Background: Long acquisition time limits the clinical utility of coronary magnetic resonance angiography (CMRA) in pediatric populations. While deep learning-based reconstruction methods such as De-Aliasing Regularization-based Compressed Sensing (DARCS) hold promise for accelerating CMRA, its clinical feasibility in pediatric populations remains unexplored.
Purpose: This study aims to reduce scan time and evaluate the image quality and diagnostic performance of DARCS-accelerated CMRA in pediatric coronary imaging, with a focus on coronary artery aneurysms (CAAs) detection.
Study design: A two-phase study including retrospective technique development and prospective clinical validation was performed.
Methods: CMRA was performed using a 3.0 T scanner with a three-dimensional diaphragm-navigated, T2 prepared gradient echo sequence. In the Phase I, pediatric CMRA k-space data were retrospectively undersampled to train and test DARCS reconstruction, with comparison to SENSE, patch-based reconstruction (PROST), and hybrid deep-learning iterative reconstruction (hybrid DL-IR). In Phase II, patients prospectively underwent both conventional 3× accelerated CMRA and 8× DARCS-CMRA. Images were assessed using quantitative image metrics (peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM)), coronary artery assessments (vessel lengths, sharpness, and visual scores). Diagnostic performance for CAA detection was evaluated at both patient and vessel levels.
Results: A total of 123 pediatric patients were included for final analysis, including 96 for the retrospective phase and 27 for the prospective phase. DARCS outperformed the second highest-performance reconstruction method in PSNR (31.74 ± 2.17 vs. 30.69 ± 2.12, P < 0.001 at 8× acceleration), improved vessel length (LAD: 75.86 ± 20.17 mm vs. 72.23 ± 20.80 mm, P < 0.001; RCA: 84.94 ± 20.36 mm vs. 80.12 ± 20.54 mm, P < 0.001), and improved subjective scoring (LAD: 3.22 ± 0.83 vs. 3.11 ± 0.89, P = 0.102 > 0.05; RCA: 3.53 ± 0.81 vs. 3.42 ± 0.81, P = 0.046 < 0.05). In the prospective phase, DARCS-CMRA achieved a 100% sensitivity and specificity in detection of CAA at both patient and vessel levels, with conventional CMRA as the reference, despite a significantly shorter scan time (92.4 ± 19.1 s vs. 208.8 ± 52.0 s).
Conclusion: DARCS offers improved reconstruction quality for accelerated CMRA compared to conventional methods, enabling preservation of CAA diagnostic accuracy despite a two-minute scan.
Recent magnetic resonance imaging (MRI) measurements have revealed proton spin-spin relaxation anisotropy in only one component of the celery stalk, even though water accounts for up to 95% of its weight and is present throughout all its tissues. Our analysis shows that this anisotropy arises from nanoconfined water within the collenchyma cell walls. In these regions, restricted molecular motion and hydrogen bonding between water molecules and cell-wall constituents give rise to residual anisotropic dipolar interactions. In contrast, water in the xylem, phloem, and parenchyma occupies larger cavities, acts like a bulk liquid, and shows no angular dependence in its nuclear relaxation behavior. These results suggest that nuclear spin-spin relaxation anisotropy may serve as a unique feature of nanoconfined water in biological tissues, contributing to the study of water behavior in plants, their microscopic structures, and aiding research on plant physiology.
Voxel-based morphometry (VBM) using T1-weighted magnetic resonance imaging is a pivotal tool for assessing brain structure and identifying subtle morphological changes associated with various neurological conditions. Conventional VBM workflows, however, face significant computational challenges, particularly during the nonlinear deformable registration stage, which impedes analysis of large-scale neuroimaging databases. In this study, we introduce FuseMorph, a deep learning-based registration method that refines initial zero-shot predictions from a pretrained model via iterative inference and targeted parameter search. By eliminating the need for full backpropagation and additional model retraining, FuseMorph significantly reduces computational demands, achieving registration accuracy comparable to state-of-the-art methods even in CPU-only environments. FuseMorph is integrated into DeepVBM, a fully automated VBM pipeline streamlines the processing of high-resolution MRI datasets and substantially reduces computation time compared to traditional pipelines, thereby facilitating the efficient analysis of large multi-center studies. The proposed approach was validated on multiple datasets, including an Alzheimer's disease cohort where DeepVBM successfully detected characteristic patterns of gray matter atrophy in regions such as the hippocampus, entorhinal cortex, and amygdala. These findings not only underscore the clinical relevance of the method but also demonstrate its potential for early detection and monitoring of neurodegenerative changes. This work contributes an accessible, efficient, and scalable solution for neuroimaging research, with potential applications extending to various neurodegenerative disease studies.
Objective: To explore the diagnostic value of Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) radiomics features and qualitative parameters for the differential diagnosis of endometrial cancer (EC) and submucosal uterine fibroidsuterine fibroids.
Methods: This retrospective study included 70 cases of endometrial cancer patients collected from our hospital between October 2022 and October 2024, assigned to the EC group, and another 35 cases of uterine leiomyoma patients during the same period were collected as the benign group according to the 2:1 matching principle. Baseline data, DCE-MRI parameters [rate constant (Kep), volume transport constant (Ktrans), and volume fraction of extracellular space (Ve)] and radiomics characteristics were compared between the two groups. The influencing factors of DCE-MRI parameters and radiomics features on EC were analyzed, as well as their diagnostic value in differentiation, and external validation of the diagnostic value in differentiation was conducted.
Results: Ktrans and Kep in EC group were higher than those in benign group, while Ve was lower (P < 0.05). The radiomic score of the EC group was higher than that of the benign group (P < 0.05). Logistic regression analysis found that Ktrans, Kep, Ve, and radiomic score were factors affecting EC (P < 0.05). The AUC values for the DCE-MRI model, radiomics model, and combined model in predicting the differential diagnosis of EC were 0.695, 0.775, and 0.867, respectively. Among them, the combined model demonstrated the highest predictive value, significantly surpassing that of the DCE-MRI and radiomics models (P < 0.05). The decision curve indicated that the clinical positive benefit achieved by the combined model in differential diagnosis of EC surpassed that of the DCE-MRI and radiomics models (P < 0.05). The calibration curve showed that the calibration curve for differential diagnosis of EC fitted well with the ideal curve. The external validation results demonstrated that the combined diagnostic model exhibited good predictive value.
Conclusion: The combination of DCE-MRI parameters and radiomics features can be used for the differential diagnosis of EC, demonstrating good predictive efficacy and clinical applicability, providing a reliable imaging diagnostic method for clinical diagnosis of EC.

