Magnetic resonance imaging最新文献

{"title":"Deep learning-based perfusion quantification and large vessel exclusion for renal multi-TI arterial spin labelling MRI","authors":"Jiaying Zhang ,&nbsp;Xiangwei Kong ,&nbsp;Xi Lin ,&nbsp;Yanbin Li ,&nbsp;Jeff L. Zhang ,&nbsp;Xiaopeng Zong","doi":"10.1016/j.mri.2025.110573","DOIUrl":"10.1016/j.mri.2025.110573","url":null,"abstract":"<div><div>The multi-TI flow-sensitive alternating inversion recovery sequence is a common ASL technique for probing renal perfusion. However, traditional method for quantifying perfusion, bolus arrival time (BAT) and bolus length (BL) from the images faces challenges due to low signal-to-noise ratio, large vessel contamination, and the absence of magnetization direction information in magnitude images. We proposed a BiLSTM-based deep learning (DL) approach for quantifying perfusion, BAT, and BL, and excluding large vessels. The network was trained on simulated pixel-wise multi-TI signals and tested using simulated and <em>in vivo</em> data. For comparison, the traditional quantification based on Buxton's model fitting was carried out, and manual cortex, medulla, and large vessel masks were drawn on fully relaxed magnitude images. For <em>in vivo</em> data, the quantification results from averages over all repetitions served as reference. In simulation, the DL approach had smaller quantification errors for perfusion and BAT but larger errors for BL than the traditional method. All <em>in vivo</em> parameters derived from the traditional method deviated more from references as number of averages decreased than those derived from DL. The DL masks excluded more high-perfusion pixels than the manual masks. Significant differences between the traditional and DL methods' quantification of <em>in vivo</em> perfusion, BAT, and BL cannot be explained by their differences observed in simulation, suggesting differences between simulated and <em>in vivo</em> data characteristics. The proposed network may serve as a useful tool for quantification in ASL, which is more accurate and more robust against noise than the traditional method.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110573"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145557172","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":"Deep learning approach for DCE-MRI parameter estimation: Evaluating signal intensity and concentration-time curve-based convolution-neural-networks","authors":"Piyush Kumar Prajapati ,&nbsp;Rakesh Kumar Gupta ,&nbsp;Anup Singh","doi":"10.1016/j.mri.2025.110559","DOIUrl":"10.1016/j.mri.2025.110559","url":null,"abstract":"<div><div>Dynamic contrast-enhanced MRI (DCE-MRI) is common technique for assessing tissue perfusion and permeability in brain tumors (e.g., gliomas), using generalized tracer kinetic (GTK) analysis. However, conventional non-linear least squares (NLLS) methods are computationally expensive and sensitive to noise and protocol variations (e.g., temporal resolution, flip angle), leading to inconsistent parameter estimates. Most recent deep learning methods use signal-intensity and pre-contrast T1, without addressing acquisition protocol variation effects. We propose CNN_CON, a concentration curve-based convolutional neural network-based approach for GTK parameter estimation, designed to adapt to various DCE-MRI protocols with faster processing and investigate its accuracy and robustness to protocol variations. CNN_CON was trained on synthetic data with protocol variations, and fine-tuned on 72 glioma patients(grade 2–4). Validation was performed on 18 test patients and two external datasets: cross-scanner(<em>n</em> = 9, 1.5 T) and cross-institutional(<em>n</em> = 6, different hospital). Performance was compared against NLLS and AIF-TK Net. CNN_CON achieved mean absolute errors of 111 ± 70 × 10⁻⁵ min⁻¹, 134 ± 53 × 10⁻⁵, and 133 ± 57 × 10⁻⁴ for <span><math><msup><mi>K</mi><mi>trans</mi></msup></math></span>, <span><math><msub><mi>v</mi><mi>p</mi></msub></math></span> and <span><math><msub><mi>v</mi><mi>e</mi></msub></math></span>, respectively, with no significant difference from NLLS(<em>p</em> &gt; 0.05), while reducing computational time from 15 min to 17 s. External validation demonstrated consistent performance across scanner and institutions. CNN_CON showed 2-3× better accuracy than AIF-TK Net and equivalent diagnostic capability for tumor grading (AUC = 0.89). Correlation with NLLS exceed 0.97 in normal tissues. The concentration-based approach provides superior robustness to protocol variations, validated across multiple centers, enabling consistent application of perfusion imaging biomarkers in clinical practice.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110559"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145482328","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":"Repeatability of in vivo MR cytometry for measuring cell size and density in healthy human livers","authors":"Xiaoyu Jiang ,&nbsp;Chaoyang Jin ,&nbsp;Hakmook Kang ,&nbsp;Gracie Piantek ,&nbsp;Zhongliang Zu ,&nbsp;John C. Gore ,&nbsp;Junzhong Xu","doi":"10.1016/j.mri.2025.110563","DOIUrl":"10.1016/j.mri.2025.110563","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the repeatability of MRI-derived cell size and density measurements in the healthy human liver using MR cytometry, a non-invasive technique for quantifying tissue microstructure,</div></div><div><h3>Methods</h3><div>Eight healthy subjects underwent two identical MR cytometry scans (2–7 days apart) on a Phillips 3 T scanner. Diffusion data were acquired using pulsed gradient spin echo (PGSE) and oscillating gradient spin echo (OGSE) sequences at multiple diffusion times. Bayes factor model selection compared different MR cytometry signal models, such as IMPULSED, which ignores water exchange between intra and extracellular compartments, and JOINT, which accounts for it. Repeatability of MRI-derived parameters were assessed via whole-volume and multi-size ROI analyses using Bland-Altman plots and correlation coefficients.</div></div><div><h3>Results</h3><div>The JOINT model was decisively preferred (&gt;90 % of voxels). High repeatability was observed for <span><math><mi>d</mi></math></span>, <span><math><msub><mi>v</mi><mi>in</mi></msub></math></span>, <span><math><msub><mi>ADC</mi><mi>ex</mi></msub></math></span>, and cell density in whole-liver and larger ROIs, with correlations up to <em>r</em> = 0.96. ROI size significantly improved consistency. Coefficients of variation for cell density decreased from 72 % (single voxel) to 28 % (7 × 7 voxels). The exchange rate <span><math><msub><mi>k</mi><mi>in</mi></msub></math></span> could not be reliably estimated.</div></div><div><h3>Conclusion</h3><div>ROI-based MR cytometry provides repeatable measurements of mean cell size and density, supporting its potential for clinical applications such as assessing steatohepatitis and detecting hepatocellular carcinoma. Protocol optimization to improve SNR is needed for reliable quantification of water exchange.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110563"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534515","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":"Enhancing and accelerating brain MRI through deep learning reconstruction using prior subject-specific imaging","authors":"Amirmohammad Shamaei ,&nbsp;Alexander Stebner ,&nbsp;Salome (Lou) Bosshart ,&nbsp;Johanna Ospel ,&nbsp;Gouri Ginde ,&nbsp;Mariana Bento ,&nbsp;Roberto Souza","doi":"10.1016/j.mri.2025.110558","DOIUrl":"10.1016/j.mri.2025.110558","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is a crucial medical imaging modality. However, long acquisition times remain a significant challenge, leading to increased costs, and reduced patient comfort. Recent studies have shown the potential of using deep learning models that incorporate information from prior subject-specific MRI scans to improve reconstruction quality of present scans. Integrating this prior information requires registration of the previous scan to the current image reconstruction, which can be time-consuming. We propose a novel deep-learning-based MRI reconstruction framework which consists of an initial reconstruction network, a deep registration model, and a transformer-based enhancement network. We validated our method on a longitudinal dataset of T1-weighted MRI scans with 2808 images from 18 subjects at four acceleration factors (R5, R10, R15, R20). Quantitative metrics confirmed our approach’s superiority over existing methods (p <span><math><mo>&lt;</mo></math></span> 0.05, Wilcoxon signed-rank test). Furthermore, we analyzed the impact of our MRI reconstruction method on the downstream task of brain segmentation and observed improved accuracy and volumetric agreement with reference segmentations. Our approach also achieved a substantial reduction in total reconstruction time compared to methods that use traditional registration algorithms, making it more suitable for real-time clinical applications. The code associated with this work is publicly available at <span><span>https://github.com/amirshamaei/longitudinal-mri-deep-recon</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110558"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570712","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":"Prediction of pathological risk factors in rectal cancer using combined extracellular volume fraction from T1 mapping and apparent diffusion coefficient","authors":"Mingyue Zhou ,&nbsp;Jianwei Zeng ,&nbsp;Chong Wang ,&nbsp;Haini Zhang ,&nbsp;Xiaohan Liu ,&nbsp;Chenzi Wang ,&nbsp;Juan Long ,&nbsp;Yingying Cui ,&nbsp;Hao Wang ,&nbsp;Yankai Meng ,&nbsp;Chunfeng Hu ,&nbsp;Kai Xu","doi":"10.1016/j.mri.2025.110576","DOIUrl":"10.1016/j.mri.2025.110576","url":null,"abstract":"<div><h3>Background</h3><div>Accurate preoperative prediction of pathological risk factors in rectal cancer is critical for guiding treatment decisions and improving patient outcomes. While the apparent diffusion coefficient (ADC) and extracellular volume fraction (ECV) each provide insights into tumor biology, their combined predictive value remains underexplored.</div></div><div><h3>Objective</h3><div>To assess the predictive performance of ECV, derived from T1 mapping, and ADC, from diffusion-weighted imaging (DWI), both individually and in combination, for evaluating pathological features in rectal cancer.</div></div><div><h3>Methods</h3><div>This retrospective study included 51 patients with histologically confirmed rectal adenocarcinoma, who underwent 3.0 T MRI between October 2023 and October 2024. Quantitative ECV and ADC values were extracted from T1 mapping and DWI, respectively. Logistic regression models, incorporating Ridge and Elastic Net regularization, were used to predict T stage, vascular invasion, and nerve invasion. Five-fold cross-validation was applied, and model performance was evaluated using AUC, sensitivity, specificity, and F1 score. DeLong's test was used to compare AUCs between models.</div></div><div><h3>Results</h3><div>ECV and ADC values were significantly associated with pathological features. ECV was higher and ADC was lower in advanced T stage (T3–4), vascular invasion-positive, and nerve invasion-positive groups (<em>P</em> &lt; 0.05). The combined ECV + ADC model achieved the highest AUCs: 0.906 for T staging, 0.811 for vascular invasion, and 0.861 for nerve invasion, outperforming single-parameter models. However, differences were not statistically significant (<em>P</em> &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>The combination of T1 mapping-derived ECV and DWI-derived ADC improves the noninvasive prediction of pathological risk factors in rectal cancer. This dual-biomarker approach may enhance preoperative assessment and support personalized treatment strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110576"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145557107","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":"GL-mamba-net: A magnetic resonance imaging restoration network with global-local mamba","authors":"Ke Liang ,&nbsp;Zhijin Lin ,&nbsp;Jie Yang ,&nbsp;Teng Yu ,&nbsp;Jieru Chi ,&nbsp;Feng Liu","doi":"10.1016/j.mri.2025.110574","DOIUrl":"10.1016/j.mri.2025.110574","url":null,"abstract":"<div><div>In clinical practice, magnetic resonance imaging is essential for disease diagnosis and evaluation, although it generally requires a prolonged scanning duration. Recently, several deep learning-based restoration techniques have been introduced to accelerate MRI acquisition. However, most existing MRI restoration methods struggle to fully capture local texture features and have limitations in fusing global and local features. To address this issue, the paper introduces a dual path Mamba network. This method uses a strategy of multi-scale local feature and global feature fusion for under-sampled single-coil image domain data, significantly improving both the quality and efficiency of image restoration. Specifically, 1) A multi-scale local Mamba block is proposed, which extracts local information from different regions through a multi-scale window mechanism, capturing diverse local features. 2) A new feature fusion block is proposed, which fuses global and local information to enhance the completeness of feature expression. 3) A dual path Mamba network architecture is proposed. This dual-path design significantly improves feature extraction capability and adaptability in complex and dynamic data environments. Through comprehensive experiments on the NAMIC, fastMRI and BraTS datasets, it is shown that the proposed network surpasses current leading methods across various evaluation metrics, particularly excelling in restoring texture details and tissue structures.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110574"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570713","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":"Quantitative proton density fat-fraction at 9.4 T using fast spin echo and asymmetric multi-echo gradient-echo pulse sequences","authors":"Radim Kořínek,&nbsp;Lucie Krátká,&nbsp;Zenon Starčuk Jr","doi":"10.1016/j.mri.2025.110550","DOIUrl":"10.1016/j.mri.2025.110550","url":null,"abstract":"<div><h3><strong>Purpose</strong></h3><div>Quantifying proton density fat fraction (PDFF) in small abdominal organs is challenging due to low <em>T</em>₁/<em>T</em>₂ contrast and susceptibility artifacts. We develop a hybrid 7-echo CSE-MRI sequence with arbitrary echo spacing, inspired by GRASE-type imaging, aiming for distortion-free PDFF mapping in small animals. The method is designed to be comparable to established conventional methods, with potential for increased robustness.</div></div><div><h3><strong>Methods</strong></h3><div>We developed a Fast Spin Echo Asymmetric Bipolar Multi-Gradient Echo (FSE-AbMGE) sequence by integrating a fast spin-echo readout with an asymmetrically placed bipolar multi-echo gradient-echo train. The sequence was implemented at 9.4 T and combined with robust phase unwrapping and water-fat reconstruction algorithms using full fat spectral modeling. Validation was performed using phantoms with known PDFF values (0–22 %) and in vivo experiments on several female mice (<em>n</em> = 2). Reference PDFF values were obtained using single-voxel ¹H-MRS.</div></div><div><h3><strong>Results</strong></h3><div>The proposed method enabled high-resolution PDFF mapping with minimal chemical shift and susceptibility artifacts. Phantom experiments showed strong agreement with both spectroscopic and ground truth values (R² &gt; 0.98, <em>p</em> &lt; 0.001). The method was also tested in vivo, demonstrating robust water-fat separation and quantification.</div></div><div><h3><strong>Conclusion</strong></h3><div>The FSE-AbMGE sequence is well-suited for accurate abdominal fat quantification in small animals. While additional validation is needed, especially in reproducibility and broader biological settings, the method shows promise for high-field fat quantification and may offer a framework adaptable to lower-field pre-clinical applications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110550"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425788","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":"Investigating microbleeds and white matter hyperintensities in TBI at a tract-level: A DTI study","authors":"Zalán Petneházy ,&nbsp;Dávid Bognár ,&nbsp;Péter Laar ,&nbsp;Tamás Dóczi ,&nbsp;Attila Schwarcz ,&nbsp;Bálint S. Környei ,&nbsp;Arnold Tóth","doi":"10.1016/j.mri.2025.110541","DOIUrl":"10.1016/j.mri.2025.110541","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to determine whether focal MRI lesions such as microbleeds (MBs) and focal white matter hyperintensities (FWMHs) serve as reliable and specific markers for tract-level white matter injury in traumatic brain injury (TBI).</div></div><div><h3>Materials &amp; methods</h3><div>Twenty-two patients with moderate-to-severe TBI and 22 age-matched healthy controls underwent MRI on a 3 T Siemens Prisma scanner. Imaging included susceptibility-weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), and diffusion tensor imaging (DTI). Focal lesions were manually identified on SWI and FLAIR and mapped onto tractography reconstructions. Diffusion metrics—fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared between lesion-affected tracts, contralateral normal-appearing white matter (NAWM), and corresponding control tracts. Statistical analyses were performed using repeated measures ANOVA with Greenhouse-Geisser correction and Bonferroni-adjusted post hoc tests for FA. Friedman tests were conducted for MD, AD, and RD, followed by Bonferroni-corrected Wilcoxon post hoc comparisons.</div></div><div><h3>Results</h3><div>In this study, we identified 27 MBs and 66 FWMHs intersecting white matter tracts. We observed notable differences in diffusion metrics when comparing lesion-affected tracts to healthy controls. In MB-affected tracts, fractional anisotropy (FA) differed significantly (<em>p</em> = 0.002), while mean diffusivity (MD) also showed a significant alteration (p = 0.002), along with radial diffusivity (RD) (<em>p</em> &lt; 0.001). Similarly, in FWMH-affected tracts, significant differences were observed in FA (<em>p</em> &lt; 0.001), MD (p &lt; 0.001), axial diffusivity (AD) (p &lt; 0.001), and RD (p &lt; 0.001). However, we did not find any significant differences between lesion-affected tracts and the contralateral normal-appearing white matter (NAWM).</div></div><div><h3>Conclusion</h3><div>MBs and FWMHs do not co-localize with axonal injury at the tract level but indicate a global white matter damage.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110541"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145329540","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":"Correction of aliasing artifact in accelerated echo-planar imaging","authors":"Silu Han ,&nbsp;Chidi P. Ugonna ,&nbsp;Nan-kuei Chen","doi":"10.1016/j.mri.2025.110556","DOIUrl":"10.1016/j.mri.2025.110556","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop a comprehensive reconstruction pipeline that simultaneously addresses 2D Nyquist and aliasing artifacts in echo-planar imaging (EPI) data acquired using various schemes, including single-shot, multi-shot, parallel, and multi-band EPI.</div></div><div><h3>Methods</h3><div>We introduce a novel 2D Nyquist artifact correction method that extends our previously reported phase-search reconstruction approach. A series of phase-corrected images are generated using a range of candidate phase values, and the corresponding coil sensitivity profiles are compared with known profiles to estimate an optimal 2D Nyquist phase correction map. In addition, we propose an integrated reconstruction procedure that corrects aliasing artifacts arising from 2D Nyquist effects, shot-to-shot motion-induced phase variations, and both in-plane and through-plane acceleration schemes. The proposed methods were evaluated using resting-state fMRI data from 30 healthy volunteers.</div></div><div><h3>Results</h3><div>The proposed method substantially reduced residual artifacts in EPI data, as measured by the ghost-to-signal ratio. The resulting default-mode network maps showed improved correspondence with known reference networks compared to those obtained using conventional 1D Nyquist artifact correction methods.</div></div><div><h3>Conclusion</h3><div>The developed reconstruction pipeline effectively corrects multiple sources of aliasing artifacts in EPI data, offering improved image quality and functional sensitivity across a wide range of EPI acquisition schemes.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110556"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145452208","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":"ASL combined with MAGiC facilitates the identification of patients with status epilepticus","authors":"Dan Luo ,&nbsp;Xinru Deng ,&nbsp;Xinlan Xiao","doi":"10.1016/j.mri.2025.110503","DOIUrl":"10.1016/j.mri.2025.110503","url":null,"abstract":"<div><div>Recent studies suggest that arterial spin labeling (ASL) can be a valuable tool in the assessment of status epilepticus (SE), as it reveals hyperperfusion in epileptogenic foci. However, ASL is not particularly effective in delineating the precise location of the lesion. In this case, the patient, a 33-year-old male with status epilepticus, underwent MAGiC sequence scanning within 4 h. The MAGiC sequence can generate ten contrast images and five quantitative maps in a single scan session. Within this framework, the diffusion-weighted imaging (DIR-WM) sequence enhances contrast by suppressing signals from cerebrospinal fluid and gray matter, thereby rendering white matter lesions, including the epileptogenic hippocampus, more discernible on the affected side.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110503"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144862325","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}