Pub Date : 2024-10-24DOI: 10.1016/j.mri.2024.110265
Emma Friesen , Madison Chisholm , Bibek Dhakal , Morgan Mercredi , Mark D. Does , John C. Gore , Melanie Martin
{"title":"Corrigendum to “Modelling white matter microstructure using diffusion OGSE MRI: Model and analysis choices” [Magnetic Resonance Imaging 113 (2024) 110221]","authors":"Emma Friesen , Madison Chisholm , Bibek Dhakal , Morgan Mercredi , Mark D. Does , John C. Gore , Melanie Martin","doi":"10.1016/j.mri.2024.110265","DOIUrl":"10.1016/j.mri.2024.110265","url":null,"abstract":"","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110265"},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142503199","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 : 2024-10-24DOI: 10.1016/j.mri.2024.110247
Moritz Becker, Filip Arvidsson, Jonas Bertilson, Elene Aslanikashvili, Jan G. Korvink, Mazin Jouda, Sören Lehmkuhl
A newly developed magnetic resonance imaging (MRI) approach is based on “Radiowave amplification by the stimulated emission of radiation” (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require radio-frequency excitation. However, RASER MRI can be “nearly unusable” as heavy distortions from nonlinear effects can occur. In this work, we show that deep learning (DL) reduces such artifacts in RASER images. We trained a two-step DL pipeline on purely synthetic data, which was generated based on a previously published, theoretical model for RASER MRI. A convolutional neural network was trained on 630′000 1D RASER projections, and a U-net on 2D random images. The DL pipeline generalizes well when applied from synthetic to experimental RASER MRI data.
{"title":"Deep learning corrects artifacts in RASER MRI profiles","authors":"Moritz Becker, Filip Arvidsson, Jonas Bertilson, Elene Aslanikashvili, Jan G. Korvink, Mazin Jouda, Sören Lehmkuhl","doi":"10.1016/j.mri.2024.110247","DOIUrl":"10.1016/j.mri.2024.110247","url":null,"abstract":"<div><div>A newly developed magnetic resonance imaging (MRI) approach is based on “Radiowave amplification by the stimulated emission of radiation” (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require radio-frequency excitation. However, RASER MRI can be “nearly unusable” as heavy distortions from nonlinear effects can occur. In this work, we show that deep learning (DL) reduces such artifacts in RASER images. We trained a two-step DL pipeline on purely synthetic data, which was generated based on a previously published, theoretical model for RASER MRI. A convolutional neural network was trained on 630′000 1D RASER projections, and a U-net on 2D random images. The DL pipeline generalizes well when applied from synthetic to experimental RASER MRI data.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110247"},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142503197","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 : 2024-10-23DOI: 10.1016/j.mri.2024.110267
Xue Bi , Xinwen Liu , Zhifeng Chen , Hongli Chen , Yajun Du , Huizu Chen , Xiaoli Huang , Feng Liu
In Magnetic Resonance Imaging (MRI), the sequential acquisition of raw complex-valued image data in Fourier space, also known as k-space, results in extended examination times. To speed up the MRI scans, k-space data are usually undersampled and processed using numerical techniques such as compressed sensing (CS). While the majority of CS-MRI algorithms primarily focus on magnitude images due to their significant diagnostic value, the phase components of complex-valued MRI images also hold substantial importance for clinical diagnosis, including neurodegenerative diseases. In this work, complex-valued MRI reconstruction is studied with a focus on the simultaneous reconstruction of both magnitude and phase images. The proposed algorithm is based on the nonsubsampled contourlet transform (NSCT) technique, which offers shift invariance in images. Instead of directly transforming the complex-valued image into the NSCT domain, we introduce a wavelet transform within the NSCT domain, reducing the size of the sparsity of coefficients. This two-level hierarchical constraint (HC) enforces sparse representation of complex-valued images for CS-MRI implementation. The proposed HC is seamlessly integrated into a proximal algorithm simultaneously. Additionally, to effectively minimize the artifacts caused by sub-sampling, thresholds related to different sub-bands in the HC are applied through an alternating optimization process. Experimental results show that the novel method outperforms existing CS-MRI techniques in phase-regularized complex-valued image reconstructions.
在磁共振成像（MRI）中，连续采集傅里叶空间（也称 k 空间）中的原始复值图像数据会导致检查时间延长。为了加快核磁共振成像扫描的速度，通常会对 k 空间数据进行低采样，并使用压缩传感（CS）等数字技术进行处理。虽然大多数 CS-MRI 算法因其重要的诊断价值而主要关注幅值图像，但复值 MRI 图像的相位分量对于临床诊断（包括神经退行性疾病）也非常重要。在这项工作中，研究了复值磁共振成像重建，重点是同时重建幅值和相位图像。所提出的算法基于非子采样等高线变换（NSCT）技术，该技术具有图像位移不变性。我们不是直接将复值图像转换到 NSCT 域，而是在 NSCT 域内引入小波变换，以减小稀疏系数的大小。这种两级分层约束（HC）为 CS-MRI 实现提供了复值图像的稀疏表示。所提出的 HC 可同时无缝集成到近端算法中。此外，为了有效减少子采样造成的伪影，通过交替优化过程应用 HC 中不同子带的相关阈值。实验结果表明，在相位规则化复值图像重建方面，新方法优于现有的 CS-MRI 技术。
{"title":"Complex-valued image reconstruction for compressed sensing MRI using hierarchical constraint","authors":"Xue Bi , Xinwen Liu , Zhifeng Chen , Hongli Chen , Yajun Du , Huizu Chen , Xiaoli Huang , Feng Liu","doi":"10.1016/j.mri.2024.110267","DOIUrl":"10.1016/j.mri.2024.110267","url":null,"abstract":"<div><div>In Magnetic Resonance Imaging (MRI), the sequential acquisition of raw complex-valued image data in Fourier space, also known as k-space, results in extended examination times. To speed up the MRI scans, k-space data are usually undersampled and processed using numerical techniques such as compressed sensing (CS). While the majority of CS-MRI algorithms primarily focus on magnitude images due to their significant diagnostic value, the phase components of complex-valued MRI images also hold substantial importance for clinical diagnosis, including neurodegenerative diseases. In this work, complex-valued MRI reconstruction is studied with a focus on the simultaneous reconstruction of both magnitude and phase images. The proposed algorithm is based on the nonsubsampled contourlet transform (NSCT) technique, which offers shift invariance in images. Instead of directly transforming the complex-valued image into the NSCT domain, we introduce a wavelet transform within the NSCT domain, reducing the size of the sparsity of coefficients. This two-level hierarchical constraint (HC) enforces sparse representation of complex-valued images for CS-MRI implementation. The proposed HC is seamlessly integrated into a proximal algorithm simultaneously. Additionally, to effectively minimize the artifacts caused by sub-sampling, thresholds related to different sub-bands in the HC are applied through an alternating optimization process. Experimental results show that the novel method outperforms existing CS-MRI techniques in phase-regularized complex-valued image reconstructions.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110267"},"PeriodicalIF":2.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142503196","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 : 2024-10-21DOI: 10.1016/j.mri.2024.110268
Yijin Yang , Boqiao Zhang , Ming Lu , Xinqiang Yan
Common-mode currents can degrade the RF coil performance and introduce potential safety hazards in MRI. Baluns are the standard method to suppress these undesired common-mode currents. Specifically, floating baluns are preferred in many applications because they are removable, allow post-installation adjustment and avoid direct soldering on the cable. However, floating baluns are typically bulky to achieve excellent common-mode suppression, taking up valuable space in the MRI bore. This is particularly severe for multi-nuclear MRI/MRS applications, as two RF systems exist. In this work, we present a novel dual-tuned floating balun that is fully removable, does not require any physical connection to the coaxial cable, and has a significantly reduced footprint. The floating design employs an inductive coupling between the cable solenoid and a floating solenoid resonator rather than a direct physical connection. Unlike the previous floating solenoid balun, this balun employs a two-layer design further to improve the mutual coupling between the two solenoids. A pole-insertion method is used to suppress common-mode currents at two user-selectable frequencies simultaneously. Bench testing of the fabricated device at 7 T demonstrated high common-mode rejection ratios at Larmor frequencies of both 1H and 23Na, even with a compact dimension (diameter 18 mm and length 12 mm). This balun's removable, compact, and multi-resonant nature enables light-weighting, allows more coil elements, and improves cable management for advanced multi-nuclear MRI/MRS systems.
{"title":"Dual-tuned floating solenoid balun for multi-nuclear MRI and MRS","authors":"Yijin Yang , Boqiao Zhang , Ming Lu , Xinqiang Yan","doi":"10.1016/j.mri.2024.110268","DOIUrl":"10.1016/j.mri.2024.110268","url":null,"abstract":"<div><div>Common-mode currents can degrade the RF coil performance and introduce potential safety hazards in MRI. Baluns are the standard method to suppress these undesired common-mode currents. Specifically, floating baluns are preferred in many applications because they are removable, allow post-installation adjustment and avoid direct soldering on the cable. However, floating baluns are typically bulky to achieve excellent common-mode suppression, taking up valuable space in the MRI bore. This is particularly severe for multi-nuclear MRI/MRS applications, as two RF systems exist. In this work, we present a novel dual-tuned floating balun that is fully removable, does not require any physical connection to the coaxial cable, and has a significantly reduced footprint. The floating design employs an inductive coupling between the cable solenoid and a floating solenoid resonator rather than a direct physical connection. Unlike the previous floating solenoid balun, this balun employs a two-layer design further to improve the mutual coupling between the two solenoids. A pole-insertion method is used to suppress common-mode currents at two user-selectable frequencies simultaneously. Bench testing of the fabricated device at 7 T demonstrated high common-mode rejection ratios at Larmor frequencies of both <sup>1</sup>H and <sup>23</sup>Na, even with a compact dimension (diameter 18 mm and length 12 mm). This balun's removable, compact, and multi-resonant nature enables light-weighting, allows more coil elements, and improves cable management for advanced multi-nuclear MRI/MRS systems.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110268"},"PeriodicalIF":2.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142503200","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 : 2024-10-18DOI: 10.1016/j.mri.2024.110256
Hsiang-Yu Yu , Cheng Jui Tsai , Tse-Hao Lee , Hsin Tung , Yen-Cheng Shih , Chien-Chen Chou , Cheng-Chia Lee , Po-Tso Lin , Syu-Jyun Peng
Background
Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults.
Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however, difficulties can arise in cases where sclerotic changes in the hippocampus are subtle or non-epilepsy-specific protocols have been implemented. In such cases, quantitative methods, such as T1-weighted axial series MRIs, are valuable additional tools to complement epilepsy-specific protocols. In the current study, we applied machine learning (ML) techniques to the analysis of brain regions of interest (ROIs), including the hippocampus, thalamus, and cortical areas, to enhance the accuracy of lesion lateralization in MRI.
Methods
This study included 104 patients diagnosed with mTLE, including 55 with lesions on the right side and 49 with lesions on the left side. FreeSurfer software was used to extract features from high-resolution T1-weighted axial brain MRI scans for use in computing lateralization indices (LI) for various brain regions. After using feature selection to pinpoint critical ROIs, the corresponding LI values were used as parameters in training the ML model.
Results
The proposed ML model demonstrated exceptional performance in the lateralization of mTLE, achieving test accuracy of 92.38 % with an AUROC of 0.97.
Conclusion
This study demonstrated the efficacy of ML in detecting instances of MTS from thin-slice T1 images. The proposed method provides valuable insights for surgical planning and treatment. Nonetheless, additional research will be required to enhance the robustness of the model and rigorously validate its effectiveness and applicability in clinical settings.
背景：颞叶中叶硬化症（MTS）是与成人耐药性颞叶中叶癫痫（mTLE）相关的最常见病理。大多数萎缩的海马可根据标准癫痫方案使用磁共振成像进行识别；但是，如果海马的硬化变化不明显，或采用了非癫痫特异性方案，就会出现困难。在这种情况下，定量方法（如 T1 加权轴向系列磁共振成像）是补充癫痫特异性方案的宝贵额外工具。在当前的研究中，我们将机器学习（ML）技术应用于分析大脑感兴趣区（ROI），包括海马、丘脑和皮质区域，以提高 MRI 中病变侧位的准确性：本研究纳入了104名确诊为mTLE的患者，其中55名患者的病变位于右侧，49名患者的病变位于左侧。研究人员使用FreeSurfer软件从高分辨率T1加权轴向脑磁共振成像扫描图像中提取特征，用于计算不同脑区的侧位指数（LI）。在使用特征选择确定关键 ROI 之后，相应的侧化指数值被用作训练 ML 模型的参数：结果：所提出的 ML 模型在 mTLE 的侧化方面表现优异，测试准确率达到 92.38%，AUROC 为 0.97：这项研究证明了 ML 在从薄片 T1 图像中检测 MTS 实例方面的功效。所提出的方法为手术规划和治疗提供了有价值的见解。不过，还需要进行更多的研究，以提高模型的稳健性，并严格验证其在临床环境中的有效性和适用性。
{"title":"Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy","authors":"Hsiang-Yu Yu , Cheng Jui Tsai , Tse-Hao Lee , Hsin Tung , Yen-Cheng Shih , Chien-Chen Chou , Cheng-Chia Lee , Po-Tso Lin , Syu-Jyun Peng","doi":"10.1016/j.mri.2024.110256","DOIUrl":"10.1016/j.mri.2024.110256","url":null,"abstract":"<div><h3>Background</h3><div>Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults.</div><div>Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however, difficulties can arise in cases where sclerotic changes in the hippocampus are subtle or non-epilepsy-specific protocols have been implemented. In such cases, quantitative methods, such as T1-weighted axial series MRIs, are valuable additional tools to complement epilepsy-specific protocols. In the current study, we applied machine learning (ML) techniques to the analysis of brain regions of interest (ROIs), including the hippocampus, thalamus, and cortical areas, to enhance the accuracy of lesion lateralization in MRI.</div></div><div><h3>Methods</h3><div>This study included 104 patients diagnosed with mTLE, including 55 with lesions on the right side and 49 with lesions on the left side. FreeSurfer software was used to extract features from high-resolution T1-weighted axial brain MRI scans for use in computing lateralization indices (LI) for various brain regions. After using feature selection to pinpoint critical ROIs, the corresponding LI values were used as parameters in training the ML model.</div></div><div><h3>Results</h3><div>The proposed ML model demonstrated exceptional performance in the lateralization of mTLE, achieving test accuracy of 92.38 % with an AUROC of 0.97.</div></div><div><h3>Conclusion</h3><div>This study demonstrated the efficacy of ML in detecting instances of MTS from thin-slice T1 images. The proposed method provides valuable insights for surgical planning and treatment. Nonetheless, additional research will be required to enhance the robustness of the model and rigorously validate its effectiveness and applicability in clinical settings.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110256"},"PeriodicalIF":2.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469127","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 : 2024-10-16DOI: 10.1016/j.mri.2024.110252
Chase J. Sakitis, Daniel B. Rowe
In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.
在 fMRI 中，捕捉任务期间的大脑活动取决于获取每个容积图像的 k 空间阵列的速度。获取完整的 k 空间阵列需要相当长的时间。对 k 空间进行低采样可缩短采集时间，但在应用反傅里叶变换 (IFT) 后会产生混叠或 "折叠 "图像。基因校准自校准部分并行采集（GRAPPA）和感度编码（SENSE）是一种并行成像技术，可从 k 空间的子采样阵列重建图像。GRAPPA 在空间频率域工作，而 SENSE 在图像空间工作，这两种技术是分开的，但可以合并，以更精确地重建子采样 k 空间阵列。在这里，我们提出了一种贝叶斯方法来处理这种合并模型，即根据先验 k 空间阵列评估未知参数的先验分布。利用先验信息来估计缺失的空间频率值，从后验分布中取消体素值的析取，并重建成全视场图像。我们的贝叶斯技术成功地重建了模拟和实验 fMRI 时间序列，没有出现混叠伪影，同时减少了时间变化，提高了任务检测能力。
{"title":"Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power","authors":"Chase J. Sakitis, Daniel B. Rowe","doi":"10.1016/j.mri.2024.110252","DOIUrl":"10.1016/j.mri.2024.110252","url":null,"abstract":"<div><div>In fMRI, capturing brain activity during a task is dependent on how quickly the <em>k</em>-space arrays for each volume image are obtained. Acquiring the full <em>k</em>-space arrays can take a considerable amount of time. Under-sampling <em>k</em>-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of <em>k</em>-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled <em>k</em>-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from <em>a priori k</em>-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110252"},"PeriodicalIF":2.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469125","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 : 2024-10-16DOI: 10.1016/j.mri.2024.110244
Jianmin Wang , Chunyan Liu , Yuxiang Zhong , Xinling Liu , Jianjun Wang
Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method has become an important technical means to accelerate MRI by reducing the data sampling rate while maintaining high-quality imaging. However, traditional undersampling reconstruction techniques such as compressed sensing mainly rely on relatively single sparse or low-rank prior information to reconstruct the image, which has limitations in capturing the comprehensive features of images, resulting in the insufficient performance of the reconstructed image in terms of details and key information. In this paper, we propose a deep plug-and-play multiple complementary priors MRI reconstruction model, which combines traditional low-rank matrix recovery model methods and deep learning methods, and integrates global, local and nonlocal priors to improve reconstruction quality. Specifically, we capture the global features of the image through the matrix nuclear norm, and use the deep convolutional neural network denoiser Swin-Conv-UNet (SCUNet) and block-matching and 3-D filtering (BM3D) algorithm to preserve the local details and structural texture of the image, respectively. In addition, we utilize an efficient half-quadratic splitting (HQS) algorithm to solve the proposed model. The experimental results show that our proposed method has better reconstruction ability than the existing popular methods in terms of visual effects and numerical results.
{"title":"Deep plug-and-play MRI reconstruction based on multiple complementary priors","authors":"Jianmin Wang , Chunyan Liu , Yuxiang Zhong , Xinling Liu , Jianjun Wang","doi":"10.1016/j.mri.2024.110244","DOIUrl":"10.1016/j.mri.2024.110244","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method has become an important technical means to accelerate MRI by reducing the data sampling rate while maintaining high-quality imaging. However, traditional undersampling reconstruction techniques such as compressed sensing mainly rely on relatively single sparse or low-rank prior information to reconstruct the image, which has limitations in capturing the comprehensive features of images, resulting in the insufficient performance of the reconstructed image in terms of details and key information. In this paper, we propose a deep plug-and-play multiple complementary priors MRI reconstruction model, which combines traditional low-rank matrix recovery model methods and deep learning methods, and integrates global, local and nonlocal priors to improve reconstruction quality. Specifically, we capture the global features of the image through the matrix nuclear norm, and use the deep convolutional neural network denoiser Swin-Conv-UNet (SCUNet) and block-matching and 3-D filtering (BM3D) algorithm to preserve the local details and structural texture of the image, respectively. In addition, we utilize an efficient half-quadratic splitting (HQS) algorithm to solve the proposed model. The experimental results show that our proposed method has better reconstruction ability than the existing popular methods in terms of visual effects and numerical results.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110244"},"PeriodicalIF":2.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469126","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 : 2024-10-12DOI: 10.1016/j.mri.2024.110254
Junxian Liao , Hongbiao Sun , Xin Chen, Qinling Jiang, Yuxin Cheng, Yi Xiao
Atrial fibrillation (AF) is the most prevalent arrhythmia in world-wild places and is associated with the development of severe secondary complications such as heart failure and stroke. Emerging evidence shows that the modified hemodynamic environment associated with AF can cause altered flow patterns in left atrial and even systemic blood associated with left atrial appendage thrombosis. Recent advances in magnetic resonance imaging (MRI) allow for the comprehensive visualization and quantification of in vivo aortic flow pattern dynamics. In particular, the technique of 4- dimensional flow MRI (4D flow MRI) offers the opportunity to derive advanced hemodynamic measures such as velocity, vortex, endothelial cell activation potential, and kinetic energy. This review introduces 4D flow MRI for blood flow visualization and quantification of hemodynamic metrics in the setting of AF, with a focus on AF and associated secondary complications.
{"title":"Advance in the application of 4-dimensional flow MRI in atrial fibrillation","authors":"Junxian Liao , Hongbiao Sun , Xin Chen, Qinling Jiang, Yuxin Cheng, Yi Xiao","doi":"10.1016/j.mri.2024.110254","DOIUrl":"10.1016/j.mri.2024.110254","url":null,"abstract":"<div><div>Atrial fibrillation (AF) is the most prevalent arrhythmia in world-wild places and is associated with the development of severe secondary complications such as heart failure and stroke. Emerging evidence shows that the modified hemodynamic environment associated with AF can cause altered flow patterns in left atrial and even systemic blood associated with left atrial appendage thrombosis. Recent advances in magnetic resonance imaging (MRI) allow for the comprehensive visualization and quantification of in vivo aortic flow pattern dynamics. In particular, the technique of 4- dimensional flow MRI (4D flow MRI) offers the opportunity to derive advanced hemodynamic measures such as velocity, vortex, endothelial cell activation potential, and kinetic energy. This review introduces 4D flow MRI for blood flow visualization and quantification of hemodynamic metrics in the setting of AF, with a focus on AF and associated secondary complications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110254"},"PeriodicalIF":2.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441941","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 : 2024-10-12DOI: 10.1016/j.mri.2024.110253
Yue Jiang , Karan Punjabi , Iain Pierce , Daniel Knight , Tina Yao , Jennifer Steeden , Alun D. Hughes , Vivek Muthurangu , Rhodri Davies
Background
The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening routine cardiac scans and in population studies.
Methods
A 3D U-Net, U-NetLR, was used to create 3D isotropic segmentations of the aorta from standard anisotropic 2D trans-axial localizers with low through-plane resolution. Training data was generated from high-resolution 3D isotropic whole heart images by simulating anisotropic images that resemble the low-resolution 2D localizers (the inputs). These inputs were paired with 3D isotropic ‘ground truth’ segmentation masks (the targets) created by a clinician from the high-resolution isotropic images. Segmentation quality was evaluated using an external dataset from UK Biobank. Segmentation accuracy was measured against ground-truth segmentations from concurrently acquired cardiac-triggered, respiratory-gated, high-resolution 3D isotropic whole heart images. Finally, the proposed method was compared to U-NetHR, a 3D U-Net variant trained directly on high-resolution 3D isotropic images. A second observer was recruited to investigate the interobserver variability.
Results
Qualitative validation on an external dataset (UK Biobank) of 180 subjects showed that 93 % of 3D segmentations with the proposed model (U-NetLR) were considered suitable for clinical use. In quantitative analysis, the proposed method (U-NetLR) showed good agreement with ground-truth segmentations from isotropic 3D images with a mean DICE score of 0.9, which is no difference from automated segmentations made directly on the high-resolution 3D isotropic aorta images (U-NetHR). When comparing measurements, there is no significant difference between U-NetLR, U-NetHR and two clinical observers in the diameter measurements at the mid ascending aorta, mid aortic arch, and descending aorta.
Conclusions
A new method of producing isotropic 3D aortic segmentations from routine CMR 2D anisotropic localizers shows good agreement with segmentation made directly from 3D isotropic images. The method has the potential to be used as a simple screening method for aortic aneurysms without the need for additional sequences.
{"title":"A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers","authors":"Yue Jiang , Karan Punjabi , Iain Pierce , Daniel Knight , Tina Yao , Jennifer Steeden , Alun D. Hughes , Vivek Muthurangu , Rhodri Davies","doi":"10.1016/j.mri.2024.110253","DOIUrl":"10.1016/j.mri.2024.110253","url":null,"abstract":"<div><h3>Background</h3><div>The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening routine cardiac scans and in population studies.</div></div><div><h3>Methods</h3><div>A 3D U-Net, U-Net<sub>LR,</sub> was used to create 3D isotropic segmentations of the aorta from standard anisotropic 2D trans-axial localizers with low through-plane resolution. Training data was generated from high-resolution 3D isotropic whole heart images by simulating anisotropic images that resemble the low-resolution 2D localizers (the inputs). These inputs were paired with 3D isotropic ‘ground truth’ segmentation masks (the targets) created by a clinician from the high-resolution isotropic images. Segmentation quality was evaluated using an external dataset from UK Biobank. Segmentation accuracy was measured against ground-truth segmentations from concurrently acquired cardiac-triggered, respiratory-gated, high-resolution 3D isotropic whole heart images. Finally, the proposed method was compared to U-Net<sub>HR</sub>, a 3D U-Net variant trained directly on high-resolution 3D isotropic images. A second observer was recruited to investigate the interobserver variability.</div></div><div><h3>Results</h3><div>Qualitative validation on an external dataset (UK Biobank) of 180 subjects showed that 93 % of 3D segmentations with the proposed model (U-Net<sub>LR</sub>) were considered suitable for clinical use. In quantitative analysis, the proposed method (U-Net<sub>LR</sub>) showed good agreement with ground-truth segmentations from isotropic 3D images with a mean DICE score of 0.9, which is no difference from automated segmentations made directly on the high-resolution 3D isotropic aorta images (U-Net<sub>HR</sub>). When comparing measurements, there is no significant difference between U-Net<sub>LR,</sub> U-Net<sub>HR</sub> and two clinical observers in the diameter measurements at the mid ascending aorta, mid aortic arch, and descending aorta.</div></div><div><h3>Conclusions</h3><div>A new method of producing isotropic 3D aortic segmentations from routine CMR 2D anisotropic localizers shows good agreement with segmentation made directly from 3D isotropic images. The method has the potential to be used as a simple screening method for aortic aneurysms without the need for additional sequences.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110253"},"PeriodicalIF":2.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469124","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 : 2024-10-12DOI: 10.1016/j.mri.2024.110255
Emma Friesen , Rubeena Gosal , Sheryl Herrera , Morgan Mercredi , Richard Buist , Kant Matsuda , Melanie Martin
Degeneration of white matter (WM) microstructure in the central nervous system is characteristic of many neurodegenerative conditions. Previous research indicates that axonal degeneration visible in ex vivo electron microscopy (EM) photomicrographs precede the onset of clinical symptoms. Measuring WM microstructural features, such as axon diameter and packing fraction, currently require these highly invasive methods of analysis and it is therefore of great importance to develop methods for in vivo measurements. Diffusion weighted Magnetic Resonance Imaging (MRI) is a non-invasive method which can be used in conjunction with temporal diffusion spectroscopy (TDS) and an oscillating gradient spin echo (OGSE) pulse sequence to probe micron-scale structures within neural tissue. The current experiment aims to compare axon diameter measurements, mean effective axon diameter (), and packing fractions calculated from EM histopathological analysis and inferred values from MR images. Mathematical models of axon diameters used for analysis include the ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD) model and the AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) model using ROI (Region of Interest) based analysis (RBA) and voxel-based analysis (VBA), respectively. Overall, it was observed that MRI inferred WM microstructural parameters overestimate those calculated from EM. This may be attributable to tissue shrinkage during EM dehydration, the sensitivity of MR pulse sequences to larger diameter axons, and/or inaccurate model assumptions. The results of the current study provide a means to characterize the precision and accuracy of RBA-ACD and VBA-AAD OGSE-TDS and highlight the need for further research investigating the relationship between ex vivo MRI and EM, with the goal of reaching in vivo MRI.
{"title":"Comparisons of MR and EM inferred tissue microstructure properties using a human autopsy corpus callosum sample","authors":"Emma Friesen , Rubeena Gosal , Sheryl Herrera , Morgan Mercredi , Richard Buist , Kant Matsuda , Melanie Martin","doi":"10.1016/j.mri.2024.110255","DOIUrl":"10.1016/j.mri.2024.110255","url":null,"abstract":"<div><div>Degeneration of white matter (WM) microstructure in the central nervous system is characteristic of many neurodegenerative conditions. Previous research indicates that axonal degeneration visible in <em>ex vivo</em> electron microscopy (EM) photomicrographs precede the onset of clinical symptoms. Measuring WM microstructural features, such as axon diameter and packing fraction, currently require these highly invasive methods of analysis and it is therefore of great importance to develop methods for <em>in vivo</em> measurements. Diffusion weighted Magnetic Resonance Imaging (MRI) is a non-invasive method which can be used in conjunction with temporal diffusion spectroscopy (TDS) and an oscillating gradient spin echo (OGSE) pulse sequence to probe micron-scale structures within neural tissue. The current experiment aims to compare axon diameter measurements, mean effective axon diameter (<span><math><mover><mi>AxD</mi><mo>¯</mo></mover></math></span><strong>)</strong>, and packing fractions calculated from EM histopathological analysis and inferred values from MR images. Mathematical models of axon diameters used for analysis include the ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD) model and the AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) model using ROI (Region of Interest) based analysis (RBA) and voxel-based analysis (VBA), respectively. Overall, it was observed that MRI inferred WM microstructural parameters overestimate those calculated from EM. This may be attributable to tissue shrinkage during EM dehydration, the sensitivity of MR pulse sequences to larger diameter axons, and/or inaccurate model assumptions. The results of the current study provide a means to characterize the precision and accuracy of RBA-ACD and VBA-AAD OGSE-TDS and highlight the need for further research investigating the relationship between <em>ex vivo</em> MRI and EM, with the goal of reaching <em>in vivo</em> MRI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110255"},"PeriodicalIF":2.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469128","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号