Journal of Magnetic Resonance Imaging最新文献

Background: Myocardial involvement is a major manifestation of exertional heatstroke (EHS), yet its short-term clinical outcome remains unclear.

Purpose: To assess serial cardiac left ventricular structural and functional changes using baseline and 3-month cardiac MRI.

Study type: Prospective.

Population: A total of 41 participants (median age, 21 years; IQR, 20-23 years) hospitalized for EHS and 27 age-, sex-, and training-matched healthy controls (HCs).

Field strength/sequence: Fast imaging employing steady-state acquisition (cine imaging), saturation methods using adaptive recovery times (native T1, extracellular volume [ECV]), phase-sensitive inversion-recovery gradient recalled echo (late gadolinium enhancement [LGE]), and multi-echo fast spin echo (T2) sequences at 3.0 T.

Assessment: Longitudinal comparisons were performed within the EHS group (baseline vs. 3-month follow-up), and cross-sectional comparisons were performed between patients and HCs. Cardiac symptoms (chest pain, dyspnea, palpitations, and syncope) at follow-up were recorded using standardized questionnaires.

Statistical tests: Paired sample t-test, independent sample t-test, analysis of variance, Kendall's τ-b. A p value < 0.05 was considered significant.

Results: Significant improvements were observed in native T1 (1492 ± 52 ms vs. 1521 ± 57 ms), ECV (23.4% ± 1.7% vs. 24.3% ± 1.8%), T2 (45.9 ± 2.2 ms vs. 47.3 ± 2.3 ms), and 2D global longitudinal strain (-16.7% ± 1.6% vs. -15.8% ± 1.1%) at 3 months follow-up compared to baseline parameters in the EHS cohort. However, native T1 (1492 ± 52 ms vs. 1456 ± 26 ms) and ECV (23.4% ± 1.7% vs. 20.6% ± 1.6%) at follow-up were significantly higher in EHS than in HCs. At 3-month follow-up, native T1, ECV, and LGE presence were associated with cardiac symptoms (Kendall's τ-b = -0.430, -0.447, and -0.398, respectively).

Data conclusion: This study demonstrated persistently elevated native T1 and ECV at 3 months following EHS, despite partial improvement. Those with residual abnormalities should not be cleared for unrestricted training.

Evidence level: 2.

Technical efficacy: Stage 2.

Background: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the diagnosis and management of nonenhancing gliomas, underscoring the need for noninvasive preoperative classification.

Purpose: To compare the value of habitat-based and whole-tumor strategies in classifying IDH mutation status in nonenhancing gliomas via transfer learning on structural magnetic resonance imaging and subtraction images.

Study type: Retrospective.

Population: Two-hundred and eighty-four patients with nonenhancing gliomas, divided into a training set (n = 198; 44 ± 12 years; 83 females) and a testing set (n = 86; 46 ± 11 years; 35 females).

Field strength/sequence: 3T, fluid-attenuated inversion recovery (FLAIR), fast spin-echo (FSE) T2-weighted imaging (T2WI), FSE T1-weighted imaging (T1WI), contrast-enhanced FSE T1-weighted imaging (T1CE).

Assessment: Based on FLAIR, T2WI, T1WI, T1CE, and subtraction images, two regions of interest input strategies were applied to construct transfer learning models, including whole-tumor strategy and habitat-based strategy. Model performance was evaluated using the area under curves (AUC) and accuracy (ACC). Finally, the optimal model was combined with clinical variables to develop integrative models.

Statistical tests: Continuous variables were analyzed by Student's t test or Wilcoxon rank-sum test; categorical variables by χ ² test or Fisher's exact test. Two-sided p < 0.05 was statistically significant.

Results: In the whole-tumor strategy, the subtraction model demonstrated significantly superior performance, achieving training and testing set AUC/ACC of 0.850/0.813 and 0.890/0.884. The habitat-based strategy significantly outperformed the whole-tumor strategy, with the T2WI model demonstrating optimal efficacy (training set, AUC/ACC = 0.898/0.899; testing set, AUC/ACC = 0.870/0.849). The integrative model (habitat-based T2WI + Age + Location) achieved the highest classification performance, with AUCs of 0.923 and 0.947 in the training and testing sets, respectively.

Data conclusion: The habitat-based strategy outperforms the whole-tumor approach, with the habitat-based T2WI model achieving optimal classification performance. Integrating age and tumor location into this model can further boost its classification capability.

Level of evidence: 3:

Technical efficacy: Stage 2.

Background: Virtual MR elastography (VMRE) and MRE have been proposed for liver fibrosis staging in metabolic dysfunction-associated steatotic liver disease (MASLD), but VMRE's diagnostic performance remains debated.

Purpose: To assess the inter-visit and inter-reader reproducibility of fat-uncorrected and fat-corrected diffusion-weighted imaging (DWI)-based VMRE and to compare their diagnostic performance with MRE for liver fibrosis staging in the MASLD population.

Study type: Prospective.

Population: Fifty four participants were enrolled: 43 with biopsy-proven MASLD (age: 57.0 ± 9.0 years; 26 males) and 11 healthy volunteers (age: 31.0 ± 15.0 years; 4 males).

Field strength/sequence: 3.0T, DWI (b-values of 0, 200, and 1500 s/mm²) for VMRE and phase-contrast MRE at 60 Hz was performed.

Assessment: VMRE-derived shifted apparent diffusion coefficients (sADC) reproducibility and diagnostic performance; MRE-derived stiffness diagnostic performance.

Statistical tests: Reproducibility was evaluated using intraclass correlation coefficients (ICC), within-subject coefficient of variation (wCV), and bias and limits of agreement (LOA) in Bland-Altman analysis. Diagnostic performance was assessed with areas under the receiver operating characteristic curve (AUC) and compared with DeLong's test. p < 0.05 was considered statistically significant.

Results: For inter-visit agreement, the ICC of fat-uncorrected and fat-corrected sADC were 0.88 and 0.83; wCV were 0.120 ± 0.30 and 0.141 ± 0.31; bias and 95% LOA were (-0.03 ± 0.18) × 10^-3 mm²/s and (-0.05 ± 0.33) × 10^-3 mm²/s, respectively. For inter-reader agreement, the ICC of fat-uncorrected and fat-corrected VMRE were 0.99 and 0.99; wCV were 0.028 ± 0.011 and 0.039 ± 0.012, respectively; bias and 95% LOA were (-0.01 ± 0.03) × 10^-3 mm²/s and (-0.02 ± 0.05) × 10^-3 mm²/s, respectively. AUC of fat-uncorrected, fat-corrected sADC, and MRE-derived stiffness for distinguishing fibrosis stages F0 versus ≥ F1 were 0.70 ± 0.17, 0.56 ± 0.18, and 0.87 ± 0.10; ≤ F1 versus ≥ F2 were 0.61 ± 0.16, 0.49 ± 0.17, and 0.86 ± 0.10; ≤ F2 versus ≥ F3 were 0.54 ± 0.16, 0.50 ± 0.16, and 0.89 ± 0.09; and ≤ F3 versus F4 were 0.58 ± 0.16, 0.55 ± 0.17, and 0.85 ± 0.11, respectively. MRE had significantly higher diagnostic performance than fat-uncorrected and fat-corrected VMRE for all fibrosis stages.

Data conclusion: VMRE has good reproducibility, but has lower fibrosis staging accuracy than MRE.

Evidence level: 1.

Technical efficacy: Stage 2.

Background: Detection of clinically significant prostate cancer (csPCa) within PI-RADS category 3 lesions remains a major diagnostic challenge.

Purpose: To develop and validate a biparametric MRI (bpMRI)-based habitat analysis model integrating deep learning features for predicting csPCa in PI-RADS 3 lesions using dual-center data.

Study type: Retrospective.

Population: This study included 551 patients with MRI-identified PI-RADS category 3 lesions and histopathological confirmation. A total of 439 patients from Center 1 were randomly assigned to a training set (n = 328) and an internal validation (in-vad) set (n = 111), while an external validation (ex-vad) set (n = 112) was obtained from Center 2.

Field strength/sequence: 3 T/1.5 T. T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences.

Assessment: Lesions were manually segmented on preoperative T2WI and DWI, and tumor subregions were determined using k-means clustering. Deep learning features were obtained from each habitat subregion, and habitat-based models were built based on selected features. A habitat whole-tumor (Habitat W) model was subsequently derived by integrating all subregions. Recursive feature elimination (RFE) was applied to select the optimal predictors from the clinical and habitat-derived features; the clinical model was constructed using the selected clinical features, while the combined model incorporated all selected features.

Statistical tests: Student's t-test, Mann-Whitney U tests, Chi-squared tests, LASSO, areas under the curve (AUC), decision curve analysis (DCA), calibration curves, RFE, SHapley Additive exPlanations (SHAP). Statistical significance was defined as p-value < 0.05.

Results: In the training, in-vad and ex-vad sets, the clinical model demonstrated AUC values of 0.893, 0.844, and 0.837, respectively. The habitat models (habitat 1, 2,3 and -W) achieved AUCs ranging from 0.857 to 0.952. The combined model yielded AUCs of 0.959, 0.963, and 0.949, respectively.

Data conclusion: The bpMRI-based deep learning Habitat W and combined model enables accurate assessment of csPCa in PI-RADS 3 lesions.

Level of evidence: 3:

Technical efficacy stage: 3.

MRI of the heart and abdominal organs provides unparalleled soft tissue contrast and quantitative biomarkers, yet remains highly susceptible to physiological motion. Contractions of the myocardium, respiratory excursions, peristalsis, vascular pulsatility, and unpredictable bulk patient movement generate artifacts that impair image quality, limit reproducibility, and may necessitate repeat scans. This review summarizes motion correction strategies in cardiac and abdominal MRI, emphasizing both clinical applications and methodological principles. Techniques to address motion can be broadly categorized into prospective and retrospective approaches. Prospective methods adjust acquisition in real time, for example through respiratory or cardiac gating, navigator echoes, or external sensors, while retrospective strategies apply corrections during or after reconstruction, using k-space binning, image registration, or model-based reconstructions. Rigid motion, such as translations or rotations of organs, can often be corrected efficiently, whereas non-rigid motion including myocardial contraction or peristalsis requires more sophisticated elastic registration or motion-compensated reconstruction. Application-specific challenges and solutions are highlighted across cardiac cine imaging, flow quantification, tagging, and quantitative mapping, as well as abdominal imaging of the liver, kidneys, and gastrointestinal tract. In each domain, examples are provided of how motion impacts diagnostic performance and how motion correction strategies can mitigate these effects. Strengths and limitations of current approaches are reviewed, from conventional breath-holding to advanced free-breathing motion-resolved imaging. Emerging trends include integration of artificial intelligence with motion-compensated reconstruction, advanced sensor technologies for real-time tracking, and hybrid approaches combining multiple strategies. While many methods remain research-focused, vendor-embedded solutions and open-source tools are increasingly available, narrowing the gap between technical advances and routine practice. Motion correction is poised to become a core feature of clinical MRI, enabling faster, more robust, and patient-friendly examinations that reduce repeat rates, improve diagnostic confidence, and expand access to high-quality imaging in challenging patient populations. EVIDENCE LEVEL: N/A. TECHNICAL EFFICACY: Stage 5.

Background: Brain segmentation using structural MRI is effective for identifying regional atrophy in Parkinsonian syndromes. However, clinical validation of the automated deep learning-based brainstem segmentation model has been limited.

Purpose: To develop and validate a two-step deep learning algorithm for automatic segmentation of brainstem substructures and classifying Parkinsonian syndromes using derived volumetric measurements.

Study type: Retrospective.

Subjects: The internal dataset comprised 300 normal cognition (NC) subjects (171 females) for segmentation and 513 subjects (265 males) for classification (207 NC, 52 progressive supranuclear palsy [PSP], 65 multiple system atrophy-cerebellar variant [MSA-C], and 189 Parkinson's disease [PD]). The external dataset comprised 82 subjects (43 males; 24 PSP, 28 MSA-C, and 30 PD).

Field strength/sequence: 3D gradient-echo T1-weighted sequence at 3 T.

Assessment: Segmentation performance was evaluated with the Dice Similarity Coefficient (DSC) by comparing model outputs against manual labels. For classification, regional brain volumes from the segmentations were used as input features for multi-class classification with support vector machine (SVM), random forest, and XGBoost models, evaluated by area under the receiver operating characteristic curve (AUROC). Five-fold cross-validation was used for internal validation and tested on an external dataset. Three radiologists analyzed an external dataset with and without the model, with a one-month washout period between sessions.

Statistical tests: For the segmentation volume, differences between groups were assessed using Student's t-test or Mann-Whitney U test. Classification performance was evaluated using a one-vs-rest approach with macro-averaging across classes.

Results: Brainstem segmentation DSC scores were 0.969 (internal) and 0.996 (external) compared to the ground-truth masks. Using regional volumetrics, the SVM achieved the highest differentiation performance, with AUROCs of 0.937 (internal) and 0.914 (external). A radiology resident achieved improved performance with the model.

Data conclusion: Our proposed two-step algorithm combining deep-learning-based brainstem segmentation and machine-learning classification enables automated differentiation of Parkinsonian syndromes using 3D T1-weighted brain MRI.

Evidence level: 3.

Technical efficacy: Stage 1.

Background: Appetite loss is a non-motor symptom in amyotrophic lateral sclerosis (ALS) linked to poorer prognosis. While the hippocampus regulates "meal memory", a key cognitive modulator of eating behavior, its structural role in ALS-related appetite loss is unknown.

Purpose: To determine if hippocampal subfield integrity influences appetite dysregulation in ALS and to evaluate the strength of neuroanatomical versus demographic factors.

Study type: Cross-sectional secondary analysis.

Population: Thirty-two patients with ALS (mean age: 58.97 ± 8.91 years; 24 males) and 22 non-neurodegenerative controls (NNDc) (mean age: 53.86 ± 9.98 years; 16 males).

Field strength/sequence: 3T; 3D T1-weighted magnetization-prepared rapid gradient-echo (MP2RAGE) and 3D T2-weighted turbo spin-echo (T2-SPACE) sequences.

Assessment: Appetite was measured using the Council on Nutrition Appetite Questionnaire (CNAQ). Hippocampal subfield volumes (CA1, CA2/3, CA4/DG, stratum radiatum/lacunosum/moleculare [SRLM], subiculum) and asymmetry indices were segmented from T1w and T2w images using the HIPS automated pipeline.

Statistical tests: Analysis of Covariance (ANCOVA) (adjusting for age, sex, body mass index (BMI), total intracranial volume (TIV), and subfield volumes/asymmetry) and hierarchical multiple regression analyses were used. Significance was set at p < 0.05.

Results: Patients with ALS (adjusted mean: 29.51 ± 0.53) had significantly lower adjusted CNAQ scores compared to controls (adjusted mean: 31.98 ± 0.66; mean difference: -2.47, partial η ² = 0.195). In the ANCOVA model, left SRLM volume was the only significant neuroanatomical covariate (F [1, 30] = 6.45, partial η ² = 0.177). However, hierarchical regression revealed that age was the only consistent independent predictor of CNAQ scores (B = -0.158), explaining the largest variance (ΔR ² = 0.165). Hippocampal volumes and asymmetry did not remain significant predictors after adjusting for age (left SRLM: p = 0.853; SRLM asymmetry: p = 0.868).

Data conclusion: Appetite loss is a non-motor symptom in ALS. While associated with lower left SRLM volume at the group level, appetite decline is more robustly and independently associated with advancing age.

Evidence level: 3.

Technical efficacy: 3.