101
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Solomon O, Patriat R, Braun H, Palnitkar TE, Moeller S, Auerbach EJ, Ugurbil K, Sapiro G, Harel N. Motion robust magnetic resonance imaging via efficient Fourier aggregation. Med Image Anal 2023; 83:102638. [PMID: 36257133 DOI: 10.1016/j.media.2022.102638] [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: 02/01/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 02/04/2023]
Abstract
We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3 T and 7 T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.
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Affiliation(s)
- Oren Solomon
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America.
| | - Rémi Patriat
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Henry Braun
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Tara E Palnitkar
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Steen Moeller
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Edward J Auerbach
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Kamil Ugurbil
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, NC, United States of America; Department of Biomedical Engineering, Duke University, NC, United States of America; Department of Computer Science, Duke University, NC, United States of America; Department of Mathematics, Duke University, NC, United States of America
| | - Noam Harel
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States of America; Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States of America
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102
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Lin D, Wang Z, Li H, Zhang H, Deng L, Ren H, Sun S, Zheng F, Zhou J, Wang M. Automated Measurement of Pancreatic Fat Deposition on Dixon MRI Using nnU-Net. J Magn Reson Imaging 2023; 57:296-307. [PMID: 35635494 DOI: 10.1002/jmri.28275] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Pancreatic fat accumulation may cause or aggravate the process of acute pancreatitis, β-cell dysfunction, T2DM disease, and even be associated with pancreatic tumors. The pathophysiology of fatty pancreas remains overlooked and lacks effective imaging diagnostics. PURPOSE To automatically measure the distribution of pancreatic fat deposition on Dixon MRI in multicenter/population datasets using nnU-Net models. STUDY TYPE Retrospective. POPULATION A total of 176 obese/nonobese subjects (90 males, 86 females; mean age, 27.2 ± 19.7) were enrolled, including a training set (N = 132) and a testing set (N = 44). FIELD STRENGTH/SEQUENCE A 3 T and 1.5 T/gradient echo T1 dual-echo Dixon. ASSESSMENT The segmentation results of four types of nnU-Net models were compared using dice similarity coefficient (DSC), positive predicted value (PPV), and sensitivity. The ground truth was the manual delineation by two radiologists according to in-phase (IP) and opposed-phase (OP) images. STATISTICAL TESTS The group difference of segmentation results of four models were assessed by the Kruskal-Wallis H test with Dunn-Bonferroni comparisons. The interobserver agreement of pancreatic fat fraction measurements across three observers and test-retest reliability of human and machine were assessed by intragroup correlation coefficient (ICC). P < 0.05 was considered statistically significant. RESULTS The three-dimensional (3D) dual-contrast model had significantly improved performance than 2D dual-contrast (DSC/sensitivity) and 3D one-contrast (IP) models (DSC/PPV/sensitivity) and had less errors than 3D one-contrast (OP) model according to higher DSC and PPV (not significant), with a mean DSC of 0.9158, PPV of 0.9105 and sensitivity of 0.9232 in the testing set. The test-retest ICC of this model was above 0.900 in all pancreatic regions, exceeded human. DATA CONCLUSION 3D Dual-contrast nnU-Net aided segmentation of pancreas on Dixon images appears to be adaptable to multicenter/population datasets. It fully automates the assessment of pancreatic fat distribution and has high reliability. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Dingyi Lin
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ziyan Wang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hong Li
- School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongxi Zhang
- School of Medicine, Children's Hospital Binjiang Campus, Department of Radiology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Liping Deng
- School of Medicine, Sir Run Run Shaw Hospital, Department of Radiology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hong Ren
- School of Medicine, Sir Run Run Shaw Hospital, Department of Radiology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuiya Sun
- School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Fenping Zheng
- School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jiaqiang Zhou
- School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Min Wang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, Zhejiang, China
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103
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Wu Y, Liu J, White GM, Deng J. Image-based motion artifact reduction on liver dynamic contrast enhanced MRI. Phys Med 2023; 105:102509. [PMID: 36565556 DOI: 10.1016/j.ejmp.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/13/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA; Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Junchi Liu
- Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
| | - Gregory M White
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Jie Deng
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA; Department of Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, TX 75235, USA.
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104
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Cui L, Song Y, Wang Y, Wang R, Wu D, Xie H, Li J, Yang G. Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis. PLoS One 2023; 18:e0278668. [PMID: 36603007 DOI: 10.1371/journal.pone.0278668] [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] [Received: 09/04/2021] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2-weighted images from the IXI public dataset were split into three datasets, i.e., training (50 cases/6500 slices), validation (5/650), and test (12/1560) sets. First, motion-corrupted k-spaces and images were simulated using a pseudo-random sampling order and random motion tracks. A convolutional neural network (CNN) model was trained to filter the motion-corrupted images. Then, the k-space of the filtered image was compared with the motion-corrupted k-space line-by-line, to detect the PE lines affected by motion. Finally, the unaffected PE lines were used to reconstruct the final image using compressed sensing (CS). For the simulated images with 35%, 40%, 45%, and 50% unaffected PE lines, the mean peak signal-to-noise ratio (PSNRs) of resulting images (mean±standard deviation) were 36.129±3.678, 38.646±3.526, 40.426±3.223, and 41.510±3.167, respectively, and the mean structural similarity (SSIMs) were 0.950±0.046, 0.964±0.035, 0.975±0.025, and 0.979±0.023, respectively. For images with more than 35% PE lines unaffected by motion, images reconstructed with proposed algorithm exhibited better quality than those images reconstructed with CS using 35% under-sampled data (PSNR 37.678±3.261, SSIM 0.964±0.028). It was proved that deep learning and k-space analysis can detect the k-space PE lines affected by motion and CS can be used to reconstruct images from unaffected data, effectively alleviating the motion artifacts.
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Affiliation(s)
- Long Cui
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yang Song
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Rui Wang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Dongmei Wu
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Haibin Xie
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Jianqi Li
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Guang Yang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
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105
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Herrmann J, Wessling D, Nickel D, Arberet S, Almansour H, Afat C, Afat S, Gassenmaier S, Othman AE. Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T. Acad Radiol 2023; 30:93-102. [PMID: 35469719 DOI: 10.1016/j.acra.2022.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/12/2022] [Accepted: 03/20/2022] [Indexed: 11/01/2022]
Abstract
To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTEDL)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTEDL and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTEDL showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTEDL provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time.
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Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Simon Arberet
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Carmen Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany; Department of Neuroradiology, University Medical Center, Mainz, Germany.
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106
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Devi S, Bakshi S, Sahoo MN. Effect of situational and instrumental distortions on the classification of brain MR images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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107
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Moses J, Sinclair B, Law M, O'Brien TJ, Vivash L. Automated Methods for Detecting and Quantitation of Enlarged Perivascular spaces on MRI. J Magn Reson Imaging 2023; 57:11-24. [PMID: 35866259 PMCID: PMC10083963 DOI: 10.1002/jmri.28369] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 02/03/2023] Open
Abstract
The brain's glymphatic system is a network of intracerebral vessels that function to remove "waste products" such as degraded proteins from the brain. It comprises of the vasculature, perivascular spaces (PVS), and astrocytes. Poor glymphatic function has been implicated in numerous diseases; however, its contribution is still unknown. Efforts have been made to image the glymphatic system to further assess its role in the pathogenesis of different diseases. Numerous imaging modalities have been utilized including two-photon microscopy and contrast-enhanced magnetic resonance imaging (MRI). However, these are associated with limitations for clinical use. PVS form a part of the glymphatic system and can be visualized on standard MRI sequences when enlarged. It is thought that PVS become enlarged secondary to poor glymphatic drainage of metabolites. Thus, quantitating PVS could be a good surrogate marker for glymphatic function. Numerous manual rating scales have been developed to measure the PVS number and size on MRI scans; however, these are associated with many limitations. Instead, automated methods have been created to measure PVS more accurately in different diseases. In this review, we discuss the imaging techniques currently available to visualize the glymphatic system as well as the automated methods currently available to measure PVS, and the strengths and limitations associated with each technique. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Jasmine Moses
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia
| | - Ben Sinclair
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Neurology, Alfred Hospital, Melbourne, Australia.,Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia
| | - Meng Law
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Neurology, Alfred Hospital, Melbourne, Australia.,Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia.,Department of Neurology, Royal Melbourne Hospital, University of Melbourne, Victoria, Australia
| | - Lucy Vivash
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia.,Department of Neurology, Alfred Hospital, Melbourne, Australia.,Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia.,Department of Neurology, Royal Melbourne Hospital, University of Melbourne, Victoria, Australia
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108
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Wang K, Li X, Liu J, Guo X, Li W, Cao X, Yang J, Xue K, Dai Y, Wang X, Qiu J, Qin N. Predicting the image quality of respiratory-gated and breath-hold 3D MRCP from the breathing curve: a prospective study. Eur Radiol 2022; 33:4333-4343. [PMID: 36543903 DOI: 10.1007/s00330-022-09293-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To compare the image quality of breath-hold magnetic resonance cholangiopancreatography (BH-MRCP) and respiratory-gating MRCP (RG-MRCP), and to explore breathing curve-based factors and patient-related data affecting image quality. METHODS A total of 126 participants who underwent RG-MRCP and BH-MRCP on a 3-T magnetic resonance (MR) scanner were enrolled from May to December 2021. The images were evaluated by three radiologists on a 5-point scale. Respiratory parameters were extracted from the breathing curves. The Wilcoxon test was used to compare the image quality between the two MRCPs. Logistic regression analyzes were performed to identify age, sex, abdominal pain, and breathing predictor variables of better image quality. RESULTS BH-MRCP performed better in visualizing intrahepatic bile ducts and overall image quality than RG-MRCP (p < 0.01). Factors predicting relatively good image quality included lower standard deviation of the respiratory amplitude (SDamp)-minimum-peak (odds ratio = 0.16, p < 0.01) for RG-MRCP and lower SDamp (OR = 0.69, p < 0.01) for BH-MRCP. CONCLUSIONS BH-MRCP had significantly better overall image quality than RG-MRCP. Respiratory conditions exerted a significant impact on MRCP image quality, and parameters derived from the breathing curve could help predict the image quality of both sequences. KEY POINTS • Both breath-hold (BH) and respiratory-gating (RG) MRCP demonstrate satisfying image quality. • BH-GRASE-MRCP is significantly better than RG-MRCP at the group level, but not for every individual. • Respiratory conditions exert a significant impact on the image quality, and the breathing curve can help predict the image quality.
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Affiliation(s)
- Ke Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xinying Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Jing Liu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xiaochao Guo
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Wei Li
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xinming Cao
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Junzhe Yang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, 2258 Chengbei Rd., Jiading District, Shanghai, 201807, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Jianxing Qiu
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
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109
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Edwards LJ, McColgan P, Helbling S, Zarkali A, Vaculčiaková L, Pine KJ, Dick F, Weiskopf N. Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis. Cereb Cortex 2022; 33:5704-5716. [PMID: 36520483 PMCID: PMC10152104 DOI: 10.1093/cercor/bhac453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 12/23/2022] Open
Abstract
Abstract
Quantitative magnetic resonance imaging (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (${R_{1}}$), effective transverse relaxation rate (${R_{2}}^{\ast }$), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (${R_{2}}^{\ast }$ at 3T, ${R_{1}}$ at 7T), endothelial cells (${R_{1}}$ and MTsat at 3T), microglia (${R_{1}}$ and MTsat at 3T, ${R_{1}}$ at 7T), and oligodendrocytes and oligodendrocyte precursor cells (${R_{1}}$ at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types.
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Affiliation(s)
- Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, DE, Germany
| | - Peter McColgan
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, DE, Germany
- Huntington’s Disease Centre, University College London , London, UK
| | - Saskia Helbling
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, DE, Germany
- Poeppel Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society , Frankfurt am Main, DE, Germany
| | - Angeliki Zarkali
- Dementia Research Centre, University College London , London, UK
| | - Lenka Vaculčiaková
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, DE, Germany
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, DE, Germany
| | - Fred Dick
- Birkbeck/UCL Centre for Neuroimaging (BUCNI) , London, UK
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, DE, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University , Leipzig, DE, Germany
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110
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Ren J, Li Y, Liu FS, Liu C, Zhu JX, Nickel MD, Wang XY, Liu XY, Zhao J, He YL, Jin ZY, Xue HD. Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality. Insights Imaging 2022; 13:193. [PMID: 36512158 DOI: 10.1186/s13244-022-01321-5] [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: 05/16/2022] [Accepted: 10/29/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2DL) applied to female pelvic MRI, using standard T2-weighted TSE (T2S) as reference. METHODS In total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2S and T2DL sequences in three planes were performed for each participant. Quantitative image evaluation was conducted by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image geometric distortion was evaluated by measuring the diameters in all three directions of the uterus and lesions. Qualitative image evaluation including overall image quality, artifacts, boundary sharpness of the uterine zonal layers, and lesion conspicuity were assessed by three radiologists using a 5-point Likert scale, with 5 indicating the best quality. Comparative analyses were conducted for the two sequences. RESULTS T2DL resulted in a 62.7% timing reduction (1:54 min for T2DL and 5:06 min for T2S in axial, sagittal, and coronal imaging, respectively). Compared to T2S, T2DL had significantly higher SNR (p ≤ 0.001) and CNR (p ≤ 0.007), and without geometric distortion (p = 0.925-0.981). Inter-observer agreement regarding qualitative evaluation was excellent (Kendall's W > 0.75). T2DL provided superior image quality (all p < 0.001), boundary sharpness of the uterine zonal layers (all p < 0.001), lesion conspicuity (p = 0.002, p < 0.001, and p = 0.021), and fewer artifacts (all p < 0.001) in sagittal, axial, and coronal imaging. CONCLUSIONS Compared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.
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Affiliation(s)
- Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Beijing, People's Republic of China
| | - Fei-Shi Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Chong Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jin-Xia Zhu
- MR Collaboration, Siemens Healthineers Ltd., Beijing, People's Republic of China
| | | | - Xiao-Ye Wang
- MR Clinical Marketing, Siemens Healthineers Ltd., Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jia Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
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Soares JF, Abreu R, Lima AC, Sousa L, Batista S, Castelo-Branco M, Duarte JV. Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis. Front Neurosci 2022; 16:1017211. [PMID: 36570849 PMCID: PMC9768441 DOI: 10.3389/fnins.2022.1017211] [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: 08/11/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes. Methods We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS. Results No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation. Discussion Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.
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Affiliation(s)
- Júlia F. Soares
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal
| | - Rodolfo Abreu
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal
| | - Ana Cláudia Lima
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Lívia Sousa
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Sónia Batista
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Valente Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal,*Correspondence: João Valente Duarte,
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Mesoscopic in vivo human T 2* dataset acquired using quantitative MRI at 7 Tesla. Neuroimage 2022; 264:119733. [PMID: 36375782 DOI: 10.1016/j.neuroimage.2022.119733] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/15/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
Abstract
Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T2* has not been performed. Here we provide a dataset containing empirical T2* measurements acquired at 0.35 × 0.35 × 0.35 mm3 voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T2* (as well as R2*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T2*-weighted (f)MRI signal.
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A unified model for reconstruction and R 2* mapping of accelerated 7T data using the quantitative recurrent inference machine. Neuroimage 2022; 264:119680. [PMID: 36240989 DOI: 10.1016/j.neuroimage.2022.119680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 09/16/2022] [Accepted: 10/10/2022] [Indexed: 11/07/2022] Open
Abstract
Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.
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Hossbach J, Splitthoff DN, Cauley S, Clifford B, Polak D, Lo WC, Meyer H, Maier A. Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction. Med Phys 2022; 50:2148-2161. [PMID: 36433748 DOI: 10.1002/mp.16119] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Intra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of the head. PURPOSE State-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. METHODS The proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free low-resolution reference scan to provide the ground truth orientation. The supervised training utilizes motion simulations based on 28 acquisitions with subject-wise training, validation, and test data splits of 70%, 23%, and 7%. During inference, MoPED is embedded in an iterative DC-driven motion correction algorithm which alternatingly updates estimates of the motion parameters and motion-corrected low-resolution k-space data. The estimated motion parameters are then used to reconstruct the final motion corrected image. The mean absolute/squared error and the Pearson correlation coefficient were used to analyze the motion parameter estimation quality on in-silico data in a quantitative evaluation. Structural similarity (SSIM), DC error and root mean squared error (RMSE) were used as metrics of image quality improvement. Furthermore, the generalization capability of the network was analyzed on two in-vivo motion volumes with 28 slices each and on one simulated T1-weighted volume. RESULTS The motion estimation achieves a Pearson correlation of 0.968 to the simulated ground-truth of the 2433 test data slices used. In-silico results indicate that MoPED decreases the time for the optimization by a factor of around 27 compared to a conventional method and is able to reduce the RMSE of the reconstructions and average DC error by more than a factor of two compared to uncorrected images. In-vivo experiments show a decrease in computation time by a factor of around 20, a RMSE decrease from 0.055 to 0.033 and an SSIM increase from 0.795 to 0.862. Furthermore, contrast independence is demonstrated as MoPED is also able to correct T1-weighted images in simulations without retraining. Due to the model-based correction, no hallucinations were observed. CONCLUSIONS Incorporating DL in a model-based motion correction algorithm shows great benefit on the optimization and computation time. The k-space-based estimation also allows a data consistent correction and therefore avoids the risk of hallucinations of image-to-image approaches.
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Affiliation(s)
- Julian Hossbach
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Massachusetts, Charlestown, USA
| | - Bryan Clifford
- Siemens Medical Solutions USA, Massachusetts, Boston, USA
| | - Daniel Polak
- Siemens Healthcare GmbH, Erlangen, Germany.,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Massachusetts, Charlestown, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Massachusetts, Boston, USA
| | | | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Wahid KA, Xu J, El-Habashy D, Khamis Y, Abobakr M, McDonald B, O’ Connell N, Thill D, Ahmed S, Sharafi CS, Preston K, Salzillo TC, Mohamed ASR, He R, Cho N, Christodouleas J, Fuller CD, Naser MA. Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy. Front Oncol 2022; 12:975902. [PMID: 36425548 PMCID: PMC9679225 DOI: 10.3389/fonc.2022.975902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/21/2022] [Indexed: 11/10/2022] Open
Abstract
BackgroundQuick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.MethodsWe used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and 6mMRI as output. Eighteen image sets were used to test model performance. Similarity metrics, including the mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were calculated between normalized synthetic 6mMRI and ground-truth 6mMRI for all test cases. In addition, a previously trained OAR DL auto-segmentation model was used to segment the right parotid gland, left parotid gland, and mandible on all test case images. Dice similarity coefficients (DSC) were calculated between 2mMRI and either ground-truth 6mMRI or synthetic 6mMRI for each OAR; two one-sided t-tests were applied between the ground-truth and synthetic 6mMRI to determine equivalence. Finally, a visual Turing test using paired ground-truth and synthetic 6mMRI was performed using three clinician observers; the percentage of images that were correctly identified was compared to random chance using proportion equivalence tests.ResultsThe median similarity metrics across the whole images were 0.19, 0.93, and 33.14 for MSE, SSIM, and PSNR, respectively. The median of DSCs comparing ground-truth vs. synthetic 6mMRI auto-segmented OARs were 0.86 vs. 0.85, 0.84 vs. 0.84, and 0.82 vs. 0.85 for the right parotid gland, left parotid gland, and mandible, respectively (equivalence p<0.05 for all OARs). The percent of images correctly identified was equivalent to chance (p<0.05 for all observers).ConclusionsUsing 2mMRI inputs, we demonstrate that DL-generated synthetic 6mMRI outputs have high similarity to ground-truth 6mMRI, but further improvements can be made. Our study facilitates the clinical incorporation of synthetic MRI in MRI-guided radiotherapy.
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Affiliation(s)
- Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | - Dina El-Habashy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Clinical Oncology and Nuclear Medicine, Menoufia University, Shebin Elkom, Egypt
| | - Yomna Khamis
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Moamen Abobakr
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brigid McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | | | - Sara Ahmed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Christina Setareh Sharafi
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kathryn Preston
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Travis C. Salzillo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Abdallah S. R. Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Clifton D. Fuller, ; Mohamed A. Naser,
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Clifton D. Fuller, ; Mohamed A. Naser,
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Wang NC, Noll DC, Srinivasan A, Gagnon-Bartsch J, Kim MM, Rao A. Simulated MRI Artifacts: Testing Machine Learning Failure Modes. BME FRONTIERS 2022; 2022:9807590. [PMID: 37850164 PMCID: PMC10521705 DOI: 10.34133/2022/9807590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/08/2022] [Indexed: 10/19/2023] Open
Abstract
Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.
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Affiliation(s)
- Nicholas C. Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, USA
| | - Douglas C. Noll
- Department of Biomedical Engineering, University of Michigan, USA
- Department of Radiology, University of Michigan, USA
| | - Ashok Srinivasan
- Department of Radiology, Division of Neuroradiology, University of Michigan, USA
- Rogel Cancer Center, University of Michigan, USA
- Frankel Cardiovascular Center, University of Michigan, USA
| | | | - Michelle M. Kim
- Department of Radiation Oncology, University of Michigan, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, USA
- Department of Radiation Oncology, University of Michigan, USA
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Bertelsen A, Bernchou U, Schytte T, Brink C, Mahmood F. Is what you see what you treat? The effect of respiration-induced target motion in 3D magnetic resonance images. Phys Imaging Radiat Oncol 2022; 24:167-172. [DOI: 10.1016/j.phro.2022.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022] Open
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Brisson NM, Krämer M, Krahl LAN, Schill A, Duda GN, Reichenbach JR. A novel multipurpose device for guided knee motion and loading during dynamic magnetic resonance imaging. Z Med Phys 2022; 32:500-513. [PMID: 35221155 PMCID: PMC9948850 DOI: 10.1016/j.zemedi.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/21/2021] [Accepted: 12/17/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION This work aimed to develop a novel multipurpose device for guided knee flexion-extension, both passively using a motorized pneumatic system and actively (muscle-driven) with the joint unloaded or loaded during dynamic MRI. Secondary objectives were to characterize the participant experience during device use, and present preliminary dynamic MRI data to demonstrate the different device capabilities. MATERIAL AND METHODS Self-reported outcomes were used to characterize the pain, physical exertion and discomfort levels experienced by 10 healthy male participants during four different active knee motion and loading protocols using the novel device. Knee angular data were recorded during the protocols to determine the maximum knee range of motion achievable. Dynamic MRI was acquired for three healthy volunteers during passive, unloaded knee motion using 2D Cartesian TSE, 2D radial GRE and 3D UTE sequences; and during active, unloaded and loaded knee motion using 2D radial GRE imaging. Because of the different MRI sequences used, spatial resolution was inherently lower for active knee motion than for passive motion acquisitions. RESULTS Depending on the protocol, some participants reported slight pain, mild discomfort and varying levels of physical exertion. On average, participants achieved ∼40° of knee flexion; loaded conditions can create knee moments up to 27Nm. High quality imaging data were obtained during different motion and loading conditions. Dynamic 3D data allowed to retrospectively extract arbitrarily oriented slices. CONCLUSION A novel multipurpose device for guided, physiologically relevant knee motion and loading during dynamic MRI was developed. Device use was well tolerated and suitable for acquiring high quality images during different motion and loading conditions. Different bone positions between loaded and unloaded conditions were likely due to out-of-plane motion, particularly because image registration was not performed. Ultimately, this device could be used to advance our understanding of physiological and pathological joint mechanics.
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Affiliation(s)
- Nicholas M Brisson
- Julius Wolff Institute, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany.
| | - Martin Krämer
- Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany; Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Leonie A N Krahl
- Julius Wolff Institute, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany
| | - Alexander Schill
- Research Workshop, Charité - Universitätsmedizin Berlin, Germany
| | - Georg N Duda
- Julius Wolff Institute, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich Schiller University Jena, Germany
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Wang WT, Li N, Papageorgiou I, Chan L, Pham DL, Butman JA. Segmented 3D Echo Planar Acquisition for Rapid Susceptibility-Weighted Imaging: Application to Microhemorrhage Detection in Traumatic Brain Injury. J Magn Reson Imaging 2022; 56:1529-1535. [PMID: 35852491 PMCID: PMC9588524 DOI: 10.1002/jmri.28326] [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: 03/18/2021] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Susceptibility-weighted imaging (SWI) provides superior image contrast of cerebral microhemorrhages (CMBs). It is based on a three-dimensional (3D) gradient echo (GRE) sequence with a relatively long imaging time. PURPOSE To evaluate whether an accelerated 3D segmented echo planar imaging SWI is comparable to GRE SWI in detecting CMBs in traumatic brain injury (TBI). STUDY TYPE Prospective. SUBJECTS Four healthy volunteers and 46 consecutive subjects (38.0 ± 14.4 years, 16 females; 12 mild, 13 moderate, and 7 severe TBI). FIELD STRENGTH/SEQUENCE A 3 T scanner/3D gradient echo and 3D segmented echo planar imaging (segEPI). ASSESSMENT Brain images were acquired using GRE and segEPI in a single session (imaging time = 9 minutes 47 seconds and 1 minute 30 seconds, respectively). The signal-to-noise ratio (SNR) calculated from healthy volunteer thalamus and centrum semiovale were compared. CMBs were counted by three raters blinded to diagnostic information. STATISTICAL TESTS A t-test was used to assess SNR difference. Pearson correlation and Wilcoxon signed-rank test were performed using CMB counts. The intermethod agreement was evaluated using Bland-Altman method. Intermethod and interrater reliabilities of image-based diffuse axonal injury (DAI) diagnoses were evaluated using Cohen's kappa and percent agreement. P ≤ 0.05 was considered statistically significant. RESULTS Thalamus SNRs were 16.9 ± 2.2 and 16.5 ± 3 for GRE and segEPI (P = 0.84), respectively. Centrum semiovale SNRs were 25.8 ± 4.6 and 21.1 ± 2.7 (P = 0.13). The correlation coefficient of CMBs was 0.93, and differences were not significant (P = 0.56-0.85). For DAI diagnoses, Cohen's kappa was 0.62-0.84 and percent agreement was 85%-94%. DATA CONCLUSION CMB counts on segEPI and GRE were highly correlated, and DAI diagnosis was made equally effectively. segEPI SWI can potentially replace GRE SWI in detecting TBI CMBs, especially when time constraints are critical. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Wen-Tung Wang
- Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, USA
| | - Ningzhi Li
- Center for Devices and Radiological Health, FDA, Silver Spring, MD, USA
| | | | - Leighton Chan
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, USA
- Rehabilitation Medicine Department, Clinical Center, NIH, Bethesda, MD, USA
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, USA
| | - John A. Butman
- Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, USA
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Bernal J, Valdés-Hernández MDC, Escudero J, Duarte R, Ballerini L, Bastin ME, Deary IJ, Thrippleton MJ, Touyz RM, Wardlaw JM. Assessment of perivascular space filtering methods using a three-dimensional computational model. Magn Reson Imaging 2022; 93:33-51. [PMID: 35932975 DOI: 10.1016/j.mri.2022.07.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/19/2022] [Accepted: 07/30/2022] [Indexed: 10/31/2022]
Abstract
Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.
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Affiliation(s)
- Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany; German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Maria D C Valdés-Hernández
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK.
| | - Javier Escudero
- Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
| | - Roberto Duarte
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Lucia Ballerini
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
| | | | - Rhian M Touyz
- Research Institute of the McGill University Health Centre, McGill University, Montréal, Canada
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
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121
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Bayer JMM, Thompson PM, Ching CRK, Liu M, Chen A, Panzenhagen AC, Jahanshad N, Marquand A, Schmaal L, Sämann PG. Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses. Front Neurol 2022; 13:923988. [PMID: 36388214 PMCID: PMC9661923 DOI: 10.3389/fneur.2022.923988] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/12/2022] [Indexed: 09/12/2023] Open
Abstract
Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.
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Affiliation(s)
- Johanna M. M. Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Andrew Chen
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States
| | - Alana C. Panzenhagen
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Neda Jahanshad
- Laboratory of Brain eScience, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, United States
| | - Andre Marquand
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboudumc, Nijmegen, Netherlands
| | - Lianne Schmaal
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
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122
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Bohra A, Vasudevan A, Kutaiba N, Van Langenberg DR. Challenges and Strategies to Optimising the Quality of Small Bowel Magnetic Resonance Imaging in Crohn’s Disease. Diagnostics (Basel) 2022; 12:diagnostics12102533. [PMID: 36292222 PMCID: PMC9600769 DOI: 10.3390/diagnostics12102533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/03/2022] [Accepted: 10/17/2022] [Indexed: 11/21/2022] Open
Abstract
Magnetic resonance enterography (MRE) is one of the most highly utilised tools in the assessment of patients with small bowel Crohn’s disease (CD). As a non-invasive modality, it has both patient and procedure-related advantages over ileocolonoscopy which is the current gold standard for Crohn’s disease activity assessment. MRE relies upon high-quality images to ensure accurate disease activity assessment; however, few studies have explored the impact of image quality on the accuracy of small bowel CD activity assessment. Bowel distension and motion artifacts are two key imaging parameters that impact the quality of images obtained through MRE. Multiple strategies have been employed to both minimise the effects of motion artifacts and improve bowel distension. This review discusses the definitions of bowel distension and motion artifacts within the literature with a particular focus on current strategies to improve bowel distension and limit motion artifacts in MRE.
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Affiliation(s)
- Anuj Bohra
- Department of Gastroenterology, Eastern Health, Box Hill 3128, Australia
- Correspondence:
| | - Abhinav Vasudevan
- Department of Gastroenterology, Eastern Health, Box Hill 3128, Australia
| | - Numan Kutaiba
- Department of Radiology, Eastern Health, Box Hill 3128, Australia
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123
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Nárai Á, Hermann P, Auer T, Kemenczky P, Szalma J, Homolya I, Somogyi E, Vakli P, Weiss B, Vidnyánszky Z. Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans. Sci Data 2022; 9:630. [PMID: 36253426 PMCID: PMC9576686 DOI: 10.1038/s41597-022-01694-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.
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Affiliation(s)
- Ádám Nárai
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Tibor Auer
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.,School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - János Szalma
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Béla Weiss
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
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124
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Tsujimura K, Shiohama T, Takahashi E. microRNA Biology on Brain Development and Neuroimaging Approach. Brain Sci 2022; 12:brainsci12101366. [PMID: 36291300 PMCID: PMC9599180 DOI: 10.3390/brainsci12101366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/22/2022] Open
Abstract
Proper brain development requires the precise coordination and orchestration of various molecular and cellular processes and dysregulation of these processes can lead to neurological diseases. In the past decades, post-transcriptional regulation of gene expression has been shown to contribute to various aspects of brain development and function in the central nervous system. MicroRNAs (miRNAs), short non-coding RNAs, are emerging as crucial players in post-transcriptional gene regulation in a variety of tissues, such as the nervous system. In recent years, miRNAs have been implicated in multiple aspects of brain development, including neurogenesis, migration, axon and dendrite formation, and synaptogenesis. Moreover, altered expression and dysregulation of miRNAs have been linked to neurodevelopmental and psychiatric disorders. Magnetic resonance imaging (MRI) is a powerful imaging technology to obtain high-quality, detailed structural and functional information from the brains of human and animal models in a non-invasive manner. Because the spatial expression patterns of miRNAs in the brain, unlike those of DNA and RNA, remain largely unknown, a whole-brain imaging approach using MRI may be useful in revealing biological and pathological information about the brain affected by miRNAs. In this review, we highlight recent advancements in the research of miRNA-mediated modulation of neuronal processes that are important for brain development and their involvement in disease pathogenesis. Also, we overview each MRI technique, and its technological considerations, and discuss the applications of MRI techniques in miRNA research. This review aims to link miRNA biological study with MRI analytical technology and deepen our understanding of how miRNAs impact brain development and pathology of neurological diseases.
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Affiliation(s)
- Keita Tsujimura
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Nagoya 4648602, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya 4648602, Japan
- Correspondence: (K.T.); (E.T.)
| | - Tadashi Shiohama
- Department of Pediatrics, Chiba University Hospital, Chiba 2608677, Japan
| | - Emi Takahashi
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Correspondence: (K.T.); (E.T.)
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125
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Dabrowski O, Courvoisier S, Falcone JL, Klauser A, Songeon J, Kocher M, Chopard B, Lazeyras F. Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets. Phys Med 2022; 102:79-87. [PMID: 36137403 DOI: 10.1016/j.ejmp.2022.09.005] [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: 03/14/2022] [Revised: 08/26/2022] [Accepted: 09/12/2022] [Indexed: 11/19/2022] Open
Abstract
MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, either prospective or retrospective. Benchmarking the latter methods requires labeled motion-corrupted datasets, which are uncommon. Up to our best knowledge, no protocol for generating labeled datasets of MRI images corrupted by controlled motion has yet been proposed. Hence, we present a methodology allowing the acquisition of reproducible motion-corrupted MRI images as well as validation of the system's performance by motion estimation through rigid-body volume registration of fast 3D echo-planar imaging (EPI) time series. A proof-of-concept is presented, to show how the protocol can be implemented to provide qualitative and quantitative results. An MRI-compatible video system displays a moving target that volunteers equipped with customized plastic glasses must follow to perform predefined head choreographies. Motion estimation using rigid-body EPI time series registration demonstrated that head position can be accurately determined (with an average standard deviation of about 0.39 degrees). A spatio-temporal upsampling and interpolation method to cope with fast motion is also proposed in order to improve motion estimation. The proposed protocol is versatile and straightforward. It is compatible with all MRI systems and may provide insights on the origins of specific motion artifacts. The MRI and artificial intelligence research communities could benefit from this work to build in-vivo labeled datasets of motion-corrupted MRI images suitable for training/testing any retrospective motion correction or machine learning algorithm.
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Affiliation(s)
- Oscar Dabrowski
- Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland.
| | - Sébastien Courvoisier
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
| | - Jean-Luc Falcone
- Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland
| | - Antoine Klauser
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
| | - Julien Songeon
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
| | - Michel Kocher
- Biomedical Imaging Group (BIG), School of Engineering, EPFL, Lausanne, Switzerland
| | - Bastien Chopard
- Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland
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126
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Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging. Diagnostics (Basel) 2022; 12:diagnostics12102370. [PMID: 36292057 PMCID: PMC9600324 DOI: 10.3390/diagnostics12102370] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBEStd), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBESR). Image analysis of 40 patients with a mean age of 56 years (range 18−84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBESR compared to VIBEStd (each p < 0.001). Lesion detectability was better for VIBESR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBEStd, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBESR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.
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127
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Nagy Z, Hutton C, David G, Hinterholzer N, Deichmann R, Weiskopf N, Vannesjo SJ. HiHi fMRI: a data-reordering method for measuring the hemodynamic response of the brain with high temporal resolution and high SNR. Cereb Cortex 2022; 33:4606-4611. [PMID: 36169574 PMCID: PMC10110425 DOI: 10.1093/cercor/bhac364] [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: 05/20/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/14/2022] Open
Abstract
There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.
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Affiliation(s)
- Zoltan Nagy
- Laboratory for Social and Neural Systems Research (SNS Lab), University Hospital Zurich, Rämistrasse 100, University of Zurich, Zurich CH-8091, Switzerland.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK
| | - Chloe Hutton
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK
| | - Gergely David
- Spinal Cord Injury Center, Balgrist University Hospital, Forchstrasse 340, University of Zurich, Zurich CH-8008, Switzerland
| | - Natalie Hinterholzer
- SCMI, Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Lengghalde 5, Zurich CH-8008, Switzerland
| | - Ralf Deichmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK.,Brain Imaging Centre, Goethe University Frankfurt, University Hospital Campus, Haus 95H, Schleusenweg 2-16, Frankfurt am Main D-60528, Germany
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig 04103, Germany
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Høgskoleringen 5, Trondheim 7491, Norway
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128
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MRI Detection of Hepatic N-Acetylcysteine Uptake in Mice. Biomedicines 2022; 10:biomedicines10092138. [PMID: 36140239 PMCID: PMC9495914 DOI: 10.3390/biomedicines10092138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
This proof-of-concept study looked at the feasibility of using a thiol–water proton exchange (i.e., CEST) MRI contrast to detect in vivo hepatic N-acetylcysteine (NAC) uptake. The feasibility of detecting NAC-induced glutathione (GSH) biosynthesis using CEST MRI was also investigated. The detectability of the GSH amide and NAC thiol CEST effect at B0 = 7 T was determined in phantom experiments and simulations. C57BL/6 mice were injected intravenously (IV) with 50 g L−1 NAC in PBS (pH 7) during MRI acquisition. The dynamic magnetisation transfer ratio (MTR) and partial Z-spectral data were generated from the acquisition of measurements of the upfield NAC thiol and downfield GSH amide CEST effects in the liver. The 1H-NMR spectroscopy on aqueous mouse liver extracts, post-NAC-injection, was performed to verify hepatic NAC uptake. The dynamic MTR and partial Z-spectral data revealed a significant attenuation of the mouse liver MR signal when a saturation pulse was applied at −2.7 ppm (i.e., NAC thiol proton resonance) after the IV injection of the NAC solution. The 1H-NMR data revealed the presence of hepatic NAC, which coincided strongly with the increased upfield MTR in the dynamic CEST data, providing strong evidence that hepatic NAC uptake was detected. However, this MTR enhancement was attributed to a combination of NAC thiol CEST and some other upfield MT-generating mechanism(s) to be identified in future studies. The detection of hepatic GSH via its amide CEST MRI contrast was inconclusive based on the current results.
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129
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Geldschläger O, Bosch D, Henning A. OTUP workflow: target specific optimization of the transmit k-space trajectory for flexible universal parallel transmit RF pulse design. NMR IN BIOMEDICINE 2022; 35:e4728. [PMID: 35297104 DOI: 10.1002/nbm.4728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/09/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
PURPOSE To optimize transmit k-space trajectories for a wide range of excitation targets and to design "universal pTx RF pulses" based on these trajectories. METHODS Transmit k-space trajectories (stack of spirals and SPINS) were optimized to best match different excitation targets using the parameters of the analytical equations of spirals and SPINS. The performances of RF pulses designed based on optimized and non-optimized trajectories were compared. The optimized trajectories were utilized for universal pulse design. The universal pulse performances were compared with subject specific tailored pulse performances. The OTUP workflow (optimization of transmit k-space trajectories and universal pulse calculation) was tested on three test target excitation patterns. For one target (local excitation of a central area in the human brain) the pulses were tested in vivo at 9.4 T. RESULTS The workflow produced appropriate transmit k-space trajectories for each test target. Utilization of an optimized trajectory was crucial for the pulse performance. Using unsuited trajectories diminished the performance. It was possible to create target specific universal pulses. However, not every test target is equally well suited for universal pulse design. There was no significant difference in the in vivo performance between subject specific tailored pulses and a universal pulse at 9.4 T. CONCLUSIONS The proposed workflow further exploited and improved the universal pulse concept by combining it with gradient trajectory optimization for stack of spirals and SPINS. It emphasized the importance of a well suited trajectory for pTx RF pulse design. Universal and tailored pulses performed with a sufficient degree of similarity in simulations and a high degree of similarity in vivo. The implemented OTUP workflow and the B0 /B1+ map data from 18 subjects measured at 9.4 T are available as open source (https://github.com/ole1965/workflow_OTUP.git).
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Affiliation(s)
- Ole Geldschläger
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Dario Bosch
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
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130
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Shangguan P, Jiang W, Wang J, Wu J, Cai C, Cai S. Multi-slice compressed sensing MRI reconstruction based on deep fusion connection network. Magn Reson Imaging 2022; 93:115-127. [DOI: 10.1016/j.mri.2022.08.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022]
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131
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Lang M, Rapalino O, Huang S, Lev MH, Conklin J, Wald LL. Emerging Techniques and Future Directions: Fast and Portable Magnetic Resonance Imaging. Magn Reson Imaging Clin N Am 2022; 30:565-582. [PMID: 35995480 DOI: 10.1016/j.mric.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Fast MRI and portable MRI are emerging as promising technologies to improve the speed, efficiency, and availability of MR imaging. Fast MRI methods are increasingly being adopted to create screening protocols for the diagnosis and management of acute pathology in the emergency department. Faster imaging can facilitate timely diagnosis, reduce motion artifacts, and improve departmental MR operations. Point-of-care and portable MRI are emerging technologies that require radiologists to reenvision the role of MRI as a tool with greater accessibility, fewer siting constraints, and the ability to provide valuable diagnostic information at the bedside. Recently introduced commercially available pulse sequences and new MRI scanners are bringing these technologies closer to the patient's clinical setting, and we expect their use to only increase over the coming decade. This article provides an overview of these emerging technologies for emergency radiologists.
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Affiliation(s)
- Min Lang
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Susie Huang
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA; Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Charleston, MA 02129, USA
| | - Michael H Lev
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
| | - Lawrence L Wald
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA; Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Charleston, MA 02129, USA
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132
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Liu Y, Wen T, Sun W, Liu Z, Song X, He X, Zhang S, Wu Z. Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:5666. [PMID: 35957222 PMCID: PMC9371218 DOI: 10.3390/s22155666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled 'black-box' by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.
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Affiliation(s)
- Yiwen Liu
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
| | - Tao Wen
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Wei Sun
- School of Computer Science, Neusoft Institute Guangdong, Foshan 528225, China;
| | - Zhenyu Liu
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Xiaoying Song
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Xuan He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China;
| | - Shuo Zhang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (S.Z.); (Z.W.)
| | - Zhenning Wu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (S.Z.); (Z.W.)
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AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Rep 2022; 12:12762. [PMID: 35882891 PMCID: PMC9325761 DOI: 10.1038/s41598-022-16609-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 07/12/2022] [Indexed: 11/09/2022] Open
Abstract
The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.
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134
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Compact pediatric cardiac magnetic resonance imaging protocols. Pediatr Radiol 2022:10.1007/s00247-022-05447-y. [PMID: 35821442 DOI: 10.1007/s00247-022-05447-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/25/2022] [Accepted: 06/30/2022] [Indexed: 10/17/2022]
Abstract
Cardiac MRI is in many respects an ideal modality for pediatric cardiovascular imaging, enabling a complete noninvasive assessment of anatomy, morphology, function and flow in one radiation-free and potentially non-contrast exam. Nonetheless, traditionally lengthy and complex imaging acquisition strategies have often limited its broader use beyond specialized centers. In this review, the author presents practical cardiac MRI imaging protocols to facilitate the performance of succinct yet successful exams that provide the most salient clinical data for the majority of congenital and acquired pediatric cardiac disease. In addition, the author reviews newer and evolving techniques that permit more rapid but similarly diagnostic MRI, including compressed sensing and artificial intelligence/machine learning reconstruction, four-dimensional flow acquisition and blood pool contrast agents. With the modern armamentarium of cardiac MRI methods, the goal of compact yet comprehensive exams in children can now be realized.
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135
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Shao HC, Li T, Dohopolski MJ, Wang J, Cai J, Tan J, Wang K, Zhang Y. Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet). Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac762c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 06/06/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Purpose. Real-time three-dimensional (3D) magnetic resonance (MR) imaging is challenging because of slow MR signal acquisition, leading to highly under-sampled k-space data. Here, we proposed a deep learning-based, k-space-driven deformable registration network (KS-RegNet) for real-time 3D MR imaging. By incorporating prior information, KS-RegNet performs a deformable image registration between a fully-sampled prior image and on-board images acquired from highly-under-sampled k-space data, to generate high-quality on-board images for real-time motion tracking. Methods. KS-RegNet is an end-to-end, unsupervised network consisting of an input data generation block, a subsequent U-Net core block, and following operations to compute data fidelity and regularization losses. The input data involved a fully-sampled, complex-valued prior image, and the k-space data of an on-board, real-time MR image (MRI). From the k-space data, under-sampled real-time MRI was reconstructed by the data generation block to input into the U-Net core. In addition, to train the U-Net core to learn the under-sampling artifacts, the k-space data of the prior image was intentionally under-sampled using the same readout trajectory as the real-time MRI, and reconstructed to serve an additional input. The U-Net core predicted a deformation vector field that deforms the prior MRI to on-board real-time MRI. To avoid adverse effects of quantifying image similarity on the artifacts-ridden images, the data fidelity loss of deformation was evaluated directly in k-space. Results. Compared with Elastix and other deep learning network architectures, KS-RegNet demonstrated better and more stable performance. The average (±s.d.) DICE coefficients of KS-RegNet on a cardiac dataset for the 5- , 9- , and 13-spoke k-space acquisitions were 0.884 ± 0.025, 0.889 ± 0.024, and 0.894 ± 0.022, respectively; and the corresponding average (±s.d.) center-of-mass errors (COMEs) were 1.21 ± 1.09, 1.29 ± 1.22, and 1.01 ± 0.86 mm, respectively. KS-RegNet also provided the best performance on an abdominal dataset. Conclusion. KS-RegNet allows real-time MRI generation with sub-second latency. It enables potential real-time MR-guided soft tissue tracking, tumor localization, and radiotherapy plan adaptation.
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136
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Chi CH, Yang FC, Chang YL. Age-related volumetric alterations in hippocampal subiculum region are associated with reduced retention of the “when” memory component. Brain Cogn 2022; 160:105877. [DOI: 10.1016/j.bandc.2022.105877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 04/18/2022] [Accepted: 04/22/2022] [Indexed: 11/02/2022]
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137
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Foreman SC, Neumann J, Han J, Harrasser N, Weiss K, Peeters JM, Karampinos DC, Makowski MR, Gersing AS, Woertler K. Deep learning-based acceleration of Compressed Sense MR imaging of the ankle. Eur Radiol 2022; 32:8376-8385. [PMID: 35751695 DOI: 10.1007/s00330-022-08919-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 05/13/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. METHODS Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6-5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9-7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen's kappa were used to compare CSAI with CS sequences. RESULTS The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86-1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005). CONCLUSIONS Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. KEY POINTS • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.
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Affiliation(s)
- Sarah C Foreman
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
| | - Jan Neumann
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Jessie Han
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Norbert Harrasser
- Department of Orthopaedic Surgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Kilian Weiss
- Philips GmbH, Röntgenstrasse 22, 22335, Hamburg, Germany
| | - Johannes M Peeters
- Philips Healthcare, Veenpluis 4-6, Building QR-0.113, 5684, Best, PC, Netherlands
| | - Dimitrios C Karampinos
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Alexandra S Gersing
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.,Department of Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377, Munich, Germany
| | - Klaus Woertler
- Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
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138
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Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI. Neuroimage 2022; 259:119411. [PMID: 35753594 DOI: 10.1016/j.neuroimage.2022.119411] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/12/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.
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139
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Chen Y, Pan Z, Meng F, Xu Q, Huang L, Pu X, Yu X, Wu Y, Lyu H, Lin X. Assessment of Rat Sciatic Nerve Using Diffusion-Tensor Imaging With Readout-Segmented Echo Planar Imaging. Front Neurosci 2022; 16:938674. [PMID: 35812234 PMCID: PMC9260505 DOI: 10.3389/fnins.2022.938674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesThis study aimed to compare readout-segmented-3, readout-segmented-5, and readout-segmented-7 echo-planar imaging (RS3-EPI, RS5-EPI, and RS7-EPI) of DTI in the assessment of rat sciatic nerve at 3T MR.MethodsEight male adult healthy Sprague-Dawley rats were scanned at 3T MR with RS-3 EPI, RS5-EPI, and RS-7 EPI DTI. The image quality of RS-3 EPI, RS-5 EPI, and RS-7 EPI in terms of the nerve morphology, distortions of the nearby femur, muscles, and homogeneity of neuromuscular were evaluated by two experienced radiologists. The correlations between the histopathological and DTI parameters, including fractional anisotropy (FA) and radial diffusivity (RD), were calculated, respectively, and compared in RS-3, RS-5, and RS-7 EPI. The image quality scores for RS-3 EPI, RS-5 EPI, and RS-7 EPI were compared using the Wilcoxon rank-sum test. The correlation between DTI and histopathological parameters was calculated using the Pearson correlation coefficient.ResultsRS-5 EPI yielded the best SNR-values corrected for the acquisition time compared to RS3-EPI and RS7-EPI. The image quality scores of RS-5 EPI were superior to those of RS-3 and RS-7 EPI (P = 0.01–0.014) and lower artifacts of the ventral/dorsal margin and femur (P = 0.008–0.016) were shown. DTT analysis yielded a significantly higher number of tracts for RS5-EPI compared to RS3-EPI (P = 0.007) but no significant difference with RS7-EPI (P = 0.071). For the three sequences, FA and RD were well-correlated with the myelin-related histopathological parameters (|r| 0.709–0.965, P = 0.001–0.049). The overall correlation coefficients of FA and RD obtained from RS-5 EPI were numerically higher than that with both RS3-EPI and RS7-EPI.ConclusionFor the rat sciatic nerve DTI imaging, RS-5 EPI offered the best image quality and SNR-values corrected for the acquisition time. The FA and RD derived from the RS-5 EPI were the most sensitive quantitative biomarkers to detect rat sciatic nerve histopathological change.
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Affiliation(s)
- Yueyao Chen
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhongxian Pan
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Fanqi Meng
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Qian Xu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Leyu Huang
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xuejia Pu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xuewen Yu
- Department of Pathology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | | | - Hanqing Lyu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- *Correspondence: Hanqing Lyu,
| | - Xiaofeng Lin
- Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Xiaofeng Lin,
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140
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Liang X, Bi Z, Yang C, Sheng R, Xia X, Zhang Z, Dai Y, Zeng M. Free-Breathing Liver Magnetic Resonance Imaging With Respiratory Frequency-Modulated Continuous-Wave Radar-Trigger Technique: A Preliminary Study. Front Oncol 2022; 12:918173. [PMID: 35719930 PMCID: PMC9200370 DOI: 10.3389/fonc.2022.918173] [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: 04/12/2022] [Accepted: 05/03/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose The aim of this study is to evaluate the performance of free-breathing liver MRI with a novel respiratory frequency-modulated continuous-wave radar-trigger (FT) technique on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) for both healthy volunteers and patients in comparison to navigator-trigger (NT) and belt-trigger (BT) techniques. Methods In this prospective study, 17 healthy volunteers and 23 patients with known or suspected liver diseases were enrolled. Six sequences (T2WI and DWI with FT, NT, and BT techniques) were performed in each subject. Quantitative evaluation and qualitative assessment were analyzed by two radiologists. Overall image quality, blurring, motion artifacts, and liver edge delineations were rated on a 4-point Likert scale. The liver and lesion signal-to-noise ratio (SNR), the lesion-to-liver contrast-to-noise ratio (CNR), as well as the apparent diffusion coefficient (ADC) value were quantitatively calculated. Results For volunteers, there were no significant differences in the image quality Likert scores and quantitative parameters on T2WI and DWI with three respiratory-trigger techniques. For patients, NT was superior to other techniques for image quality on T2WI; conversely, little difference was found on DWI in qualitative assessment. The mean SNR of the liver on T2WI and DWI with BT, NT, and FT techniques was similar in patients, which is in line with volunteers. FT performed better in terms of higher SNR (705.13 ± 434.80) and higher CNR (504.41 ± 400.69) on DWI at b50 compared with BT (SNR: 651.83 ± 401.16; CNR:429.24 ± 404.11) and NT (SNR: 639.41 ± 407.98; CNR: 420.64 ± 416.61) (p < 0.05). The mean ADC values of the liver and lesion with different techniques in both volunteers and patients showed non-significant difference. Conclusion For volunteers, the performance of T2WI as well as DWI with three respiratory-trigger techniques was similarly good. As for patients, FT-DWI is superior to BT and NT techniques in terms of higher lesion SNR and CNR at b50.
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Affiliation(s)
- Xinyue Liang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.,Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Zhenghong Bi
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xinyuan Xia
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Zheng Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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141
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Stępień I, Oszust M. A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. J Imaging 2022; 8:160. [PMID: 35735959 PMCID: PMC9224540 DOI: 10.3390/jimaging8060160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/31/2022] [Accepted: 06/01/2022] [Indexed: 02/08/2023] Open
Abstract
No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.
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Affiliation(s)
- Igor Stępień
- Doctoral School of Engineering and Technical Sciences, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland;
| | - Mariusz Oszust
- Department of Computer and Control Engineering, Rzeszow University of Technology, Wincentego Pola 2, 35-959 Rzeszow, Poland
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142
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Backhausen LL, Herting MM, Tamnes CK, Vetter NC. Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders. Neuropsychol Rev 2022; 32:400-418. [PMID: 33893904 PMCID: PMC9090677 DOI: 10.1007/s11065-021-09496-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 03/02/2021] [Indexed: 11/25/2022]
Abstract
Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.
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Affiliation(s)
- Lea L. Backhausen
- Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitaet Dresden, Dresden, Germany
| | - Megan M. Herting
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Christian K. Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora C. Vetter
- Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitaet Dresden, Dresden, Germany
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143
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Barragán-Montero A, Bibal A, Dastarac MH, Draguet C, Valdés G, Nguyen D, Willems S, Vandewinckele L, Holmström M, Löfman F, Souris K, Sterpin E, Lee JA. Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Phys Med Biol 2022; 67:10.1088/1361-6560/ac678a. [PMID: 35421855 PMCID: PMC9870296 DOI: 10.1088/1361-6560/ac678a] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/14/2022] [Indexed: 01/26/2023]
Abstract
The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.
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Affiliation(s)
- Ana Barragán-Montero
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Adrien Bibal
- PReCISE, NaDI Institute, Faculty of Computer Science, UNamur and CENTAL, ILC, UCLouvain, Belgium
| | - Margerie Huet Dastarac
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Camille Draguet
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - Gilmer Valdés
- Department of Radiation Oncology, Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, United States of America
| | - Siri Willems
- ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium
| | | | | | | | - Kevin Souris
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - John A Lee
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
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144
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Arya R, Ervin B, Buroker J, Greiner HM, Byars AW, Rozhkov L, Skoch J, Horn PS, Frink C, Scholle C, Leach JL, Mangano FT, Glauser TA, Holland KD. Neuronal Circuits Supporting Development of Visual Naming Revealed by Intracranial Coherence Modulations. Front Neurosci 2022; 16:867021. [PMID: 35663562 PMCID: PMC9160526 DOI: 10.3389/fnins.2022.867021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background Improvement in visual naming abilities throughout the childhood and adolescence supports development of higher-order linguistic skills. We investigated neuronal circuits underlying improvement in the speed of visual naming with age, and age-related dynamics of these circuits. Methods Response times were electronically measured during an overt visual naming task in epilepsy patients undergoing stereo-EEG monitoring. Coherence modulations among pairs of neuroanatomic parcels were computed and analyzed for relationship with response time and age. Results During the overt visual naming task, mean response time (latency) significantly decreased from 4 to 23 years of age. Coherence modulations during visual naming showed that increased connectivity between certain brain regions, particularly that between left fusiform gyrus/left parahippocampal gyrus and left frontal operculum, is associated with improvement in naming speed. Also, decreased connectivity in other brain regions, particularly between left angular and supramarginal gyri, is associated with decreased mean response time. Further, coherence modulations between left frontal operculum and both left fusiform and left posterior cingulate gyri significantly increase, while that between left angular and supramarginal gyri significantly decrease, with age. Conclusion Naming speed continues to improve from pre-school years into young adulthood. This age-related improvement in efficiency of naming environmental objects occurs likely because of strengthened direct connectivity between semantic and phonological nodes, and elimination of intermediate higher-order cognitive steps.
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Affiliation(s)
- Ravindra Arya
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States
| | - Brian Ervin
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States
| | - Jason Buroker
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Hansel M. Greiner
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Anna W. Byars
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Leonid Rozhkov
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Jesse Skoch
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Pediatric Neurosurgery, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Paul S. Horn
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Clayton Frink
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Craig Scholle
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - James L. Leach
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Pediatric Neuroradiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Francesco T. Mangano
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Pediatric Neurosurgery, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Tracy A. Glauser
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Katherine D. Holland
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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Sundermann B, Billebaut B, Bauer J, Iacoban CG, Alykova O, Schülke C, Gerdes M, Kugel H, Neduvakkattu S, Bösenberg H, Mathys C. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 1-3D Acquisitions, Dixon Techniques and Artefact Reduction. ROFO-FORTSCHR RONTG 2022; 194:1100-1108. [PMID: 35545104 DOI: 10.1055/a-1800-8692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Recently introduced MRI techniques offer improved image quality and facilitate examinations of patients even when artefacts are expected. They pave the way for novel diagnostic imaging strategies in neuroradiology. These methods include improved 3D imaging, movement and metal artefact reduction techniques as well as Dixon techniques. METHODS Narrative review with an educational focus based on current literature research and practical experiences of different professions involved (physicians, MRI technologists/radiographers, physics/biomedical engineering). Different hardware manufacturers are considered. RESULTS AND CONCLUSIONS 3D FLAIR is an example of a versatile 3D Turbo Spin Echo sequence with broad applicability in routine brain protocols. It facilitates detection of smaller lesions and more precise measurements for follow-up imaging. It also offers high sensitivity for extracerebral lesions. 3D techniques are increasingly adopted for imaging arterial vessel walls, cerebrospinal fluid spaces and peripheral nerves. Improved hybrid-radial acquisitions are available for movement artefact reduction in a broad application spectrum. Novel susceptibility artefact reduction techniques for targeted application supplement previously established metal artefact reduction sequences. Most of these techniques can be further adapted to achieve the desired diagnostic performances. Dixon techniques allow for homogeneous fat suppression in transition areas and calculation of different image contrasts based on a single acquisition. KEY POINTS · 3D FLAIR can replace 2 D FLAIR for most brain imaging applications and can be a cornerstone of more precise and more widely applicable protocols.. · Further 3D TSE sequences are increasingly replacing 2D TSE sequences for specific applications.. · Improvement of artefact reduction techniques increase the potential for effective diagnostic MRI exams despite movement or near metal implants.. · Dixon techniques facilitate homogeneous fat suppression and simultaneous acquisition of multiple contrasts.. CITATION FORMAT · Sundermann B, Billebaut B, Bauer J et al. Practical Aspects of novel MRI Techniques in Neuroradiology: Part 1-3D Acquisitions, Dixon Techniques and Artefact Reduction. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1800-8692.
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Affiliation(s)
- Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Germany.,Clinic for Radiology, University Hospital Münster, Germany
| | - Benoit Billebaut
- Clinic for Radiology, University Hospital Münster, Germany.,School for Radiologic Technologists, University Hospital Münster, Germany
| | - Jochen Bauer
- Clinic for Radiology, University Hospital Münster, Germany
| | - Catalin George Iacoban
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | - Olga Alykova
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | | | - Maike Gerdes
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | - Harald Kugel
- Clinic for Radiology, University Hospital Münster, Germany
| | | | - Holger Bösenberg
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus University of Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Germany.,Department of Diagnostic and Interventional Radiology, University of Düsseldorf, Germany
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146
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Liu C, Wang Q, Zhang J. NeuRec: Incorporating Interpatient prior to Sparse-View Image Reconstruction for Neurorehabilitation. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5426643. [PMID: 35586813 PMCID: PMC9110181 DOI: 10.1155/2022/5426643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/31/2022] [Indexed: 12/02/2022]
Abstract
Medical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) imaging are indispensable for contemporary neurorehabilitation diagnostics, intervention, and monitoring. It would be desirable to reconstruct images from sparse measurements to reduce the ionizing radiation and motion artifacts. Although recent coordinate-based representation methods have shown promise advances for sparse-view reconstruction, they overfit a single MLP on a single patient. In this work, we generalize it across many patients by incorporating an interpatient prior into the ill-posed inverse/reconstruction problem, which is the missing ingredient in the previous works. The experiment demonstrates that our method significantly improves image quality over the state-of-the-art both qualitatively and quantitatively. Thus, our method provides a powerful and principled means to deal with the measurement-scarce problem.
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Affiliation(s)
- Cong Liu
- Faculty of Business Information, Shanghai Business School, Shanghai 200235, China
- The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou 213003, China
- Center of Medical Physics, Nanjing Medical University, Changzhou 213003, China
| | - Qingbin Wang
- Ruijin Hospital, Shanghai Jiao Tong University, Shanghai 200031, China
| | - Jing Zhang
- Faculty of Business Information, Shanghai Business School, Shanghai 200235, China
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147
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Brackenier Y, Cordero‐Grande L, Tomi‐Tricot R, Wilkinson T, Bridgen P, Price A, Malik SJ, De Vita E, Hajnal JV. Data‐driven motion‐corrected brain
MRI
incorporating pose‐dependent
B
0
fields. Magn Reson Med 2022; 88:817-831. [PMID: 35526212 PMCID: PMC9324873 DOI: 10.1002/mrm.29255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022]
Abstract
Purpose To develop a fully data‐driven retrospective intrascan motion‐correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose‐dependent changes in polarizing magnetic (B0) fields. Theory and Methods Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics‐inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo. Results The applicability of the physics‐inspired B0 model was confirmed in vivo. Simulations show the need to include the pose‐dependent B0 fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion We have developed a motion‐correction framework that accounts for and estimates pose‐dependent B0 fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B0 dependencies cannot be neglected. The use of a compact physics‐inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.
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Affiliation(s)
- Yannick Brackenier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Lucilio Cordero‐Grande
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación Universidad Politécnica de Madrid and CIBER‐BNN Madrid Spain
| | - Raphael Tomi‐Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- MR Research Collaborations Siemens Healthcare Limited Frimley United Kingdom
| | - Thomas Wilkinson
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Philippa Bridgen
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Anthony Price
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
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148
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Williams B, Roesch E, Christakou A. Systematic validation of an automated thalamic parcellation technique using anatomical data at 3T. Neuroimage 2022; 258:119340. [DOI: 10.1016/j.neuroimage.2022.119340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 05/20/2022] [Accepted: 05/28/2022] [Indexed: 11/24/2022] Open
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149
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Anwar I, McCabe B, Simcock C, Harvey-Lloyd J, Malamateniou C. Paediatric magnetic resonance imaging adaptations without the use of sedation or anaesthesia: A narrative review. J Med Imaging Radiat Sci 2022; 53:505-514. [DOI: 10.1016/j.jmir.2022.04.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/06/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022]
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150
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Verschuur AS, Boswinkel V, Tax CM, van Osch JA, Nijholt IM, Slump CH, de Vries LS, van Wezel‐Meijler G, Leemans A, Boomsma MF. Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices. J Neuroimaging 2022; 32:480-492. [PMID: 35253956 PMCID: PMC9314603 DOI: 10.1111/jon.12985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE To apply and evaluate an intensity-based interpolation technique, enabling segmentation of motion-affected neonatal brain MRI. METHODS Moderate-late preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderate-late Preterm infants "BIMP-study") between August 2017 and November 2019. T2-weighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderate-severe motion (n = 33 [29%]) were included. Motion-affected slices were re-estimated using intensity-based shape-preserving cubic spline interpolation, and automatically segmented in eight structures. Quality of interpolation and segmentation was visually assessed for errors after interpolation. Reliability was tested using interpolated control group scans (18/54 axial slices). Structural similarity index (SSIM) was used to compare T2-weighted scans, and Sørensen-Dice was used to compare segmentation before and after interpolation. Finally, volumes of brain structures of the control group were used assessing sensitivity (absolute mean fraction difference) and bias (confidence interval of mean difference). RESULTS Visually, segmentation of 25 scans (22%) with motion artifacts improved with interpolation, while segmentation of eight scans (7%) with adjacent motion-affected slices did not improve. Average SSIM was .895 and Sørensen-Dice coefficients ranged between .87 and .97. Absolute mean fraction difference was ≤0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14-3.07 cm3 ), cerebrospinal fluid (0.39-1.65 cm3 ), deep gray matter (0.74-1.01 cm3 ), and brainstem volumes (0.07-0.28 cm3 ) and a negative bias in white matter volumes (-4.47 to -1.65 cm3 ). CONCLUSION According to qualitative and quantitative assessment, intensity-based interpolation reduced the percentage of discarded scans from 29% to 7%.
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Affiliation(s)
- Anouk S. Verschuur
- Department of RadiologyIsalaZwolleThe Netherlands
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Vivian Boswinkel
- Women and Children's HospitalIsalaZwolleThe Netherlands
- UMC Utrecht Brain CenterUtrecht UniversityUtrechtThe Netherlands
| | - Chantal M.W. Tax
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
- Cardiff University Brain Research Imaging CentreCardiffUK
| | | | | | - Cornelis H. Slump
- Department of Robotics and MechatronicsUniversity of TwenteEnschedeThe Netherlands
| | - Linda S. de Vries
- Department of NeonatologyWilhelmina Children's HospitalUtrechtThe Netherlands
| | | | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
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