Journal of Magnetic Resonance Imaging最新文献

{"title":"Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models.","authors":"Markus Wennmann, Lukas T Rotkopf, Fabian Bauer, Thomas Hielscher, Jessica Kächele, Elias K Mai, Niels Weinhold, Marc-Steffen Raab, Hartmut Goldschmidt, Tim F Weber, Heinz-Peter Schlemmer, Stefan Delorme, Klaus Maier-Hein, Peter Neher","doi":"10.1002/jmri.29442","DOIUrl":"10.1002/jmri.29442","url":null,"abstract":"<p><strong>Background: </strong>Radiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large-scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test-retest experiments, which might help to overcome the problem of limited generalizability to external data.</p><p><strong>Purpose: </strong>To evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi-MRI-scanner test-retest-study, on the performance and generalizability of radiomics models.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Patients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2-8).</p><p><strong>Field strength/sequence: </strong>1.5T and 3.0T; T1-weighted turbo spin echo.</p><p><strong>Assessment: </strong>The task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2-8).</p><p><strong>Statistical tests: </strong>Pearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z-transformation, Wilcoxon signed-rank test, Wilcoxon rank-sum test; significance level P < 0.05.</p><p><strong>Results: </strong>When using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10).</p><p><strong>Conclusion: </strong>Using only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model.</p><p><strong>Level of evidence: 3: </strong></p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"676-686"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling\".","authors":"Thais Maria S Bezerra, Almir G V Bitencourt","doi":"10.1002/jmri.29453","DOIUrl":"10.1002/jmri.29453","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"942-943"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Generalizing Diffusion Tensor Imaging of the Physis and Metaphysis\".","authors":"Emanuele Siravo","doi":"10.1002/jmri.29459","DOIUrl":"10.1002/jmri.29459","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"805-806"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141097150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients.","authors":"Jia Guo, Yi-Ming Li, Hongwei Guo, Da-Peng Hao, Jing-Xu Xu, Chen-Cui Huang, Hua-Wei Han, Feng Hou, Shi-Feng Yang, Jian-Ling Cui, He-Xiang Wang","doi":"10.1002/jmri.29474","DOIUrl":"10.1002/jmri.29474","url":null,"abstract":"<p><strong>Background: </strong>Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative.</p><p><strong>Purpose: </strong>To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images.</p><p><strong>Study type: </strong>Retrospective/prospective.</p><p><strong>Population: </strong>354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort.</p><p><strong>Field strength/sequence: </strong>1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted.</p><p><strong>Assessment: </strong>DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months.</p><p><strong>Statistical tests: </strong>Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant.</p><p><strong>Results: </strong>The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS.</p><p><strong>Data conclusion: </strong>The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans.</p><p><strong>Level of evidence: 4: </strong></p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"807-819"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141300830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models\".","authors":"Mohammad Sabati, Anil Chauhan","doi":"10.1002/jmri.29500","DOIUrl":"10.1002/jmri.29500","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"687-689"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141468579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exercise MR of Skeletal Muscles, the Heart, and the Brain.","authors":"Melissa T Hooijmans, Jeroen A L Jeneson, Harald T Jørstad, Adrianus J Bakermans","doi":"10.1002/jmri.29445","DOIUrl":"10.1002/jmri.29445","url":null,"abstract":"<p><p>Magnetic resonance (MR) imaging (MRI) is routinely used to evaluate organ morphology and pathology in the human body at rest or in combination with pharmacological stress as an exercise surrogate. With MR during actual physical exercise, we can assess functional characteristics of tissues and organs under real-life stress conditions. This is particularly relevant in patients with limited exercise capacity or exercise intolerance, and where complaints typically present only during physical activity, such as in neuromuscular disorders, inherited metabolic diseases, and heart failure. This review describes practical and physiological aspects of exercise MR of skeletal muscles, the heart, and the brain. The acute effects of physical exercise on these organs are addressed in the light of various dynamic quantitative MR readouts, including phosphorus-31 MR spectroscopy (³¹P-MRS) of tissue energy metabolism, phase-contrast MRI of blood flow and muscle contraction, real-time cine MRI of cardiac performance, and arterial spin labeling MRI of muscle and brain perfusion. Exercise MR will help advancing our understanding of underlying mechanisms that contribute to exercise intolerance, which often proceed structural and anatomical changes in disease. Its potential to detect disease-driven alterations in organ function, perfusion, and metabolism under physiological stress renders exercise MR stress testing a powerful noninvasive imaging modality to aid in disease diagnosis and risk stratification. Although not yet integrated in most clinical workflows, and while some applications still require thorough validation, exercise MR has established itself as a comprehensive and versatile modality for characterizing physiology in health and disease in a noninvasive and quantitative way. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"535-560"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140896977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Hepatic Glucose and Palmitic Acid Metabolism in Rodents on High-Fat Diet Using Deuterium Metabolic Imaging.","authors":"Viktoria Ehret, Usevalad Ustsinau, Joachim Friske, Thomas Scherer, Clemens Fürnsinn, Thomas H Helbich, Cécile Philippe, Martin Krššák","doi":"10.1002/jmri.29437","DOIUrl":"10.1002/jmri.29437","url":null,"abstract":"<p><strong>Background: </strong>One of the main features of several metabolic disorders is dysregulation of hepatic glucose and lipid metabolism. Deuterium metabolic imaging (DMI) allows for assessing the uptake and breakdown of ²H-labeled substrates, giving specific insight into nutrient processing in healthy and diseased organs. Thus, DMI could be a useful approach for analyzing the differences in liver metabolism of healthy and diseased subjects to gain a deeper understanding of the alterations related to metabolic disorders.</p><p><strong>Purpose: </strong>Evaluating the feasibility of DMI as a tool for the assessment of metabolic differences in rodents with healthy and fatty livers (FLs).</p><p><strong>Study type: </strong>Animal Model.</p><p><strong>Population: </strong>18 male Sprague Dawley rats on standard (SD, n = 9, healthy) and high-fat diet (HFD, n = 9, FL disease).</p><p><strong>Field strength/sequence: </strong>Phase-encoded 1D pulse-acquire sequence and anatomy co-registered phase-encoded 3D pulse-acquire chemical shift imaging for ²H at 9.4T.</p><p><strong>Assessment: </strong>Localized and nonlocalized liver spectroscopy was applied at eight time points over 104 minutes post injection. The obtained spectra were preprocessed and quantified using jMRUI (v7.0) and the resulting amplitudes translated to absolute concentration (mM) according to the ²H natural abundance water peak.</p><p><strong>Statistical tests: </strong>Two-way repeated measures ANOVA were employed to assess between-group differences, with statistical significance at P < 0.05.</p><p><strong>Results: </strong>DMI measurements demonstrated no significant difference (P = 0.98) in the uptake of [6,6'-²H₂]glucose between healthy and impaired animals (AUC_SD = 1966.0 ± 151.5 mM - minutes vs. AUC_HFD = 2027.0 ± 167.6 mM·minutes). In the diseased group, the intrahepatic uptake of palmitic acid d-31 was higher (AUC_HFD = 57.4 ± 17.0 mM·minutes, AUC_SD = 33.3 ± 10.5 mM·minutes), but without statistical significance owing to substantial in-group variation (P = 0.73).</p><p><strong>Data conclusion: </strong>DMI revealed higher concentrations of palmitic acid in rats with FL disease and no difference in hepatic glucose concentration between healthy and impaired animals. Thus, DMI appears to be a useful tool for evaluating metabolism in rodents with FL disease.</p><p><strong>Level of evidence: </strong>2 TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"958-967"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140896956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method.","authors":"Kuiyuan Liu, Zixuan Lin, Tianshu Zheng, Ruicheng Ba, Zelin Zhang, Haotian Li, Hongxi Zhang, Assaf Tal, Dan Wu","doi":"10.1002/jmri.29434","DOIUrl":"10.1002/jmri.29434","url":null,"abstract":"<p><strong>Background: </strong>Accurately fitting diffusion-time-dependent diffusion MRI (t_d-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition.</p><p><strong>Purpose: </strong>Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum.</p><p><strong>Field strength/sequence: </strong>3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE).</p><p><strong>Assessment: </strong>The Bayesian method's performance in fitting cell diameter ( <math><mrow><mi>d</mi></mrow> </math> ), intracellular volume fraction ( <math> <mrow><msub><mi>f</mi> <mtext>in</mtext></msub> </mrow> </math> ), and extracellular diffusion coefficient ( <math> <mrow><msub><mi>D</mi> <mi>ex</mi></msub> </mrow> </math> ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology.</p><p><strong>Statistical tests: </strong>T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05.</p><p><strong>Results: </strong>Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively for <math><mrow><mi>d</mi></mrow> </math> , <math> <mrow><msub><mi>f</mi> <mtext>in</mtext></msub> </mrow> </math> , and <math> <mrow><msub><mi>D</mi> <mi>ex</mi></msub> </mrow> </math> compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 for <math> <mrow><msub><mi>f</mi> <mtext>in</mtext></msub> </mrow> </math> compared to r = 0.698 using NLLS, P = 0.5764).</p><p><strong>Data conclusion: </strong>The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"724-734"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"A Nomogram Based on MRI Visual Decision Tree to Evaluate Vascular Endothelial Growth Factor in Hepatocellular Carcinoma\".","authors":"Felix Busch, Lena Hoffmann, Lisa C Adams","doi":"10.1002/jmri.29504","DOIUrl":"10.1002/jmri.29504","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"983-984"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141492319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques.","authors":"Sana Mohammadi, Sadegh Ghaderi, Ali Fathi Jouzdani, Iman Azinkhah, Sanaz Alibabaei, Mobin Azami, Vida Omrani","doi":"10.1002/jmri.29473","DOIUrl":"10.1002/jmri.29473","url":null,"abstract":"<p><strong>Background: </strong>Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.</p><p><strong>Purpose: </strong>To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.</p><p><strong>Study type: </strong>Systematic review and meta-analysis.</p><p><strong>Population: </strong>Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).</p><p><strong>Field strength/sequence: </strong>Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T.</p><p><strong>Assessment: </strong>Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.</p><p><strong>Statistical tests: </strong>Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I² statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant.</p><p><strong>Results: </strong>HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).</p><p><strong>Data conclusion: </strong>ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.</p><p><strong>Level of evidence: 1: </strong></p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"758-768"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141427036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}