Objective: This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).
Materials and methods: In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.
Results: The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.
Discussion: The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.
{"title":"Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement.","authors":"Atita Suwannasak, Salita Angkurawaranon, Prapatsorn Sangpin, Itthi Chatnuntawech, Kittichai Wantanajittikul, Uten Yarach","doi":"10.1007/s10334-024-01165-8","DOIUrl":"10.1007/s10334-024-01165-8","url":null,"abstract":"<p><strong>Objective: </strong>This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).</p><p><strong>Materials and methods: </strong>In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.</p><p><strong>Results: </strong>The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.</p><p><strong>Discussion: </strong>The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-06-22DOI: 10.1007/s10334-024-01180-9
Manuel Villegas-Martinez, Victor de Villedon de Naide, Vivek Muthurangu, Aurélien Bustin
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.
{"title":"The beating heart: artificial intelligence for cardiovascular application in the clinic.","authors":"Manuel Villegas-Martinez, Victor de Villedon de Naide, Vivek Muthurangu, Aurélien Bustin","doi":"10.1007/s10334-024-01180-9","DOIUrl":"10.1007/s10334-024-01180-9","url":null,"abstract":"<p><p>Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141440585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-07-23DOI: 10.1007/s10334-024-01173-8
Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
{"title":"Deep learning for accelerated and robust MRI reconstruction.","authors":"Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron","doi":"10.1007/s10334-024-01173-8","DOIUrl":"10.1007/s10334-024-01173-8","url":null,"abstract":"<p><p>Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141748522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-25DOI: 10.1007/s10334-024-01172-9
Hideki Ota, Yoshiaki Morita, Diana Vucevic, Satoshi Higuchi, Hidenobu Takagi, Hideaki Kutsuna, Yuichi Yamashita, Paul Kim, Mitsue Miyazaki
Purpose: To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric ky-kz k-space trajectory combined with high-resolution deep learning reconstruction (HR-DLR).
Methods: All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric ky-kz trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables.
Results: The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5 min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15 min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5 min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR.
Conclusion: Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput.
{"title":"Motion robust coronary MR angiography using zigzag centric ky-kz trajectory and high-resolution deep learning reconstruction.","authors":"Hideki Ota, Yoshiaki Morita, Diana Vucevic, Satoshi Higuchi, Hidenobu Takagi, Hideaki Kutsuna, Yuichi Yamashita, Paul Kim, Mitsue Miyazaki","doi":"10.1007/s10334-024-01172-9","DOIUrl":"10.1007/s10334-024-01172-9","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric k<sub>y</sub>-k<sub>z</sub> k-space trajectory combined with high-resolution deep learning reconstruction (HR-DLR).</p><p><strong>Methods: </strong>All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric k<sub>y</sub>-k<sub>z</sub> trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables.</p><p><strong>Results: </strong>The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5 min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15 min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5 min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR.</p><p><strong>Conclusion: </strong>Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141446481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-21DOI: 10.1007/s10334-024-01170-x
Stanislaw Mitew, Ling Yun Yeow, Chi Long Ho, Prakash K N Bhanu, Oliver James Nickalls
Rationale and objectives: Defacing research MRI brain scans is often a mandatory step. With current defacing software, there are issues with Windows compatibility and researcher doubt regarding the adequacy of preservation of brain voxels in non-T1w scans. To address this, we developed PyFaceWipe, a multiplatform software for multiple MRI contrasts, which was evaluated based on its anonymisation ability and effect on downstream processing.
Materials and methods: Multiple MRI brain scan contrasts from the OASIS-3 dataset were defaced with PyFaceWipe and PyDeface and manually assessed for brain voxel preservation, remnant facial features and effect on automated face detection. Original and PyFaceWipe-defaced data from locally acquired T1w structural scans underwent volumetry with FastSurfer and brain atlas generation with ANTS.
Results: 214 MRI scans of several contrasts from OASIS-3 were successfully processed with both PyFaceWipe and PyDeface. PyFaceWipe maintained complete brain voxel preservation in all tested contrasts except ASL (45%) and DWI (90%), and PyDeface in all tested contrasts except ASL (95%), BOLD (25%), DWI (40%) and T2* (25%). Manual review of PyFaceWipe showed no failures of facial feature removal. Pinna removal was less successful (6% of T1 scans showed residual complete pinna). PyDeface achieved 5.1% failure rate. Automated detection found no faces in PyFaceWipe-defaced scans, 19 faces in PyDeface scans compared with 78 from the 224 original scans. Brain atlas generation showed no significant difference between atlases created from original and defaced data in both young adulthood and late elderly cohorts. Structural volumetry dice scores were ≥ 0.98 for all structures except for grey matter which had 0.93. PyFaceWipe output was identical across the tested operating systems.
Conclusion: PyFaceWipe is a promising multiplatform defacing tool, demonstrating excellent brain voxel preservation and competitive defacing in multiple MRI contrasts, performing favourably against PyDeface. ASL, BOLD, DWI and T2* scans did not produce recognisable 3D renders and hence should not require defacing. Structural volumetry dice scores (≥ 0.98) were higher than previously published FreeSurfer results, except for grey matter which were comparable. The effect is measurable and care should be exercised during studies. ANTS atlas creation showed no significant effect from PyFaceWipe defacing.
{"title":"PyFaceWipe: a new defacing tool for almost any MRI contrast.","authors":"Stanislaw Mitew, Ling Yun Yeow, Chi Long Ho, Prakash K N Bhanu, Oliver James Nickalls","doi":"10.1007/s10334-024-01170-x","DOIUrl":"https://doi.org/10.1007/s10334-024-01170-x","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Defacing research MRI brain scans is often a mandatory step. With current defacing software, there are issues with Windows compatibility and researcher doubt regarding the adequacy of preservation of brain voxels in non-T1w scans. To address this, we developed PyFaceWipe, a multiplatform software for multiple MRI contrasts, which was evaluated based on its anonymisation ability and effect on downstream processing.</p><p><strong>Materials and methods: </strong>Multiple MRI brain scan contrasts from the OASIS-3 dataset were defaced with PyFaceWipe and PyDeface and manually assessed for brain voxel preservation, remnant facial features and effect on automated face detection. Original and PyFaceWipe-defaced data from locally acquired T1w structural scans underwent volumetry with FastSurfer and brain atlas generation with ANTS.</p><p><strong>Results: </strong>214 MRI scans of several contrasts from OASIS-3 were successfully processed with both PyFaceWipe and PyDeface. PyFaceWipe maintained complete brain voxel preservation in all tested contrasts except ASL (45%) and DWI (90%), and PyDeface in all tested contrasts except ASL (95%), BOLD (25%), DWI (40%) and T2* (25%). Manual review of PyFaceWipe showed no failures of facial feature removal. Pinna removal was less successful (6% of T1 scans showed residual complete pinna). PyDeface achieved 5.1% failure rate. Automated detection found no faces in PyFaceWipe-defaced scans, 19 faces in PyDeface scans compared with 78 from the 224 original scans. Brain atlas generation showed no significant difference between atlases created from original and defaced data in both young adulthood and late elderly cohorts. Structural volumetry dice scores were ≥ 0.98 for all structures except for grey matter which had 0.93. PyFaceWipe output was identical across the tested operating systems.</p><p><strong>Conclusion: </strong>PyFaceWipe is a promising multiplatform defacing tool, demonstrating excellent brain voxel preservation and competitive defacing in multiple MRI contrasts, performing favourably against PyDeface. ASL, BOLD, DWI and T2* scans did not produce recognisable 3D renders and hence should not require defacing. Structural volumetry dice scores (≥ 0.98) were higher than previously published FreeSurfer results, except for grey matter which were comparable. The effect is measurable and care should be exercised during studies. ANTS atlas creation showed no significant effect from PyFaceWipe defacing.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141432260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-21DOI: 10.1007/s10334-024-01174-7
Victor Casula, Abdul Wahed Kajabi
Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.
骨关节炎(OA)是一种致残性慢性疾病,涉及关节结构的逐渐退化,引起疼痛和功能障碍。磁共振成像(MRI)已被广泛用作评估 OA 相关变化的非侵入性工具。解剖核磁共振成像仅限于对关节结构进行形态学评估,而定量核磁共振成像(qMRI)可在分子水平上测量组织的生物物理特性。定量磁共振成像技术已被用于描述组织的结构完整性、生化含量和机械特性。其应用范围扩展到研究退行性改变、早期 OA 检测和评估治疗干预。本文综述了用于肌肉骨骼组织评估的 qMRI 技术,尤其侧重于关节软骨。其目的是描述 qMRI 参数的基本机制和主要局限性、它们与组织生理特性的关联以及它们在检测导致 OA 发生的组织退化方面的潜力,主要侧重于基础研究和临床前研究。此外,该综述还强调了 qMRI 的一些临床应用,讨论了基于纹理的放射组学和机器学习在推进 OA 研究中的作用。
{"title":"Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications.","authors":"Victor Casula, Abdul Wahed Kajabi","doi":"10.1007/s10334-024-01174-7","DOIUrl":"https://doi.org/10.1007/s10334-024-01174-7","url":null,"abstract":"<p><p>Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141432229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1007/s10334-024-01160-z
Emma Friesen, Kamya Hari, Maxina Sheft, Jonathan D. Thiessen, Melanie Martin
Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler’s Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.
{"title":"Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review","authors":"Emma Friesen, Kamya Hari, Maxina Sheft, Jonathan D. Thiessen, Melanie Martin","doi":"10.1007/s10334-024-01160-z","DOIUrl":"https://doi.org/10.1007/s10334-024-01160-z","url":null,"abstract":"<p>Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler’s Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T<sub>1</sub> Weighted (T<sub>1</sub>W) and T<sub>2</sub> Weighted (T<sub>2</sub>W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1007/s10334-024-01156-9
Rodrigo Pommot Berto, Hanna Bugler, Gabriel Dias, Mateus Oliveira, Lucas Ueda, Sergio Dertkigil, Paula D. P. Costa, Leticia Rittner, Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun, Abdul Qayyum, Cristobal Rodero, Steven Niederer, Roberto Souza, Ashley D. Harris
Purpose
Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.
Methods
There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.
Results
Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.
Conclusion
DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.
{"title":"Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time","authors":"Rodrigo Pommot Berto, Hanna Bugler, Gabriel Dias, Mateus Oliveira, Lucas Ueda, Sergio Dertkigil, Paula D. P. Costa, Leticia Rittner, Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun, Abdul Qayyum, Cristobal Rodero, Steven Niederer, Roberto Souza, Ashley D. Harris","doi":"10.1007/s10334-024-01156-9","DOIUrl":"https://doi.org/10.1007/s10334-024-01156-9","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.1007/s10334-024-01158-7
Vaddadi Venkatesh, Raji Susan Mathew, Phaneendra K. Yalavarthy
Objective
Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM.
Materials and methods
This work proposes a Schatten (textit{p})-norm-driven model-based deep learning framework for QSM with a learnable norm parameter (textit{p}) to adapt to the data. In contrast to other model-based architectures that enforce the l(_{text {2}})-norm or l(_{text {1}})-norm for the denoiser, the proposed approach can enforce any (textit{p})-norm ((text {0}<textit{p}le text {2})) on a trainable regulariser.
Results
The proposed method was compared with deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits.
Conclusion
The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.
{"title":"Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping","authors":"Vaddadi Venkatesh, Raji Susan Mathew, Phaneendra K. Yalavarthy","doi":"10.1007/s10334-024-01158-7","DOIUrl":"https://doi.org/10.1007/s10334-024-01158-7","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>This work proposes a Schatten <span>(textit{p})</span>-norm-driven model-based deep learning framework for QSM with a learnable norm parameter <span>(textit{p})</span> to adapt to the data. In contrast to other model-based architectures that enforce the <i>l</i><span>(_{text {2}})</span>-norm or <i>l</i><span>(_{text {1}})</span>-norm for the denoiser, the proposed approach can enforce any <span>(textit{p})</span>-norm (<span>(text {0}<textit{p}le text {2})</span>) on a trainable regulariser.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The proposed method was compared with deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}