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Sanchez T, Esteban O, Gomez Y, Pron A, Koob M, Dunet V, Girard N, Jakab A, Eixarch E, Auzias G, Bach Cuadra M. FetMRQC: A robust quality control system for multi-centric fetal brain MRI. Med Image Anal 2024; 97:103282. [PMID: 39053168 DOI: 10.1016/j.media.2024.103282] [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: 12/15/2023] [Revised: 06/28/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.
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Affiliation(s)
- Thomas Sanchez
- CIBM - Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Oscar Esteban
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yvan Gomez
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Spain; Department Woman-Mother-Child, CHUV, Lausanne, Switzerland
| | - Alexandre Pron
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France
| | - Mériam Koob
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nadine Girard
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France; Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital Timone, AP-HM, Marseilles, France
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
| | - Elisenda Eixarch
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Spain; IDIBAPS and CIBERER, Barcelona, Spain
| | - Guillaume Auzias
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France
| | - Meritxell Bach Cuadra
- CIBM - Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Xu H, Shi W, Sun J, Zheng T, Xu X, Sun C, Yi S, Wang G, Wu D. A motion assessment method for reference stack selection in fetal brain MRI reconstruction based on tensor rank approximation. NMR IN BIOMEDICINE 2024:e5248. [PMID: 39231762 DOI: 10.1002/nbm.5248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 07/12/2024] [Accepted: 08/08/2024] [Indexed: 09/06/2024]
Abstract
Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.
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Affiliation(s)
- Haoan Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Wen Shi
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jiwei Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Sun Yi
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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Machura B, Kucharski D, Bozek O, Eksner B, Kokoszka B, Pekala T, Radom M, Strzelczak M, Zarudzki L, Gutiérrez-Becker B, Krason A, Tessier J, Nalepa J. Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies. Comput Med Imaging Graph 2024; 116:102401. [PMID: 38795690 DOI: 10.1016/j.compmedimag.2024.102401] [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: 01/12/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/28/2024]
Abstract
Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and - importantly - it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.
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Affiliation(s)
| | - Damian Kucharski
- Graylight Imaging, Gliwice, Poland; Silesian University of Technology, Gliwice, Poland.
| | - Oskar Bozek
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland.
| | - Bartosz Eksner
- Department of Radiology and Nuclear Medicine, ZSM Chorzów, Chorzów, Poland.
| | - Bartosz Kokoszka
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland.
| | - Tomasz Pekala
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland.
| | - Mateusz Radom
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
| | - Marek Strzelczak
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
| | - Benjamín Gutiérrez-Becker
- Roche Pharma Research and Early Development, Informatics, Roche Innovation Center Basel, Basel, Switzerland.
| | - Agata Krason
- Roche Pharma Research and Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland.
| | - Jean Tessier
- Roche Pharma Research and Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland.
| | - Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Silesian University of Technology, Gliwice, Poland.
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Van Roy S, Hsu L, Ho J, Scirica BM, Fischer D, Snider SB, Lee JW. Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging. Neurocrit Care 2024:10.1007/s12028-024-02087-y. [PMID: 39164537 DOI: 10.1007/s12028-024-02087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 07/29/2024] [Indexed: 08/22/2024]
Abstract
BACKGROUND Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. METHODS The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. RESULTS Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). CONCLUSIONS Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.
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Affiliation(s)
- Sam Van Roy
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA
| | - Liangge Hsu
- Division of Neuroradiology, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Joseph Ho
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA
| | - Benjamin M Scirica
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David Fischer
- Department of Neurology, Hospital of Pennsylvania, Philadelphia, PA, USA
| | - Samuel B Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jong Woo Lee
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA.
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5
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Bhattacharya S, Price AN, Uus A, Sousa HS, Marenzana M, Colford K, Murkin P, Lee M, Cordero-Grande L, Teixeira RPAG, Malik SJ, Deprez M. In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences. Magn Reson Med 2024; 92:715-729. [PMID: 38623934 DOI: 10.1002/mrm.30094] [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: 10/26/2023] [Revised: 02/18/2024] [Accepted: 03/08/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. METHODS Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. RESULTS The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. CONCLUSION The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.
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Affiliation(s)
- Suryava Bhattacharya
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Helena S Sousa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Kathleen Colford
- Centre for the Developing Brain, King's College London, London, UK
| | - Peter Murkin
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maggie Lee
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, ETSI Telecomunicración, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Rui Pedro A G Teixeira
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Shaihan J Malik
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
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Calixto C, Taymourtash A, Karimi D, Snoussi H, Velasco-Annis C, Jaimes C, Gholipour A. Advances in Fetal Brain Imaging. Magn Reson Imaging Clin N Am 2024; 32:459-478. [PMID: 38944434 PMCID: PMC11216711 DOI: 10.1016/j.mric.2024.03.004] [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: 07/01/2024]
Abstract
Over the last 20 years, there have been remarkable developments in fetal brain MR imaging analysis methods. This article delves into the specifics of structural imaging, diffusion imaging, functional MR imaging, and spectroscopy, highlighting the latest advancements in motion correction, fetal brain development atlases, and the challenges and innovations. Furthermore, this article explores the clinical applications of these advanced imaging techniques in comprehending and diagnosing fetal brain development and abnormalities.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
| | - Athena Taymourtash
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Spitalgasse 23, Wien 1090, Austria
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Haykel Snoussi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Camilo Jaimes
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02215, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
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7
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He J, Ma H, Guo M, Wang J, Wang Z, Fan G. Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization. Quant Imaging Med Surg 2024; 14:5109-5130. [PMID: 39022237 PMCID: PMC11250356 DOI: 10.21037/qims-24-67] [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: 01/12/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024]
Abstract
Background Super-resolution (SR) refers to the use of hardware or software methods to enhance the resolution of low-resolution (LR) images and produce high-resolution (HR) images. SR is applied frequently across a variety of medical imaging contexts, particularly in the enhancement of neuroimaging, with specific techniques including SR microscopy-used for diagnostic biomarkers-and functional magnetic resonance imaging (fMRI)-a neuroimaging method for the measurement and mapping of brain activity. This bibliometric analysis of the literature related to SR in medical imaging was conducted to identify the global trends in this field, and visualization via graphs was completed to offer insights into future research prospects. Methods In order to perform a bibliometric analysis of the SR literature, this study sourced all publications from the Web of Science Core Collection (WoSCC) database published from January 1, 2000, to October 11, 2023. A total of 3,262 articles on SR in medical imaging were evaluated. VOSviewer was used to perform co-occurrence and co-authorship analysis, and network visualization of the literature data, including author, journal, publication year, institution, and keywords, was completed. Results From 2000 to 2023, the annual publication volume surged from 13 to 366. The top three journals in this field in terms of publication volume were as follows: (I) Scientific Reports (86 publications), (II) IEEE Transactions on Medical Imaging (74 publications), and (III) IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (56 publications). The most prolific country, institution, and author were the United States (1,017 publications; 31,301 citations), the Chinese Academy of Sciences (124 publications; 2,758 citations), and Dinggang Shen (20 publications; 671 citations), respectively. A cluster analysis of the top 100 keywords was conducted, which revealed the presence of five co-occurrence clusters: (I) SR and artificial intelligence (AI) for medical image enhancement, (II) SR and inverse problem processing concepts for positron emission tomography (PET) image processing, (III) SR ultrasound through microbubbles, (IV) SR microscopy for Alzheimer and Parkinson diseases, and (V) SR in brain fMRI: rapid acquisition and precise imaging. The most recent high-frequency keywords were deep learning (DL), magnetic resonance imaging (MRI), and convolutional neural networks (CNNs). Conclusions Over the past two decades, the output of publications by countries, institutions, and authors in the field of SR in medical imaging has steadily increased. Based on bibliometric analysis of international trends, the resurgence of SR in medical imaging has been facilitated by advancements in AI. The increasing need for multi-center and multi-modal medical images has further incentivized global collaboration, leading to the diverse research paths in SR medical imaging among prominent scientists.
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Affiliation(s)
- Jiachuan He
- Department of Radiology, the First Hospital of China Medical University, Shenyang, China
| | - He Ma
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Miaoran Guo
- Department of Radiology, the First Hospital of China Medical University, Shenyang, China
| | - Jiaqi Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, China
| | - Zhongqing Wang
- Department of Information Center, the First Hospital of China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, the First Hospital of China Medical University, Shenyang, China
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8
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Snoussi H, Karimi D, Afacan O, Utkur M, Gholipour A. HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI. ARXIV 2024:arXiv:2406.20042v1. [PMID: 38979484 PMCID: PMC11230346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomogeneities result in artifacts and data scattering across spatial and angular domains. The effects of those artifacts are more pronounced in high-angular resolution fetal dMRI, where signal-to-noise ratio is very low. Those effects lead to biased estimates and compromise the consistency and reliability of dMRI analysis. This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction, advanced motion correction for model-free and robust reconstruction, optimized multi-shell design for enhanced information capture and increased tolerance to motion, and outlier detection for improved reconstruction fidelity. The framework is open-source, flexible, and can be used to process any type of fetal dMRI data including single-echo or single-shell acquisitions, but is most effective when used with multi-shell multi-echo fetal dMRI data that cannot be processed with any of the existing tools. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling, including fiber orientation distribution function estimation. These advancements pave the way for more reliable analysis of the fetal brain microstructure and tractography under challenging imaging conditions.
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Affiliation(s)
- Haykel Snoussi
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Davood Karimi
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Onur Afacan
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Mustafa Utkur
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Ali Gholipour
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
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9
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Martins-Costa C, Wiegers A, Pham VA, Sidhaye J, Doleschall B, Novatchkova M, Lendl T, Piber M, Peer A, Möseneder P, Stuempflen M, Chow SYA, Seidl R, Prayer D, Höftberger R, Kasprian G, Ikeuchi Y, Corsini NS, Knoblich JA. ARID1B controls transcriptional programs of axon projection in an organoid model of the human corpus callosum. Cell Stem Cell 2024; 31:866-885.e14. [PMID: 38718796 DOI: 10.1016/j.stem.2024.04.014] [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: 05/17/2023] [Revised: 02/13/2024] [Accepted: 04/17/2024] [Indexed: 06/09/2024]
Abstract
Mutations in ARID1B, a member of the mSWI/SNF complex, cause severe neurodevelopmental phenotypes with elusive mechanisms in humans. The most common structural abnormality in the brain of ARID1B patients is agenesis of the corpus callosum (ACC), characterized by the absence of an interhemispheric white matter tract that connects distant cortical regions. Here, we find that neurons expressing SATB2, a determinant of callosal projection neuron (CPN) identity, show impaired maturation in ARID1B+/- neural organoids. Molecularly, a reduction in chromatin accessibility of genomic regions targeted by TCF-like, NFI-like, and ARID-like transcription factors drives the differential expression of genes required for corpus callosum (CC) development. Through an in vitro model of the CC tract, we demonstrate that this transcriptional dysregulation impairs the formation of long-range axonal projections, causing structural underconnectivity. Our study uncovers new functions of the mSWI/SNF during human corticogenesis, identifying cell-autonomous axonogenesis defects in SATB2+ neurons as a cause of ACC in ARID1B patients.
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Affiliation(s)
- Catarina Martins-Costa
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria; Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, 1030 Vienna, Austria
| | - Andrea Wiegers
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Vincent A Pham
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Jaydeep Sidhaye
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Balint Doleschall
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria; Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, 1030 Vienna, Austria
| | - Maria Novatchkova
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Thomas Lendl
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Marielle Piber
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Angela Peer
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Paul Möseneder
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Marlene Stuempflen
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Siu Yu A Chow
- Institute of Industrial Science, The University of Tokyo, 153-8505 Tokyo, Japan; Institute for AI and Beyond, The University of Tokyo, 113-0032 Tokyo, Japan
| | - Rainer Seidl
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Romana Höftberger
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Yoshiho Ikeuchi
- Institute of Industrial Science, The University of Tokyo, 153-8505 Tokyo, Japan; Institute for AI and Beyond, The University of Tokyo, 113-0032 Tokyo, Japan
| | - Nina S Corsini
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria.
| | - Jürgen A Knoblich
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria; Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria.
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10
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Tan J, Zhang X, Qing C, Xu X. Fourier Domain Robust Denoising Decomposition and Adaptive Patch MRI Reconstruction. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7299-7311. [PMID: 37015441 DOI: 10.1109/tnnls.2022.3222394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The sparsity of the Fourier transform domain has been applied to magnetic resonance imaging (MRI) reconstruction in k -space. Although unsupervised adaptive patch optimization methods have shown promise compared to data-driven-based supervised methods, the following challenges exist in MRI reconstruction: 1) in previous k -space MRI reconstruction tasks, MRI with noise interference in the acquisition process is rarely considered. 2) Differences in transform domains should be resolved to achieve the high-quality reconstruction of low undersampled MRI data. 3) Robust patch dictionary learning problems are usually nonconvex and NP-hard, and alternate minimization methods are often computationally expensive. In this article, we propose a method for Fourier domain robust denoising decomposition and adaptive patch MRI reconstruction (DDAPR). DDAPR is a two-step optimization method for MRI reconstruction in the presence of noise and low undersampled data. It includes the low-rank and sparse denoising reconstruction model (LSDRM) and the robust dictionary learning reconstruction model (RDLRM). In the first step, we propose LSDRM for different domains. For the optimization solution, the proximal gradient method is used to optimize LSDRM by singular value decomposition and soft threshold algorithms. In the second step, we propose RDLRM, which is an effective adaptive patch method by introducing a low-rank and sparse penalty adaptive patch dictionary and using a sparse rank-one matrix to approximate the undersampled data. Then, the block coordinate descent (BCD) method is used to optimize the variables. The BCD optimization process involves valid closed-form solutions. Extensive numerical experiments show that the proposed method has a better performance than previous methods in image reconstruction based on compressed sensing or deep learning.
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11
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Pogledic I, Bobić‐Rasonja M, Mitter C, Štajduhar A, Schwartz E, Milković‐Periša M, Baltzer PA, Lequin M, Krampl‐Bettelheim E, Kasprian G, Judaš M, Prayer D, Jovanov‐Milosevic N. Fetal indusium griseum is a possible biomarker of the regularity of brain midline development in 3T MR imaging: A retrospective observational study. Acta Obstet Gynecol Scand 2024; 103:897-906. [PMID: 38339766 PMCID: PMC11019519 DOI: 10.1111/aogs.14781] [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: 09/21/2023] [Revised: 12/04/2023] [Accepted: 01/03/2024] [Indexed: 02/12/2024]
Abstract
INTRODUCTION This study aimed to assess the visibility of the indusium griseum (IG) in magnetic resonance (MR) scans of the human fetal brain and to evaluate its reliability as an imaging biomarker of the normality of brain midline development. MATERIAL AND METHODS The retrospective observational study encompassed T2-w 3T MR images from 90 post-mortem fetal brains and immunohistochemical sections from 41 fetal brains (16-40 gestational weeks) without cerebral pathology. Three raters independently inspected and evaluated the visibility of IG in post-mortem and in vivo MR scans. Weighted kappa statistics and regression analysis were used to determine inter- and intra-rater agreement and the type and strength of the association of IG visibility with gestational age. RESULTS The visibility of the IG was the highest between the 25 and 30 gestational week period, with a very good inter-rater variability (kappa 0.623-0.709) and excellent intra-rater variability (kappa 0.81-0.93). The immunochemical analysis of the histoarchitecture of IG discloses the expression of highly hydrated extracellular molecules in IG as the substrate of higher signal intensity and best visibility of IG during the mid-fetal period. CONCLUSIONS The knowledge of developmental brain histology and fetal age allows us to predict the IG-visibility in magnetic resonance imaging (MRI) and use it as a biomarker to evaluate the morphogenesis of the brain midline. As a biomarker, IG is significant for post-mortem pathological examination by MRI. Therefore, in the clinical in vivo imaging examination, IG should be anticipated when an assessment of the brain midline structures is needed in mid-gestation, including corpus callosum thickness measurements.
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Affiliation(s)
- Ivana Pogledic
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image‐guided Therapy, Allgemeines KrankenhausMedical University of ViennaViennaAustria
| | - Mihaela Bobić‐Rasonja
- Department of BiologySchool of Medicine, University of ZagrebZagrebCroatia
- Croatian Institute for Brain Research, Scientific Center of Excellence for Basic, Clinical and Translational NeuroscienceSchool of Medicine University of ZagrebZagrebCroatia
| | - Christian Mitter
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image‐guided Therapy, Allgemeines KrankenhausMedical University of ViennaViennaAustria
- Department of Systematic Anatomy, Center for Anatomy and Cell BiologyMedical University of ViennaViennaAustria
| | - Andrija Štajduhar
- Croatian Institute for Brain Research, Scientific Center of Excellence for Basic, Clinical and Translational NeuroscienceSchool of Medicine University of ZagrebZagrebCroatia
- School of MedicineSchool of Public Health “Andrija Štampar” University of ZagrebZagrebCroatia
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Marija Milković‐Periša
- Department of Pathology and Cytology, School of MedicineUniversity Hospital Center Zagreb, University of ZagrebZagrebCroatia
| | - Pascal A. Baltzer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image‐guided Therapy, Allgemeines KrankenhausMedical University of ViennaViennaAustria
| | - Maarten Lequin
- Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | | | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image‐guided Therapy, Allgemeines KrankenhausMedical University of ViennaViennaAustria
| | - Miloš Judaš
- Croatian Institute for Brain Research, Scientific Center of Excellence for Basic, Clinical and Translational NeuroscienceSchool of Medicine University of ZagrebZagrebCroatia
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image‐guided Therapy, Allgemeines KrankenhausMedical University of ViennaViennaAustria
| | - Natasa Jovanov‐Milosevic
- Department of BiologySchool of Medicine, University of ZagrebZagrebCroatia
- Croatian Institute for Brain Research, Scientific Center of Excellence for Basic, Clinical and Translational NeuroscienceSchool of Medicine University of ZagrebZagrebCroatia
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12
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Ciceri T, Casartelli L, Montano F, Conte S, Squarcina L, Bertoldo A, Agarwal N, Brambilla P, Peruzzo D. Fetal brain MRI atlases and datasets: A review. Neuroimage 2024; 292:120603. [PMID: 38588833 DOI: 10.1016/j.neuroimage.2024.120603] [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: 11/03/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Florian Montano
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Stefania Conte
- Psychology Department, State University of New York at Binghamton, New York, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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13
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Zhang W, Zhang X, Li L, Liao L, Zhao F, Zhong T, Pei Y, Xu X, Yang C, Zhang H, Li G. A joint brain extraction and image quality assessment framework for fetal brain MRI slices. Neuroimage 2024; 290:120560. [PMID: 38431181 DOI: 10.1016/j.neuroimage.2024.120560] [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: 10/13/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.
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Affiliation(s)
- Wenhao Zhang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Xin Zhang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
| | - Lingyi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Lufan Liao
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Yuchen Pei
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Xiangmin Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Chaoxiang Yang
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
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14
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Lamon S, de Dumast P, Sanchez T, Dunet V, Pomar L, Vial Y, Koob M, Bach Cuadra M. Assessment of fetal corpus callosum biometry by 3D super-resolution reconstructed T2-weighted magnetic resonance imaging. Front Neurol 2024; 15:1358741. [PMID: 38595845 PMCID: PMC11002102 DOI: 10.3389/fneur.2024.1358741] [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: 12/20/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Objective To assess the accuracy of corpus callosum (CC) biometry, including sub-segments, using 3D super-resolution fetal brain MRI (SR) compared to 2D or 3D ultrasound (US) and clinical low-resolution T2-weighted MRI (T2WS). Method Fetal brain biometry was conducted by two observers on 57 subjects [21-35 weeks of gestational age (GA)], including 11 cases of partial CC agenesis. Measures were performed by a junior observer (obs1) on US, T2WS and SR and by a senior neuroradiologist (obs2) on T2WS and SR. CC biometric regression with GA was established. Statistical analysis assessed agreement within and between modalities and observers. Results This study shows robust SR to US concordance across gestation, surpassing T2WS. In obs1, SR aligns with US, except for genu and CC length (CCL), enhancing splenium visibility. In obs2, SR closely corresponds to US, differing in rostrum and CCL. The anterior CC (rostrum and genu) exhibits higher variability. SR's regression aligns better with literature (US) for CCL, splenium and body than T2WS. SR is the method with the least missing values. Conclusion SR yields CC biometry akin to US (excluding anterior CC). Thanks to superior 3D visualization and better through plane spatial resolution, SR allows to perform CC biometry more frequently than T2WS.
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Affiliation(s)
- Samuel Lamon
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Thomas Sanchez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Léo Pomar
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Yvan Vial
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
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15
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Chen J, Lu R, Jing B, Zhang H, Chen G, Shen D. One model, two brains: Automatic fetal brain extraction from MR images of twins. Comput Med Imaging Graph 2024; 112:102330. [PMID: 38262133 DOI: 10.1016/j.compmedimag.2024.102330] [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: 07/19/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024]
Abstract
Fetal brain extraction from magnetic resonance (MR) images is of great importance for both clinical applications and neuroscience studies. However, it is a challenging task, especially when dealing with twins, which are commonly existing in pregnancy. Currently, there is no brain extraction method dedicated to twins, raising significant demand to develop an effective twin fetal brain extraction method. To this end, we propose the first twin fetal brain extraction framework, which possesses three novel features. First, to narrow down the region of interest and preserve structural information between the two brains in twin fetal MR images, we take advantage of an advanced object detector to locate all the brains in twin fetal MR images at once. Second, we propose a Twin Fetal Brain Extraction Network (TFBE-Net) to further suppress insignificant features for segmenting brain regions. Finally, we propose a Two-step Training Strategy (TTS) to learn correlation features of the single fetal brain for further improving the performance of TFBE-Net. We validate the proposed framework on a twin fetal brain dataset. The experiments show that our framework achieves promising performance on both quantitative and qualitative evaluations, and outperforms state-of-the-art methods for fetal brain extraction.
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Affiliation(s)
- Jian Chen
- School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, Fujian, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, 100069, China
| | - Ranlin Lu
- School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, Fujian, China
| | - Bin Jing
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, 100069, China; School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Geng Chen
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Dinggang Shen
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, 201210, China; Shanghai Clinical Research and Trial Center, Shanghai, 201210, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200230, China.
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16
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Mufti N, Aertsen M, Thomson D, De Vloo P, Demaerel P, Deprest J, Melbourne A, David AL. Longitudinal MRI in the context of in utero surgery for open spina bifida: A descriptive study. Acta Obstet Gynecol Scand 2024; 103:322-333. [PMID: 37984808 PMCID: PMC10823411 DOI: 10.1111/aogs.14711] [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: 06/16/2023] [Revised: 09/17/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023]
Abstract
INTRODUCTION Fetal surgery for open spina bifida (OSB) requires comprehensive preoperative assessment using imaging for appropriate patient selection and to evaluate postoperative efficacy and complications. We explored patient access and conduct of fetal magnetic resonance imaging (MRI) for prenatal assessment of OSB patients eligible for fetal surgery. We compared imaging acquisition and reporting to the International Society of Ultrasound in Obstetrics and Gynecology MRI performance guidelines. MATERIAL AND METHODS We surveyed access to fetal MRI for OSB in referring fetal medicine units (FMUs) in the UK and Ireland, and two NHS England specialist commissioned fetal surgery centers (FSCs) at University College London Hospital, and University Hospitals KU Leuven Belgium. To study MRI acquisition protocols, we retrospectively analyzed fetal MRI images before and after fetal surgery for OSB. RESULTS MRI for fetal OSB was accessible with appropriate specialists available to supervise, perform, and report scans. The average time to arrange a fetal MRI appointment from request was 4 ± 3 days (range, 0-10), the average scan time available was 37 ± 16 min (range, 20-80 min), with 15 ± 11 min (range, 0-30 min) extra time to repeat sequences as required. Specific MRI acquisition protocols, and MRI reporting templates were available in only 32% and 18% of units, respectively. Satisfactory T2-weighted (T2W) brain imaging acquired in three orthogonal planes was achieved preoperatively in all centers, and 6 weeks postoperatively in 96% of FSCs and 78% of referring FMUs. However, for T2W spine image acquisition referring FMUs were less able to provide three orthogonal planes presurgery (98% FSC vs. 50% FMU, p < 0.001), and 6 weeks post-surgery (100% FSC vs. 48% FMU, p < 0.001). Other standard imaging recommendations such as T1-weighted (T1W), gradient echo (GE) or echoplanar fetal brain and spine imaging in one or two orthogonal planes were more likely available in FSCs compared to FMUs pre- and post-surgery (p < 0.001). CONCLUSIONS There was timely access to supervised MRI for OSB fetal surgery assessment. However, the provision of images of the fetal brain and spine in sufficient orthogonal planes, which are required for determining eligibility and to determine the reversal of hindbrain herniation after fetal surgery, were less frequently acquired. Our evidence suggests the need for specific guidance in relation to fetal MRI for OSB. We propose an example guidance for MRI acquisition and reporting.
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Affiliation(s)
- Nada Mufti
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - Michael Aertsen
- Department of RadiologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Dominic Thomson
- Pediatric Neurosurgery DepartmentGreat Ormond Street Hospital for ChildrenLondonUK
| | - Phillippe De Vloo
- Department of NeurosurgeryUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Philippe Demaerel
- Department of RadiologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Jan Deprest
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynecologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
- Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynecologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
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17
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Chen J, Lu R, Ye S, Guang M, Tassew TM, Jing B, Zhang G, Chen G, Shen D. Image Recovery Matters: A Recovery-Extraction Framework for Robust Fetal Brain Extraction From MR Images. IEEE J Biomed Health Inform 2024; 28:823-834. [PMID: 37995170 DOI: 10.1109/jbhi.2023.3333953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
The extraction of the fetal brain from magnetic resonance (MR) images is a challenging task. In particular, fetal MR images suffer from different kinds of artifacts introduced during the image acquisition. Among those artifacts, intensity inhomogeneity is a common one affecting brain extraction. In this work, we propose a deep learning-based recovery-extraction framework for fetal brain extraction, which is particularly effective in handling fetal MR images with intensity inhomogeneity. Our framework involves two stages. First, the artifact-corrupted images are recovered with the proposed generative adversarial learning-based image recovery network with a novel region-of-darkness discriminator that enforces the network focusing on artifacts of the images. Second, we propose a brain extraction network for more effective fetal brain segmentation by strengthening the association between lower- and higher-level features as well as suppressing task-irrelevant features. Thanks to the proposed recovery-extraction strategy, our framework is able to accurately segment fetal brains from artifact-corrupted MR images. The experiments show that our framework achieves promising performance in both quantitative and qualitative evaluations, and outperforms state-of-the-art methods in both image recovery and fetal brain extraction.
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18
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Nichols ES, Grace M, Correa S, de Vrijer B, Eagleson R, McKenzie CA, de Ribaupierre S, Duerden EG. Sex- and age-based differences in fetal and early childhood hippocampus maturation: a cross-sectional and longitudinal analysis. Cereb Cortex 2024; 34:bhad421. [PMID: 37950876 PMCID: PMC10793584 DOI: 10.1093/cercor/bhad421] [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: 08/09/2023] [Revised: 10/13/2023] [Accepted: 10/14/2023] [Indexed: 11/13/2023] Open
Abstract
The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38 weeks' gestational age), newborns (0-4 weeks' postnatal age), infants (5-50 weeks' postnatal age), and toddlers (2-3 years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M = 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2 years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.
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Affiliation(s)
- Emily S Nichols
- Department of Applied Psychology, Faculty of Education, Western University, 1137 Western Road, London, Ontario, Canada
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Michael Grace
- Department of Physiology and Pharmacology, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Susana Correa
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Barbra de Vrijer
- Department of Obstetrics & Gynaecology, Schulich School of Medicine & Dentistry, Western University, London Health Sciences Centre-Victoria Hospital, B2-401, London, Ontario N6H 5W9, Canada
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
| | - Roy Eagleson
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
- Department of Biomedical Engineering, Western University, Canada
- Department of Electrical and Computer Engineering, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Charles A McKenzie
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, Canada
| | - Sandrine de Ribaupierre
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
- Department of Biomedical Engineering, Western University, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, Canada
- Department of Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, Canada
| | - Emma G Duerden
- Department of Applied Psychology, Faculty of Education, Western University, 1137 Western Road, London, Ontario, Canada
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
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19
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Nakaki A, Crovetto F, Urru A, Piella G, Borras R, Comte V, Vellvé K, Paules C, Segalés L, Dacal M, Gomez Y, Youssef L, Casas R, Castro-Barquero S, Martín-Asuero A, Oller Guzmán T, Morilla I, Martínez-Àran A, Camacho A, Pascual Tutusaus M, Arranz A, Rebollo-Polo M, Gomez-Chiari M, Bargallo N, Pozo ÓJ, Gomez-Gomez A, Izquierdo Renau M, Eixarch E, Vieta E, Estruch R, Crispi F, Gonzalez-Ballester MA, Gratacós E. Effects of Mediterranean diet or mindfulness-based stress reduction on fetal and neonatal brain development: a secondary analysis of a randomized clinical trial. Am J Obstet Gynecol MFM 2023; 5:101188. [PMID: 37839546 DOI: 10.1016/j.ajogmf.2023.101188] [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: 07/14/2023] [Revised: 09/13/2023] [Accepted: 10/09/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Maternal suboptimal nutrition and high stress levels are associated with adverse fetal and infant neurodevelopment. OBJECTIVE This study aimed to investigate if structured lifestyle interventions involving a Mediterranean diet or mindfulness-based stress reduction during pregnancy are associated with differences in fetal and neonatal brain development. STUDY DESIGN This was a secondary analysis of the randomized clinical trial Improving Mothers for a Better Prenatal Care Trial Barcelona that was conducted in Barcelona, Spain, from 2017 to 2020. Participants with singleton pregnancies were randomly allocated into 3 groups, namely Mediterranean diet intervention, stress reduction program, or usual care. Participants in the Mediterranean diet group received monthly individual sessions and free provision of extra-virgin olive oil and walnuts. Pregnant women in the stress reduction group underwent an 8-week mindfulness-based stress reduction program adapted for pregnancy. Magnetic resonance imaging of 90 fetal brains was performed at 36 to 39 weeks of gestation and the Neonatal Neurobehavioral Assessment Scale was completed for 692 newborns at 1 to 3 months. Fetal outcomes were the total brain volume and lobular or regional volumes obtained from a 3-dimensional reconstruction and semiautomatic segmentation of magnetic resonance images. Neonatal outcomes were the 6 clusters scores of the Neonatal Neurobehavioral Assessment Scale. Multiple regression analyses were conducted to assess the association between the interventions and the fetal and neonatal outcomes. RESULTS When compared with the usual care group, the offspring exposed to a maternal Mediterranean diet had a larger total fetal brain volume (mean, 284.11 cm3; standard deviation, 23.92 cm3 vs 294.01 cm3; standard deviation, 26.29 cm3; P=.04), corpus callosum (mean, 1.16 cm3; standard deviation, 0.19 cm3 vs 1.26 cm3; standard deviation, 0.22 cm3; P=.03), and right frontal lobe (44.20; standard deviation, 4.09 cm3 vs 46.60; standard deviation, 4.69 cm3; P=.02) volumes based on magnetic resonance imaging measures and higher scores in the Neonatal Neurobehavioral Assessment Scale clusters of autonomic stability (mean, 7.4; standard deviation, 0.9 vs 7.6; standard deviation, 0.7; P=.04), social interaction (mean, 7.5; standard deviation, 1.5 vs 7.8; standard deviation, 1.3; P=.03), and range of state (mean, 4.3; standard deviation, 1.3 vs 4.5; standard deviation, 1.0; P=.04). When compared with the usual care group, offspring from the stress reduction group had larger fetal left anterior cingulate gyri volume (1.63; standard deviation, 0.32 m3 vs 1.79; standard deviation, 0.30 cm3; P=.03) based on magnetic resonance imaging and higher scores in the Neonatal Neurobehavioral Assessment Scale for regulation of state (mean, 6.0; standard deviation, 1.8 vs 6.5; standard deviation, 1.5; P<.01). CONCLUSION Maternal structured lifestyle interventions involving the promotion of a Mediterranean diet or stress reduction during pregnancy were associated with changes in fetal and neonatal brain development.
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Affiliation(s)
- Ayako Nakaki
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain (Drs Nakaki, Bargallo, Eixarch, Crispi and Gratacos); Department of Surgery and Surgical specializations, Faculty of Medicine and Helath Sciences, University of Barcelona, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Castro-Barquero, Arranz, Eixarch, Crispi and Gratacos)
| | - Francesca Crovetto
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain (Drs Crovetto, Izquierdo Renau, and Gratacos)
| | - Andrea Urru
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (Drs Urru and Piella, Mr Comte, and Dr Gonzalez-Ballester)
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (Drs Urru and Piella, Mr Comte, and Dr Gonzalez-Ballester)
| | - Roger Borras
- Cardiovascular Institute, Hospital Clínic, IDIBAPS, Universitat Autònoma de Barcelona, Barcelona, Spain (Mr Borras); Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain (Mr Borras and Dr Bargallo)
| | - Valentin Comte
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (Drs Urru and Piella, Mr Comte, and Dr Gonzalez-Ballester)
| | - Kilian Vellvé
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Cristina Paules
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Laura Segalés
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Marta Dacal
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Yvan Gomez
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Lina Youssef
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Josep Carreras Leukaemia Research Institute, Hospital Clinic, University of Barcelona Campus, Barcelona, Spain (Dr Youssef)
| | - Rosa Casas
- Department of Internal Medicine Hospital Clinic, IDIBAPS, University of Barcelona, Barcelona, Spain (Drs Casas, Castro-Barquero, and Estruch); Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERON), Madrid, Spain (Drs Casas, Castro-Barquero, and Estruch)
| | - Sara Castro-Barquero
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Department of Internal Medicine Hospital Clinic, IDIBAPS, University of Barcelona, Barcelona, Spain (Drs Casas, Castro-Barquero, and Estruch); Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERON), Madrid, Spain (Drs Casas, Castro-Barquero, and Estruch)
| | - Andrés Martín-Asuero
- Instituto esMindfulness, Barcelona, Spain (Dr Martín-Asuero and Ms Oller Guzmán)
| | - Teresa Oller Guzmán
- Instituto esMindfulness, Barcelona, Spain (Dr Martín-Asuero and Ms Oller Guzmán)
| | - Ivette Morilla
- Department of Psychiatry and Psychology, Hospital Clinic, Neuroscience Institute, IDIBAPS, University of Barcelona, CIBERSAM, Barcelona, Spain (Drs Morilla, Martínez-Àran, and Vieta)
| | - Anabel Martínez-Àran
- Department of Psychiatry and Psychology, Hospital Clinic, Neuroscience Institute, IDIBAPS, University of Barcelona, CIBERSAM, Barcelona, Spain (Drs Morilla, Martínez-Àran, and Vieta)
| | - Alba Camacho
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Mireia Pascual Tutusaus
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Angela Arranz
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos)
| | - Monica Rebollo-Polo
- Diagnostic Imaging and Image Guided Therapy, Institut de Recerca Sant Joan de Dèu, Esplugues de Llobregat, Spain (Drs Rebollo-Polo and Gomez-Chiari); Radiology Department, Hôpitaux Universitaires de Genève, Geneva, Switzerland (Dr Rebollo-Polo)
| | - Marta Gomez-Chiari
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Diagnostic Imaging and Image Guided Therapy, Institut de Recerca Sant Joan de Dèu, Esplugues de Llobregat, Spain (Drs Rebollo-Polo and Gomez-Chiari); Diagnostic Imaging Department, Hospital Sant Joan de Dèu, Esplugues de Llobregat, Spain (Dr Gomez-Chiari)
| | - Nuria Bargallo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain (Drs Nakaki, Bargallo, Eixarch, Crispi and Gratacos); Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain (Mr Borras and Dr Bargallo); Radiology Department, Center of Image Diagnostic, Hospital Clínic. Facultad de Medicina, Universidad de Barcelona, Barcelona, Spain (Dr Bargallo)
| | - Óscar J Pozo
- Applied Metabolomics Research Group, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain (Drs Pozo and Gomez-Gomez)
| | - Alex Gomez-Gomez
- Applied Metabolomics Research Group, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain (Drs Pozo and Gomez-Gomez)
| | - Montserrat Izquierdo Renau
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain (Drs Crovetto, Izquierdo Renau, and Gratacos); Neonatology Department, Hospital Sant Joan de Déu, University of Barcelona, Barcelona, Spain (Dr Izquierdo Renau)
| | - Elisenda Eixarch
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain (Drs Nakaki, Bargallo, Eixarch, Crispi and Gratacos); Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain (Drs Eixarch, Crispi, and Gratacos)
| | - Eduard Vieta
- Department of Psychiatry and Psychology, Hospital Clinic, Neuroscience Institute, IDIBAPS, University of Barcelona, CIBERSAM, Barcelona, Spain (Drs Morilla, Martínez-Àran, and Vieta)
| | - Ramon Estruch
- Department of Internal Medicine Hospital Clinic, IDIBAPS, University of Barcelona, Barcelona, Spain (Drs Casas, Castro-Barquero, and Estruch); Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERON), Madrid, Spain (Drs Casas, Castro-Barquero, and Estruch)
| | - Fàtima Crispi
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain (Drs Nakaki, Bargallo, Eixarch, Crispi and Gratacos); Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain (Drs Eixarch, Crispi, and Gratacos).
| | - Miguel Angel Gonzalez-Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (Drs Urru and Piella, Mr Comte, and Dr Gonzalez-Ballester); ICREA, Barcelona, Spain (Dr Gonzalez-Ballester)
| | - Eduard Gratacós
- BCNatal Fetal Medicine Research Center, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain (Drs Nakaki, Crovetto, Vellvé, Paules, Segalés, Ms Dacal, Drs Gomez, Youssef, Castro-Barquero, Mses Camacho and Pascual Tutsaus, and Drs Arranz, Gomez-Chairi, Eixarch, Crispi and Gratacos); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain (Drs Nakaki, Bargallo, Eixarch, Crispi and Gratacos); Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain (Drs Crovetto, Izquierdo Renau, and Gratacos); Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain (Drs Eixarch, Crispi, and Gratacos)
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20
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Mufti N, Chappell J, Aertsen M, Ebner M, Fidon L, Deprest J, David AL, Melbourne A. Assessment of longitudinal brain development using super-resolution magnetic resonance imaging following fetal surgery for open spina bifida. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:707-720. [PMID: 37161647 PMCID: PMC10947002 DOI: 10.1002/uog.26244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/18/2023] [Accepted: 05/01/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVES Prenatal surgery is offered for selected fetuses with open spina bifida (OSB) to improve long-term outcome. We studied the effect of fetal OSB surgery on brain development using advanced magnetic resonance imaging (MRI) techniques to quantify the volume, surface area and shape of cerebral structures and to analyze surface curvature by means of parameters that correspond to gyrification. METHODS We compared MRI data from 29 fetuses with OSB before fetal surgery (mean gestational age (GA), 23 + 3 weeks) and at 1 and 6 weeks after surgery, with that of 36 GA-matched control fetuses (GA range, 21 + 2 to 36 + 2 weeks). Automated super-resolution reconstruction provided three-dimensional isotropic volumetric brain images. Unmyelinated white matter, cerebellum and ventricles were segmented automatically and refined manually, after which volume, surface area and shape parameter (volume/surface area) were quantified. Mathematical markers (shape index (SI) and curvedness) were used to measure gyrification. Parameters were assessed according to lesion type (myelomeningocele vs myeloschisis (MS)), postoperative persistence of hindbrain herniation (HH) and the presence of supratentorial anomalies, namely partial agenesis of the corpus callosum (pACC) and heterotopia (HT). RESULTS Growth in ventricular volume per week and change in shape parameter per week were higher at 6 weeks after surgery in fetuses with OSB compared with controls (median, 2500.94 (interquartile range (IQR), 1689.70-3580.80) mm3 /week vs 708.21 (IQR, 474.50-925.00) mm3 /week; P < 0.001 and 0.075 (IQR, 0.047-0.112) mm/week vs 0.022 (IQR, 0.009-0.042) mm/week; P = 0.046, respectively). Ventricular volume growth increased 6 weeks after surgery in cases with pACC (P < 0.001) and those with persistent HH (P = 0.002). During that time period, the change in unmyelinated white-matter shape parameter per week was decreased in OSB fetuses compared with controls (0.056 (IQR, 0.044-0.092) mm/week vs 0.159 (IQR, 0.100-0.247) mm/week; P = 0.002), particularly in cases with persistent HH (P = 0.011), MS (P = 0.015), HT (P = 0.022), HT with corpus callosum anomaly (P = 0.017) and persistent HH with corpus callosum anomaly (P = 0.007). At 6 weeks postoperatively, despite OSB fetuses having a lower rate of change in curvedness compared with controls (0.061 (IQR, 0.040-0.093) mm-1 /week vs 0.094 (IQR, 0.070-0.146) mm-1 /week; P < 0.001), reversing the trend seen at 1 week after surgery (0.144 (IQR, 0.099-0.236) mm-1 /week vs 0.072 (IQR, 0.059-0.081) mm-1 /week; P < 0.001), gyrification, as determined using SI, appeared to be increased in OSB fetuses overall compared with controls. This observation was more prominent in fetuses with pACC and those with severe ventriculomegaly (P-value range, < 0.001 to 0.006). CONCLUSIONS Following fetal OSB repair, volume, shape and curvedness of ventricles and unmyelinated white matter differed significantly compared with those of normal fetuses. Morphological brain changes after fetal surgery were not limited to effects on the circulation of cerebrospinal fluid. These observations may have implications for postnatal neurocognitive outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- N. Mufti
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - J. Chappell
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - M. Aertsen
- Department of RadiologyUniversity Hospitals Katholieke Universiteit (KU) LeuvenLeuvenBelgium
| | - M. Ebner
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - L. Fidon
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - J. Deprest
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynaecologyUniversity Hospitals Katholieke Universiteit (KU) LeuvenLeuvenBelgium
| | - A. L. David
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynaecologyUniversity Hospitals Katholieke Universiteit (KU) LeuvenLeuvenBelgium
- National Institute for Health and Care Research University College London Hospitals Biomedical Research CentreLondonUK
| | - A. Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
- Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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McPhee S, Kershaw LE, Daniel CR, Peña Fernández M, Cillán-García E, Taylor SE, Wolfram U. QCT-based computational bone strength assessment updated with MRI-derived 'hidden' microporosity. J Mech Behav Biomed Mater 2023; 147:106094. [PMID: 37741181 DOI: 10.1016/j.jmbbm.2023.106094] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/10/2023] [Accepted: 08/26/2023] [Indexed: 09/25/2023]
Abstract
Microdamage accumulated through sustained periods of cyclic loading or single overloading events contributes to bone fragility through a reduction in stiffness and strength. Monitoring microdamage in vivo remains unattainable by clinical imaging modalities. As such, there are no established computational methods for clinical fracture risk assessment that account for microdamage that exists in vivo at any specific timepoint. We propose a method that combines multiple clinical imaging modalities to identify an indicative surrogate, which we term 'hidden porosity', that incorporates pre-existing bone microdamage in vivo. To do so, we use the third metacarpal bone of the equine athlete as an exemplary model for fatigue induced microdamage, which coalesces in the subchondral bone. N = 10 metacarpals were scanned by clinical quantitative computed tomography (QCT) and magnetic resonance imaging (MRI). We used a patch-based similarity method to quantify the signal intensity of a fluid sensitive MRI sequence in bone regions where microdamage coalesces. The method generated MRI-derived pseudoCT images which were then used to determine a pre-existing damage (Dpex) variable to quantify the proposed surrogate and which we incorporate into a nonlinear constitutive model for bone tissue. The minimum, median, and maximum detected Dpex of 0.059, 0.209, and 0.353 reduced material stiffness by 5.9%, 20.9%, and 35.3% as well as yield stress by 5.9%, 20.3%, and 35.3%. Limb-specific voxel-based finite element meshes were equipped with the updated material model. Lateral and medial condyles of each metacarpal were loaded to simulate physiological joint loading during gallop. The degree of detected Dpex correlated with a relative reduction in both condylar stiffness (p = 0.001, R2 > 0.74) and strength (p < 0.001, R2 > 0.80). Our results illustrate the complementary value of looking beyond clinical CT, which neglects the inclusion of microdamage due to partial volume effects. As we use clinically available imaging techniques, our results may aid research beyond the equine model on fracture risk assessment in human diseases such as osteoarthritis, bone cancer, or osteoporosis.
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Affiliation(s)
- Samuel McPhee
- School of Engineering and Physical Sciences, Institute of Mechanical, Process and Energy Engineering, Heriot-Watt University, Edinburgh, UK
| | - Lucy E Kershaw
- Centre for Cardiovascular Sciences and Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK
| | - Carola R Daniel
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, The University of Edinburgh, Edinburgh, UK
| | - Marta Peña Fernández
- School of Engineering and Physical Sciences, Institute of Mechanical, Process and Energy Engineering, Heriot-Watt University, Edinburgh, UK
| | | | - Sarah E Taylor
- Royal (Dick) School of Veterinary Studies and the Roslin Institute, The University of Edinburgh, Edinburgh, UK
| | - Uwe Wolfram
- School of Engineering and Physical Sciences, Institute of Mechanical, Process and Energy Engineering, Heriot-Watt University, Edinburgh, UK.
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22
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Vahedifard F, Liu X, Adepoju JO, Zhao S, Ai HA, Marathu KK, Supanich M, Byrd SE, Deng J. Automatic Localization of the Pons and Vermis on Fetal Brain MR Imaging Using a U-Net Deep Learning Model. AJNR Am J Neuroradiol 2023; 44:1191-1200. [PMID: 37652583 PMCID: PMC10549940 DOI: 10.3174/ajnr.a7978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/02/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND AND PURPOSE An MRI of the fetus can enhance the identification of perinatal developmental disorders, which improves the accuracy of ultrasound. Manual MRI measurements require training, time, and intra-variability concerns. Pediatric neuroradiologists are also in short supply. Our purpose was developing a deep learning model and pipeline for automatically identifying anatomic landmarks on the pons and vermis in fetal brain MR imaging and suggesting suitable images for measuring the pons and vermis. MATERIALS AND METHODS We retrospectively used 55 pregnant patients who underwent fetal brain MR imaging with a HASTE protocol. Pediatric neuroradiologists selected them for landmark annotation on sagittal single-shot T2-weighted images, and the clinically reliable method was used as the criterion standard for the measurement of the pons and vermis. A U-Net-based deep learning model was developed to automatically identify fetal brain anatomic landmarks, including the 2 anterior-posterior landmarks of the pons and 2 anterior-posterior and 2 superior-inferior landmarks of the vermis. Four-fold cross-validation was performed to test the accuracy of the model using randomly divided and sorted gestational age-divided data sets. A confidence score of model prediction was generated for each testing case. RESULTS Overall, 85% of the testing results showed a ≥90% confidence, with a mean error of <2.22 mm, providing overall better estimation results with fewer errors and higher confidence scores. The anterior and posterior pons and anterior vermis showed better estimation (which means fewer errors in landmark localization) and accuracy and a higher confidence level than other landmarks. We also developed a graphic user interface for clinical use. CONCLUSIONS This deep learning-facilitated pipeline practically shortens the time spent on selecting good-quality fetal brain images and performing anatomic measurements for radiologists.
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Affiliation(s)
- Farzan Vahedifard
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Xuchu Liu
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Jubril O Adepoju
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Shiqiao Zhao
- Department of Biostatistics (S.Z.), Yale School of Public Health, New Haven, Connecticut
| | - H Asher Ai
- Division for Diagnostic Medical Physics (H.A.A., M.S.), Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois
| | - Kranthi K Marathu
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Mark Supanich
- Division for Diagnostic Medical Physics (H.A.A., M.S.), Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois
| | - Sharon E Byrd
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Jie Deng
- Department of Radiation Oncology (J.D.), Division of Medical Physics & Engineering, University of Texas Southwestern Medical Center, Dallas, Texas
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23
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Ciceri T, Squarcina L, Giubergia A, Bertoldo A, Brambilla P, Peruzzo D. Review on deep learning fetal brain segmentation from Magnetic Resonance images. Artif Intell Med 2023; 143:102608. [PMID: 37673558 DOI: 10.1016/j.artmed.2023.102608] [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: 12/21/2022] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alice Giubergia
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; University of Padua, Padova Neuroscience Center, Padua, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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24
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Li C, Fleck JS, Martins-Costa C, Burkard TR, Themann J, Stuempflen M, Peer AM, Vertesy Á, Littleboy JB, Esk C, Elling U, Kasprian G, Corsini NS, Treutlein B, Knoblich JA. Single-cell brain organoid screening identifies developmental defects in autism. Nature 2023; 621:373-380. [PMID: 37704762 PMCID: PMC10499611 DOI: 10.1038/s41586-023-06473-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/21/2023] [Indexed: 09/15/2023]
Abstract
The development of the human brain involves unique processes (not observed in many other species) that can contribute to neurodevelopmental disorders1-4. Cerebral organoids enable the study of neurodevelopmental disorders in a human context. We have developed the CRISPR-human organoids-single-cell RNA sequencing (CHOOSE) system, which uses verified pairs of guide RNAs, inducible CRISPR-Cas9-based genetic disruption and single-cell transcriptomics for pooled loss-of-function screening in mosaic organoids. Here we show that perturbation of 36 high-risk autism spectrum disorder genes related to transcriptional regulation uncovers their effects on cell fate determination. We find that dorsal intermediate progenitors, ventral progenitors and upper-layer excitatory neurons are among the most vulnerable cell types. We construct a developmental gene regulatory network of cerebral organoids from single-cell transcriptomes and chromatin modalities and identify autism spectrum disorder-associated and perturbation-enriched regulatory modules. Perturbing members of the BRG1/BRM-associated factor (BAF) chromatin remodelling complex leads to enrichment of ventral telencephalon progenitors. Specifically, mutating the BAF subunit ARID1B affects the fate transition of progenitors to oligodendrocyte and interneuron precursor cells, a phenotype that we confirmed in patient-specific induced pluripotent stem cell-derived organoids. Our study paves the way for high-throughput phenotypic characterization of disease susceptibility genes in organoid models with cell state, molecular pathway and gene regulatory network readouts.
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Affiliation(s)
- Chong Li
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria.
| | - Jonas Simon Fleck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Catarina Martins-Costa
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Thomas R Burkard
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Jan Themann
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Marlene Stuempflen
- Department of Radiodiagnostics, Medical University of Vienna, Vienna, Austria
| | - Angela Maria Peer
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Ábel Vertesy
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Jamie B Littleboy
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Christopher Esk
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
| | - Ulrich Elling
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Gregor Kasprian
- Department of Radiodiagnostics, Medical University of Vienna, Vienna, Austria
| | - Nina S Corsini
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Barbara Treutlein
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
| | - Juergen A Knoblich
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria.
- Department of Neurology, Medical University of Vienna, Vienna, Austria.
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25
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Dudley JA, Nagaraj UD, Merhar S, Mangano FT, Kline-Fath BM, Ou X, Acheson A, Yuan W. DTI of Opioid-Exposed Fetuses Using ComBat Harmonization: A Bi-Institutional Study. AJNR Am J Neuroradiol 2023; 44:1084-1089. [PMID: 37562830 PMCID: PMC10494946 DOI: 10.3174/ajnr.a7951] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/25/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND AND PURPOSE The underlying mechanisms leading to altered cognitive, behavioral, and vision outcomes in children with prenatal opioid exposure are yet to be fully understood. Some studies suggest WM alterations in infants and children with prenatal opioid exposure; however, the time course of WM changes is unknown. We aimed to evaluate differences in diffusion tensor imaging MRI parameters in the brain between opioid exposed fetuses and normal controls. MATERIALS AND METHODS This is a pilot, prospective cohort study in which subjects in the third trimester of pregnancy underwent fetal DTI of the brain with 20 noncolinear diffusion directions and a b-value of 500 s/mm2 at 2.5-mm isotropic resolution. RESULTS The study included a total of 26 fetuses, 11 opioid-exposed (mean gestational age, 32.61 [SD, 2.35] weeks) and 15 unexposed controls (mean gestational age, 31.77 [SD, 1.68] weeks). After we adjusted for gestational age, fractional anisotropy values were significantly higher in opioid-exposed fetuses relative to controls in 8 WM tracts: the bilateral lemniscus (left: P = .017; right: P = .020), middle cerebellar peduncle (P = .027), left inferior cerebellar peduncle (P = .026), right sagittal stratum (P = .040), right fornix stria terminalis (P = .022), right inferior fronto-occipital fasciculus (P = .011), and the right uncinate fasciculus (P = .033). Significant alteration was also identified in other DTI indices involving a series of brain regions. CONCLUSIONS Our data demonstrate initial evidence of cerebral WM microstructural differences between opioid-exposed fetuses and unexposed controls. Further studies in larger patient populations will be needed to fully understand these findings.
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Affiliation(s)
- J A Dudley
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
| | - U D Nagaraj
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
| | - S Merhar
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
- Perinatal Institute, Division of Neonatology (S.M.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - F T Mangano
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
- Department of Neurosurgery (F.T.M.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - B M Kline-Fath
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
| | - X Ou
- Departments of Radiology (X.O.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Departments of Pediatrics (X.O.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - A Acheson
- Department of Psychiatry (A.A.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - W Yuan
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
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26
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Stuempflen M, Taymourtash A, Kienast P, Schmidbauer VU, Schwartz E, Mitter C, Binder J, Prayer D, Kasprian G. Ganglionic eminence: volumetric assessment of transient brain structure utilizing fetal magnetic resonance imaging. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:405-413. [PMID: 37099530 DOI: 10.1002/uog.26232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/27/2023] [Accepted: 04/15/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVE To provide quantitative magnetic resonance imaging (MRI) super-resolution-based three-dimensional volumetric reference data on the growth dynamics of the ganglionic eminence (GE) relative to cortical and total fetal brain volumes (TBV). METHODS This was a retrospective study of fetuses without structural central nervous system anomalies or other confounding comorbidities that were referred for fetal MRI. Super-resolution reconstructions of 1.5- and 3-Tesla T2-weighted images were generated. Semiautomatic segmentation of TBV and cortical volume and manual segmentation of the GE were performed. Cortical volume, TBV and GE volume were quantified and three-dimensional reconstructions were generated to visualize the developmental dynamics of the GE. RESULTS Overall, 120 fetuses that underwent 127 MRI scans at a mean gestational age of 27.23 ± 4.81 weeks (range, 20-37 weeks) were included. In the investigated gestational-age range, GE volume ranged from 74.88 to 808.75 mm3 and was at its maximum at 21 gestational weeks, followed by a linear decrease (R2 = 0.559) throughout the late second and third trimesters. A pronounced reduction in GE volume relative to cortical volume and TBV occurred in the late second trimester, with a decline in this reduction observed in the third trimester (R2 = 0.936 and 0.924, respectively). Three-dimensional rendering allowed visualization of a continuous change in the shape and size of the GE throughout the second and third trimesters. CONCLUSIONS Even small compartments of the fetal brain, which are not easily accessible by standardized two-dimensional modalities, can be assessed precisely by super-resolution processed fetal MRI. The inverse growth dynamics of GE volume compared with TBV and cortical volume reflects the transitory nature and physiological involution of this (patho-)physiologically important brain structure. The normal development and involution of the GE is mandatory for normal cortical development. Pathological changes of this transient organ precede impairment of cortical structures, and their detection may allow an earlier diagnosis of such anomalies. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- M Stuempflen
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - A Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - P Kienast
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - V U Schmidbauer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - E Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - C Mitter
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - J Binder
- Department of Obstetrics and Feto-maternal Medicine, Medical University of Vienna, Vienna, Austria
| | - D Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - G Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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27
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Lin J, Miao QI, Surawech C, Raman SS, Zhao K, Wu HH, Sung K. High-Resolution 3D MRI With Deep Generative Networks via Novel Slice-Profile Transformation Super-Resolution. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2023; 11:95022-95036. [PMID: 37711392 PMCID: PMC10501177 DOI: 10.1109/access.2023.3307577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.
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Affiliation(s)
- Jiahao Lin
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Electrical and Computer Engineering, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Q I Miao
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Chuthaporn Surawech
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
- Division of Diagnostic Radiology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Steven S Raman
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Kai Zhao
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
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28
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Payette K, Uus A, Verdera JA, Zampieri CA, Hall M, Story L, Deprez M, Rutherford MA, Hajnal JV, Ourselin S, Tomi-Tricot R, Hutter J. An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field. ARXIV 2023:arXiv:2308.04903v1. [PMID: 37608939 PMCID: PMC10441444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R2 >0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.
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Affiliation(s)
- Kelly Payette
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Megan Hall
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Women & Children’s Health, King’s College London, London, UK: MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Sebastien Ourselin
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Raphael Tomi-Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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29
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Sarmah M, Neelima A, Singh HR. Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images. Vis Comput Ind Biomed Art 2023; 6:15. [PMID: 37495817 PMCID: PMC10371974 DOI: 10.1186/s42492-023-00142-7] [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: 02/28/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
Three-dimensional (3D) reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units. In the coming years, most patient care will shift toward this new paradigm. However, development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved, most of which are dependent on human expertise. In this review, a survey of pre-processing steps was conducted, and reconstruction techniques for several organs in medical diagnosis were studied. Various methods and principles related to 3D reconstruction were highlighted. The usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.
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Affiliation(s)
- Mriganka Sarmah
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India.
| | - Arambam Neelima
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India
| | - Heisnam Rohen Singh
- Department of Information Technology, Nagaland University, Nagaland, 797112, India
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30
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Vahedifard F, Ai HA, Supanich MP, Marathu KK, Liu X, Kocak M, Ansari SM, Akyuz M, Adepoju JO, Adler S, Byrd S. Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements. Diagnostics (Basel) 2023; 13:2355. [PMID: 37510099 PMCID: PMC10378043 DOI: 10.3390/diagnostics13142355] [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: 06/15/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.
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Affiliation(s)
- Farzan Vahedifard
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - H Asher Ai
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Mark P Supanich
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Kranthi K Marathu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Xuchu Liu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Shehbaz M Ansari
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Melih Akyuz
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Jubril O Adepoju
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Seth Adler
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
| | - Sharon Byrd
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Rush Medical College, Chicago, IL 60612, USA
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31
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Correa S, Nichols ES, Mueller ME, de Vrijer B, Eagleson R, McKenzie CA, de Ribaupierre S, Duerden EG. Default mode network functional connectivity strength in utero and the association with fetal subcortical development. Cereb Cortex 2023; 33:9144-9153. [PMID: 37259175 PMCID: PMC10350815 DOI: 10.1093/cercor/bhad190] [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: 03/28/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
The default mode network is essential for higher-order cognitive processes and is composed of an extensive network of functional and structural connections. Early in fetal life, the default mode network shows strong connectivity with other functional networks; however, the association with structural development is not well understood. In this study, resting-state functional magnetic resonance imaging and anatomical images were acquired in 30 pregnant women with singleton pregnancies. Participants completed 1 or 2 MR imaging sessions, on average 3 weeks apart (43 data sets), between 28- and 39-weeks postconceptional ages. Subcortical volumes were automatically segmented. Activation time courses from resting-state functional magnetic resonance imaging were extracted from the default mode network, medial temporal lobe network, and thalamocortical network. Generalized estimating equations were used to examine the association between functional connectivity strength between default mode network-medial temporal lobe, default mode network-thalamocortical network, and subcortical volumes, respectively. Increased functional connectivity strength in the default mode network-medial temporal lobe network was associated with smaller right hippocampal, left thalamic, and right caudate nucleus volumes, but larger volumes of the left caudate. Increased functional connectivity strength in the default mode network-thalamocortical network was associated with smaller left thalamic volumes. The strong associations seen among the default mode network functional connectivity networks and regionally specific subcortical volume development indicate the emergence of short-range connectivity in the third trimester.
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Affiliation(s)
- Susana Correa
- Neuroscience Program, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
| | - Emily S Nichols
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
| | - Megan E Mueller
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
| | - Barbra de Vrijer
- Obstetrics & Gynaecology, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Roy Eagleson
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
| | - Charles A McKenzie
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Sandrine de Ribaupierre
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Emma G Duerden
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
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32
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Clark A, Flouri D, Mufti N, James J, Clements E, Aughwane R, Aertsen M, David A, Melbourne A. Developments in functional imaging of the placenta. Br J Radiol 2023; 96:20211010. [PMID: 35234516 PMCID: PMC10321248 DOI: 10.1259/bjr.20211010] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/26/2022] [Accepted: 02/22/2022] [Indexed: 12/21/2022] Open
Abstract
The placenta is both the literal and metaphorical black box of pregnancy. Measurement of the function of the placenta has the potential to enhance our understanding of this enigmatic organ and serve to support obstetric decision making. Advanced imaging techniques are key to support these measurements. This review summarises emerging imaging technology being used to measure the function of the placenta and new developments in the computational analysis of these data. We address three important examples where functional imaging is supporting our understanding of these conditions: fetal growth restriction, placenta accreta, and twin-twin transfusion syndrome.
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Affiliation(s)
- Alys Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | | | - Joanna James
- Department of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Eleanor Clements
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Rosalind Aughwane
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
| | - Michael Aertsen
- Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium
| | - Anna David
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
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Ciceri T, Squarcina L, Pigoni A, Ferro A, Montano F, Bertoldo A, Persico N, Boito S, Triulzi FM, Conte G, Brambilla P, Peruzzo D. Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester. Neuroinformatics 2023; 21:549-563. [PMID: 37284977 PMCID: PMC10406722 DOI: 10.1007/s12021-023-09635-5] [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] [Accepted: 05/20/2023] [Indexed: 06/08/2023]
Abstract
Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Adele Ferro
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Florian Montano
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nicola Persico
- Department of Woman, Child and Newborn, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Simona Boito
- Department of Woman, Child and Newborn, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Maria Triulzi
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Services and Preventive Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio Conte
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Services and Preventive Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Vahedifard F, Adepoju JO, Supanich M, Ai HA, Liu X, Kocak M, Marathu KK, Byrd SE. Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging. World J Clin Cases 2023; 11:3725-3735. [PMID: 37383127 PMCID: PMC10294149 DOI: 10.12998/wjcc.v11.i16.3725] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/30/2023] [Accepted: 05/06/2023] [Indexed: 06/02/2023] Open
Abstract
Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.
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Affiliation(s)
- Farzan Vahedifard
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Jubril O Adepoju
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Mark Supanich
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
| | - Hua Asher Ai
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
| | - Xuchu Liu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Kranthi K Marathu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Sharon E Byrd
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
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35
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Xu J, Moyer D, Gagoski B, Iglesias JE, Grant PE, Golland P, Adalsteinsson E. NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1707-1719. [PMID: 37018704 PMCID: PMC10287191 DOI: 10.1109/tmi.2023.3236216] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.
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36
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Arain Z, Iliodromiti S, Slabaugh G, David AL, Chowdhury TT. Machine learning and disease prediction in obstetrics. Curr Res Physiol 2023; 6:100099. [PMID: 37324652 PMCID: PMC10265477 DOI: 10.1016/j.crphys.2023.100099] [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: 03/10/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.
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Affiliation(s)
- Zara Arain
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Stamatina Iliodromiti
- Women's Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK
| | - Gregory Slabaugh
- Digital Environment Research Institute, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 1HH, UK
| | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, Medical School Building, Huntley Street, London, WC1E 6AU, UK
| | - Tina T. Chowdhury
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
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37
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Uus AU, Kyriakopoulou V, Makropoulos A, Fukami-Gartner A, Cromb D, Davidson A, Cordero-Grande L, Price AN, Grigorescu I, Williams LZJ, Robinson EC, Lloyd D, Pushparajah K, Story L, Hutter J, Counsell SJ, Edwards AD, Rutherford MA, Hajnal JV, Deprez M. BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537347. [PMID: 37131820 PMCID: PMC10153133 DOI: 10.1101/2023.04.18.537347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain_bounti_tissue).
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | | | | | - Daniel Cromb
- Centre for the Developing Brain, King's College London, London, UK
| | - Alice Davidson
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid and CIBER-BBN, ISCII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, King's College London, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Logan Z J Williams
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emma C Robinson
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Lloyd
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Kuberan Pushparajah
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - A David Edwards
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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38
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Deprest T, Fidon L, De Keyzer F, Ebner M, Deprest J, Demaerel P, De Catte L, Vercauteren T, Ourselin S, Dymarkowski S, Aertsen M. Application of Automatic Segmentation on Super-Resolution Reconstruction MR Images of the Abnormal Fetal Brain. AJNR Am J Neuroradiol 2023; 44:486-491. [PMID: 36863845 PMCID: PMC10084897 DOI: 10.3174/ajnr.a7808] [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: 02/06/2022] [Accepted: 02/06/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND AND PURPOSE Fetal brain MR imaging is clinically used to characterize fetal brain abnormalities. Recently, algorithms have been proposed to reconstruct high-resolution 3D fetal brain volumes from 2D slices. By means of these reconstructions, convolutional neural networks have been developed for automatic image segmentation to avoid labor-intensive manual annotations, usually trained on data of normal fetal brains. Herein, we tested the performance of an algorithm specifically developed for segmentation of abnormal fetal brains. MATERIALS AND METHODS This was a single-center retrospective study on MR images of 16 fetuses with severe CNS anomalies (gestation, 21-39 weeks). T2-weighted 2D slices were converted to 3D volumes using a super-resolution reconstruction algorithm. The acquired volumetric data were then processed by a novel convolutional neural network to perform segmentations of white matter and the ventricular system and cerebellum. These were compared with manual segmentation using the Dice coefficient, Hausdorff distance (95th percentile), and volume difference. Using interquartile ranges, we identified outliers of these metrics and further analyzed them in detail. RESULTS The mean Dice coefficient was 96.2%, 93.7%, and 94.7% for white matter and the ventricular system and cerebellum, respectively. The Hausdorff distance was 1.1, 2.3, and 1.6 mm, respectively. The volume difference was 1.6, 1.4, and 0.3 mL, respectively. Of the 126 measurements, there were 16 outliers among 5 fetuses, discussed on a case-by-case basis. CONCLUSIONS Our novel segmentation algorithm obtained excellent results on MR images of fetuses with severe brain abnormalities. Analysis of the outliers shows the need to include pathologies underrepresented in the current data set. Quality control to prevent occasional errors is still needed.
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Affiliation(s)
- T Deprest
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - L Fidon
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
| | - F De Keyzer
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - M Ebner
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
- Department of Medical Physics and Biomedical Engineering (M.E., T.V.), University College London, London, UK
| | - J Deprest
- Gynaecology and Obstetrics (J.D., L.D.C., T.V.), University Hospitals Leuven, Belgium
- Institute for Women's Health (J.D.)
| | - P Demaerel
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - L De Catte
- Gynaecology and Obstetrics (J.D., L.D.C., T.V.), University Hospitals Leuven, Belgium
| | - T Vercauteren
- Gynaecology and Obstetrics (J.D., L.D.C., T.V.), University Hospitals Leuven, Belgium
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
- Department of Medical Physics and Biomedical Engineering (M.E., T.V.), University College London, London, UK
| | - S Ourselin
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
| | - S Dymarkowski
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - M Aertsen
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
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39
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Karimi D, Rollins CK, Velasco-Annis C, Ouaalam A, Gholipour A. Learning to segment fetal brain tissue from noisy annotations. Med Image Anal 2023; 85:102731. [PMID: 36608414 PMCID: PMC9974964 DOI: 10.1016/j.media.2022.102731] [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: 05/12/2022] [Revised: 11/17/2022] [Accepted: 12/23/2022] [Indexed: 01/03/2023]
Abstract
Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.
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Affiliation(s)
- Davood Karimi
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Abdelhakim Ouaalam
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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40
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Automatic placental and fetal volume estimation by a convolutional neural network. Placenta 2023; 134:23-29. [PMID: 36863128 DOI: 10.1016/j.placenta.2023.02.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023]
Abstract
INTRODUCTION We aimed to develop an artificial intelligence (AI) deep learning algorithm to efficiently estimate placental and fetal volumes from magnetic resonance (MR) scans. METHODS Manually annotated images from an MRI sequence was used as input to the neural network DenseVNet. We included data from 193 normal pregnancies at gestational week 27 and 37. The data were split into 163 scans for training, 10 scans for validation and 20 scans for testing. The neural network segmentations were compared to the manual annotation (ground truth) using the Dice Score Coefficient (DSC). RESULTS The mean ground truth placental volume at gestational week 27 and 37 was 571 cm3 (Standard Deviation (SD) 293 cm3) and 853 cm3 (SD 186 cm3), respectively. Mean fetal volume was 979 cm3 (SD 117 cm3) and 2715 cm3 (SD 360 cm3). The best fitting neural network model was attained at 22,000 training iterations with mean DSC 0.925 (SD 0.041). The neural network estimated mean placental volumes at gestational week 27-870 cm3 (SD 202 cm3) (DSC 0.887 (SD 0.034), and to 950 cm3 (SD 316 cm3) at gestational week 37 (DSC 0.896 (SD 0.030)). Mean fetal volumes were 1292 cm3 (SD 191 cm3) and 2712 cm3 (SD 540 cm3), with mean DSC of 0.952 (SD 0.008) and 0.970 (SD 0.040). The time spent for volume estimation was reduced from 60 to 90 min by manual annotation, to less than 10 s by the neural network. CONCLUSION The correctness of neural network volume estimation is comparable to human performance; the efficiency is substantially improved.
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Urru A, Nakaki A, Benkarim O, Crovetto F, Segalés L, Comte V, Hahner N, Eixarch E, Gratacos E, Crispi F, Piella G, González Ballester MA. An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107334. [PMID: 36682108 DOI: 10.1016/j.cmpb.2023.107334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/29/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. METHODS In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. RESULTS Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. CONCLUSIONS These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.
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Affiliation(s)
- Andrea Urru
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ayako Nakaki
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Francesca Crovetto
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain
| | - Laura Segalés
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain
| | - Valentin Comte
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nadine Hahner
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Eduard Gratacos
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Fàtima Crispi
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel A González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.
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Cordero-Grande L, Ortuno-Fisac JE, Del Hoyo AA, Uus A, Deprez M, Santos A, Hajnal JV, Ledesma-Carbayo MJ. Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:810-822. [PMID: 36288233 DOI: 10.1109/tmi.2022.3217725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with ifdefined tmiformat R2.5a state of the art method methods in the literature in a cohort of 72 fetal datasets in the range of 20-36 weeks gestational age. Results suggest improved image resolution Quantitative as well as radiological assessment suggest improved image quality and more accurate prediction of gestational age at scan is obtained when comparing to a state of the art reconstruction method methods. In addition, gestational age prediction results from our volumetric reconstructions compare favourably are competitive with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of 0.618 weeks ( R2=0.958 ) is achieved when combining fetal brain and trunk information.
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Salome P, Sforazzini F, Grugnara G, Kudak A, Dostal M, Herold-Mende C, Heiland S, Debus J, Abdollahi A, Knoll M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers (Basel) 2023; 15:cancers15030965. [PMID: 36765922 PMCID: PMC9913466 DOI: 10.3390/cancers15030965] [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: 01/07/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
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Affiliation(s)
- Patrick Salome
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
| | - Francesco Sforazzini
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
| | - Gianluca Grugnara
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Andreas Kudak
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Matthias Dostal
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Brain Tumour Group, European Organization for Research and Treatment of Cancer, 1200 Brussels, Belgium
- Division of Neurosurgical Research, Department of Neurosurgery, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jürgen Debus
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Amir Abdollahi
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
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Wu Q, Li Y, Sun Y, Zhou Y, Wei H, Yu J, Zhang Y. An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation. IEEE J Biomed Health Inform 2023; 27:1004-1015. [PMID: 37022393 DOI: 10.1109/jbhi.2022.3223106] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-dimensional (3D) HR image acquisition typically requests long scan time and, results in small spatial coverage and low signal-to-noise ratio (SNR). Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach scale-specific projection between LR and HR images, thus these methods can only deal with fixed up-sampling rates. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the LR image and the HR image are represented using the same implicit neural voxel function with different sampling rates. Due to the continuity of the learned implicit function, a single ArSSR model is able to achieve arbitrary and infinite up-sampling rate reconstructions of HR images from any input LR image. Then the SR task is converted to approach the implicit voxel function via deep neural networks from a set of paired HR and LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.
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Emam D, Aertsen M, Van der Veeken L, Fidon L, Patkee P, Kyriakopoulou V, De Catte L, Russo F, Demaerel P, Vercauteren T, Rutherford M, Deprest J. Longitudinal MRI Evaluation of Brain Development in Fetuses with Congenital Diaphragmatic Hernia around the Time of Fetal Endotracheal Occlusion. AJNR Am J Neuroradiol 2023; 44:205-211. [PMID: 36657946 PMCID: PMC9891331 DOI: 10.3174/ajnr.a7760] [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: 04/28/2022] [Accepted: 12/10/2022] [Indexed: 01/21/2023]
Abstract
BACKGROUND AND PURPOSE Congenital diaphragmatic hernia is associated with high mortality and morbidity, including evidence suggesting neurodevelopmental comorbidities after birth. The aim of this study was to document longitudinal changes in brain biometry and the cortical folding pattern in fetuses with congenital diaphragmatic hernia compared with healthy fetuses. MATERIALS AND METHODS This is a retrospective cohort study including fetuses with isolated congenital diaphragmatic hernia between January 2007 and May 2019, with at least 2 MR imaging examinations. For controls, we used images from fetuses who underwent MR imaging for an unrelated condition that did not compromise fetal brain development and fetuses from healthy pregnant women. Biometric measurements and 3D segmentations of brain structures were used as well as qualitative and quantitative grading of the supratentorial brain. Brain development was correlated with disease-severity markers. RESULTS Forty-two fetuses were included, with a mean gestational age at first MR imaging of 28.0 (SD, 2.1) weeks and 33.2 (SD, 1.3) weeks at the second imaging. The mean gestational age in controls was 30.7 (SD, 4.2) weeks. At 28 weeks, fetuses with congenital diaphragmatic hernia had abnormal qualitative and quantitative maturation, more extra-axial fluid, and larger total skull volume. By 33 weeks, qualitative grading scores were still abnormal, but quantitative scoring was in the normal range. In contrast, the extra-axial fluid volume remained abnormal with increased ventricular volume. Normal brain parenchymal volumes were found. CONCLUSIONS Brain development in fetuses with congenital diaphragmatic hernia around 28 weeks appears to be delayed. This feature is less prominent at 33 weeks. At this stage, there was also an increase in ventricular and extra-axial space volume.
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Affiliation(s)
- D Emam
- From the Department of Development and Regeneration (D.E., L.V.d.V., L.D.C., F.R., J.D.), Cluster Woman and Child, Group Biomedical Sciences, KU Leuven University of Leuven, Leuven, Belgium
- Department Obstetrics and Gynaecology (D.E., L.F.), Faculty of Medicine, Tanta University, Tanta, Egypt
| | - M Aertsen
- Department of Imaging and Pathology (M.A., P.D.), Clinical Department of Radiology, University Hospitals, KU Leuven, Leuven, Belgium
| | - L Van der Veeken
- From the Department of Development and Regeneration (D.E., L.V.d.V., L.D.C., F.R., J.D.), Cluster Woman and Child, Group Biomedical Sciences, KU Leuven University of Leuven, Leuven, Belgium
- Clinical Department Obstetrics and Gynaecology (L.V.d.V., L.D.C., F.R., J.D.), University Hospitals Leuven, Leuven, Belgium
| | - L Fidon
- Department Obstetrics and Gynaecology (D.E., L.F.), Faculty of Medicine, Tanta University, Tanta, Egypt
- Division of Imaging Sciences and Biomedical Engineering, Perinatal Imaging and Health and School of Biomedical Engineering and Imaging Sciences (L.F., T.V., J.D.), King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - P Patkee
- Centre for the Developing Brain (P.P., V.K., M.R., J.D.)
| | | | - L De Catte
- From the Department of Development and Regeneration (D.E., L.V.d.V., L.D.C., F.R., J.D.), Cluster Woman and Child, Group Biomedical Sciences, KU Leuven University of Leuven, Leuven, Belgium
- Clinical Department Obstetrics and Gynaecology (L.V.d.V., L.D.C., F.R., J.D.), University Hospitals Leuven, Leuven, Belgium
| | - F Russo
- From the Department of Development and Regeneration (D.E., L.V.d.V., L.D.C., F.R., J.D.), Cluster Woman and Child, Group Biomedical Sciences, KU Leuven University of Leuven, Leuven, Belgium
- Clinical Department Obstetrics and Gynaecology (L.V.d.V., L.D.C., F.R., J.D.), University Hospitals Leuven, Leuven, Belgium
| | - P Demaerel
- Department of Imaging and Pathology (M.A., P.D.), Clinical Department of Radiology, University Hospitals, KU Leuven, Leuven, Belgium
| | - T Vercauteren
- Division of Imaging Sciences and Biomedical Engineering, Perinatal Imaging and Health and School of Biomedical Engineering and Imaging Sciences (L.F., T.V., J.D.), King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - M Rutherford
- Centre for the Developing Brain (P.P., V.K., M.R., J.D.)
| | - J Deprest
- From the Department of Development and Regeneration (D.E., L.V.d.V., L.D.C., F.R., J.D.), Cluster Woman and Child, Group Biomedical Sciences, KU Leuven University of Leuven, Leuven, Belgium
- Clinical Department Obstetrics and Gynaecology (L.V.d.V., L.D.C., F.R., J.D.), University Hospitals Leuven, Leuven, Belgium
- Centre for the Developing Brain (P.P., V.K., M.R., J.D.)
- Division of Imaging Sciences and Biomedical Engineering, Perinatal Imaging and Health and School of Biomedical Engineering and Imaging Sciences (L.F., T.V., J.D.), King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Institute for Women's Health (J.D.), University College London, London, UK
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Fang Y, Bakian-Dogaheh K, Moghaddam M. Real-Time 3D Microwave Medical Imaging With Enhanced Variational Born Iterative Method. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:268-280. [PMID: 36166569 DOI: 10.1109/tmi.2022.3210494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green's function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects. Compared with the Born iterative method and distorted Born iterative method, VBIM requires less computational time to reach the convergence threshold. The graphics processing unit based acceleration technique is implemented for real-time imaging. To demonstrate the efficiency and accuracy of this VBIM-RIS method, synthetic analysis of a complex multi-layer spherical phantom is first conducted. Then, the algorithm is tested with measured data using our new MWI system prototype. Finally, a synthetic brain-tumor phantom model under a thermal therapy procedure is monitored to exemplify the real-time imaging with about 5 seconds per reconstruction frame.
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Shi W, Xu H, Sun C, Sun J, Li Y, Xu X, Zheng T, Zhang Y, Wang G, Wu D. AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:209-219. [PMID: 36129858 DOI: 10.1109/tmi.2022.3208277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.
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Xu X, Sun C, Sun J, Shi W, Shen Y, Zhao R, Luo W, Li M, Wang G, Wu D. Spatiotemporal Atlas of the Fetal Brain Depicts Cortical Developmental Gradient. J Neurosci 2022; 42:9435-9449. [PMID: 36323525 PMCID: PMC9794379 DOI: 10.1523/jneurosci.1285-22.2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 11/12/2022] Open
Abstract
The fetal brains experience rapid and complex development in utero during the second and third trimesters. In utero MRI of the fetal brain in this period enables us to quantify normal fetal brain development in the spatiotemporal domain. In this study, we established a high-quality spatiotemporal atlas between 23 and 38 weeks gestational age (GA) from 90 healthy Chinese human fetuses of both sexes using a pairwise and groupwise registration pipeline. We quantified the fetal cortical morphology indices and characterized their spatiotemporal developmental pattern. The cortical thickness exhibited a biphasic pattern that first increased and then decreased; the curvature fitted well into the Gompertz growth model; sulcal depth increased linearly, while surface area expanded exponentially. The cortical thickness and curvature trajectories consistently pointed to a characteristic time point around GA of 31 weeks. The characteristic GA and growth rate obtained from individual cortical regions suggested a central-to-peripheral developmental gradient, with the earliest development in the parietal lobe, and we also observed a superior-to-inferior gradient within the temporal lobe. These findings may be linked to biophysical events, such as dendritic arborization and thalamocortical fibers ingrowth. The proposed atlas was also compared with an existing fetal atlas from a white/mixed population. Finally, we examined the structural asymmetry of the fetal brains and found extensive asymmetry that dynamically changed with development. The current study depicted a comprehensive profile of fetal cortical development, and the established atlas could be used as a normative reference for neurodevelopmental and diagnostic purposes, especially in the Chinese population.SIGNIFICANCE STATEMENT We generated a high-quality 4D spatiotemporal atlas of the normal fetal brain development from 23 to 38 gestational weeks in a Chinese population and characterized the spatiotemporal developmental pattern of cortical morphology. According to the cortical development trajectories, the fetal cerebral cortex development follows a central-to-peripheral developmental gradient that may be related to the underlying cellular events. The majority of cortical regions already exhibit significant asymmetry during the fetal period.
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Affiliation(s)
- Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, P. R. China
| | - Jiwei Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Wen Shi
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287
| | - Yao Shen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Ruoke Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, P. R. China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
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Hufnagel S, Metzner S, Kerkering KM, Aigner CS, Kofler A, Schulz-Menger J, Schaeffter T, Kolbitsch C. 3D model-based super-resolution motion-corrected cardiac T1 mapping. Phys Med Biol 2022; 67. [PMID: 36265478 DOI: 10.1088/1361-6560/ac9c40] [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: 07/25/2022] [Accepted: 10/20/2022] [Indexed: 12/13/2022]
Abstract
Objective. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR).Approach. Due to signal-to-noise ratio limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6-8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6-8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5-2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects.Main results. Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%.Significance. The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3 × 1.3 × 1.5-2 mm3). The approach led to improvements in the visualization of small structures and precise T1 values.
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Affiliation(s)
- Simone Hufnagel
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Selma Metzner
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | | | - Andreas Kofler
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité Medical Faculty University Medicine, Berlin, Germany.,Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center (ECRC), Charité Humboldt University Berlin, DZHK partner site Berlin, Berlin, Germany.,Department of Cardiology and Nephrology, HELIOS Klinikum Berlin Buch, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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De Asis-Cruz J, Limperopoulos C. Harnessing the Power of Advanced Fetal Neuroimaging to Understand In Utero Footprints for Later Neuropsychiatric Disorders. Biol Psychiatry 2022; 93:867-879. [PMID: 36804195 DOI: 10.1016/j.biopsych.2022.11.019] [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: 08/01/2022] [Revised: 11/03/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Adverse intrauterine events may profoundly impact fetal risk for future adult diseases. The mechanisms underlying this increased vulnerability are complex and remain poorly understood. Contemporary advances in fetal magnetic resonance imaging (MRI) have provided clinicians and scientists with unprecedented access to in vivo human fetal brain development to begin to identify emerging endophenotypes of neuropsychiatric disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and schizophrenia. In this review, we discuss salient findings of normal fetal neurodevelopment from studies using advanced, multimodal MRI that have provided unparalleled characterization of in utero prenatal brain morphology, metabolism, microstructure, and functional connectivity. We appraise the clinical utility of these normative data in identifying high-risk fetuses before birth. We highlight available studies that have investigated the predictive validity of advanced prenatal brain MRI findings and long-term neurodevelopmental outcomes. We then discuss how ex utero quantitative MRI findings can inform in utero investigations toward the pursuit of early biomarkers of risk. Lastly, we explore future opportunities to advance our understanding of the prenatal origins of neuropsychiatric disorders using precision fetal imaging.
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