distribution in hepatic radioembolization using an innovative interventional hybrid C-arm scanner. Given that subtraction of SPECT images is challenging due to high noise levels and poor resolution, we compared four methods to obtain a difference image in terms of image quality and quantitative accuracy. These methods included (i) image subtraction: subtraction of independently reconstructed SPECT images, (ii) projection subtraction: reconstruction of a SPECT image from subtracted projections, (iii) projection addition: reconstruction by addition of projections as a background term during the iterative reconstruction, and (iv) image addition: simultaneous reconstruction of the difference image and the subtracted image. RESULTS Digital simulations (XCAT) and phantom studies (NEMA-IQ and anthropomorphic torso) showed that all four methods were able to generate difference images but their performance on specific metrics varied substantially. Image subtraction had the best quantitative performance (activity recovery coefficient) but had the worst visual quality (contrast-to-noise ratio) due to high noise levels. Projection subtraction showed a slightly better visual quality than image subtraction, but also a slightly worse quantitative accuracy. Projection addition had a substantial bias in its quantitative accuracy which increased with less counts in the projections. Image addition resulted in the best visual image quality but had a quantitative bias when the two images to subtract contained opposing features. CONCLUSION All four investigated methods of SPECT subtraction demonstrated the capacity to generate a feasible difference image from two SPECT images. Image subtraction is recommended when the user is only interested in quantitative values, whereas image addition is recommended when the user requires the best visual image quality. Since quantitative accuracy is most important for the dosimetric investigation of AT2 in radioembolization, we recommend using the image subtraction method for this purpose.
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Affiliation(s)
- Camiel E M Kerckhaert
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, Netherlands.
| | - Hugo W A M de Jong
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, Netherlands
| | - Marjolein B M Meddens
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, Netherlands
| | - Rob van Rooij
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, Netherlands
| | - Maarten L J Smits
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, Netherlands
| | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Martijn M A Dietze
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, Netherlands
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van Hoof S, Kreye J, Cordero-Gómez C, Hoffmann J, Momsen Reincke S, Sánchez-Sendin E, Duong SL, Upadhya M, Dhangar D, Michór P, Woodhall GL, Küpper M, Oder A, Kuchling J, Koch SP, Mueller S, Boehm-Sturm P, von Kries JP, Finke C, Kirschstein T, Wright SK, Prüss H. Human cerebrospinal fluid monoclonal CASPR2 autoantibodies induce changes in electrophysiology, functional MRI, and behavior in rodent models. Brain Behav Immun 2024; 122:266-278. [PMID: 39142424 DOI: 10.1016/j.bbi.2024.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 07/02/2024] [Accepted: 08/11/2024] [Indexed: 08/16/2024] Open
Abstract
Anti-contactin associated protein receptor 2 (CASPR2) encephalitis is a severe autoimmune encephalitis with a variable clinical phenotype including behavioral abnormalities, cognitive decline, epileptic seizures, peripheral nerve hyperexcitability and neuropathic pain. The detailed mechanisms of how CASPR2 autoantibodies lead to synaptic dysfunction and clinical symptoms are largely unknown. Aiming for analyses from the molecular to the clinical level, we isolated antibody-secreting cells from the cerebrospinal fluid of two patients with CASPR2 encephalitis. From these we cloned four anti-CASPR2 human monoclonal autoantibodies (mAbs) with strong binding to brain and peripheral nerves. All were highly hypermutated and mainly of the IgG4 subclass. Mutagenesis studies determined selective binding to the discoidin domain of CASPR2. Surface plasmon resonance revealed affinities with dissociation constants KD in the pico- to nanomolar range. CASPR2 mAbs interrupted the interaction of CASPR2 with its binding partner contactin 2 in vitro and were internalized after binding to CASPR2-expressing cells. Electrophysiological recordings of rat hippocampal slices after stereotactic injection of CASPR2 mAbs showed characteristic afterpotentials following electrical stimulation. In vivo experiments with intracerebroventricular administration of human CASPR2 mAbs into mice and rats showed EEG-recorded brain hyperexcitability but no spontaneous recurrent seizures. Behavioral assessment of infused mice showed a subtle clinical phenotype, mainly affecting sociability. Mouse brain MRI exhibited markedly reduced resting-state functional connectivity without short-term structural changes. Together, the experimental data support the direct pathogenicity of CASPR2 autoantibodies. The minimally invasive EEG and MRI techniques applied here may serve as novel objective, quantifiable tools for improved animal models, in particular for subtle neuropsychiatric phenotypes or repeated measurements.
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Affiliation(s)
- Scott van Hoof
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Helmholtz Innovation Lab BaoBab (Brain Antibody-omics and B-cell Lab), Berlin, Germany
| | - Jakob Kreye
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Helmholtz Innovation Lab BaoBab (Brain Antibody-omics and B-cell Lab), Berlin, Germany; Department of Pediatric Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - César Cordero-Gómez
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - Julius Hoffmann
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - S Momsen Reincke
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Elisa Sánchez-Sendin
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Helmholtz Innovation Lab BaoBab (Brain Antibody-omics and B-cell Lab), Berlin, Germany
| | - Sophie L Duong
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany
| | - Manoj Upadhya
- Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Divya Dhangar
- Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Paulina Michór
- Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Gavin L Woodhall
- Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Maraike Küpper
- Oscar Langendorff Institute of Physiology, University of Rostock, Germany, Center of Transdisciplinary Neurosciences Rostock (CTNR), Germany
| | - Andreas Oder
- Screening Unit, Leibniz Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany
| | - Joseph Kuchling
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Neurocure Cluster of Excellence, NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Stefan Paul Koch
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Center for Stroke Research Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Germany; Charité 3R, Replace, Reduce, Refine, Charité - Universitätsmedizin Berlin, Germany
| | - Susanne Mueller
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Center for Stroke Research Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Germany; Charité 3R, Replace, Reduce, Refine, Charité - Universitätsmedizin Berlin, Germany
| | - Philipp Boehm-Sturm
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Center for Stroke Research Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Germany; Charité 3R, Replace, Reduce, Refine, Charité - Universitätsmedizin Berlin, Germany
| | - Jens Peter von Kries
- Screening Unit, Leibniz Forschungsinstitut für Molekulare Pharmakologie, 13125 Berlin, Germany
| | - Carsten Finke
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Neurocure Cluster of Excellence, NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Timo Kirschstein
- Oscar Langendorff Institute of Physiology, University of Rostock, Germany, Center of Transdisciplinary Neurosciences Rostock (CTNR), Germany
| | - Sukhvir K Wright
- Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK; Department of Paediatric Neurology, The Birmingham Women's and Children's Hospital National Health Service Foundation Trust, Birmingham, UK
| | - Harald Prüss
- German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Berlin, 10117 Berlin, Germany; Helmholtz Innovation Lab BaoBab (Brain Antibody-omics and B-cell Lab), Berlin, Germany.
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14
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Quintero P, Wu C, Otazo R, Cervino L, Harris W. On-board synthetic 4D MRI generation from 4D CBCT for radiotherapy of abdominal tumors: A feasibility study. Med Phys 2024. [PMID: 39137256 DOI: 10.1002/mp.17347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/03/2024] [Accepted: 07/20/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Magnetic resonance-guided radiotherapy with an MR-guided LINAC represents potential clinical benefits in abdominal treatments due to the superior soft-tissue contrast compared to kV-based images in conventional treatment units. However, due to the high cost associated with this technology, only a few centers have access to it. As an alternative, synthetic 4D MRI generation based on artificial intelligence methods could be implemented. Nevertheless, appropriate MRI texture generation from CT images might be challenging and prone to hallucinations, compromising motion accuracy. PURPOSE To evaluate the feasibility of on-board synthetic motion-resolved 4D MRI generation from prior 4D MRI, on-board 4D cone beam CT (CBCT) images, motion modeling information, and deep learning models using the digital anthropomorphic phantom XCAT. METHODS The synthetic 4D MRI corresponds to phases from on-board 4D CBCT. Each synthetic MRI volume in the 4D MRI was generated by warping a reference 3D MRI (MRIref, end of expiration phase from a prior 4D MRI) with a deformation field map (DFM) determined by (I) the eigenvectors from the principal component analysis (PCA) motion-modeling of the prior 4D MRI, and (II) the corresponding eigenvalues predicted by a convolutional neural network (CNN) model using the on-board 4D CBCT images as input. The CNN was trained with 1000 deformations of one reference CT (CTref, same conditions as MRIref) generated by applying 1000 DFMs computed by randomly sampling the original eigenvalues from the prior 4D MRI PCA model. The evaluation metrics for the CNN model were root-mean-square error (RMSE) and mean absolute error (MAE). Finally, different on-board 4D-MRI generation scenarios were assessed by changing the respiratory period, the amplitude of the diaphragm, and the chest wall motion of the 4D CBCT using normalized root-mean-square error (nRMSE) and structural similarity index measure (SSIM) for image-based evaluation, and volume dice coefficient (VDC), volume percent difference (VPD), and center-of-mass shift (COMS) for contour-based evaluation of liver and target volumes. RESULTS The RMSE and MAE values of the CNN model reported 0.012 ± 0.001 and 0.010 ± 0.001, respectively for the first eigenvalue predictions. SSIM and nRMSE were 0.96 ± 0.06 and 0.22 ± 0.08, respectively. VDC, VPD, and COMS were 0.92 ± 0.06, 3.08 ± 3.73 %, and 2.3 ± 2.1 mm, respectively, for the target volume. The more challenging synthetic 4D-MRI generation scenario was for one 4D-CBCT with increased chest wall motion amplitude, reporting SSIM and nRMSE of 0.82 and 0.51, respectively. CONCLUSIONS On-board synthetic 4D-MRI generation based on predicting actual treatment deformation from on-board 4D-CBCT represents a method that can potentially improve the treatment-setup localization in abdominal radiotherapy treatments with a conventional kV-based LINAC.
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Affiliation(s)
- Paulo Quintero
- Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Can Wu
- Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Ricardo Otazo
- Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Laura Cervino
- Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Wendy Harris
- Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, USA
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15
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Isumi S, Futamura D, Hanasaki T, Sako Y, Miyata S, Kan H, Suzuki Y, Hasegawa N, Mushiake H, Kametaka S, Uchiyama Y, Osanai M, Lee-Hotta S. Association of medullary reticular formation ventral part with spasticity in mice suffering from photothrombotic stroke. Neuroimage 2024; 298:120791. [PMID: 39147291 DOI: 10.1016/j.neuroimage.2024.120791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 07/15/2024] [Accepted: 08/13/2024] [Indexed: 08/17/2024] Open
Abstract
Strokes cause spasticity via stretch reflex hyperexcitability in the spinal cord, and spastic paralysis due to involuntary muscle contraction in the hands and fingers can severely restrict skilled hand movements. However, the underlying neurological mechanisms remain unknown. Using a mouse model of spasticity after stroke, we demonstrate changes in neuronal activity with and without electrostimulation of the afferent nerve to induce the stretch reflex, measured using quantitative activation-induced manganese-enhanced magnetic resonance imaging. Neuronal activity increased within the ventral medullary reticular formation (MdV) in the contralesional brainstem during the acute post-stroke phase, and this increase was characterised by activation of circuits involved in spasticity. Interestingly, ascending electrostimulation inhibited the MdV activity on the stimulation side in normal conditions. Moreover, immunohistochemical staining showed that, in the acute phase, the density of GluA1, one of the α-amino-3 hydroxy‑5 methyl -4 isoxazolepropionic acid receptor (AMPAR) subunits, at the synapses of MdV neurons was significantly increased. In addition, the GluA1/GluA2 ratio in these receptors was altered at 2 weeks post-stroke, confirming homeostatic plasticity as the underlying mechanisms of spasticity. These results provide new insights into the relationship between impaired skilled movements and spasticity at the acute post-stroke phase.
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Affiliation(s)
- Shogo Isumi
- Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Daiki Futamura
- Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Takuto Hanasaki
- Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Yukito Sako
- Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Shotaro Miyata
- Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Hirohito Kan
- Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Yumika Suzuki
- Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Naoki Hasegawa
- Department of Radiological Imaging and Informatics, School of Medicine, Tohoku University, Sendai, Japan
| | - Hajime Mushiake
- Department of Physiology, School of Medicine, Tohoku University, Sendai, Japan
| | - Satoshi Kametaka
- Division of Biofunctional Sciences, Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Yasushi Uchiyama
- Division of Creative Physical Therapy, Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, 1-1-20 Daiko-Minami, Higashi-ku, Nagoya, Aichi 461-8673, Japan
| | - Makoto Osanai
- Department of Physiology, School of Medicine, Tohoku University, Sendai, Japan; Laboratory for Physiological Functional Imaging, Department of Medical Physics and Engineering, Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Sachiko Lee-Hotta
- Division of Creative Physical Therapy, Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, 1-1-20 Daiko-Minami, Higashi-ku, Nagoya, Aichi 461-8673, Japan.
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16
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Yuan W, Cheng J, Gong Y, He L, Zhang J. MACG-Net: Multi-axis cross gating network for deformable medical image registration. Comput Biol Med 2024; 178:108673. [PMID: 38905891 DOI: 10.1016/j.compbiomed.2024.108673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 04/18/2024] [Accepted: 05/26/2024] [Indexed: 06/23/2024]
Abstract
Deformable Image registration is a fundamental yet vital task for preoperative planning, intraoperative information fusion, disease diagnosis and follow-ups. It solves the non-rigid deformation field to align an image pair. Latest approaches such as VoxelMorph and TransMorph compute features from a simple concatenation of moving and fixed images. However, this often leads to weak alignment. Moreover, the convolutional neural network (CNN) or the hybrid CNN-Transformer based backbones are constrained to have limited sizes of receptive field and cannot capture long range relations while full Transformer based approaches are computational expensive. In this paper, we propose a novel multi-axis cross grating network (MACG-Net) for deformable medical image registration, which combats these limitations. MACG-Net uses a dual stream multi-axis feature fusion module to capture both long-range and local context relationships from the moving and fixed images. Cross gate blocks are integrated with the dual stream backbone to consider both independent feature extractions in the moving-fixed image pair and the relationship between features from the image pair. We benchmark our method on several different datasets including 3D atlas-based brain MRI, inter-patient brain MRI and 2D cardiac MRI. The results demonstrate that the proposed method has achieved state-of-the-art performance. The source code has been released at https://github.com/Valeyards/MACG.
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Affiliation(s)
- Wei Yuan
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jun Cheng
- Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore
| | - Yuhang Gong
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Ling He
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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17
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Pan Z, Qi N, Meng Q, Pan B, Feng T, Zhao J, Gong NJ. Fast SPECT/CT planar bone imaging enabled by deep learning enhancement. Med Phys 2024; 51:5414-5426. [PMID: 38652084 DOI: 10.1002/mp.17094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/21/2024] [Accepted: 03/23/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND The application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of high-count bone scintigraphy images from low-count counterparts. Few studies have been conducted and validated on real clinical pairs due to the misalignment inherent in multiple scan procedures. PURPOSE To generate high quality whole-body bone images from 2× and 3× fast scans using deep learning based enhancement method. MATERIALS AND METHODS Seventy-six cases who underwent whole-body bone scans were enrolled in this prospective study. All patients went through a standard scan at a speed of 20 cm/min, which followed by fast scans consisting of 2× and 3× accelerations at speeds of 40 and 60 cm/min. A content-attention image restoration approach based on Residual-in-Residual Dense Block (RRDB) is introduced to effectively recover high-quality images from fast scans with fine-details and less noise. Our approach is robust with misalignment introduced from patient's metabolism, and shows valid count-level consistency. Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID) are employed in evaluating the similarity to the standard bone images. To further prove our method practical in clinical settings, image quality of the anonymous images was evaluated by two experienced nuclear physicians on a 5-point Likert scale (5 = excellent) . RESULTS The proposed method reaches the state-of-the-art performance on FID and LPIPS with 0.583 and 0.176 for 2× fast scans and 0.583 and 0.185 for 3× fast scans. Clinic evaluation further demonstrated the restored images had a significant improvement compared to fast scan in image quality, technetium 99m-methyl diphosphonate (Tc-99 m MDP) distribution, artifacts, and diagnostic confidence. CONCLUSIONS Our method was validated for accelerating whole-body bone scans by introducing real clinical data. Confirmed by nuclear medicine physicians, the proposed method can effectively enhance image diagnostic value, demonstrating potential for efficient high-quality fast bone imaging in practical settings.
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Affiliation(s)
| | - Na Qi
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qingyuan Meng
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Boyang Pan
- RadioDynamic Healthcare, Shanghai, China
| | - Tao Feng
- Laboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Beijing, China
| | - Jun Zhao
- Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Nan-Jie Gong
- Laboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Beijing, China
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18
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Bracha S, Johnson HJ, Pranckevicius NA, Catto F, Economides AE, Litvinov S, Hassi K, Rigoli MT, Cheroni C, Bonfanti M, Valenti A, Stucchi S, Attreya S, Ross PD, Walsh D, Malachi N, Livne H, Eshel R, Krupalnik V, Levin D, Cobb S, Koumoutsakos P, Caporale N, Testa G, Aguzzi A, Koshy AA, Sheiner L, Rechavi O. Engineering Toxoplasma gondii secretion systems for intracellular delivery of multiple large therapeutic proteins to neurons. Nat Microbiol 2024; 9:2051-2072. [PMID: 39075233 PMCID: PMC11306108 DOI: 10.1038/s41564-024-01750-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 06/05/2024] [Indexed: 07/31/2024]
Abstract
Delivering macromolecules across biological barriers such as the blood-brain barrier limits their application in vivo. Previous work has demonstrated that Toxoplasma gondii, a parasite that naturally travels from the human gut to the central nervous system (CNS), can deliver proteins to host cells. Here we engineered T. gondii's endogenous secretion systems, the rhoptries and dense granules, to deliver multiple large (>100 kDa) therapeutic proteins into neurons via translational fusions to toxofilin and GRA16. We demonstrate delivery in cultured cells, brain organoids and in vivo, and probe protein activity using imaging, pull-down assays, scRNA-seq and fluorescent reporters. We demonstrate robust delivery after intraperitoneal administration in mice and characterize 3D distribution throughout the brain. As proof of concept, we demonstrate GRA16-mediated brain delivery of the MeCP2 protein, a putative therapeutic target for Rett syndrome. By characterizing the potential and current limitations of the system, we aim to guide future improvements that will be required for broader application.
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Affiliation(s)
- Shahar Bracha
- Department of Neurobiology, Biochemistry and Biophysics, Wise Faculty of Life Sciences and Sagol School for Neuroscience, Tel Aviv University, Tel Aviv, Israel.
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.
| | - Hannah J Johnson
- Neuroscience Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA
- Departments of Neurology and Immunobiology, College of Medicine, and BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | - Nicole A Pranckevicius
- Centre for Parasitology, School of Infection and Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Francesca Catto
- Institute of Neuropathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Athena E Economides
- Institute of Neuropathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sergey Litvinov
- Computational Science and Engineering Laboratory, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Karoliina Hassi
- Centre for Parasitology, School of Infection and Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Marco Tullio Rigoli
- Human Technopole, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Cristina Cheroni
- Human Technopole, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | | | - Alessia Valenti
- Human Technopole, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Sarah Stucchi
- Human Technopole, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Shruti Attreya
- Undergraduate Biology Research Program, University of Arizona, Tucson, AZ, USA
| | - Paul D Ross
- Institute of Neuroscience and Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Daniel Walsh
- Centre for Parasitology, School of Infection and Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | | | | | | | | | | | - Stuart Cobb
- Institute of Neuroscience and Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Petros Koumoutsakos
- Computational Science and Engineering Laboratory, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Nicolò Caporale
- Human Technopole, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Giuseppe Testa
- Human Technopole, Milan, Italy.
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy.
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy.
| | - Adriano Aguzzi
- Institute of Neuropathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Anita A Koshy
- Departments of Neurology and Immunobiology, College of Medicine, and BIO5 Institute, University of Arizona, Tucson, AZ, USA.
| | - Lilach Sheiner
- Centre for Parasitology, School of Infection and Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
| | - Oded Rechavi
- Department of Neurobiology, Biochemistry and Biophysics, Wise Faculty of Life Sciences and Sagol School for Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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19
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Simons DC, Buser MAD, Fitski M, van de Ven CP, Ten Haken B, Wijnen MHWA, Tan CO, van der Steeg AFW. Multi-modal 3-Dimensional Visualization of Pediatric Neuroblastoma: Aiding Surgical Planning Beyond Anatomical Information. J Pediatr Surg 2024; 59:1575-1581. [PMID: 38461108 DOI: 10.1016/j.jpedsurg.2024.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND Patient-specific 3D models of neuroblastoma and relevant anatomy are useful tools for surgical planning. However, these models do not represent the heterogenous biology of neuroblastoma. This heterogeneity is visualized with the ADC and 123I-MIGB-SPECT-CT imaging. Combining these multi-modal data into preoperative 3D heatmaps, may allow differentiation of the areas of vital and non-vital tumor tissue. We developed a workflow to create multi-modal preoperative 3D models for neuroblastoma surgery. METHODS We included 7 patients who underwent neuroblastoma surgery between 2022 and 2023. We developed 3D models based on the contrast enhanced T1-weighted MRI scans. Subsequently, we aligned the corresponding ADC and 123I-MIBG-SPECT-CT images using rigid transformation. We estimated registration precision using the Dice score and the target registration error (TRE). 3D heatmaps were computed based on ADC and 123I-MIBG uptake. RESULTS The registration algorithm had a median Dice score of 0.81 (0.75-0.90) for ADC and 0.77 (0.65-0.91) for 123I-MIBG-SPECT. For the ADC registration, the median TRE of renal vessels was 4.90 mm (0.86-10.18) and of the aorta 4.67 mm (1.59-12.20). For the 123I -MIBG-SPECT imaging the TRE of the renal vessels was 5.52 mm (1.71-10.97) and 5.28 mm (3.33-16.77) for the aorta. CONCLUSIONS We successfully developed a registration workflow to create multi-modal 3D models which allows the surgeon to visualize the tumor and its biological behavior in relation to the surrounding tissue. Future research will include linking of pathological results to imaging data, to validate these multi-modal 3D models. LEVEL OF EVIDENCE Level IV. TYPE OF STUDY Clinical Research.
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Affiliation(s)
- Dominique C Simons
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands; University of Twente, Technical Medicine, Hallenweg 5, 7522, NH, Enschede, the Netherlands
| | - Myrthe A D Buser
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Matthijs Fitski
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Cornelis P van de Ven
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Bernhard Ten Haken
- University of Twente, Magnetic Detection and Imaging, Enschede, the Netherlands
| | - Marc H W A Wijnen
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Can Ozan Tan
- University of Twente, Electrical Engineering, Mathematics, and Computer Science, Hallenweg 5, 7522, NH, Enschede, the Netherlands
| | - Alida F W van der Steeg
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands.
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20
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van Voorst H, Pitkänen J, van Poppel L, de Vries L, Mojtahedi M, Martou L, Emmer BJ, Roos YBWEM, van Oostenbrugge R, Postma AA, Marquering HA, Majoie CBLM, Curtze S, Melkas S, Bentley P, Caan MWA. Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke. Eur Radiol 2024; 34:5080-5093. [PMID: 38285103 DOI: 10.1007/s00330-024-10584-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/12/2023] [Accepted: 12/16/2023] [Indexed: 01/30/2024]
Abstract
BACKGROUND Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT. METHODS We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR). RESULTS In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62-79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given. CONCLUSION DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed. CLINICAL RELEVANCE STATEMENT White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied. KEY POINTS White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment.
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Affiliation(s)
- Henk van Voorst
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands.
| | - Johanna Pitkänen
- Department of Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Laura van Poppel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
| | - Lucas de Vries
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| | - Mahsa Mojtahedi
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
| | - Laura Martou
- Department of Brain Sciences, Imperial College London, Charing Cross Hospital, London, England
| | - Bart J Emmer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Yvo B W E M Roos
- Department of Neurology, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
| | - Robert van Oostenbrugge
- Department of Neurology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Alida A Postma
- Department of Radiology and Nuclear Medicine, School for Mental health and sciences (Mhens), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Henk A Marquering
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
| | - Charles B L M Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Sami Curtze
- Department of Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Susanna Melkas
- Department of Neurology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Paul Bentley
- Department of Brain Sciences, Imperial College London, Charing Cross Hospital, London, England
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands
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Pham XL, Luu MH, van Walsum T, Mai HS, Klein S, Le NH, Chu DT. CMAN: Cascaded Multi-scale Spatial Channel Attention-guided Network for large 3D deformable registration of liver CT images. Med Image Anal 2024; 96:103212. [PMID: 38830326 DOI: 10.1016/j.media.2024.103212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 03/27/2024] [Accepted: 05/17/2024] [Indexed: 06/05/2024]
Abstract
Deformable image registration is an essential component of medical image analysis and plays an irreplaceable role in clinical practice. In recent years, deep learning-based registration methods have demonstrated significant improvements in convenience, robustness and execution time compared to traditional algorithms. However, registering images with large displacements, such as those of the liver organ, remains underexplored and challenging. In this study, we present a novel convolutional neural network (CNN)-based unsupervised learning registration method, Cascaded Multi-scale Spatial-Channel Attention-guided Network (CMAN), which addresses the challenge of large deformation fields using a double coarse-to-fine registration approach. The main contributions of CMAN include: (i) local coarse-to-fine registration in the base network, which generates the displacement field for each resolution and progressively propagates these local deformations as auxiliary information for the final deformation field; (ii) global coarse-to-fine registration, which stacks multiple base networks for sequential warping, thereby incorporating richer multi-layer contextual details into the final deformation field; (iii) integration of the spatial-channel attention module in the decoder stage, which better highlights important features and improves the quality of feature maps. The proposed network was trained using two public datasets and evaluated on another public dataset as well as a private dataset across several experimental scenarios. We compared CMAN with four state-of-the-art CNN-based registration methods and two well-known traditional algorithms. The results show that the proposed double coarse-to-fine registration strategy outperforms other methods in most registration evaluation metrics. In conclusion, CMAN can effectively handle the large-deformation registration problem and show potential for application in clinical practice. The source code is made publicly available at https://github.com/LocPham263/CMAN.git.
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Affiliation(s)
- Xuan Loc Pham
- FET, VNU University of Engineering and Technology, Hanoi, Viet Nam; Diagnostic Image Analysis Group, Radboud UMC, Nijmegen, The Netherlands
| | - Manh Ha Luu
- FET, VNU University of Engineering and Technology, Hanoi, Viet Nam; AVITECH, VNU University of Engineering and Technology, Hanoi, Viet Nam; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
| | - Theo van Walsum
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Hong Son Mai
- Department of Nuclear Medicine, Hospital 108, Hanoi, Viet Nam
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Ngoc Ha Le
- Department of Nuclear Medicine, Hospital 108, Hanoi, Viet Nam
| | - Duc Trinh Chu
- FET, VNU University of Engineering and Technology, Hanoi, Viet Nam
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Ghoul A, Pan J, Lingg A, Kubler J, Krumm P, Hammernik K, Rueckert D, Gatidis S, Kustner T. Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3013-3026. [PMID: 39088484 DOI: 10.1109/tmi.2024.3385024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/03/2024]
Abstract
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling. We combine local and global dependencies to perform simultaneous coarse and fine motion estimation. The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI. The impact of motion estimation accuracy on the downstream task of motion-compensated reconstruction was analyzed. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories (Cartesian and radial) and acceleration factors of up to 16x for cardiac motion and 30x for respiratory motion and achieves superior image quality in motion-compensated reconstruction qualitatively and quantitatively compared to conventional and recent deep learning-based approaches. The code is publicly available at https://github.com/lab-midas/GMARAFT.
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Ariz M, Martínez M, Alvarez I, Fernández-Seara MA, Castellanos G, Pastor P, Pastor MA, Ortiz de Solórzano C. Automatic Segmentation and Quantification of Nigrosome-1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease. J Magn Reson Imaging 2024; 60:534-547. [PMID: 37915245 DOI: 10.1002/jmri.29073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow-tail" in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. PURPOSE Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. STUDY TYPE Prospective. SUBJECTS Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). FIELD STRENGTH/SEQUENCE 3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI). ASSESSMENT N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. STATISTICAL TESTS Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05. RESULTS N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC = 22.55 ± 1.49; NM-CRPD = 19.79 ± 1.92; NM-nVolHC = 2.69 × 10-5 ± 1.02 × 10-5; NM-nVolPD = 1.18 × 10-5 ± 0.96 × 10-5; Iron-CRHC = 10.51 ± 2.64; Iron-CRPD = 19.35 ± 7.88; Iron-nVolHC = 0.72 × 10-5 ± 0.81 × 10-5; Iron-nVolPD = 2.82 × 10-5 ± 2.04 × 10-5). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR = -0.31; ρiron-CR = 0.43; ρiron-nVol = 0.46) and the motor status (ρNM-nVol = -0.27; ρiron-CR = 0.33; ρiron-nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. DATA CONCLUSION This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mikel Ariz
- Ciberonc and Biomedical Engineering Program, CIMA University of Navarra, Pamplona, Spain
- Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, Pamplona, Spain
| | - Martín Martínez
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
| | - Ignacio Alvarez
- Movement Disorders Unit, Neurology, University Hospital Mútua de Terrassa, Terrassa, Barcelona, Spain
| | - Maria A Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Gabriel Castellanos
- Department of Physiological Sciences, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Pau Pastor
- Unit of Neurodegenerative Diseases, Department of Neurology, University Hospital Germans Trias i Pujol, and Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain
| | - Maria A Pastor
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
- Movement Disorders Unit, Neurology, University of Navarra, Pamplona, Spain
| | - Carlos Ortiz de Solórzano
- Ciberonc and Biomedical Engineering Program, CIMA University of Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
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24
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Tao B, Xu J, Gao J, He S, Jiang S, Wang F, Chen X, Wu Y. Deep learning-based automatic segmentation of bone graft material after maxillary sinus augmentation. Clin Oral Implants Res 2024; 35:964-972. [PMID: 38033189 DOI: 10.1111/clr.14221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 12/02/2023]
Abstract
OBJECTIVES To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone-beam computed tomography (CBCT) images. MATERIALS AND METHODS One hundred paired CBCT scans (a preoperative scan and a postoperative scan) were collected and randomly allocated to training (n = 82) and testing (n = 18) subsets. The ground truths of graft materials were labeled by three observers together (two experienced surgeons and a computer engineer). A deep learning model including a 3D V-Net and a 3D Attention V-Net was developed. The overall performance of the model was assessed through the testing data set. The comparative accuracy and inference time consumption of the model-driven and manual segmentation (by two surgeons with 3 years of experience in dental implant surgery) were conducted on 10 CBCT scans from the test samples. RESULTS The deep learning model had a Dice coefficient (Dice) of 90.36 ± 2.53%, a 95% Hausdorff distance (HD) of 1.59 ± 0.82 mm, and an average surface distance (ASD) of 0.38 ± 0.11 mm. The proposed model only needed 7.2 s, while the surgeon took 19.15 min on average to complete a segmentation task. The overall performances of the model were significantly superior to those of surgeons. CONCLUSIONS The proposed deep learning model yielded a more accurate and efficient performance of automatic segmentation of graft material after SA than that of the two surgeons. The proposed model could facilitate a powerful system for volumetric change evaluation, dental implant planning, and digital dentistry.
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Affiliation(s)
- Baoxin Tao
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Jiangchang Xu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Gao
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Shamin He
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Shuanglin Jiang
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Feng Wang
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiqun Wu
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
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Bhat I, Kuijf HJ, Viergever MA, Pluim JPW. Influence of learned landmark correspondences on lung CT registration. Med Phys 2024; 51:5321-5336. [PMID: 38713916 DOI: 10.1002/mp.17120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/04/2024] [Accepted: 04/25/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Disease or injury may cause a change in the biomechanical properties of the lungs, which can alter lung function. Image registration can be used to measure lung ventilation and quantify volume change, which can be a useful diagnostic aid. However, lung registration is a challenging problem because of the variation in deformation along the lungs, sliding motion of the lungs along the ribs, and change in density. PURPOSE Landmark correspondences have been used to make deformable image registration robust to large displacements. METHODS To tackle the challenging task of intra-patient lung computed tomography (CT) registration, we extend the landmark correspondence prediction model deep convolutional neural network-Match by introducing a soft mask loss term to encourage landmark correspondences in specific regions and avoid the use of a mask during inference. To produce realistic deformations to train the landmark correspondence model, we use data-driven synthetic transformations. We study the influence of these learned landmark correspondences on lung CT registration by integrating them into intensity-based registration as a distance-based penalty. RESULTS Our results on the public thoracic CT dataset COPDgene show that using learned landmark correspondences as a soft constraint can reduce median registration error from approximately 5.46 to 4.08 mm compared to standard intensity-based registration, in the absence of lung masks. CONCLUSIONS We show that using landmark correspondences results in minor improvements in local alignment, while significantly improving global alignment.
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Affiliation(s)
- Ishaan Bhat
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Josien P W Pluim
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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26
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Friedlander Y, Munidasa S, Thakar A, Ragunayakam N, Venegas C, Kjarsgaard M, Zanette B, Capaldi DPI, Santyr G, Nair P, Svenningsen S. Phase-Resolved Functional Lung (PREFUL) MRI to Quantify Ventilation: Feasibility and Physiological Relevance in Severe Asthma. Acad Radiol 2024; 31:3416-3426. [PMID: 38378325 DOI: 10.1016/j.acra.2024.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/28/2024] [Accepted: 01/28/2024] [Indexed: 02/22/2024]
Abstract
RATIONALE AND OBJECTIVES Emergent evidence in several respiratory diseases supports translational potential for Phase-Resolved Functional Lung (PREFUL) MRI to spatially quantify ventilation but its feasibility and physiological relevance have not been demonstrated in patients with asthma. This study compares PREFUL-derived ventilation defect percent (VDP) in severe asthma patients to healthy controls and measures its responsiveness to bronchodilator therapy and relation to established measures of airways disease. MATERIALS AND METHODS Forty-one adults with severe asthma and seven healthy controls performed same-day free-breathing 1H MRI, 129Xe MRI, spirometry, and oscillometry. A subset of participants (n = 23) performed chest CT and another subset of participants with asthma (n = 19) repeated 1H MRI following the administration of a bronchodilator. VDP was calculated for both PREFUL and 129Xe MRI. Additionally, the percent of functional small airways disease was determined from CT parametric response maps (PRMfSAD). RESULTS PREFUL VDP measured pre-bronchodilator (19.1% [7.4-43.3], p = 0.0002) and post-bronchodilator (16.9% [6.1-38.4], p = 0.0007) were significantly greater than that of healthy controls (7.5% [3.7-15.5]) and was significantly decreased post-bronchodilator (from 21.9% [10.1-36.9] to 16.9% [6.1-38.4], p = 0.0053). PREFUL VDP was correlated with spirometry (FEV1%pred: r = -0.46, p = 0.0023; FVC%pred: r = -0.35, p = 0.024, FEV1/FVC: r = -0.46, p = 0.0028), 129Xe MRI VDP (r = 0.39, p = 0.013), and metrics of small airway disease (CT PRMfSAD: r = 0.55, p = 0.021; Xrs5 Hz: r = -0.44, p = 0.0046, and AX: r = 0.32, p = 0.044). CONCLUSION PREFUL-derived VDP is responsive to bronchodilator therapy in asthma and is associated with measures of airflow obstruction and small airway dysfunction. These findings validate PREFUL VDP as a physiologically relevant and accessible ventilation imaging outcome measure in asthma.
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Affiliation(s)
- Yonni Friedlander
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Samal Munidasa
- Translational Medicine Program, The Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Ashutosh Thakar
- Department of Medicine, McMaster University, Hamilton, Canada
| | | | - Carmen Venegas
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Canada; Department of Medicine, McMaster University, Hamilton, Canada
| | - Melanie Kjarsgaard
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Canada; Department of Medicine, McMaster University, Hamilton, Canada
| | - Brandon Zanette
- Translational Medicine Program, The Hospital for Sick Children, Toronto, Canada
| | - Dante P I Capaldi
- Department of Radiation Oncology, Division of Physics, University of California, San Francisco, CA
| | - Giles Santyr
- Translational Medicine Program, The Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Parameswaran Nair
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Canada; Department of Medicine, McMaster University, Hamilton, Canada
| | - Sarah Svenningsen
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Canada; Department of Medicine, McMaster University, Hamilton, Canada.
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27
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Chang Y, Liang Y, Wu H, Li L, Yang B, Jiang L, Ren Q, Pei X. Adaptive assessment based on fractional CBCT images for cervical cancer. J Appl Clin Med Phys 2024:e14462. [PMID: 39072895 DOI: 10.1002/acm2.14462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
PURPOSE Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images. METHODS Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA. RESULTS The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2 mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan. CONCLUSION This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.
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Affiliation(s)
- Yankui Chang
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Yongguang Liang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Haotian Wu
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Lingyan Li
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Bo Yang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Lipeng Jiang
- Department of Radiation Oncology, First Affiliated Hospital of Jinzhou Medical University, Shenyang, China
| | - Qiang Ren
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Xi Pei
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
- Anhui Wisdom Technology Company Limited, Hefei, China
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28
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Amiri S, Vrtovec T, Mustafaev T, Deufel CL, Thomsen HS, Sillesen MH, Brandt EGS, Andersen MB, Müller CF, Ibragimov B. Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation. Med Phys 2024. [PMID: 39031886 DOI: 10.1002/mp.17300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/22/2024] Open
Abstract
BACKGROUND The pancreas is a complex abdominal organ with many anatomical variations, and therefore automated pancreas segmentation from medical images is a challenging application. PURPOSE In this paper, we present a framework for segmenting individual pancreatic subregions and the pancreatic duct from three-dimensional (3D) computed tomography (CT) images. METHODS A multiagent reinforcement learning (RL) network was used to detect landmarks of the head, neck, body, and tail of the pancreas, and landmarks along the pancreatic duct in a selected target CT image. Using the landmark detection results, an atlas of pancreases was nonrigidly registered to the target image, resulting in anatomical probability maps for the pancreatic subregions and duct. The probability maps were augmented with multilabel 3D U-Net architectures to obtain the final segmentation results. RESULTS To evaluate the performance of our proposed framework, we computed the Dice similarity coefficient (DSC) between the predicted and ground truth manual segmentations on a database of 82 CT images with manually segmented pancreatic subregions and 37 CT images with manually segmented pancreatic ducts. For the four pancreatic subregions, the mean DSC improved from 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shifted windowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, when utilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperforming the standard approaches and existing methods on different datasets. CONCLUSIONS The resulting accuracy of the proposed RL-based segmentation framework demonstrates an improvement against segmentation with standard U-Net architectures.
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Affiliation(s)
- Sepideh Amiri
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Tomaž Vrtovec
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
| | | | | | - Henrik S Thomsen
- Department of Radiology, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Martin Hylleholt Sillesen
- Department of Organ Surgery and Transplantation, and CSTAR, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Michael Brun Andersen
- Department of Radiology, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Copenhagen University, Copenhagen, Denmark
| | - Christoph Felix Müller
- Department of Radiology, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Bulat Ibragimov
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
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Zhang Y, Huang Y, Xiong X, Liu Y, Qi J. A multi-task generative model for simultaneous post-contrast MR image synthesis and brainstem glioma segmentation. Magn Reson Imaging 2024; 113:110210. [PMID: 39033886 DOI: 10.1016/j.mri.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 06/25/2024] [Accepted: 07/16/2024] [Indexed: 07/23/2024]
Abstract
OBJECTIVES This study aims to generate post-contrast MR images reducing the exposure of gadolinium-based contrast agents (GBCAs) for brainstem glioma (BSG) detection, simultaneously delineating the BSG lesion, and providing high-resolution contrast information. METHODS A retrospective cohort of 30 patients diagnosed with brainstem glioma was included. Multi-contrast images, including pre-contrast T1 weighted (pre-T1w), T2 weighted (T2w), arterial spin labeling (ASL) and post-contrast T1w images, were collected. A multi-task generative model was developed to synthesize post-contrast T1w images and simultaneously segment BSG masks from the multi-contrast inputs. Performance evaluation was conducted using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) metrics. A perceptual study was also undertaken to assess diagnostic quality. RESULTS The proposed model achieved SSIM of 0.86 ± 0.04, PSNR of 26.33 ± 0.05 and MAE of 57.20 ± 20.50 for post-contrast T1w image synthesis. Automated delineation of the BSG lesions achieved Dice similarity coefficient (DSC) score of 0.88 ± 0.27. CONCLUSIONS The proposed model can synthesize high-quality post-contrast T1w images and accurately segment the BSG region, yielding satisfactory DSC scores. CLINICAL RELEVANCE STATEMENT The synthesized post-contrast MR image presented in this study has the potential to reduce the usage of gadolinium-based contrast agents, which may pose risks to patients. Moreover, the automated segmentation method proposed in this paper aids radiologists in accurately identifying the brainstem glioma lesion, facilitating the diagnostic process.
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Affiliation(s)
| | - Yanxin Huang
- University of Electronic Science and Technology of China, Chengdu, China
| | | | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jin Qi
- University of Electronic Science and Technology of China, Chengdu, China
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Smits HJG, Bennink E, Ruiter LN, Breimer GE, Willems SM, Dankbaar JW, Philippens MEP. Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study. Transl Oncol 2024; 48:102051. [PMID: 39018773 DOI: 10.1016/j.tranon.2024.102051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/09/2024] [Accepted: 07/01/2024] [Indexed: 07/19/2024] Open
Abstract
In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.
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Affiliation(s)
- Hilde J G Smits
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Edwin Bennink
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Lilian N Ruiter
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stefan M Willems
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan W Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
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Raudales R, Kim G, Kelly SM, Hatfield J, Guan W, Zhao S, Paul A, Qian Y, Li B, Huang ZJ. Specific and comprehensive genetic targeting reveals brain-wide distribution and synaptic input patterns of GABAergic axo-axonic interneurons. eLife 2024; 13:RP93481. [PMID: 39012795 PMCID: PMC11251723 DOI: 10.7554/elife.93481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024] Open
Abstract
Axo-axonic cells (AACs), also called chandelier cells (ChCs) in the cerebral cortex, are the most distinctive type of GABAergic interneurons described in the neocortex, hippocampus, and basolateral amygdala (BLA). AACs selectively innervate glutamatergic projection neurons (PNs) at their axon initial segment (AIS), thus may exert decisive control over PN spiking and regulate PN functional ensembles. However, the brain-wide distribution, synaptic connectivity, and circuit function of AACs remain poorly understood, largely due to the lack of specific and reliable experimental tools. Here, we have established an intersectional genetic strategy that achieves specific and comprehensive targeting of AACs throughout the mouse brain based on their lineage (Nkx2.1) and molecular (Unc5b, Pthlh) markers. We discovered that AACs are deployed across essentially all the pallium-derived brain structures, including not only the dorsal pallium-derived neocortex and medial pallium-derived hippocampal formation, but also the lateral pallium-derived claustrum-insular complex, and the ventral pallium-derived extended amygdaloid complex and olfactory centers. AACs are also abundant in anterior olfactory nucleus, taenia tecta, and lateral septum. AACs show characteristic variations in density across neocortical areas and layers and across subregions of the hippocampal formation. Neocortical AACs comprise multiple laminar subtypes with distinct dendritic and axonal arborization patterns. Retrograde monosynaptic tracing from AACs across neocortical, hippocampal, and BLA regions reveal shared as well as distinct patterns of synaptic input. Specific and comprehensive targeting of AACs facilitates the study of their developmental genetic program and circuit function across brain structures, providing a ground truth platform for understanding the conservation and variation of a bona fide cell type across brain regions and species.
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Affiliation(s)
- Ricardo Raudales
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Program in Neurobiology, Stony Brook UniversityStony BrookUnited States
| | - Gukhan Kim
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
| | - Sean M Kelly
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Program in Neurobiology, Stony Brook UniversityStony BrookUnited States
| | - Joshua Hatfield
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Department of Neurobiology, Duke UniversityDurhamUnited States
| | - Wuqiang Guan
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
| | - Shengli Zhao
- Department of Neurobiology, Duke UniversityDurhamUnited States
| | - Anirban Paul
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Department of Neural and Behavioral Sciences, Penn State College of MedicineHersheyUnited States
| | - Yongjun Qian
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Department of Neurobiology, Duke UniversityDurhamUnited States
| | - Bo Li
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Department of Neurobiology, Duke UniversityDurhamUnited States
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Z Josh Huang
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
- Department of Neurobiology, Duke UniversityDurhamUnited States
- Department of Biomedical Engineering, Duke UniversityDurhamUnited States
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32
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Ravano V, Andelova M, Piredda GF, Sommer S, Caneschi S, Roccaro L, Krasensky J, Kudrna M, Uher T, Corredor-Jerez RA, Disselhorst JA, Maréchal B, Hilbert T, Thiran JP, Richiardi J, Horakova D, Vaneckova M, Kober T. Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis. J Neurol 2024:10.1007/s00415-024-12568-x. [PMID: 39003428 DOI: 10.1007/s00415-024-12568-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 07/01/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND AND OBJECTIVES In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. METHODS We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. RESULTS AND CONCLUSIONS Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.
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Affiliation(s)
- Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland.
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University, Prague, Czech Republic
| | - Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
| | - Stefan Sommer
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Swiss Center for Muscoloskeletal Imaging (SCMI) Balgrist Campus, Zurich, Switzerland
| | - Samuele Caneschi
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lucia Roccaro
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jan Krasensky
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Matej Kudrna
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University, Prague, Czech Republic
| | - Ricardo A Corredor-Jerez
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonathan A Disselhorst
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University, Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Schlaffke L, Rehmann R, Froeling M, Güttsches AK, Vorgerd M, Enax-Krumova E, Forsting J. Quantitative muscle MRI in sporadic inclusion body myositis (sIBM): A prospective cohort study. J Neuromuscul Dis 2024:JND240053. [PMID: 39031378 DOI: 10.3233/jnd-240053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
Background Sporadic inclusion body myositis (sIBM) is the predominant idiopathic inflammatory myopathy (IIM) in older people. Limitations of classical clinical assessments have been discussed as possible explanations for failed clinical trials, underlining the need for more sensitive outcome measures. Quantitative muscle MRI (qMRI) is a promising candidate for evaluating and monitoring sIBM. Objective Longitudinal assessment of qMRI in sIBM patients. Methods We evaluated fifteen lower extremity muscles of 12 sIBM patients (5 females, mean age 69.6, BMI 27.8) and 12 healthy age- and gender-matched controls. Seven patients and matched controls underwent a follow-up evaluation after one year. Clinical assessment included testing for muscle strength with Quick Motor Function Measure (QMFM), IBM functional rating scale (IBM-FRS), and gait analysis (6-minute walking distance). 3T-MRI scans of the lower extremities were performed, including a Dixon-based sequence, T2 mapping and Diffusion Tensor Imaging. The qMRI-values fat-fraction (FF), water T2 relaxation time (wT2), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ1), and radial diffusivity (RD) were analysed. Results Compared to healthy controls, significant differences for all qMRI parameters averaged over all muscles were found in sIBM using a MANOVA (p < 0.001). In low-fat muscles (FF < 10% ), a significant increase of wT2 and FA with an accompanying decrease of MD, λ1, and RD was observed (p≤0.020). The highest correlation with clinical assessments was found for wT2 values in thigh muscles (r≤-0.634). Significant changes of FF (+3.0% ), wT2 (+0.6 ms), MD (-0.04 10 - 3mm2/s), λ1 (-0.05 10 - 3mm2/s), and RD (-0.03 10 - 3mm2/s) were observed in the longitudinal evaluation of sIBM patients (p≤0.001). FA showed no significant change (p = 0.242). Conclusion qMRI metrics correlate with clinical findings and can reflect different ongoing pathophysiological mechanisms. While wT2 is an emerging marker of disease activity, the role of diffusion metrics, possibly reflecting changes in fibre size and intracellular deposits, remains subject to further investigations.
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Affiliation(s)
- Lara Schlaffke
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Robert Rehmann
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Department of Neurology, Klinikum Dortmund, University Witten-Herdecke, Dortmund, Germany
| | - Martijn Froeling
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Anne-Katrin Güttsches
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Matthias Vorgerd
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Elena Enax-Krumova
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Johannes Forsting
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
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Li G, Wang G, Wei W, Li Z, Xiao Q, He H, Luo D, Chen L, Li J, Zhang X, Song Y, Bai S. Cardiorespiratory motion characteristics and their dosimetric impact on cardiac stereotactic body radiotherapy. Med Phys 2024. [PMID: 38994881 DOI: 10.1002/mp.17284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Cardiac stereotactic body radiotherapy (CSBRT) is an emerging and promising noninvasive technique for treating refractory arrhythmias utilizing highly precise, single or limited-fraction high-dose irradiations. This method promises to revolutionize the treatment of cardiac conditions by delivering targeted therapy with minimal exposure to surrounding healthy tissues. However, the dynamic nature of cardiorespiratory motion poses significant challenges to the precise delivery of dose in CSBRT, introducing potential variabilities that can impact treatment efficacy. The complexities of the influence of cardiorespiratory motion on dose distribution are compounded by interplay and blurring effects, introducing additional layers of dose uncertainty. These effects, critical to the understanding and improvement of the accuracy of CSBRT, remain unexplored, presenting a gap in current clinical literature. PURPOSE To investigate the cardiorespiratory motion characteristics in arrhythmia patients and the dosimetric impact of interplay and blurring effects induced by cardiorespiratory motion on CSBRT plan quality. METHODS The position and volume variations in the substrate target and cardiac substructures were evaluated in 12 arrhythmia patients using displacement maximum (DMX) and volume metrics. Moreover, a four-dimensional (4D) dose reconstruction approach was employed to examine the dose uncertainty of the cardiorespiratory motion. RESULTS Cardiac pulsation induced lower DMX than respiratory motion but increased the coefficient of variation and relative range in cardiac substructure volumes. The mean DMX of the substrate target was 0.52 cm (range: 0.26-0.80 cm) for cardiac pulsation and 0.82 cm (range: 0.32-2.05 cm) for respiratory motion. The mean DMX of the cardiac structure ranged from 0.15 to 1.56 cm during cardiac pulsation and from 0.35 to 1.89 cm during respiratory motion. Cardiac pulsation resulted in an average deviation of -0.73% (range: -4.01%-4.47%) in V25 between the 3D and 4D doses. The mean deviations in the homogeneity index (HI) and gradient index (GI) were 1.70% (range: -3.10%-4.36%) and 0.03 (range: -0.14-0.11), respectively. For cardiac substructures, the deviations in D50 due to cardiac pulsation ranged from -1.88% to 1.44%, whereas the deviations in Dmax ranged from -2.96% to 0.88% of the prescription dose. By contrast, the respiratory motion led to a mean deviation of -1.50% (range: -10.73%-4.23%) in V25. The mean deviations in HI and GI due to respiratory motion were 4.43% (range: -3.89%-13.98%) and 0.18 (range: -0.01-0.47) (p < 0.05), respectively. Furthermore, the deviations in D50 and Dmax in cardiac substructures for the respiratory motion ranged from -0.28% to 4.24% and -4.12% to 1.16%, respectively. CONCLUSIONS Cardiorespiratory motion characteristics vary among patients, with the respiratory motion being more significant. The intricate cardiorespiratory motion characteristics and CSBRT plan complexity can induce substantial dose uncertainty. Therefore, assessing individual motion characteristics and 4D dose reconstruction techniques is critical for implementing CSBRT without compromising efficacy and safety.
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Affiliation(s)
- Guangjun Li
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Guangyu Wang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Weige Wei
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhibin Li
- Department of Radiotherapy & Oncology, The First Affiliated Hospital of Soochow University, Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Haiping He
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dashuang Luo
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Chen
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jing Li
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiangyu Zhang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ying Song
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Brazovskaja A, Gomes T, Holtackers R, Wahle P, Körner C, He Z, Schaffer T, Eckel JC, Hänsel R, Santel M, Seimiya M, Denecke T, Dannemann M, Brosch M, Hampe J, Seehofer D, Damm G, Camp JG, Treutlein B. Cell atlas of the regenerating human liver after portal vein embolization. Nat Commun 2024; 15:5827. [PMID: 38992008 PMCID: PMC11239663 DOI: 10.1038/s41467-024-49236-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/28/2024] [Indexed: 07/13/2024] Open
Abstract
The liver has the remarkable capacity to regenerate. In the clinic, regeneration is induced by portal vein embolization, which redirects portal blood flow, resulting in liver hypertrophy in locations with increased blood supply, and atrophy of embolized segments. Here, we apply single-cell and single-nucleus transcriptomics on healthy, hypertrophied, and atrophied patient-derived liver samples to explore cell states in the regenerating liver. Our data unveils pervasive upregulation of genes associated with developmental processes, cellular adhesion, and inflammation in post-portal vein embolization liver, disrupted portal-central hepatocyte zonation, and altered cell subtype composition of endothelial and immune cells. Interlineage crosstalk analysis reveals mesenchymal cells as an interaction hub between immune and endothelial cells, and highlights the importance of extracellular matrix proteins in liver regeneration. Moreover, we establish tissue-scale iterative indirect immunofluorescence imaging for high-dimensional spatial analysis of perivascular microenvironments, uncovering changes to tissue architecture in regenerating liver lobules. Altogether, our data is a rich resource revealing cellular and histological changes in human liver regeneration.
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Affiliation(s)
| | - Tomás Gomes
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
| | - Rene Holtackers
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Philipp Wahle
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Christiane Körner
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital, Leipzig University, Leipzig, Germany
| | - Zhisong He
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Theresa Schaffer
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Julian Connor Eckel
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital, Leipzig University, Leipzig, Germany
| | - René Hänsel
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital, Leipzig University, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany
| | - Malgorzata Santel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Makiko Seimiya
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, Leipzig University, Leipzig, Germany
| | - Michael Dannemann
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mario Brosch
- Medical Department 1, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden (CRTD), Technical University Dresden, Dresden, Germany
| | - Jochen Hampe
- Medical Department 1, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden (CRTD), Technical University Dresden, Dresden, Germany
| | - Daniel Seehofer
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital, Leipzig University, Leipzig, Germany
| | - Georg Damm
- Department of Hepatobiliary Surgery and Visceral Transplantation, University Hospital, Leipzig University, Leipzig, Germany.
| | - J Gray Camp
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
| | - Barbara Treutlein
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
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36
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Pranzatelli TJ, Perez P, Ku A, Matuck B, Huynh K, Sakai S, Abed M, Jang SI, Yamada E, Dominick K, Ahmed Z, Oliver A, Wasikowski R, Easter QT, Baer AN, Pelayo E, Khavandgar Z, Kleiner DE, Magone MT, Gupta S, Lessard C, Farris AD, Burbelo PD, Martin D, Morell RJ, Zheng C, Rachmaninoff N, Maldonado-Ortiz J, Qu X, Aure M, Dezfulian MH, Lake R, Teichmann S, Barber DL, Tsoi LC, Sowalsky AG, Tyc KM, Liu J, Gudjonsson J, Byrd KM, Johnson PL, Chiorini JA, Warner BM. GZMK+CD8+ T cells Target A Specific Acinar Cell Type in Sjögren's Disease. RESEARCH SQUARE 2024:rs.3.rs-3601404. [PMID: 38196575 PMCID: PMC10775371 DOI: 10.21203/rs.3.rs-3601404/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Sjögren's Disease (SjD) is a systemic autoimmune disease without a clear etiology or effective therapy. Utilizing unbiased single-cell and spatial transcriptomics to analyze human minor salivary glands in health and disease we developed a comprehensive understanding of the cellular landscape of healthy salivary glands and how that landscape changes in SjD patients. We identified novel seromucous acinar cell types and identified a population of PRR4+CST3+WFDC2- seromucous acinar cells that are particularly targeted in SjD. Notably, GZMK+CD8 T cells, enriched in SjD, exhibited a cytotoxic phenotype and were physically associated with immune-engaged epithelial cells in disease. These findings shed light on the immune response's impact on transitioning acinar cells with high levels of secretion and explain the loss of this specific cell population in SjD. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's Disease.
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Affiliation(s)
- Thomas J.F. Pranzatelli
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Department of Biology, University of Maryland College Park, MD, USA
| | - Paola Perez
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Anson Ku
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Bethesda, MD, USA
| | - Bruno Matuck
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Khoa Huynh
- Department of Biostatistics, Virginia Commonwealth University, VA, USA
| | - Shunsuke Sakai
- T-lymphocyte Biology Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Mehdi Abed
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Shyh-Ing Jang
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Eiko Yamada
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Kalie Dominick
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Zara Ahmed
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Amanda Oliver
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Rachael Wasikowski
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Quinn T. Easter
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | - Alan N. Baer
- Sjögren’s Clinical Investigations Team, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Eileen Pelayo
- Sjögren’s Clinical Investigations Team, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Zohreh Khavandgar
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Sjögren’s Clinical Investigations Team, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - David E. Kleiner
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda MD, USA
| | - M. Teresa Magone
- Consult Services Section, National Eye Institute, National Institutes of Health, Bethesda MD, USA
| | - Sarthak Gupta
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Lupus Clinical Trials Unit, National Institute of Arthritis and Musculoskeletal and Skin, Diseases, National Institutes of Health, Bethesda MD, USA
| | - Christopher Lessard
- Genes & Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - A. Darise Farris
- Arthritis & Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Peter D. Burbelo
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Martin
- Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
| | - Robert J. Morell
- Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
| | - Changyu Zheng
- Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
| | | | - Jose Maldonado-Ortiz
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Sjögren’s Clinical Investigations Team, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Xufeng Qu
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Marit Aure
- Matrix and Morphogenesis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | | | - Ross Lake
- Laboratory of Genitourinary Cancer Pathogenesis (LCGP) Microscopy Core Facility, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Bethesda, MD, USA
| | - Sarah Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Daniel L. Barber
- T-lymphocyte Biology Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Lam C. Tsoi
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam G. Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Bethesda, MD, USA
| | - Katarzyna M. Tyc
- Department of Biostatistics, Virginia Commonwealth University, VA, USA
| | - Jinze Liu
- Department of Biostatistics, Virginia Commonwealth University, VA, USA
| | - Johann Gudjonsson
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Kevin M. Byrd
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
| | | | - John A. Chiorini
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Blake M. Warner
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
- Sjögren’s Clinical Investigations Team, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
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Forsting J, Rehmann R, Rohm M, Kocabas A, De Lorenzo A, Güttsches AK, Vorgerd M, Froeling M, Schlaffke L. Prospective longitudinal cohort study of quantitative muscle magnetic resonance imaging in a healthy control population. NMR IN BIOMEDICINE 2024:e5214. [PMID: 38982853 DOI: 10.1002/nbm.5214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 06/03/2024] [Accepted: 06/18/2024] [Indexed: 07/11/2024]
Abstract
Quantitative muscle magnetic resonance imaging (qMRI) is a valuable methodology for assessing muscular injuries and neuromuscular disorders. Notably, muscle diffusion tensor imaging (DTI) gives insights into muscle microstructural and macrostructural characteristics. However, the long-term reproducibility and robustness of these measurements remain relatively unexplored. The purpose of this prospective longitudinal cohort study was to assess the long-term robustness and range of variation of qMRI parameters, especially DTI metrics, in the lower extremity muscles of healthy controls under real-life conditions. Twelve volunteers (seven females, age 44.1 ± 12.1 years, body mass index 23.3 ± 2.0 kg/m2) underwent five leg muscle MRI sessions every 20 ± 4 weeks over a total period of 1.5 years. A multiecho gradient-echo Dixon-based sequence, a multiecho spin-echo T2-mapping sequence, and a spin-echo echo planar imaging diffusion-weighted sequence were acquired bilaterally with a Philips 3-T Achieva MR System using a 16-channel torso coil. Fifteen leg muscles were segmented in both lower extremities. qMRI parameters, including fat fraction (FF), water T2 relaxation time, and the diffusion metrics fractional anisotropy (FA) and mean diffusivity (MD), were evaluated. Coefficients of variance (wsCV) and intraclass correlation coefficients (ICCs) were calculated to assess the reproducibility of qMRI parameters. The standard error of measurement (SEM) and the minimal detectable change (MDC) were calculated to determine the range of variation. All tests were applied to all muscles and, subsequently, to each muscle separately. wsCV showed good reproducibility (≤ 10%) for all qMRI parameters in all muscles. The ICCs revealed excellent agreement between time points (FF = 0.980, water T2 = 0.941, FA = 0.952, MD = 0.948). Random measurement errors assessed by SEM and the MDC were low (< 12%). In conclusion, in this study, we showed that qMRI parameters in healthy volunteers living normal lives are stable over 18 months, thereby defining a benchmark for the expected range of variation over time.
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Affiliation(s)
- Johannes Forsting
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Robert Rehmann
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Department of Neurology, Klinikum Dortmund, University Witten-Herdecke, Dortmund, Germany
| | - Marlena Rohm
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Abdulhadi Kocabas
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Alice De Lorenzo
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Anne-Katrin Güttsches
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Matthias Vorgerd
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Martijn Froeling
- Department of Radiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Lara Schlaffke
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
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Howe JR, Chan CL, Lee D, Blanquart M, Romero HK, Zadina AN, Lemieux ME, Mills F, Desplats PA, Tye KM, Root CM. Control of innate olfactory valence by segregated cortical amygdala circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600895. [PMID: 38979308 PMCID: PMC11230396 DOI: 10.1101/2024.06.26.600895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Animals perform innate behaviors that are stereotyped responses to specific evolutionarily relevant stimuli in the absence of prior learning or experience. These behaviors can be reduced to an axis of valence, whereby specific odors evoke approach or avoidance. The cortical amygdala (plCoA) mediates innate attraction and aversion to odor. However, little is known about how this brain area gives rise to behaviors of opposing motivational valence. Here, we sought to define the circuit features of plCoA that give rise to innate olfactory behaviors of valence. We characterized the physiology, gene expression, and projections of this structure, identifying a divergent, topographic organization that selectively controls innate attraction and avoidance to odor. First, we examined odor-evoked responses in these areas and found sparse encoding of odor identity, but not valence. We next considered a topographic organization and found that optogenetic stimulation of the anterior and posterior domains of plCoA elicits attraction and avoidance, respectively, suggesting a functional axis for valence. Using single cell and spatial RNA sequencing, we identified the molecular cell types in plCoA, revealing an anteroposterior gradient in cell types, whereby anterior glutamatergic neurons preferentially express Slc17a6 and posterior neurons express Slc17a7. Activation of these respective cell types recapitulates appetitive and aversive valence behaviors, and chemogenetic inhibition reveals partial necessity for valence responses to innate appetitive or aversive odors. Finally, we identified topographically organized circuits defined by projections, whereby anterior neurons preferentially project to medial amygdala, and posterior neurons preferentially project to nucleus accumbens, which are respectively sufficient and necessary for innate negative and positive olfactory valence. Together, these data advance our understanding of how the olfactory system generates stereotypic, hardwired attraction and avoidance, and supports a model whereby distinct, topographically distributed plCoA populations direct innate olfactory valence responses by signaling to divergent valence-specific targets, linking upstream olfactory identity to downstream valence behaviors, through a population code. This represents a novel circuit motif in which valence encoding is represented not by the firing properties of individual neurons, but by population level identity encoding that is routed through divergent targets to mediate distinct valence.
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Affiliation(s)
- James R Howe
- Department of Biological Sciences, Section of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA
- These authors contributed equally
| | - Chung-Lung Chan
- Department of Biological Sciences, Section of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
- These authors contributed equally
| | - Donghyung Lee
- Department of Biological Sciences, Section of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
| | - Marlon Blanquart
- Department of Biological Sciences, Section of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
| | - Haylie K Romero
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Circadian Biology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Abigail N Zadina
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, 10027, USA
| | | | - Fergil Mills
- Salk Institute for Biological Sciences, La Jolla, CA 92037, USA
| | - Paula A Desplats
- Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Circadian Biology, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Pathology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kay M Tye
- Department of Biological Sciences, Section of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
- Salk Institute for Biological Sciences, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, La Jolla, CA 92037, USA
| | - Cory M Root
- Department of Biological Sciences, Section of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
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Aliotta E, Paudyal R, Diplas B, Han J, Hu YC, Hun Oh J, Hatzoglou V, Jensen N, Zhang P, Aristophanous M, Riaz N, Deasy JO, Lee NY, Shukla-Dave A. Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy. Phys Imaging Radiat Oncol 2024; 31:100603. [PMID: 39040433 PMCID: PMC11261256 DOI: 10.1016/j.phro.2024.100603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
Background and purpose Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methods Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. Results Significant correlations with volume loss were observed for baseline FDG SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. Conclusions Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.
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Affiliation(s)
- Eric Aliotta
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Bill Diplas
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - James Han
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Naomi Jensen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Peng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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Brewer M, Migas LG, Clouthier KA, Allen JL, Anderson DM, Pingry E, Farrow M, Quardokus EM, Spraggins JM, Van de Plas R, de Caestecker MP. Validation of an organ mapping antibody panel for cyclical immunofluorescence microscopy on normal human kidneys. Am J Physiol Renal Physiol 2024; 327:F91-F102. [PMID: 38721662 DOI: 10.1152/ajprenal.00426.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/11/2024] [Accepted: 03/26/2024] [Indexed: 06/21/2024] Open
Abstract
The lack of standardization in antibody validation remains a major contributor to irreproducibility of human research. To address this, we have applied a standardized approach to validate a panel of antibodies to identify 18 major cell types and 5 extracellular matrix compartments in the human kidney by immunofluorescence (IF) microscopy. We have used these to generate an organ mapping antibody panel for two-dimensional (2-D) and three-dimensional (3-D) cyclical IF (CyCIF) to provide a more detailed method for evaluating tissue segmentation and volumes using a larger panel of markers than would normally be possible using standard fluorescence microscopy. CyCIF also makes it possible to perform multiplexed IF microscopy of whole slide images, which is a distinct advantage over other multiplexed imaging technologies that are applicable to limited fields of view. This enables a broader view of cell distributions across larger anatomical regions, allowing a better chance to capture localized regions of dysfunction in diseased tissues. These methods are broadly accessible to any laboratory with a fluorescence microscope, enabling spatial cellular phenotyping in normal and disease states. We also provide a detailed solution for image alignment between CyCIF cycles that can be used by investigators to perform these studies without programming experience using open-sourced software. This ability to perform multiplexed imaging without specialized instrumentation or computational skills opens the door to integration with more highly dimensional molecular imaging modalities such as spatial transcriptomics and imaging mass spectrometry, enabling the discovery of molecular markers of specific cell types, and how these are altered in disease.NEW & NOTEWORTHY We describe here validation criteria used to define on organ mapping panel of antibodies that can be used to define 18 cell types and five extracellular matrix compartments using cyclical immunofluorescence (CyCIF) microscopy. As CyCIF does not require specialized instrumentation, and image registration required to assemble CyCIF images can be performed by any laboratory without specialized computational skills, this technology is accessible to any laboratory with access to a fluorescence microscope and digital scanner.
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Affiliation(s)
- Maya Brewer
- Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Lukasz G Migas
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Kelly A Clouthier
- Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Jamie L Allen
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - David M Anderson
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Ellie Pingry
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Melissa Farrow
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Chemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Raf Van de Plas
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Mark P de Caestecker
- Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
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Pan J, Huang W, Rueckert D, Kustner T, Hammernik K. Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion Estimation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2420-2433. [PMID: 38354077 DOI: 10.1109/tmi.2024.3364504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments. The code is available at https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion.
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Zou J, Song Y, Liu L, Aviles-Rivero AI, Qin J. Unsupervised lung CT image registration via stochastic decomposition of deformation fields. Comput Med Imaging Graph 2024; 115:102397. [PMID: 38735104 DOI: 10.1016/j.compmedimag.2024.102397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 01/30/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024]
Abstract
We address the problem of lung CT image registration, which underpins various diagnoses and treatments for lung diseases. The main crux of the problem is the large deformation that the lungs undergo during respiration. This physiological process imposes several challenges from a learning point of view. In this paper, we propose a novel training scheme, called stochastic decomposition, which enables deep networks to effectively learn such a difficult deformation field during lung CT image registration. The key idea is to stochastically decompose the deformation field, and supervise the registration by synthetic data that have the corresponding appearance discrepancy. The stochastic decomposition allows for revealing all possible decompositions of the deformation field. At the learning level, these decompositions can be seen as a prior to reduce the ill-posedness of the registration yielding to boost the performance. We demonstrate the effectiveness of our framework on Lung CT data. We show, through extensive numerical and visual results, that our technique outperforms existing methods.
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Affiliation(s)
- Jing Zou
- Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Youyi Song
- Department of Data Science, School of Science, China Pharmaceutical University, Nan Jing, 210009, China
| | - Lihao Liu
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB30WA, UK
| | - Angelica I Aviles-Rivero
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB30WA, UK
| | - Jing Qin
- Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
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Tsekas G, Zachiu C, Bol GH, van den Dobbelsteen M, Meijers LT, van Lier AL, de Boer JC, Raaymakers BW. Investigating the use of comprehensive motion monitoring for intrafraction 3D drift assessment of hypofractionated prostate cancer patients on a 1.5T magnetic resonance imaging radiotherapy system. Phys Imaging Radiat Oncol 2024; 31:100596. [PMID: 39104731 PMCID: PMC11298924 DOI: 10.1016/j.phro.2024.100596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/28/2024] [Accepted: 05/28/2024] [Indexed: 08/07/2024] Open
Abstract
This work investigates the use of a multi-2D cine magnetic resonance imaging-based comprehensive motion monitoring (CMM) system for the assessment of prostate intrafraction 3D drifts. The data of six healthy volunteers were analyzed and the values of a clinically-relevant registration quality factor metric exported by CMM were presented. Additionally, the CMM-derived prostate motion was compared to a 3D-based reference and the 2D-3D tracking agreement was reported. Due to the low quality of SI motion tracking (often > 2 mm tracking mismatch between anatomical planes) we conclude that further improvements are desirable prior to clinical introduction of CMM for prostate drift corrections.
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Affiliation(s)
- Georgios Tsekas
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Cornel Zachiu
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Gijsbert H. Bol
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Madelon van den Dobbelsteen
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Lieke T.C. Meijers
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Astrid L.H.M.W. van Lier
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Johannes C.J. de Boer
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Bas W. Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Wang Z, Yu J, Zhai M, Wang Z, Sheng K, Zhu Y, Wang T, Liu M, Wang L, Yan M, Zhang J, Xu Y, Wang X, Ma L, Hu W, Cheng H. System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning. Cell Res 2024; 34:493-503. [PMID: 38605178 PMCID: PMC11217450 DOI: 10.1038/s41422-024-00956-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.
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Affiliation(s)
- Zichen Wang
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
- Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, Jiangsu, China
| | - Jing Yu
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
- Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, Jiangsu, China
| | - Muyue Zhai
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Zehua Wang
- Wangxuan Institute of Computer Technology, Peking University, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Kaiwen Sheng
- Beijing Academy of Artificial Intelligence, Beijing, China
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Yu Zhu
- Beijing Academy of Artificial Intelligence, Beijing, China
| | - Tianyu Wang
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Mianzhi Liu
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Lu Wang
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Miao Yan
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Jue Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- College of Engineering, Peking University, Beijing, China
| | - Ying Xu
- Jiangsu Key Laboratory of Neuropsychiatric Diseases and Cambridge-Su Genomic Resource Center, Medical School of Soochow University, Suzhou, Jiangsu, China
| | - Xianhua Wang
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
- Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, Jiangsu, China
| | - Lei Ma
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.
- Beijing Academy of Artificial Intelligence, Beijing, China.
| | - Wei Hu
- Wangxuan Institute of Computer Technology, Peking University, Beijing, China.
| | - Heping Cheng
- National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.
- Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, Jiangsu, China.
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Wang Y, Huang C, Chang W, Lu W, Hui Q, Jiang S, Ouyang X, Kong D. UDRSNet: An unsupervised deformable registration module based on image structure similarity. Med Phys 2024; 51:4811-4826. [PMID: 38353628 DOI: 10.1002/mp.16986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/03/2024] [Accepted: 01/28/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. PURPOSE To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. METHODS We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adoptedl 2 $l_2$ -norm to regularize the deformation field instead of the traditionall 1 $l_1$ -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. RESULTS Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical t $t$ -test results also proved that these improvements of our method have statistical significance. CONCLUSIONS In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.
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Affiliation(s)
- Yun Wang
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Chongfei Huang
- China Mobile (Hangzhou) Information Technology Co., Ltd., Hangzhou, China
| | - Wanru Chang
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Wenliang Lu
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
| | - Qinglei Hui
- School of Mathematics and Statistics, Anyang Normal University, Anyang, China
| | - Siyuan Jiang
- Zhejiang Demetics Medical Technology Co., Ltd, Hangzhou, China
| | - Xiaoping Ouyang
- State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, China
- Zhejiang Qiushi Institute for Mathematical Medicine, Hangzhou, China
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Trägårdh E, Ulén J, Enqvist O, Edenbrandt L, Larsson M. Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images. Clin Physiol Funct Imaging 2024; 44:332-339. [PMID: 38563413 DOI: 10.1111/cpf.12879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND We developed a fully automated artificial intelligence (AI)AI-based-based method for detecting suspected lymph node metastases in prostate-specific membrane antigen (PSMA)(PSMA) positron emission tomography-computed tomography (PET-CT)(PET-CT) images of prostate cancer patients by using data augmentation that adds synthetic lymph node metastases to the images to expand the training set. METHODS Synthetic data were derived from original training images to which new synthetic lymph node metastases were added. Thus, the original training set from a previous study (n = 420) was expanded by one synthetic image for every original image (n = 840), which was used to train an AI model. The performance of the AI model was compared to that of nuclear medicine physicians and a previously developed AI model. The human readers were alternately used as a reference and compared to either another reading or AI model. RESULTS The new AI model had an average sensitivity of 84% for detecting lymph node metastases compared with 78% for human readings. Our previously developed AI method without synthetic data had an average sensitivity of 79%. The number of false positive lesions were slightly higher for the new AI model (average 3.3 instances per patient) compared to human readings and the previous AI model (average 2.8 instances per patient), while the number of false negative lesions was lower. CONCLUSIONS Creating synthetic lymph node metastases, as a form of data augmentation, on [18F]PSMA-1007F]PSMA-1007 PETPET-CT-CT images improved the sensitivity of an AI model for detecting suspected lymph node metastases. However, the number of false positive lesions increased somewhat.
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Affiliation(s)
- Elin Trägårdh
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden
- Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden
| | | | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Truong NCD, Bangalore Yogananda CG, Wagner BC, Holcomb JM, Reddy D, Saadat N, Hatanpaa KJ, Patel TR, Fei B, Lee MD, Jain R, Bruce RJ, Pinho MC, Madhuranthakam AJ, Maldjian JA. Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma. Radiol Artif Intell 2024; 6:e230218. [PMID: 38775670 PMCID: PMC11294953 DOI: 10.1148/ryai.230218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 06/21/2024]
Abstract
Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.
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Affiliation(s)
- Nghi C. D. Truong
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Chandan Ganesh Bangalore Yogananda
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Benjamin C. Wagner
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - James M. Holcomb
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Divya Reddy
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Niloufar Saadat
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Kimmo J. Hatanpaa
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Toral R. Patel
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Baowei Fei
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Matthew D. Lee
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Rajan Jain
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Richard J. Bruce
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Marco C. Pinho
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Ananth J. Madhuranthakam
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Joseph A. Maldjian
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
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Hiremath A, Viswanathan VS, Bera K, Shiradkar R, Yuan L, Armitage K, Gilkeson R, Ji M, Fu P, Gupta A, Lu C, Madabhushi A. Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study. Comput Biol Med 2024; 177:108643. [PMID: 38815485 PMCID: PMC11188049 DOI: 10.1016/j.compbiomed.2024.108643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity.
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Affiliation(s)
- Amogh Hiremath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA; Picture Health, Cleveland, OH, USA
| | | | - Kaustav Bera
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | | | - Lei Yuan
- Renmin Hospital of Wuhan University, Department of Information Center, Wuhan, Hubei, China
| | - Keith Armitage
- University Hospitals Cleveland Medical Center, Department of Infectious Diseases, Cleveland, OH, USA
| | - Robert Gilkeson
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Mengyao Ji
- Renmin Hospital of Wuhan University, Department of Gastroenterology, Wuhan, Hubei, China
| | - Pingfu Fu
- Case Western Reserve University, Department of Population and Quantitative Health Sciences, Cleveland, OH, USA
| | - Amit Gupta
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Cheng Lu
- Guangdong Provincial People's Hospital, Department of Radiology, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial People's Hospital, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial People's Hospital, Medical Research Center, Guangdong Academy of Medical Sciences, China
| | - Anant Madabhushi
- Georgia Institute of Technology and Emory University, Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, GA, USA; Atlanta Veterans Administration Medical Center, GA, USA.
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49
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Wang DC, Santos-Valencia F, Song JH, Franks KM, Luo L. Embryonically active piriform cortex neurons promote intracortical recurrent connectivity during development. Neuron 2024:S0896-6273(24)00414-8. [PMID: 38964330 DOI: 10.1016/j.neuron.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 04/28/2024] [Accepted: 06/11/2024] [Indexed: 07/06/2024]
Abstract
Neuronal activity plays a critical role in the maturation of circuits that propagate sensory information into the brain. How widely does early activity regulate circuit maturation across the developing brain? Here, we used targeted recombination in active populations (TRAP) to perform a brain-wide survey for prenatally active neurons in mice and identified the piriform cortex as an abundantly TRAPed region. Whole-cell recordings in neonatal slices revealed preferential interconnectivity within embryonically TRAPed piriform neurons and their enhanced synaptic connectivity with other piriform neurons. In vivo Neuropixels recordings in neonates demonstrated that embryonically TRAPed piriform neurons exhibit broad functional connectivity within piriform and lead spontaneous synchronized population activity during a transient neonatal period, when recurrent connectivity is strengthening. Selectively activating or silencing these neurons in neonates enhanced or suppressed recurrent synaptic strength, respectively. Thus, embryonically TRAPed piriform neurons represent an interconnected hub-like population whose activity promotes recurrent connectivity in early development.
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Affiliation(s)
- David C Wang
- Howard Hughes Medical Institute and Department of Biology, Stanford University, Stanford, CA 94305, USA; Stanford MSTP, Stanford, CA 94305, USA
| | | | - Jun H Song
- Howard Hughes Medical Institute and Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Kevin M Franks
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA.
| | - Liqun Luo
- Howard Hughes Medical Institute and Department of Biology, Stanford University, Stanford, CA 94305, USA.
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Xie J, Shao HC, Li Y, Zhang Y. Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction. Phys Med Biol 2024; 69:135008. [PMID: 38870947 PMCID: PMC11218670 DOI: 10.1088/1361-6560/ad580d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/29/2024] [Accepted: 06/13/2024] [Indexed: 06/15/2024]
Abstract
Objective.Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction.Approach.PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Main results.PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning.Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.
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Affiliation(s)
- Jiacheng Xie
- The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Hua-Chieh Shao
- The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Yunxiang Li
- The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - You Zhang
- The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
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