Journal of Magnetic Resonance Imaging最新文献

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: The growing population of patients with active implantable medical devices (AIMDs) presents challenges for MRI access. Safety concerns regarding device malfunction and patient risk, demand standardized management strategies.

Purpose: To evaluate the safety and feasibility of a structured multidisciplinary safety protocol for 1.5T MRI in AIMD carriers.

Study type: Retrospective.

Population: 514 consecutive patients with AIMDs (cardiac implantable electronic devices (CIEDs) (n = 347), auditory implants (n = 85), neurostimulators (n = 61), and drug delivery systems (n = 21)) scheduled for clinical MRI (head (34.3%), spine (19.0%), prostate (10.5%), abdomen/pelvis (9.5%), knee (8.6%), heart (3.8%), and other sites (14.3%)) between 2019 and 2025.

Field strength/sequence: MRI was performed on 1.5T scanners using predefined protocols, with automatic compliance to device-specific SAR limits through the Philips ScanWise Implant software.

Assessment: The safety protocol included device verification, MRI-mode programming, compliance with manufacturer safety conditions, physiologic monitoring, and post-MRI device interrogation. MRI completion rates, exclusion frequencies, and adverse events were recorded, and exclusion proportions were compared across AIMD categories.

Statistical tests: Descriptive statistics.

Results: Of 514 patients, 50 (9.7%) with MR-unsafe, MR-nonconditional, or artifact-prone devices were not scanned: MR-unsafe (n = 23), MR-nonconditional (n = 22), or anticipated severe artifacts (n = 3). Four hundred and sixty-four (90.3%) patients successfully completed diagnostic MRI. Two brain examinations were terminated early due to implant-site pain. No device resets, malfunctions, or significant changes in sensing/pacing thresholds occurred. Internal magnet displacement was observed in three (3.5%) auditory implants, with two requiring surgical repositioning. Exclusion rates varied by device type, ranging from 11% to 14% for CIEDs to 29% for specific neurostimulators.

Data conclusion: A structured, multidisciplinary protocol enables safe MRI in the majority of patients with MR-conditional AIMDs. Standardized pre-MRI screening and management support safe implementation, and improve MRI accessibility for this growing and complex patient population.

Level of evidence: 4:

Technical efficacy stage: 5.

Background: Silicosis is an occupational lung disease characterized by inflammation and fibrosis. As it is irreversible, early identification of high-risk individuals is clinically important, but biomarkers for progression remain lacking.

Purpose: To determine whether ventilation and perfusion defects quantified by phase-resolved functional lung (PREFUL) MRI can predict silicosis progression.

Study type: Prospective.

Subjects: Thirty participants with silicosis (29 males and 1 female) and 30 healthy controls (29 males and 1 female).

Sequence: 2D spoiled gradient echo, 3.0 T.

Assessment: All participants underwent baseline PREFUL MRI, pulmonary function tests (PFTs), and chest CT, with quantitative calculation of ventilation defect percentages (VDP_RVent and VDP_FVL-CM) and perfusion defect percentage (QDP). Silicosis was followed for 1 year with assessments including forced vital capacity percent predicted (FVC% predicted), diffusing capacity of the lungs for carbon monoxide percent predicted (DL_co% predicted), symptoms, and CT. Disease progression was defined by any two of: (a) CT evidence of progression, (b) worsening symptoms, or (c) ≥ 10% decline in FVC% predicted or ≥ 15% decline in DLco% predicted.

Statistical tests: Spearman correlation coefficients were used to evaluate the correlation between ventilation/perfusion metrics and PFT parameters. Receiver operating characteristic (ROC) curves were used to assess the ability of PREFUL MRI parameters to classify disease progression, reporting the area under the curve (AUC), sensitivity, and specificity. Significance was set at p < 0.05.

Results: Eight patients progressed and 22 remained stable. Baseline VDP_RVent, VDP_FVL-CM, and QDP were significantly higher in progressors (36%, 34%, 40%) than in non-progressors (22%, 15%, 22%). QDP showed strong predictive performance with AUC of 0.72 (95% CI: 0.51-0.93) for radiological progression, 0.90 (95% CI: 0.79-1.00) for PFTs decline, and 0.97 (95% CI: 0.92-1.00) for global progression.

Data conclusion: Increased ventilation and perfusion defects on PREFUL MRI are associated with silicosis progression.

Evidence level: 2.

Technical efficacy: Stage 2.

Trial registration: NCT06431555.

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 χ² 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.