Magnetic resonance imaging最新文献

{"title":"Multi-task magnetic resonance imaging reconstruction using meta-learning","authors":"Wanyu Bian ,&nbsp;Albert Jang ,&nbsp;Fang Liu","doi":"10.1016/j.mri.2024.110278","DOIUrl":"10.1016/j.mri.2024.110278","url":null,"abstract":"<div><div>Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity among image datasets with different types of contrast leads to suboptimal learning performance.</div><div>This paper proposes a meta-learning approach to efficiently learn image features from multiple MRI datasets. Our algorithm can perform multi-task learning to simultaneously reconstruct MRI images acquired using different imaging sequences with various image contrasts. We have developed a proximal gradient descent-inspired optimization method to learn image features across image and k-space domains, ensuring high-performance learning for every image contrast. Meanwhile, meta-learning, a “learning-to-learn” process, is incorporated into our framework to improve the learning of mutual features embedded in multiple image contrasts.</div><div>The experimental results reveal that our proposed multi-task meta-learning approach surpasses state-of-the-art single-task learning methods at high acceleration rates. Our meta-learning consistently delivers accurate and detailed reconstructions, achieves the lowest pixel-wise errors, and significantly enhances qualitative performance across all tested acceleration rates.</div><div>We have demonstrated the ability of our new meta-learning reconstruction method to successfully reconstruct highly-undersampled k-space data from multiple MRI datasets simultaneously, outperforming other compelling reconstruction methods previously developed for single-task learning.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"116 ","pages":"Article 110278"},"PeriodicalIF":2.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695521","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}

{"title":"Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction","authors":"Uten Yarach ,&nbsp;Itthi Chatnuntawech ,&nbsp;Congyu Liao ,&nbsp;Surat Teerapittayanon ,&nbsp;Siddharth Srinivasan Iyer ,&nbsp;Tae Hyung Kim ,&nbsp;Justin Haldar ,&nbsp;Jaejin Cho ,&nbsp;Berkin Bilgic ,&nbsp;Yuxin Hu ,&nbsp;Brian Hargreaves ,&nbsp;Kawin Setsompop","doi":"10.1016/j.mri.2024.110277","DOIUrl":"10.1016/j.mri.2024.110277","url":null,"abstract":"<div><div>Purpose: BUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of more complex reconstruction that is computationally prohibitive. In this work we develop rapid reconstruction pipeline for BUDA-cEPI to pave the way for its deployment in routine clinical and neuroscientific applications. The proposed reconstruction includes the development of ML-based unrolled reconstruction as well as rapid ML-based B0 and eddy current estimations that are needed. The architecture of the unroll network was designed so that it can mimic S-LORAKS regularization well, with the addition of virtual coil channels.</div><div>Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also introduced into the reconstruction to effectively take advantage of the smooth phase prior and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.</div><div>Results: The introduction of the Virtual Coil concept into the unrolled network was shown to be key to achieving high-quality reconstruction for BUDA-cEPI. With the inclusion of an additional non-diffusion image (b-value = 0 s/mm²), a slight improvement was observed, with the normalized root mean square error further reduced by approximately 5 %. The reconstruction times for S-LORAKS and the proposed unrolled networks were approximately 225 and 3 s per slice, respectively.</div><div>Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ∼88× when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110277"},"PeriodicalIF":2.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142682124","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}

{"title":"Radiomic features of dynamic contrast-enhanced MRI can predict Ki-67 status in head and neck squamous cell carcinoma","authors":"Lu Yang ,&nbsp;Longwu Yu ,&nbsp;Guangzi Shi ,&nbsp;Lingjie Yang ,&nbsp;Yu Wang ,&nbsp;Riyu Han ,&nbsp;Fengqiong Huang ,&nbsp;Yinfeng Qian ,&nbsp;Xiaohui Duan","doi":"10.1016/j.mri.2024.110276","DOIUrl":"10.1016/j.mri.2024.110276","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to investigate the potential of radiomic features derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting Ki-67 and p16 status in head and neck squamous cell carcinoma (HNSCC).</div></div><div><h3>Materials and methods</h3><div>A cohort of 124 HNSCC patients who underwent pre-surgery DCE-MRI were included and divided into training and test set (7:3), further subgroup analysis was performed for 104 cases with oral squamous cell carcinoma (OSCC). Radiomics features were extracted from DCE images. The least absolute shrinkage and selection operator (LASSO) was used for radiomics features selection, and receiver operating characteristics analysis for predictive performance assessment. The nomogram's performance was evaluated using decision curve analysis (DCA).</div></div><div><h3>Results</h3><div>Ten DCE-MRI features were identified to build the predictive model of HNSCC, demonstrating excellent predictive value for Ki-67 status in both the training set (AUC of 0.943) and test set (AUC of 0.801). The nomograms based on the predictive model showed good fit in the calibration curves (<em>p</em> &gt; 0.05), and DCA indicated its high clinical usefulness. In subgroup analysis of OSCC, fourteen features were selected to build the predictive model for Ki-67 status with an AUC of 0.960 in training set and 0.817 in test set. No features could be included to establish a model to predict p16 status.</div></div><div><h3>Conclusion</h3><div>The radiomics model utilizing DCE-MRI features could effectively predict Ki-67 status in HNSCC patients, offering potential for noninvasive preoperative prediction of Ki-67 status.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"116 ","pages":"Article 110276"},"PeriodicalIF":2.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687383","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}

{"title":"GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction","authors":"Shahzad Ahmed ,&nbsp;Feng Jinchao ,&nbsp;Javed Ferzund ,&nbsp;Muhammad Usman Ali ,&nbsp;Muhammad Yaqub ,&nbsp;Malik Abdul Manan ,&nbsp;Atif Mehmood","doi":"10.1016/j.mri.2024.110279","DOIUrl":"10.1016/j.mri.2024.110279","url":null,"abstract":"<div><h3>Purpose</h3><div>This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural detail, leading to suboptimal image quality. GraFMRI leverages Graph Neural Networks (GNNs) to transform multi-modal MRI data (T1, T2, PD) into a graph-based representation, enabling the model to capture intricate spatial relationships and inter-modality dependencies.</div></div><div><h3>Methods</h3><div>The framework integrates Graph-Based Non-Local Means (NLM) Filtering for effective noise suppression and Adversarial Training to reduce artifacts. A dynamic attention mechanism enables the model to focus on key anatomical regions, even when fully-sampled reference images are unavailable. GraFMRI was evaluated on the IXI and fastMRI datasets using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) as metrics for reconstruction quality.</div></div><div><h3>Results</h3><div>GraFMRI consistently outperforms traditional and self-supervised reconstruction techniques. Significant improvements in multi-modal fusion were observed, with better preservation of information across modalities. Noise suppression through NLM filtering and artifact reduction via adversarial training led to higher PSNR and SSIM scores across both datasets. The dynamic attention mechanism further enhanced the accuracy of the reconstructions by focusing on critical anatomical regions.</div></div><div><h3>Conclusion</h3><div>GraFMRI provides a scalable, robust solution for multi-modal MRI reconstruction, addressing noise and artifact challenges while enhancing diagnostic accuracy. Its ability to fuse information from different MRI modalities makes it adaptable to various clinical applications, improving the quality and reliability of reconstructed images.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"116 ","pages":"Article 110279"},"PeriodicalIF":2.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142676371","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}

{"title":"Progress in MRI is NOT ubiquitous","authors":"John C. Gore","doi":"10.1016/j.mri.2024.110273","DOIUrl":"10.1016/j.mri.2024.110273","url":null,"abstract":"<div><div>There has been tremendous progress in MRI over the past 40+ years, driven by advances in technology as well as human ingenuity, with considerable impact in medicine. However, our understanding of how to account for, and interpret, MRI properties <em>quantitatively</em> lags behind these technical advances. This lack of understanding will limit our ability to make full use of quantitative metrics in the future, and much more work is needed to bridge this knowledge gap.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110273"},"PeriodicalIF":2.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648472","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}

{"title":"Manual data labeling, radiology, and artificial intelligence: It is a dirty job, but someone has to do it","authors":"Teodoro Martín-Noguerol ,&nbsp;Pilar López-Úbeda ,&nbsp;Félix Paulano-Godino ,&nbsp;Antonio Luna","doi":"10.1016/j.mri.2024.110280","DOIUrl":"10.1016/j.mri.2024.110280","url":null,"abstract":"<div><div>In this letter to the editor, authors highlight the key role of data labeling in training AI models for medical imaging, discussing the complexities, resource demands, costs, and the relevance of quality control in the labeling process including the potential and limitations of AI tools for automated labeling. The article underlines that labeling quality is essential for the accuracy of AI models and the safety of their clinical applications, highlighting the legal responsibilities of labelers in cases where improper labeling leads to AI errors.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"116 ","pages":"Article 110280"},"PeriodicalIF":2.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667820","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}

{"title":"The effects of reference selection methods on PROPELLER MRI","authors":"Yilong Liu ,&nbsp;Bing Zhang ,&nbsp;Jintang Ye ,&nbsp;Zhe Zhang ,&nbsp;Renyuan Liu ,&nbsp;Ming Li ,&nbsp;Xiaodong Chen ,&nbsp;Taihui Yu ,&nbsp;Biling Liang ,&nbsp;Xiaoying Wang ,&nbsp;Rui Li ,&nbsp;Chun Yuan ,&nbsp;Hua Guo","doi":"10.1016/j.mri.2024.110275","DOIUrl":"10.1016/j.mri.2024.110275","url":null,"abstract":"<div><div>PROPELLER MRI has been shown effective for rigid motion compensation, while the performance of existing PROPELLER reconstruction methods critically depend on selecting a proper reference blade.</div><div>In this work, we proposed a robust implementation for PROPELLER reconstruction, which was incorporated with different reference selection methods, including single blade reference (SBR), combined blades reference (CBR), grouped blades reference (GBR) and Pipe et al.'s revised method, which requires no blade reference (NBR).</div><div>Both simulation and <em>in vivo</em> studies were performed to evaluate the precision and robustness of motion estimation for reference selection methods. <em>In vivo</em> data sets from 10 volunteers with instructed motion and 11 patients with random motion were collected and images were scored independently and blindly by two experienced radiologists.</div><div>Both simulation and <em>in vivo</em> studies demonstrate that the four reference selection methods have similar performances according to visual inspection. In our tests, one iteration for the motion estimation can be sufficient for SBR, CBR, or GBR, and comparable to NBR in terms of image quality for clinical diagnosis. With two iterations, SBR, CBR, and GBR are comparable to NBR in terms of motion estimation precision. With our proposed PROPELLER reconstruction, reference selection is not critical for robust motion correction. NBR with no iterations and SBR, CBR, and GBR with two iterations are recommended for accurate motion correction.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"116 ","pages":"Article 110275"},"PeriodicalIF":2.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667770","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}

{"title":"Detection challenges of temporal encephaloceles in epilepsy: A retrospective analysis","authors":"Alexander V. Ortiz ,&nbsp;Jarrod J. Eisma ,&nbsp;Dann Martin ,&nbsp;Andre H. Lagrange ,&nbsp;Cari Motuzas ,&nbsp;William Nobis ,&nbsp;Bassel W. Abou-Khalil ,&nbsp;Victoria L. Morgan ,&nbsp;Jonah Fox","doi":"10.1016/j.mri.2024.110272","DOIUrl":"10.1016/j.mri.2024.110272","url":null,"abstract":"<div><div>Temporal encephaloceles (TEs) are herniations of cerebral parenchyma through structural defects in the floor of the middle cranial fossa. They are a relatively common, but only relatively recently identified potential cause of drug-resistant epilepsy. Uncontrolled epilepsy is associated with many negative long term health consequences including a heightened risk of death. The most effective treatment for drug-resistant epilepsy is surgery. One of the most predictive factors associated with successful surgery is identification of an abnormality on imaging. However, TEs can be difficult to detect and are often overlooked on neuroimaging studies. Improving our ability to accurately detect TEs by MRI is an important step in improving surgical outcomes in patients with drug-resistant epilepsy. We performed a review on existing imaging modalities for detecting TEs and report on our attempt to use a voxel-based morphometry (VBM) algorithm to detect TEs in T1-weighted MRIs of 81 patients from a database comprised of 25 patients with confirmed encephaloceles and 56 controls. Our program's sensitivity and specificity were compared to those of two neuroradiologists and two epileptologists using visualization during surgery as the gold standard. On average, the neuroradiologists and epileptologists had sensitivities of 41 % and 58 % and specificities of 81 % and 60 % while our VBM-based approach had sensitivities and specificities ranging from 11 % to 50 % and 0.2 % to 17 %, respectively. This work provides an overview of the different imaging modalities utilized in the detection of TEs and highlights the difficulties associated with their detection for both experienced physicians and cutting-edge computational methods. Our findings suggest that VBM-based methods could potentially be used to enhance clinicians' ability to detect TEs thereby facilitating surgical planning, improving surgical outcomes by allowing for more specific targeting, and bettering the long-term health and well-being of patients with drug-resistant epilepsy secondary to TEs.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110272"},"PeriodicalIF":2.1,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622520","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}

{"title":"Cystic Fibrosis or asthma? Discerning dyspnea with hyperpolarizaed xenon gas magnetic resonance imaging","authors":"David Wang ,&nbsp;Cody Thornburgh ,&nbsp;Harjeet Singh ,&nbsp;Zach Holliday","doi":"10.1016/j.mri.2024.110271","DOIUrl":"10.1016/j.mri.2024.110271","url":null,"abstract":"<div><div>Hyperpolarized Xenon MRI (HPG MRI) has been studied for its potential use in assessing lung function in patients with cystic fibrosis (CF) and in patients with asthma. We present a case of a man with overlapping cystic fibrosis and allergic asthma with severe obstructive lung disease in which spirometry and computed topography (CT) imaging was unable to determine the primary cause for his uncontrolled symptoms. HPG MRI was used to guide a tissue biopsy and determine the primary driver to be allergic asthma. After starting targeted therapy for severe asthma, his symptoms have greatly improved.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110271"},"PeriodicalIF":2.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142568622","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}

{"title":"Intravascular enhancement sign at 3D T1-weighted turbo spin echo sequence is associated with cerebral atherosclerotic stenosis","authors":"Bo Wang ,&nbsp;Feng Ouyang ,&nbsp;Qin Wu ,&nbsp;Jingting Chen ,&nbsp;Jie Liu ,&nbsp;Zihe Xu ,&nbsp;Lianjiang Lv ,&nbsp;Nianzu Yu ,&nbsp;Xianjun Zeng","doi":"10.1016/j.mri.2024.110270","DOIUrl":"10.1016/j.mri.2024.110270","url":null,"abstract":"<div><h3>Objective</h3><div>Intravascular enhancement sign (IVES) at three-dimensional T1-weighted turbo spin echo (3D T1W TSE) sequence may be a simple hemodynamic maker. This study aims to investigate the association between IVES and features of intracranial atherosclerotic stenosis (ICAS).</div></div><div><h3>Method</h3><div>Retrospective analysis of clinical and imaging data of patients who underwent high resolution-vessel wall imaging (HR-VWI) examination from May 2021 to May 2023. The number of IVES vessels and ICAS features at HR-VWI were extracted by two neuroradiologists. Paired comparisons and correlation analysis on these indicators were performed.</div></div><div><h3>Results</h3><div>A total of 118 patients with ICAS in the first segment of the middle cerebral artery and accompanied by unilateral IVES were enrolled. Compared to the non-IVES side, a higher incidence of ischemic events and intraplaque hemorrhage (IPH), higher degree of vascular stenosis and enhancement, lower remodeling index, and lower signal intensity ratio (SIR) were found in subjects with IVES. In the ICAS with IVES, 79.66 % showed severe stenosis and occlusion; in the ICAS with severe stenosis and occlusion, 89.5 % showed IVES in the distal. A multivariable logistic regression model identified the vascular stenosis degree (OR = 1.922; 95 %CI [1.37–2.692]; <em>P</em> &lt; 0.001), enhanced-degree (OR = 2.486; 95 %CI [1.315–4.698]; <em>P</em> = 0.005), position (OR = 2.869; 95 %CI [1.255–6.560]; <em>P</em> = 0.012), and SIR (OR = 0.032; 95 %CI [0.004–0.275]; <em>P</em> = 0.002) were independent association with the presence of IVES. The area under the curve was 0.911 for the use of IVES vessel quantities to identify severe stenosis and occlusion of arterial lumen.</div></div><div><h3>Conclusion</h3><div>The number of IVES vessels was associated with the local features of ICAS, which may indicate severe stenosis and occlusion in the major branches of the proximal artery.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"115 ","pages":"Article 110270"},"PeriodicalIF":2.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142568699","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}