JOURNAL OF MAGNETIC RESONANCE IMAGING磁共振成像杂志
JOURNAL OF MAGNETIC RESONANCE IMAGING(英文缩写 J MAGN RESON IMAGING),ISSN 1053-1807,eISSN 1522-2586,中文译名:磁共振成像杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 5.119 | Q1 |
| 2022 | 4.400 | Q1 |
| 2023 | 3.300 | Q1 |
| 2024 | 3.500 | Q1 |
| 2025 | 4.300 | Q1 |
JOURNAL OF MAGNETIC RESONANCE IMAGING 最新收录文献
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1. Slice-Wise Quality Assessment of High b Value Breast DWI via Deep Learning-Based Artifact Detection.
1. 基于深度学习伪影检测的高b值乳腺DWI逐层质量评估PMID:日期:2026-10-05Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast MRI. Therefore, DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. However, especially high b value DWI can be prone to intensity artifacts that can affect diagnostic image assessment. To detect hyper- and hypointense artifacts on high b value DWI (b = 1500 s/mm) using deep learning, employing either a binary classification (artifact presence) or a multiclass classification (artifact intensity) approach on a slice-wise dataset. Retrospective. 11,806 DWI slices were acquired from 156 examinations performed on female patients between 2022 and mid-2023. For both hyper- and hypointense artifact classification, the slices were split into test, validation, and holdout test set with 8164/1806/1836 and 8164/1820/1822 slices in these sets respectively. 3 T spin-echo planar diffusion-weighted sequence with b values: 50, 750, and 1500 s/mm. Three convolutional neural network (CNN) architectures (DenseNet121, ResNet18, and SEResNet50) were trained for binary classification of hyper- and hypointense artifacts on slice-wise labeled data. The best performing model (DenseNet121) was applied to an independent holdout test set and further trained separately for multiclass classification. Performance of the networks was evaluated using accuracy, precision, recall, and areas under the receiver operating characteristic curve (AUROC) and under the precision recall curve (AUPRC). Radiologist evaluated bounding box positions on a 5-point Likert-like scale across 150 slices for validation and 200 slices for the holdout test set, derived from the network's Grad-CAM heatmaps. DenseNet121 achieved AUROCs of 0.91 and 0.95 for hyper- and hypointense artifact detection, respectively, and weighted AUROCs of 0.84 and 0.90 for multiclass classification on single-slice high b value diffusion-weighted images, achieving mean scores of 3.52 ± 0.81 for hyperintense artifacts and 3.62 ± 0.65 for hypointense artifacts. Hyper- and hypointense artifact detection in slice-wise breast DWI MRI dataset (b = 1500 s/mm) using CNNs, particularly DenseNet121, suggests potential reliability and requires further validation. 3. Stage 3.
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2. Editorial for "Slice-Wise Quality Assessment of High b-Value Breast DWI via Deep Learning-Based Artifact Detection".
2. 关于“基于深度学习伪影检测的高b值乳腺DWI逐层质量评估”的社论PMID:日期:2026-10-05该文献暂无摘要。
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3. Editorial for "Evaluation of Pre-Operative Cartilage and Meniscus Ultrashort Echo Time T2* as Biomarkers of Long-Term Clinical Outcomes Following Anterior Cruciate Ligament Reconstruction".
3. “评估术前软骨和半月板超短回波时间T2*作为前交叉韧带重建术后长期临床结局生物标志物”的社论PMID:日期:2026-10-05该文献暂无摘要。
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4. Evaluation of Pre-Operative Cartilage and Meniscus Ultrashort Echo Time T2* as Biomarkers of Long-Term Clinical Outcomes Following Anterior Cruciate Ligament Reconstruction.
PMID:日期:2026-10-05Ultrashort echo time T2* (UTE-T2*) mapping is sensitive to the composition of deep cartilage and menisci. UTE-T2* elevations are common after anterior cruciate ligament (ACL) injuries, but their long-term prognostic importance is unknown. Higher pre-operative UTE-T2* values will associate with greater symptoms and radiographic knee osteoarthritis (KOA) at long-term follow-up. Retrospective longitudinal cohort study using prospectively acquired baseline MRI and follow-up clinical outcomes. Twenty-two participants (13 female, mean [SD] age: 29 [9] years) with ACL reconstruction (ACLR). 3T, 3D acquisition-weighted stack of spirals. Pre-operative UTE-T2* maps from participants who underwent ACLR (2009-2012) were compared to 11-year follow-up Knee Injury and Osteoarthritis Outcome Scores (KOOS), the Marx Activity Rating Scale scores, and radiographs. Linear regression models assessed relationships between pre-operative cartilage and meniscus UTE-T2* and follow-up KOOS Symptoms or Marx scores. Mann-Whitney U-tests assessed differences in pre-operative UTE-T2* by radiographic KOA status (Kellgren-Lawrence grade ≥ 2). two-sided α < 0.05. KOOS and Marx scores were available for 22 participants at a median of 11.3 years (range: 10.3-12.4) after ACLR; radiographs were available for 11 participants. After adjustment for meniscus tear status, higher pre-operative UTE-T2* values in the deep medial femoral cartilage and medial meniscus were associated with worse KOOS Symptoms at 11 years (adjusted R = 0.146-0.332). Higher femoral cartilage UTE-T2* trended with lower Marx scores (adjusted R = 0.208, p = 0.056), while higher posterior meniscus UTE-T2* was associated with higher Marx scores (adjusted R = 0.308). In the small radiographic subset, median pre-operative posterior meniscus UTE-T2* was 69% higher in patients with radiographic KOA. Pre-operative UTE-T2* in medial compartment cartilage and menisci were associated with knee symptoms and activity levels 11 years after ACLR. These findings support the potential utility of elevated cartilage and meniscus UTE-T2* as markers of pre-osteoarthritis reflecting symptomatic and structural KOA risk after ACLR. Level 3. Stage 3 Diagnostic Thinking.
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5. Personalized Cine Cardiac MRI Protocol Optimization Using a Retrieval-Augmented Large Language Model: A Prospective Comparison With Technologist-Optimized Protocol.
5. 使用检索增强大语言模型的个性化电影心脏MRI协议优化：与技师优化协议的前瞻性比较PMID:日期:2026-10-04Cine cardiac magnetic resonance imaging (MRI) protocols are typically based on static templates, which may not optimally address individual patient limitations such as arrhythmia or limited breath-hold capacity. To test whether an artificial intelligence (AI) framework using a large language model with retrieval-augmented generation (LLM-RAG) can provide cine cardiac MRI protocols that are equivalent or noninferior to those of technologist-optimized protocols. Prospective, intra-individual comparative, nonrandomized. Sixty-eight patients (mean age 46 ± 18 years; 39 women) referred for clinical cardiac MRI, including patients with normal function as well as those with arrhythmia, implants, or limited breath-hold capacity. 1.5 T, cine cardiac MRI. Each participant underwent cine cardiac MRI twice in one session. First with a technologist-optimized protocol, then with an AI-optimized protocol (fixed order). Image quality and artifact suppression were assessed using four-point Likert scales by two blinded readers. Left ventricular (LV) functional parameters and repeat acquisitions were recorded. Wilcoxon signed-rank test, McNemar's test, paired t-tests, and Bland-Altman analysis. Significance set at p < 0.05. Compared to technologist-, AI-optimized cine protocols demonstrated significantly higher image quality (3.69 ± 0.47 vs. 3.00 ± 0.49, p < 0.001) and artifact suppression (3.19 ± 0.35 vs. 2.17 ± 0.45, p < 0.001). The AI-optimized protocol reduced repeat acquisitions by 68% (6 vs. 19 repeats, p = 0.028). No significant differences were observed in LV functional parameters between both protocols (all p > 0.05). Bland-Altman analysis showed minimal bias for all LV functional parameters. In this nonrandomized study, a LLM-RAG framework generated cine cardiac MRI protocols that were noninferior to technologist-optimized protocols for image quality and demonstrated high agreement for LV functional measurements with fewer repeat acquisitions. These findings demonstrated the feasibility of AI-guided patient-adaptive protocoling. Randomized trials are warranted. 2. Stage 2.
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6. Diffusion Basis Spectrum Imaging (DBSI): A Multicompartment Diffusion Model Linking Regional Microstructural Heterogeneity With Proliferation and Survival in High-Grade Gliomas.
6. 弥散基础谱成像（DBSI）：一种多 compartment 弥散模型，将区域微结构异质性与高级别胶质瘤的增殖和生存相关联PMID:日期:2026-10-02Diffusion basis spectrum imaging (DBSI) has emerged as an advanced model capable of characterizing central nervous system tissue injury and glioma pathology. However, the associations of DBSI with key characteristics of high-grade gliomas (HGGs), including proliferation and survival outcomes, remain underexplored. To evaluate the associations between DBSI-derived histogram features with Ki-67 level and overall survival (OS) in HGGs. Retrospective. Ninety-one patients (36 women, 53.41 ± 12.62 years) with HGGs. 3-T, spin-echo echo-planar imaging (SE-EPI) sequence with q-space Cartesian grid sampling. DBSI was used to estimate restricted fraction, hindered fraction, water fraction, anisotropic fraction, and mean isotropic apparent diffusion coefficient (iso ADC). Histogram features were extracted from both the contrast-enhancing regions (CER) and non-enhancing T2-weighted imaging (T2WI)/fluid attenuated inversion recovery (FLAIR) hyperintense regions (NEHR). Patients were stratified by Ki-67 labeling index (LI), and OS was defined as the time from surgery to death or last follow-up. Receiver operating characteristic (ROC) curve analysis and Cox regression were applied to evaluate features in relation to Ki-67 LI and OS, respectively. p < 0.05 was considered significant. In HGGs, CER showed higher restricted and anisotropic fractions, whereas NEHR showed a higher hindered fraction, water fraction, and iso ADC. For Ki-67 assessment, the robust mean absolute deviation (RMAD) of restricted fraction in CER was the best single feature (AUC = 0.714), and the combination of DBSI features improved the AUC to 0.791. Furthermore, DBSI features from NEHR, together with multifocality, were significant factors associated with OS. DBSI-derived histogram analysis provides a noninvasive quantitative characterization of HGG heterogeneity. Proliferative activity may be primarily reflected within CER, while survival outcomes may be related to DBSI features in NEHR. 3. Stage 3.
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7. Restricted Spectrum Imaging for Diagnosis of High-Risk Metabolic Dysfunction-Associated Steatohepatitis.
7. 限制性频谱成像用于诊断高风险代谢功能障碍相关脂肪性肝炎PMID:日期:2026-09-30[中文摘要] 传统扩散MRI对代谢功能障碍相关脂肪性肝病（MASLD）异质性微结构变化的表征不充分。高风险代谢功能障碍相关脂肪性肝炎（MASH）的最优限制谱成像（RSI）模型及参数仍不明确。优化RSI建模并评估其用于高风险MASH的性能。前瞻性横断面研究。96例经活检证实的MASLD参与者（中位年龄46岁[IQR：36-58]；51例女性[53.1%]），其中64例为高风险MASH。3T；脂肪抑制单次激发回波平面成像联合多b值扩散加权成像、基于EPI的MR弹性成像及3D化学位移编码质子密度脂肪分数成像。采用贝叶斯信息准则（BIC）比较二至五室RSI模型。评估RSI分数、表观扩散系数、FIB-4及基于MRI的MASH评分（MAST）与组织病理学特征及高风险MASH的关系。Spearman相关、Mann-Whitney U检验、logistic回归、受试者工作特征分析、DeLong检验、bootstrap内部验证、决策曲线分析（阈值0.20-0.60）及组内相关系数（ICC）。p < 0.05为显著。五室模型显示最低BIC（1472.30；ΔBIC = 0）。RSI测量显示优异的观察者间一致性（ICC，0.955-0.994）。RSI-C1（r = 0.357）和RSI-C2（r = 0.283）与脂肪变性显著相关；RSI-C2（r = 0.273）和RSI-C5（r = 0.282）与炎症相关；RSI-C2（r = 0.326）、RSI-C4（r = 0.400）和RSI-C5（r = 0.386）与纤维化相关（均p < 0.05）。RSI-C2 + C4 + C5模型对高风险MASH的AUC为0.800，与FIB-4（AUC = 0.707；p = 0.219）或MAST（AUC = 0.899；p = 0.146）相比无显著差异。在0.20-0.60阈值范围内，RSI平均净获益（0.504）超过FIB-4（0.438），但未超过MAST（0.559）。RSI提供了与MASLD组织学相关的区室特异性微结构生物标志物，或可补充无创工具用于高风险MASH评估。2. 第二阶段。
[英文摘要] Conventional diffusion MRI inadequately characterizes heterogeneous microstructural changes in metabolic dysfunction-associated steatotic liver disease (MASLD). The optimal restricted spectrum imaging (RSI) model and value for high-risk metabolic dysfunction-associated steatohepatitis (MASH) remain unclear. To optimize RSI modeling and evaluate its performance for high-risk MASH. Prospective cross-sectional study. 96 biopsy-proven MASLD participants (median age, 46 years [IQR: 36-58]; 51 women [53.1%]), including 64 with high-risk MASH. 3T; fat-suppressed single-shot echo-planar imaging with multi-b-value diffusion-weighted imaging, EPI-based MR elastography, and 3D chemical shift-encoded proton density fat-fraction imaging. Two- to five-compartment RSI models were compared using Bayesian information criterion (BIC). RSI fractions, apparent diffusion coefficient, FIB-4, and MRI-based MASH score (MAST) were evaluated against histopathologic features and high-risk MASH. Spearman correlation, Mann-Whitney U test, logistic regression, receiver operating characteristic analysis, DeLong test, bootstrap internal validation, decision curve analysis (thresholds, 0.20-0.60), and intraclass correlation coefficient (ICC). p < 0.05 was significant. The five-compartment model showed the lowest BIC (1472.30; ΔBIC = 0). RSI measurements showed excellent inter-observer agreement (ICC, 0.955-0.994). RSI-C1 (r = 0.357) and RSI-C2 (r = 0.283) significantly correlated with steatosis; RSI-C2 (r = 0.273) and RSI-C5 (r = 0.282) with inflammation; and RSI-C2 (r = 0.326), RSI-C4 (r = 0.400), and RSI-C5 (r = 0.386) with fibrosis (all p < 0.05). The RSI-C2 + C4 + C5 model yielded an AUC of 0.800 for high-risk MASH, without significant difference versus FIB-4 (AUC = 0.707; p = 0.219) or MAST (AUC = 0.899; p = 0.146). Across thresholds of 0.20-0.60, RSI mean net benefit (0.504) exceeded FIB-4 (0.438) but not MAST (0.559). RSI provides compartment-specific microstructural biomarkers associated with MASLD histology and may complement noninvasive tools for high-risk MASH assessment. 2. Stage 2.
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8. Editorial for "Effects of Single and Multiple Dose Tiotropium/Olodaterol in Chronic Obstructive Pulmonary Disease on Alveolar Gas Exchange Measured by Hyperpolarized 129Xe MRI".
8. 关于“单次和多次给予噻托溴铵/奥达特罗对慢性阻塞性肺疾病患者经超极化129Xe磁共振成像测得的肺泡气体交换影响”的社论PMID:日期:2026-09-26该文献暂无摘要。
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10. Preoperative Prediction of Ductal Carcinoma In Situ Upstaging Using Machine Learning-Based Peritumoral Breast MRI Radiomics.
10. 基于机器学习的瘤周乳腺MRI影像组学对导管原位癌升级的术前预测PMID:日期:2026-09-25A subset of biopsy-confirmed ductal carcinoma in situ (DCIS) cases can be upstaged to invasive breast cancer at surgery. Preoperative identification of upstaging risk is essential for treatment planning. To develop clinical and breast MRI radiomics models to preoperatively predict upstaging in DCIS. Retrospective and prospective. Three hundred forty-five women (median age, 59 years [IQR, 49-67]) with 362 DCIS lesions diagnosed via core-needle biopsy (training: n = 216, internal testing: n = 53, external testing: n = 93). 1.5 T 3D T1-weighted spoiled gradient-echo MRI: pre-contrast non-fat-suppressed and dynamic contrast-enhanced (DCE) fat-suppressed sequences. Clinical and radiomics features were extracted from radiologist-segmented lesions on DCE breast MRI. Seven machine learning algorithms were evaluated across clinical-only (demographic and clinicopathologic variables), radiomics-only (whole-lesion/peritumoral), and combined models, using nested cross-validation and internal and external testing. Feature importance was assessed using Shapley additive explanation (SHAP). Mann-Whitney, Chi-square, DeLong tests; area under the receiver operating characteristic curve (AUC), sensitivity, negative predictive value (NPV). The clinical model achieved AUC 0.70 (95% CI: 0.51-0.89), with 36% sensitivity and 84% NPV. A multilayer perceptron integrating clinical and peritumoral radiomics non-significantly increased AUC to 0.75 (95% CI: 0.58-0.92; ΔAUC = +0.05; False Discovery Rate p-adj = 0.65), improving sensitivity to 82% (Δ = +46%) and NPV to 92% (Δ = +8%). On external testing, AUC was 0.63 (95% CI: 0.50-0.76) with no significant change over the clinical baseline (ΔAUC = -0.03; FDR p-adj = 0.857) but demonstrated superior sensitivity (68%; Δ = +30%) and NPV (84%; Δ = +3%). SHAP identified peritumoral center-of-mass shift as the dominant predictor. Clinical-peritumoral modeling did not significantly improve discrimination over the clinical baseline but showed higher NPV and sensitivity, suggesting the tumor microenvironment may provide complementary information for upstaging risk stratification. LEVEL OF EVIDENCE: 3 Stage 2.