Journal of Magnetic Resonance Imaging最新文献

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.

Background: Diffusion-weighted imaging (DWI) in breast MRI requires balancing image quality with acquisition time. Reducing scan time without sacrificing diagnostic performance could improve patient comfort and workflow.

Purpose: To compare 3-scan trace DWI (tDWI) and diagonal DWI (dDWI) for breast lesion evaluation using both phantom and clinical assessments, focusing on image quality metrics and diagnostic performance.

Study type: Retrospective comparative study.

Population: A commercially available breast diffusion phantom (Caliber MRI; Boulder, CO, USA) and 92 consecutive participants were initially enrolled. After excluding 23 due to having no confirmed lesion, and 15 with non-mass lesions, 54 patients (median age 55.5 years, range 21-82) were analyzed.

Field strength/sequence: 3-T, tDWI (120 s) and dDWI (74 s) with identical parameters except for diffusion gradient directions, with dDWI reducing scan time by 38%.

Assessment: Phantom studies measured apparent diffusion coefficient (ADC) and estimated signal-to-noise ratio (eSNR). Clinical studies evaluated ADC, contrast-to-noise ratio (CNR), and contrast ratio (CR) from ROIs in lesions, normal breast tissue, and fat. Three radiologists scored lesion conspicuity and image quality.

Statistical tests: Due to non-normal data distribution, the Wilcoxon signed-rank test compared metrics between sequences. The ADC coefficient of variation (CV) was calculated. A p-value < 0.05 was considered significant.

Results: dDWI significantly improved image quality by reducing artifacts, especially those originating from the nipple and in the breast tissue periphery, and slightly better lesion conspicuity. No significant difference was found for eSNR in breast phantom studies (p = 0.31 for b = 0; and p = 0.84 for b = 800 s/mm²). tDWI demonstrated significantly higher CNR for breast tissue and fat. tDWI also showed lower CR values for tumor/breast tissue and lower values for tumor/fat. ADC measurements were similar between techniques (CV = 4.20%).

Data conclusion: dDWI provides a 38% shorter acquisition time than tDWI while maintaining comparable quantitative performance. dDWI demonstrates improved image quality, particularly in challenging anatomical regions, though tDWI yields higher contrast-to-noise ratio values.

Evidence level: 3.

Technical efficacy stage: 2.

Magnetic particle imaging (MPI) is an emerging noninvasive, ionization-free three-dimensional tracer imaging technology that achieves imaging by leveraging the nonlinear magnetization response of superparamagnetic nanoparticles. This study conducts a systematic review of the principles and key technologies of the MPI system through literature searches in databases including PubMed, Web of Science, and Google Scholar. First, this review introduces MPI's imaging principles, image reconstruction processes, and the technical characteristics of different types of MPI devices, aiming to deepen the understanding of device applications. Second, it presents the development history of three categories of MPI systems: closed-bore, open-bore, and single-sided. Finally, this review conducts a comparative analysis of the advantages and limitations of each category and discusses the future development trends and challenges of the MPI system. This review aims to provide researchers in the field with a systematic theoretical understanding of the MPI system, promote knowledge sharing, and further encourage more scientific efforts to engage in this highly promising area of molecular imaging. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 1.

Background: 2D multi-slice MRI techniques for evaluating regional ventilation in neonatal lung diseases, including bronchopulmonary dysplasia (BPD), have limited through-plane resolution, potentially missing heterogeneous lung abnormalities. 3D ultra-short echo time (UTE) MRI phase-resolved functional lung (PREFUL) improves spatial resolution but has not been used to evaluate infants with BPD.

Purpose: To demonstrate the feasibility of 3D UTE MRI for structural and functional assessment in infants with BPD.

Study type: Retrospective.

Population: A total of 28 infants with BPD (female: male = 8:20; 14 required invasive ventilation at MRI, 14 required non-invasive ventilation).

Field strength/sequence: Free-breathing, respiratory bellows-gated (acquired 1.19-1.25 mm³ isotropic) 3D gradient echo UTE MRI on a 1.5 T scanner.

Assessment: Images were retrospectively reconstructed into 24 respiratory phases with motion-resolved reconstruction using compressed sensing. Regional ventilation (RVent), flow-volume loop cross correlation metric (FVL-CM), and corresponding ventilation defect maps (VDP_RVent, VDP_FVL-CM, and the combination, VDP_combined) were derived using the 3D PREFUL method. Structural lung abnormalities were assessed by two readers using a modified Ochiai scoring system, and parenchyma was defined as normal intensity, hypointense, or hyperintense.

Statistical tests: The Mann-Whitney U Test, Spearman's correlation, and the Kruskal-Wallis test with Dunn's multiple-comparisons tests were used. Statistical significance was defined as p < 0.05.

Results: In the mechanically ventilated infants VDP_FVL-CM (median [IQR] = 45.8 [28.5-55.9]%) and VDP_combined (57.2% [35.2-69.3]%) were significantly higher as compared to non-ventilated patients (VDP_FVL-CM = 22.8% [17.2-34.7]% and VDP_combined = 30.7 [25.1-43.9]%). All PREFUL MRI VDP measures significantly correlated with total lung score (all ρ ≥ 0.45). RVent was significantly lower in hyperintense regions (0.06 [0.04-0.08] mL/mL) compared to normal intensity regions (0.09 [0.07-0.13] mL/mL), whereas FVL-CM was significantly decreased in hypointense regions (79 [66-87]%) compared to normal (92 [90-95]%) and hyperintense regions (91 [81-96]%).

Data conclusion: UTE MRI is feasible for assessing regional functional lung abnormalities in infants with BPD that directly correlate with reader-based assessments of parenchymal disease severity.

Evidence level: 4.

Technical efficacy: Stage 1.

Background: Pathophysiological changes affect tissue cell composition and density. For example, neurodegenerative disorders and brain tumors are associated with cell loss and abnormal accumulation, respectively. In these scenarios, if monitored and tracked, tissue cellularity might be used to inform clinical diagnosis and management.

Purpose: To propose and evaluate a new marker of tissue cellularity, called susceptibility-Derived Cellularity Index (χDCI), that would be readily available for clinical applications with fast acquisition and at high resolution.

Study type: Retrospective study.

Population/subjects: 24 healthy subjects (7/17 M/F, 70 ± 11 years) and 21 patients with IDH-wild type glioblastoma (16/5 M/F, 65 ± 8 years).

Field strength/sequence: 3 T MRI sequences including 3D T1w pre- and post-contrast agent injection, 3D T2w, 3D FLAIR, 3D multi-echo gradient recalled echo, 2D diffusion weighted imaging.

Assessment: χDCI was computed based on parameters estimated with DECOMPOSE-QSM. The Neurite Density Index (NDI) was estimated with the NODDI model. T1w images were used for region of interest (ROIs) segmentations with FreeSurfer (i.e., cortical gray matter, white matter, thalamus, caudate, putamen, pallidum, hippocampus and amygdala). For the patients with glioblastoma, regions of contrast enhancement, necrosis, and edema were also included in the analysis.

Statistical tests: Pearson's correlation analysis between mean χDCI and NDI values in the ROIs was carried out separately for the two cohorts of participants (significance level = 0.05, after correction for multiple comparisons).

Results: Significant correlations were observed between χDCI and NDI in white matter (r = 0.56) and putamen (r = 0.69) for the healthy participants. Significant positive correlations were also found in white matter (r = 0.6), pallidum (r = 0.48), putamen (r = 0.79), thalamus (r = 0.64) and edema (r = 0.69) for the patient cohort.

Data conclusion: χDCI is proposed as a marker of tissue cellularity. The significant associations between χDCI and NDI in several regions investigated in the present study support the potential of χDCI as a proxy of intracellular volume fraction.

Level of evidence: 3:

Technical efficacy: Stage 1.

Background: Accurate identification of benign and malignant bile duct dilatation (BDD) is needed to determine its management plan. Conventional imaging evaluation is subjective, whereas deep learning (DL) offers potential for automated objective assessment.

Purpose: To construct and evaluate DL models and ensemble strategies based on magnetic resonance cholangiopancreatography (MRCP) images for identifying benign and malignant BDD.

Study type: Retrospective and prospective.

Population: A retrospective cohort (n = 378; median age, 60 years [range: 14, 90]; 194 male) from two institutions and a prospective cohort (n = 60; median age, 62.5 years [range: 15, 86]; 30 male) were included. Retrospective data were randomly stratified split into training, validation, and internal test sets (2:1:1) and an independent external test set. Benign cases were downsampled to balance class distribution.

Field strength/sequence: 3 T MRCP (3D turbo spin echo: VISTA and SPACE).

Assessment: The primary retrospective endpoint was area under the curve (AUC) across DL algorithms and ensembles. Prospectively, the accuracy, sensitivity, and specificity of the model was compared with those of three radiologists.

Statistical tests: Group comparisons used Mann-Whitney U and Chi-square tests (p < 0.05). Model performance was evaluated using the Hosmer-Lemeshow test, DeLong's test with Bonferroni correction (α = 0.005), and McNemar's test.

Results: The Xception model achieved AUCs of 0.816 (95% CI, 0.788-0.844) on the internal test set and 0.807 (95% CI, 0.779-0.835) on the external test set. The ensemble model incorporating logistic regression yielded higher patient-level AUCs of 0.890 and 0.885, with good calibration (p = 0.109). No significant differences were observed among the five ensemble strategies (minimum adjusted p = 0.62). In the prospective cohort, the model showed 90.0% accuracy, sensitivity, and specificity, comparable to radiologists (76.7%-86.7%) without a significant difference (p = 0.143, 0.302, and 0.774, respectively).

Data conclusion: The Xce-LR model shows potential for automating BDD differentiation using MRCP.

Level of evidence: 2:

Technical efficacy: Stage 2.