Magnetic Resonance Materials in Physics, Biology and Medicine最新文献_第7页

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs. 利用多对比度输入的三维卷积神经网络在纵向磁共振成像上自动进行腹部脂肪组织分割和体积量化。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-02-01 DOI: 10.1007/s10334-023-01146-3

Sevgi Gokce Kafali, Shu-Fu Shih, Xinzhou Li, Grace Hyun J Kim, Tristan Kelly, Shilpy Chowdhury, Spencer Loong, Jeremy Moretz, Samuel R Barnes, Zhaoping Li, Holden H Wu

{"title":"Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.","authors":"Sevgi Gokce Kafali, Shu-Fu Shih, Xinzhou Li, Grace Hyun J Kim, Tristan Kelly, Shilpy Chowdhury, Spencer Loong, Jeremy Moretz, Samuel R Barnes, Zhaoping Li, Holden H Wu","doi":"10.1007/s10334-023-01146-3","DOIUrl":"10.1007/s10334-023-01146-3","url":null,"abstract":"Objective: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.Materials and methods: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).Results: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis.Discussion: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"491-506"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139651021","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}

引用次数: 0

Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI. 学习深度学习:选择UNet架构增强MRI的统计和范式测试。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2023-11-21 DOI: 10.1007/s10334-023-01127-6

Rishabh Sharma, Panagiotis Tsiamyrtzis, Andrew G Webb, Ernst L Leiss, Nikolaos V Tsekos

{"title":"Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.","authors":"Rishabh Sharma, Panagiotis Tsiamyrtzis, Andrew G Webb, Ernst L Leiss, Nikolaos V Tsekos","doi":"10.1007/s10334-023-01127-6","DOIUrl":"10.1007/s10334-023-01127-6","url":null,"abstract":"Objective: This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.Materials and methods: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI). We employed both Analysis of Variance (ANOVA) and Mixed Effects Model (MEM) to assess the statistical significance of the independent parameters, aiming to compare their efficacy in revealing differences and interactions among fixed parameters.Results: ANOVA analysis showed that, except for the acquisition protocol, fixed variables were statistically insignificant. In contrast, MEM analysis revealed that all fixed parameters and their interactions held statistical significance. This emphasizes the need for advanced statistical analysis in comparative studies, where MEM can uncover finer distinctions often overlooked by ANOVA.Discussion: These findings highlight the importance of utilizing appropriate statistical analysis when comparing different deep learning models. Additionally, the surprising effectiveness of the UNet architecture in enhancing various acquisition protocols underscores the potential for developing improved methods for characterizing and training deep learning models. This study serves as a stepping stone toward enhancing the transparency and comparability of deep learning techniques for medical imaging applications.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"507-528"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138291349","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}

引用次数: 0

ESMRMB 2024 focus topic: MR beyond trends-fact-checking MR. ESMRMB 2024 焦点话题：超越趋势的 MR--事实核查 MR。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-06-28 DOI: 10.1007/s10334-024-01177-4

Christian Langkammer, Lena Václavů, Thomas Kuestner, Melanie Bauer, Najat Salameh, Marco Palombo, Raquel Perez Lopez

引用次数: 0

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement. 基于深度学习的 1.5 T 脑结构磁共振成像超分辨率：应用于定量体积测量。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-05-17 DOI: 10.1007/s10334-024-01165-8

Atita Suwannasak, Salita Angkurawaranon, Prapatsorn Sangpin, Itthi Chatnuntawech, Kittichai Wantanajittikul, Uten Yarach

{"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":"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.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"465-475"},"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}

引用次数: 0

The beating heart: artificial intelligence for cardiovascular application in the clinic. 跳动的心脏：人工智能在心血管领域的临床应用。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-06-22 DOI: 10.1007/s10334-024-01180-9

Manuel Villegas-Martinez, Victor de Villedon de Naide, Vivek Muthurangu, Aurélien Bustin

{"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":"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.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"369-382"},"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}

引用次数: 0

Deep learning for accelerated and robust MRI reconstruction. 用于加速和稳健磁共振成像重建的深度学习。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-07-23 DOI: 10.1007/s10334-024-01173-8

Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron

引用次数: 0

Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review 在神经变性小鼠模型中鉴定铜绿素诱导的脱髓鞘的磁共振指标：综述

IF 2.3 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-04-18 DOI: 10.1007/s10334-024-01160-z

Emma Friesen, Kamya Hari, Maxina Sheft, Jonathan D. Thiessen, Melanie Martin

{"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":"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.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"468 1","pages":""},"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}

引用次数: 0

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time 减少 GABA 编辑 MRS 采集时间的 2023 ISBI 挑战赛结果

IF 2.3 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-04-13 DOI: 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

{"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>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.<h3 data-test=\"abstract-sub-heading\">Methods</h3>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.<h3 data-test=\"abstract-sub-heading\">Results</h3>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.<h3 data-test=\"abstract-sub-heading\">Conclusion</h3>DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"41 1","pages":""},"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}

引用次数: 0

Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping Spinet-QSM：基于模型的深度学习与 Schatten p-norm 正则化，用于改进定量易感性绘图

IF 2.3 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-04-10 DOI: 10.1007/s10334-024-01158-7

Vaddadi Venkatesh, Raji Susan Mathew, Phaneendra K. Yalavarthy

{"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>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.<h3 data-test=\"abstract-sub-heading\">Materials and methods</h3>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.<h3 data-test=\"abstract-sub-heading\">Results</h3>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.<h3 data-test=\"abstract-sub-heading\">Conclusion</h3>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.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"30 1","pages":""},"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}

引用次数: 0

Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter 从定量参数映射到髓鞘水分数的转换图：与白质中的 R1-R2* 和髓鞘水分数进行比较

IF 2.3 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-04-06 DOI: 10.1007/s10334-024-01155-w

Shun Kitano, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Hiroaki Hayashi, Yuki Matsumoto, Kosuke Ito, Yoshitaka Bito, Akihiro Haga

{"title":"Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter","authors":"Shun Kitano, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Hiroaki Hayashi, Yuki Matsumoto, Kosuke Ito, Yoshitaka Bito, Akihiro Haga","doi":"10.1007/s10334-024-01155-w","DOIUrl":"https://doi.org/10.1007/s10334-024-01155-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3>To clarify the relationship between myelin water fraction (MWF) and R1⋅R2* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWFQPM).<h3 data-test=\"abstract-sub-heading\">Materials and methods</h3>Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R1⋅R2* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R1·R2* and the two-pool model-MWF. The R1·R2* map was converted to MWFQPM map. MWF atlas template was generated using converted to MWF from R1·R2* per WM region.<h3 data-test=\"abstract-sub-heading\">Results</h3>The mean MWF and R1·R2* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s−2, respectively. A non-linear relationship in 48 regions of the WM between MWF and R1·R2* values was observed by quadratic polynomial approximation (R2 ≥ 0.963, P < 0.0001).<h3 data-test=\"abstract-sub-heading\">Discussion</h3>MWFQPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWFQPM may be generated with combined multiple WM regions.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"41 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574463","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}

引用次数: 0