Journal of Magnetic Resonance Imaging最新文献

Background: Cystic fibrosis (CF) monitoring relies on computed tomography (CT), but ultra-short echo time MRI (UTE-MRI) offers a radiation-free alternative. However, its clinical adoption is hindered by the laborious and subjective manual analysis, which prevents standardized quantification of bronchial abnormalities.

Purpose: To develop a deep learning (DL) system for the segmentation of CF bronchial abnormalities on UTE-MRI and assess clinical relevance in patients undergoing cystic fibrosis transmembrane conductance regulator (CFTR) modulator treatment.

Study type: Retrospective.

Population: One-hundred and sixty-six CF patients were included (age = 23 ± 11, 48% male), comprising a training set (n = 97), a test set (n = 25), and an independent clinical validation cohort (n = 44).

Field strength/sequence: 1.5T/UTE-MRI 3D gradient-echo Spiral Volume Interpolated Breath-hold Examination (VIBE) sequence.

Assessment: The RiSeNet architecture was trained using paired UTE-MRI and CT scans. Its technical performance was evaluated against expert-refined segmentations and compared to state-of-the-art segmentation models using topology-aware metrics: Normalized Surface Dice (NSD) and CenterLine Dice (clDice). Clinical validation was performed by correlating automated measurements at baseline (M0) and 1-year post-CFTR modulator treatment (M12) with Bhalla scores and pulmonary function tests (FEV1%p).

Statistical tests: Student's t-test, Mann-Whitney, Wilcoxon, and Chi-square tests were used for group comparisons. The Spearman test was used to assess correlations. A p value < 0.05 was considered statistically significant.

Results: In the test group, RiSeNet achieved significantly superior performance versus state-of-the-art with NSD scores of 0.84 for bronchiectasis, 0.90 for wall thickening, and 0.75 for mucus; and clDice scores of 0.69, 0.61, and 0.64, respectively. In the clinical validation group, significant correlations with Bhalla (ρ = -0.92/-0.85) and FEV1%p (ρ = -0.68/-0.67) were observed pre/post-CFTR modulator. Post-CFTR modulator, FEV1%p improved (69%-92%) with significant reductions in bronchiectasis (3.88-1.25), wall thickening (30.43-3.05), and mucus (53.30-11.80).

Data conclusion: RiSeNet may enable semantic segmentation of CF abnormalities on radiation-free UTE-MRI.

Evidence level: 3 TECHNICAL EFFICACY: 4.

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.