Journal of Magnetic Resonance Imaging最新文献

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.

Despite its unequivocal value in radiological diagnosis, access to conventional high-field MRI systems remains extremely uneven across the world. Access is particularly limited in underfunded and remote settings, due to the high cost and infrastructure requirements of MRI systems. Low-field MRI offers a range of benefits including affordability, portability, suitability for use in intensive care units, and for point-of-care imaging. Different low-field configurations enhance flexibility in various clinical scenarios, including imaging claustrophobic or obese subjects, accommodating different body postures, extremity-focused investigations, and neonatal imaging. Moreover, lower field strengths offer important safety benefits. However, the overarching assumption that lower fields are safe without exception may foster a false sense of security, potentially leading to hazardous situations. On behalf of the International Society for Magnetic Resonance in Medicine, this paper provides a comprehensive review of important safety considerations for low-field MRI, aiming to inform users and stakeholders of both its benefits and limitations, and to empower them toward its safe use. These recommendations are likely to evolve as new evidence becomes available. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: Stage 5.

Integrated FDG PET-MRI images suggest increased risk for underlying Alzheimer's disease pathology in 73 year old with memory loss and word finding difficulty. On MRI there is severe hippocampal atrophy (A; axial FLAIR; arrow), mild biparietal sulcal prominence (B; coronal MPRAGE; arrows) and mild white matter changes (C; axial FLAIR; arrow). FDG surface maps demonstrate symmetric confluent parietotemporal lobe FDG hypometabolism (D; arrows). By Shepherd and Dogra (60–78)

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 η² = 0.195). In the ANCOVA model, left SRLM volume was the only significant neuroanatomical covariate (F [1, 30] = 6.45, partial η² = 0.177). However, hierarchical regression revealed that age was the only consistent independent predictor of CNAQ scores (B = -0.158), explaining the largest variance (ΔR² = 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.

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.