Journal of Magnetic Resonance Imaging最新文献

Background: Sturge-Weber syndrome (SWS) is a rare neurocutaneous disorder associated with venous capillary malformations, atrophy, and calcifications. Longitudinal imaging is limited by risks of sedation and gadolinium exposure in children.

Purpose: To evaluate whether strategically acquired gradient echo (STAGE), a rapid multi-contrast quantitative MRI method, can reliably detect vascular and parenchymal abnormalities in SWS compared with conventional pre-/post-contrast MRI.

Study type: Observational cross-sectional.

Population: Twenty-two patients with unilateral SWS diagnosed by previous MRI (13 female; ages 2-24 years).

Field strength/sequence: 3T/T1-weighted (T1W) and T2-weighted (T2W) turbo-spin-echo, fluid attenuated inversion recovery, and a 3D gradient echo-based STAGE sequence providing T1, proton density (PD), T2*, and R2* maps, susceptibility-weighted imaging (SWI), quantitative susceptibility mapping (QSM), T1W with enhanced gray matter to white matter contrast (T1WE), and synthetic images of T2W, FLAIR, and gradient echo images.

Assessment: Conventional MRI and STAGE images were reviewed in 10 patients (training group), side-by-side, to determine the STAGE-derived images that identify SWS abnormalities, including leptomeningeal venous capillary malformations (LVCM), enlarged deep medullary veins, choroid plexus enlargement, cerebral atrophy, and calcifications. In the remaining test group of 12 patients, three reviewers scored these abnormalities on STAGE images and compared them with scores from conventional MRI.

Statistical tests: Interrater reliability with intraclass correlation coefficient (ICC), Spearman's rank correlation, Wilcoxon signed-ranked test, Mann-Whitney U-test, Fisher's exact test. Statistical significance level was set as p < 0.05.

Results: LVCMs were visualized on STAGE with SWI and R2*. Calcifications were differentiated from venous abnormalities using PD, T1WE, synthetic gradient echo, and QSM. STAGE-derived scores had excellent interrater reliability (ICCs > 0.90) and were similar to the conventional MRI scores despite some minor differences in some individual cases (total scores from conventional MRI vs. STAGE 8.9 vs. 8.7, p = 0.29).

Data conclusion: STAGE provided rapid, non-contrast, multi-parametric imaging that reliably detected vascular and parenchymal SWS abnormalities seen on conventional MRI.

Evidence level: 2.

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: Dysfunction of cerebral perforating arteries is a major contributor to cerebral small vessel disease. Developing a reliable MRI technique for assessing cerebral perforating arteries on widely accessible 3T systems would be advantageous.

Purpose: To evaluate the feasibility and reliability of dual-velocity encoding (dual-VENC) PC-MRI at 3T for assessing pulsatility of cerebral perforating arteries.

Study type: Prospective.

Subjects: Twelve healthy young adults (2 female, 24.0 ± 3.99 years) and 31 older adults with and without vascular risk factors (21 female, 67.72 ± 8.48 years).

Field strength/sequence: Dual-VENC 2D PC-MRI at 3T and 7T.

Assessment: The number of perforators (N_perforator) and pulsatility index (PI) measured using 3T dual-VENC PC-MRI were evaluated through test-retest and comparison against those by 7T dual-VENC PC-MRI on the younger participants. The associations of PI and N_perforator with age, cognition, and vascular risk factors were investigated in the elderly cohort.

Statistical tests: Paired t-tests, two-sample t-tests, Bland-Altman analysis, coefficient of variation (CV), Shapiro-Wilk Test, one-way ANOVA, and multivariable regression models. Significance level: 0.05.

Results: 3T dual-VENC PC-MRI provided better reproducibility with CV values of 10% and 14% for PI and N_perforator, respectively, compared to single VENCs (high VENC: 21% and 21%, low VENC: 13% and 14%). 3T dual-VENC PC-MRI showed no significant difference in N_perforator and PI measurements with 7T dual-VENC (p = 0.16, 0.38, respectively). Among the older participants, aging and cognitive impairment were both significantly associated with increased PI but not with N_perforator (p = 0.17 and 0.365); global vascular risk burden, as well as individual vascular risk factors, including pulse pressure and hypercholesterolemia, showed a significant association with PI but not with N_perforator (p = 0.858, 0.345, and 0.476).

Data conclusion: 3T dual-VENC PC-MRI provides high-fidelity pulsatility assessment of cerebral perforating arteries and may be a useful tool at widely accessible 3T.

Level of evidence: Level 2.

Technical efficacy: Stage 2.

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: Vascular cognitive impairment and dementia (VCID) is the second leading cause of dementia. Cerebrovascular reactivity (CVR) is a promising biomarker for VCID. However, CVR is not commonly measured in clinical practice due to logistical difficulties in applying a hypercapnia challenge during MR imaging.

Purpose: To investigate whether CVR measured by intermittent breath modulation (bm-CVR) without hypercapnia gas-inhalation can be a sensitive biomarker in VCID.

Study type: Prospective cohort study.

Subjects: Eighty-two participants (aged 66.8 ± 6.8 years, 54 females, 28 males) with normal cognition (N = 36), mild cognitive impairment (MCI, N = 37), or mild dementia (N = 9).

Field strength/sequence: 3.0 T; Blood-oxygenation-level-dependence (BOLD) MRI.

Assessment: CVR was measured with BOLD MRI using intermittent breath modulation. Cognitive function was measured with Montreal Cognitive Assessment (MoCA) score, cognitive domains scores, and a global composite cognitive score. Physical function was measured with gait-speed and chair-stand test scores.

Statistical tests: Multi-linear regression models were performed within the participants to test the associations between bm-CVR and measures of cognitive and physical functions. A p value < 0.05 was considered significant. Bonferroni multiple comparison correction was performed when multiple cognitive domains were tested.

Results: Whole-brain bm-CVR values were significantly associated with the diagnosis group of normal, MCI, and early dementia (β = -3.00%/mm Hg, 95% confidence interval (CI) [-5.42, -0.58]), and were lower in the impaired participants relative to participants with normal cognition (β = -2.18, CI [-3.91, -0.45]). Bm-CVR was positively associated with global cognition measured by both MoCA (β = 14.73, CI [1.74, 27.73]) and composite cognitive score (β = 2.22, CI [0.77, 3.66]). For domain-specific cognitive scores, bm-CVR was significantly associated with processing speed (β = 3.19, CI [1.36, 5.02]) and language (β = 2.81, CI [0.70, 4.94]), but not with executive function (p = 0.43) or episodic memory (p = 0.79).

Data conclusion: Bm-CVR is a sensitive biomarker for VCID. This gas-free CVR method may be a more practical approach than hypercapnia gas-inhalation CVR for characterizing vascular pathology.

Evidence level: 2.

Technical efficacy: Stage 2.

Background: Preoperative identification of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy holds substantial clinical importance. The significance of virtual MR elastography (vMRE) in identifying CR-POPF risk remains unclear.

Purpose: To explore the application of vMRE in assessing pancreatic fibrosis and preoperatively identifying CR-POPF with inter-device validation.

Study type: Retrospective.

Population: Three hundred twenty patients (212 male; mean age = 56.93 ± 9.34 years) receiving pancreatoduodenectomy were assessed and divided into two cohorts (cohort 1: 228; cohort 2: 92) based on two MRI scanners.

Field strength/sequence: 3 T, diffusion-weighted spin echo planar imaging and MRE using spin-echo echo planar imaging sequence.

Assessment: Optimal combination of b values was identified for calculating shifted apparent diffusion coefficient (sADC). The diffusion-based tissue shear modulus (μ_diff) was constructed using optimal sADC. Association between μ_diff and pancreatic fibrosis grades was evaluated. CR-POPF was diagnosed postoperatively.

Statistical tests: Spearman correlation analysis, linear regression analysis, univariate and multivariate logistic regression analysis, and receiver operating characteristic (ROC) analysis were used. Significance was set at p < 0.05.

Results: Seventy-six patients developed (23.75%) CR-POPF. sADC generated from b values of 400 and 1500 s/mm² was identified for fitting μ_diff. μ_diff was significantly associated with histopathologic fibrosis grade (Spearman ρ = 0.763) and achieved performance for identifying CR-POPF with area under the curve (AUC) of 0.765. μ_diff, body mass index (BMI), and main pancreatic duct (MPD) were revealed as independent risk factors of CR-POPF. Their combination significantly improved the performance to AUC of 0.923 (AUC of μ_diff = 0.769; AUC of BMI = 0.673; AUC of MPD = 0.666). Results were confirmed in cohort 2.

Data conclusion: Diffusion-based vMRE could effectively assess pancreatic fibrosis grade and provide a noninvasive biomarker for identifying CR-POPF preoperatively. The combination of μ_diff, BMI, and MPD demonstrated superior discriminative performance.

Evidence level: 3.

Technical efficacy: Stage 2.

Background: 3D T1 may assess the superior ophthalmic vein (SOV) congestion, yet its role in the intravenous glucocorticoid (IVGC) therapy remains unclear.

Purpose: To evaluate SOV congestion by 3D T1 in thyroid eye disease (TED) and explore its correlation with IVGC therapeutic response.

Study type: Retrospective.

Subjects: One hundred thirty-three TED patients, classified as 80 IVGC responders (45.7 ± 10.9 years, 32 males) and 53 non-responders (47.6 ± 9.7 years, 23 males); 290 healthy controls (HCs) (47.9 ± 16.4 years, 145 males).

Field strength/sequence: 3 T; 3D T1-weighted spoiled gradient-recalled echo, T2 iterative decomposition of water and fat with echo asymmetric and least-squares estimation, and fat-suppressed (FS) T2 mapping.

Assessment: The SOV diameter (mm), along with the volume ratio, FS T2 relaxation time (T2RT [ms]), and water fraction (WF [%]) of the extraocular muscles (EOMs) and orbital fat (OF) were manually measured.

Statistical tests: Student's t-test, Mann-Whitney U test, one-way analysis of variance, interclass correlation coefficient, and correlation analysis. p < 0.05 defined statistical significance.

Results: TED patients presented significantly larger SOV diameters than HCs (2.21 ± 0.45 vs. 1.72 ± 0.26). Among them, the responsive group had significantly larger baseline SOV diameters (2.26 ± 0.49 vs. 2.15 ± 0.39), along with higher FS T2RT (71.30 ± 14.32 vs. 67.65 ± 13.33) and WF (79.31 ± 13.68 vs. 75.17 ± 14.47) in the EOMs compared to the nonresponsive group. The SOV diameter demonstrated moderate-to-good positive correlations with FS T2RT (r = 0.59) and WF (r = 0.67) in responders. Following IVGC therapy, a significant reduction in SOV diameter was observed exclusively in the responsive group (2.26 ± 0.49 vs. 1.98 ± 0.47).

Data conclusion: Patients exhibiting more severe SOV congestion showed a better response to IVGC therapy, potentially due to an enhanced inflammatory state in the EOMs.

Evidence level: 4.

Technical efficacy: Stage 4.

Brain age is an emerging concept that reflects complex, time-dependent changes in brain structure, identifying departures from expected neurodevelopmental patterns. In the developing brain, accurate MRI-based age estimation is a quantitative biomarker for detecting atypical neurodevelopment, facilitating early diagnosis, guiding clinical decision-making, and potentially improving long-term outcomes. Data-driven models applied to neuroimaging have provided valuable insights into the pathogenesis of various congenital and acquired pediatric conditions. In particular, advanced deep learning approaches have recently gained prominence in a wide range of pediatric neuroimaging studies, offering state-of-the-art performance in estimating developmental brain age. In this survey, we provide a comprehensive review of the current MRI applications of deep learning methodologies for developmental brain age (fetal stage—2 years) estimation. We provide details on both clinical and technical aspects, open-access developmental MRI datasets, and compare the performance of these models utilizing evaluation metrics. Additionally, we discuss the applications of brain age estimation in clinical research contexts, highlighting its importance in understanding neurodevelopmental disorders. Finally, we address the challenges faced and propose future research directions to advance the field of brain age estimation. We aim to provide valuable insights for researchers and practitioners, facilitating advancements in both theoretical understanding and practical applications of MRI-based deep learning brain age estimation of the developing brain.

Evidence Level: 3.

Technical Efficacy: Stage 2.