Diffusion magnetic resonance imaging (dMRI) is essential for studying the microstructure of the developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomo-geneities lead to amplified artifacts and data scattering, compromising the consistency of dMRI analysis. This work introduces HAITCH, a novel open-source framework for correcting and reconstructing high-angular resolution dMRI data from challenging fetal scans. Our multi-stage approach incorporates an optimized multi-shell design for increased information capture and motion tolerance, a blip-reversed dual-echo multi-shell acquisition for dynamic distortion correction, advanced motion correction for robust and model-free reconstruction, and outlier detection for improved reconstruction fidelity. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH effectively removes artifacts and reconstructs high-fidelity dMRI data suitable for advanced diffusion modeling and tractography.

Although 7 Tesla (7T) field strength MR imaging offers higher signal-to-noise ratio and spatial resolution and improves certain types of tissue contrast, the incorporation of these systems into clinical pediatric neuroradiology has been relatively limited. Following a discussion of available hardware, current regulations, and pediatric specific safety considerations, this article briefly reviews the underlying principles behind the improved image quality attainable with certain techniques at 7T. Subsequently, specific high-performance sequences and techniques are highlighted including MP2RAGE, T2-weighted, and T2*-weighted sequences as well as MR angiography, all with sample images and comparison with standard field strengths. Finally, current clinical neuroradiological applications of 7T are explored with particular focus on focal epilepsy, multiple sclerosis, vascular diseases, and cerebral microbleeds. Ongoing and future innovations in hardware design and sequence development promise continued advancement in 7T neuroimaging and further applications to pediatric neuroradiology.

PURPOSE: To improve the quality of abdominal diffusion-weighted MR images (DW-MRI) when acquired using single-repetition (NEX = 1) protocols, and thereby increase apparent diffusion coefficient (ADC) map accuracy and lesion conspicuity at high b-values. We aim to reduce the effect of blurring due to motion that obscures small lesions when averaging multiple repetition images at each b-value, which is the current clinical standard.

METHODS: We propose a self-supervised denoising diffusion probabilistic model (ssDDPM) to improve DW-MRI quality given noisy single-repetition acquisitions in pediatric abdominal scans. The ssDDPM is designed for multi-b-value DW-MRI and incorporates diffusion signal decay model (i.e., ADC model) constraints into its loss term. The model is trained to denoise single-repetition images from multiple b-values while ensuring that the output adheres to the signal decay model. Training was performed on a dataset of 120 pediatric subjects with liver tumors. The performance of ssDDPM was compared with non-local means (NLM) filtering and deep image prior (DIP) denoising techniques. These techniques have the capability to denoise single repetition images unlike the other techniques in literature that requires multiple direction or repetition images. Evaluation included qualitative radiologist's image quality assessment, receiver operating characteristic (ROC) analysis for lesion detection, and ADC fitting accuracy compared with motion-free, breath-hold reference data.

RESULTS: The ssDDPM demonstrated superior performance over comparison methods in terms of image quality, lesion conspicuity, and ADC map accuracy in NEX = 1 images. It received higher scores in radiologist assessments and showed better lesion discrimination in ROC analysis. Additionally, ssDDPM provided more precise and accurate ADC estimates when compared with the motion-free, breath-hold reference data.

CONCLUSION: The ssDDPM effectively reduces motion related deblurring and enhances the quality of DW-MRI images by directly denoising single-repetition (NEX = 1) images while respecting signal decay model constraints. This method improves the assessment of pediatric liver lesions, offering a more accurate and efficient diagnostic tool with reduced scan times, when compared with current clinical practice and other denoising techniques.

PURPOSE: To address the unmet need for a cross-platform, multiparametric relaxometry technique to facilitate data harmonization across different sites.

METHODS: A simultaneous T1 and T2 mapping technique, 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS), was implemented using the open-source vendor-agnostic Pulseq platform. The technique was tested on four 3 T scanners from two vendors across two sites, evaluating cross-scanner, cross-software version, cross-site, and cross-vendor variability. The cross-vendor reproducibility was assessed using both the vendor-native and Pulseq-based implementations. A National Institute of Standards and Technology/International Society for Magnetic Resonance in Medicine system phantom and three human subjects were evaluated. The acquired T1 and T2 maps from the different 3D-QALAS runs were compared using linear regression, Bland-Altman plots, coefficient of variation (CV), and intraclass correlation coefficient (ICC).

RESULTS: Pulseq-QALAS demonstrated high linearity (R2 = 0.994 for T1, R2 = 0.999 for T2) and correlation (ICC = 0.99 [0.98-0.99]) against temperature-corrected NMR reference values in the system phantom. Compared to vendor-native sequences, the Pulseq implementation showed significantly higher reproducibility in phantom T2 values (CV, 2.3% vs. 17%; p < 0.001), and improved T1 reproducibility (CV, 3.4% vs. 4.9%; p = 0.71, not significant). The Pulseq implementation reduced cross-vendor variability to a level comparable to cross-scanner (within-vendor) variability. In vivo, Pulseq-QALAS exhibited reduced cross-vendor variability, particularly for T2 values in gray matter with a twofold reduction in variability (CV, 2.3 vs. 5.9%; p < 0.001).

CONCLUSION: An identical implementation across different scanners and vendors, combined with consistent reconstruction and fitting pipelines, can improve relaxometry measurement reproducibility across platforms.

Accurate characterization of in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building upon previous efforts to construct spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, which is a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from carefully processed MRI scans of 160 fetuses with typically-developing brains using a diffeomorphic deformable registration framework integrated with kernel regression on age. CRL-2025 uniquely includes detailed tissue segmentations, transient white matter compartments, and parcellation into 126 anatomical regions. This atlas offers significantly enhanced anatomical details over the CRL-2017 atlas, and is released along with the CRL diffusion MRI atlas with its newly created tissue segmentation and labels as well as deep learning-based multiclass segmentation models for fine-grained fetal brain MRI segmentation. The CRL-2025 atlas and its associated tools provide a robust and scalable platform for fetal brain MRI segmentation, groupwise analysis, and early neurodevelopmental research, and these materials are publicly released to support the broader research community.

BACKGROUND: Accurate diagnosis of anterior cruciate ligament (ACL) tears on magnetic resonance imaging (MRI) is critical for timely treatment planning. Deep learning (DL) approaches have shown promise in assisting clinicians, but many prior studies are limited by small datasets, lack of surgical confirmation, or exclusion of partial tears.

AIM: To evaluate the performance of multiple convolutional neural network (CNN) architectures, including a proposed CustomCNN, for ACL tear detection using a surgically validated dataset.

METHODS: A total of 8,086 proton density-weighted sagittal knee MRI slices were obtained from patients whose ACL status (intact, partial, or complete tear) was confirmed arthroscopically. Eleven deep learning models, including CustomCNN, DenseNet121, and InceptionResNetV2, were trained and evaluated with strict patient-level separation to avoid data leakage. Model performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS: The CustomCNN model achieved the highest diagnostic performance, with an accuracy of 91.5% (95% CI: 89.5-93.1), sensitivity of 92.4% (95% CI: 90.4-94.2), and an AUC of 0.913. The inclusion of both partial and complete tears enhanced clinical relevance, and patient-level splitting reduced the risk of inflated metrics from correlated slices. Compared with previous reports, the proposed approach demonstrated competitive results while addressing key methodological limitations.

CONCLUSION: The CustomCNN model enables rapid and reliable detection of ACL tears, including partial lesions, and may serve as a valuable decision-support tool for radiologists and orthopedic surgeons. The use of a surgically validated dataset and rigorous methodology enhances clinical credibility. Future work should expand to multicenter datasets, diverse MRI protocols, and prospective reader studies to establish generalizability and facilitate integration into real-world workflows.

Background: Magnetic resonance imaging (MRI) is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics. Acquiring high-quality MRI data is of paramount importance. Super-resolution reconstruction serves as a post-acquisition method capable of improving MRI data quality. Current methods predominantly utilize convolutional neural networks in super-resolution reconstruction. However, convolutional layers have inherent limitations in capturing extensive spatial dependencies due to their localized nature. Methods: We developed a new methodology that enables rapid and high-quality MRI data acquisition through a novel super-resolution approach. We proposed an innovative architecture using transformers to exploit long-range spatial dependencies present in images, allowing for an unsupervised learning framework specifically designed for super-resolution tasks tailored to individual subject. We validated our approach using both simulated data and clinical data comprising 40 scans acquired with a 3-T MRI system. Results: We obtained images with T2 contrast at an isotropic spatial resolution of 500 μm in just 4 min of imaging time, and simultaneously, the signal-to-noise ratio and contrast-to-noise ratio were improved by 13.23% and 18.45%, respectively, in comparison to current leading super-resolution techniques. Conclusions: The results demonstrated that incorporating long-range spatial dependencies substantially improved super-resolution reconstruction, thereby allowing for the acquisition of high-quality MRI data with reduced imaging time.

Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head motion during fMRI scans, occurring between shots of slice acquisition, can result in distortion, biased analyses, and increased costs due to the need for scan repetitions. Therefore, retrospective slice-level motion correction through slice-to-volume registration (SVR) is crucial. Previous studies have utilized deep learning (DL) based models to address the SVR task; however, they overlooked the uncertainty stemming from the input stack of slices and did not assign weighting or scoring to each slice. Treating all slices equally ignores the variability in their relevance, leading to suboptimal predictions. In this work, we introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume, incorporating a self-attention mechanism to enhance robustness against input data variations and uncertainties. Our SVR model utilizes independent slice and volume encoders and a self-attention module to assign pixel-wise scores for each slice. We used the publicly available Healthy Brain Network (HBN) dataset. We split the volumes into training (64%), validation (16%), and test (20%) sets. To conduct the simulated motion study, we synthesized rigid transformations across a wide range of parameters and applied them to the reference volumes. Slices were then sampled according to the acquisition protocol to generate 2,000, 500, and 200 3D volume-2D slice pairs for the training, validation, and test sets, respectively. Our experimental results demonstrate that our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods (Euclidean distance of 0.93 [mm] vs. 1.86 [mm], a paired t-test with a p-value of p<0.03). Furthermore, our approach exhibits faster registration speed compared to conventional iterative methods (0.096 s vs. 1.17 s). Our end-to-end SVR model facilitates real-time head motion tracking during fMRI acquisition, ensuring reliability and robustness against uncertainties in the inputs.