Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from MRI scans of 159 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. It offers significantly enhanced anatomical details over the CRL-2017 atlas and is presented along with a re-release of the CRL diffusion MRI atlas featuring newly created tissue segmentation and labels. We release de-identified, processed subject-level fetal MRI datasets used to generate CRL-2025, providing input-output transparency and reproducibility. We also provide FetalSEG, a deep learning-based multiclass segmentation tool to facilitate automatic fetal brain MRI segmentation. The CRL-2025 atlas and its tools enable scalable fetal brain MRI segmentation, analysis, and neurodevelopmental research for the broader community.

BACKGROUND: Measuring differential renal function (DRF) is critical for managing children with congenital and acquired urological diseases. MAG-3 renal scintigraphy is the most widely used clinical technique; dynamic contrast-enhanced MRI (DCE-MRI) provides a radiation-free and high-resolution alternative method for assessing DRF.

OBJECTIVE: To evaluate the agreement between DRF values derived from DCE-MRI and MAG-3 scintigraphy-derived DRF in pediatric patients.

MATERIALS AND METHODS: This retrospective study included 23 pediatric patients (age range, 3 months-20 years; mean±SD, 8.3±7.4 years) who underwent DCE-MRI embedded in MR urography (MRU) protocol and MAG-3 studies within 6 months. DCE-MRI was performed using a golden-angle radial dynamic stack-of-stars acquisition with golden-angle sparse parallel (GRASP) reconstruction, and analyzed the renal filtration rate with a two-compartment tracer kinetic model. DRF values from DCE-MRI were compared with those from MAG-3, using both default (n=23) and C-shaped background regions for MAG-3 analysis, where raw data were available (n=16). Agreement was evaluated using Bland-Altman analysis, Pearson and concordance correlations, and paired statistical tests.

RESULTS: With default MAG-3 background analysis (n=23), mean DRF difference (MAG-3 minus DCE-MRI) was 0.4% (95% CI, -3.3% to 4.2%), with 95% limits of agreement of -16.7% to +17.5%. Concordance correlation coefficient (CCC) was 0.870 (95% CI, 0.718-0.942). Within the pre-defined clinically acceptable margin of ±10%, 78.3% (18/23) of cases fell, with a mean absolute difference of 6.1%. With C-shaped background subtraction in the subset with available raw MAG-3 data (n=16), mean difference was -2.3% (95% CI, -6.7% to 2.0%), limits of agreement -18.3% to +13.6%, and CCC 0.845 (95% CI, 0.614-0.943). Pearson correlation was strong in both analyses (n=23: r=0.872, 95% CI, 0.719-0.945; n=16: r=0.866, 95% CI, 0.648-0.953; both P<0.001). No clinically significant systematic or proportional bias was detected in either analysis.

CONCLUSION: DCE-MRI demonstrates strong linear association and concordance with MAG-3 scintigraphy for pediatric differential renal function assessment with no clinically significant systematic bias, though the observed measurement variability indicates caution is warranted when comparing values near surgical decision thresholds. C-shaped background correction is recommended for MAG-3 in hydronephrotic kidneys to optimize inter-modality agreement.

BACKGROUND: The stria of Gennari (SoG) is a densely myelinated band within layer IVb of the primary visual cortex (V1) and represents the only cortical laminar structure visible macroscopically in vivo. Ultrahigh-field MRI may improve its detection and conspicuity.

PURPOSE: To quantitatively and qualitatively compare the in vivo appearance of the SoG at 3 versus 7 T using matched high-resolution T2-weighted fast spin-echo MRI.

STUDY TYPE: Prospective.

POPULATION: A total of 12 subjects (6 female, 6 male; median age, 17 years; range, 15-24 years) with pediatric-onset epilepsy.

FIELD STRENGTH/SEQUENCE: 3 and 7 T MRI, T2-weighted fast spin-echo, acquired within 1 month of each other.

ASSESSMENT: Cortical line profiles per subject were extracted orthogonally to the calcarine sulcus in V1. Quantitative metrics included peak-valley distance, Δ-signal (peak-valley difference), and contrast ratio (CR). Two blinded neuroradiologists and a radiologist independently rated SoG conspicuity using a 5-point Likert scale.

STATISTICAL TESTS: McNemar's test compared detection rates; paired t-tests or Wilcoxon signed-rank tests compared quantitative metrics; reader preferences were analyzed using Wilcoxon tests; inter-reader agreement was assessed using weighted Cohen's κ. No multiple-comparison correction was applied (α = 0.05).

RESULTS: SoG-consistent valleys were detected in 31% of profiles at 3 T and 65% at 7 T, significantly. Peak-valley distance remained stable across field strengths (0.45 ± 0.06 depth units; p = 0.37). Mean Δ-signal (33.5 vs. 36.1; p = 0.81) and contrast ratio (0.013 vs. 0.000; p = 0.54) did not differ significantly. Both readers demonstrated a strong preference for 7 T images (pseudo-median +1 to +1.5; significant), with fair inter-reader agreement (κ = 0.36).

DATA CONCLUSION: The SoG can be visualized in vivo at both 3 and 7 T, with higher detection frequency and greater subjective conspicuity at 7 T. Quantitative laminar metrics remained stable across field strengths, suggesting that improved detectability at 7 T likely reflects enhanced spatial definition rather than measurable changes in signal contrast.

TECHNICAL EFFICACY: Stage 2.

BACKGROUND AND PURPOSE: The increased SNR at 7T enables higher spatial resolution for neurovascular imaging, yet its application in pediatric MRA remains underexplored. This study systematically evaluates the advantages of 7T TOF-MRA compared with 3T in pediatric patients, hypothesizing that 7T would provide superior vessel contrast and increased vascular volume, given the use of smaller voxels, as well as higher SNR, despite these smaller voxels.

MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act (HIPAA)-compliant, institutional review board-approved retrospective study included pediatric patients (younger than 19 years of age) who underwent 7T TOF-MRA. Controls consisted of either same-subject 3T MRAs within 6 months (when available) or age- and sex-matched 3T MRA subjects. Imaging parameters were optimized for spatial resolution at 7T to achieve 0.3- to 0.4-mm isotropic voxels. Quantitative analysis included contrast ratio and SNR measurements for the ICA, M1 to M4 arterial segments, and the lenticulostriate perforating arteries. Vascular volume was assessed using 3D segmentation. Semiquantitative vessel conspicuity ratings and motion artifact scoring were performed by blinded neuroradiologists.

RESULTS: Fifteen patients (10 with 7T MRA, 5 with matched 3T controls) and 20 MRAs were analyzed. Contrast ratio was significantly higher at 7T for perforators, M3, and M4 branches (P < .05), with the greatest improvement in the M4 branches. Vascular volume was 147% greater at 7T (P = .018), reflecting improved small-vessel depiction and segmentation. Semiquantitative analysis showed significantly better vessel conspicuity at 7T for the M4 branches and lenticulostriate perforators (P < .01). Motion artifact scores were similar between field strengths (P = .118).

CONCLUSIONS: 7T TOF-MRA significantly enhances vascular contrast and improves visualization of small arteries compared with 3T, making it a valuable tool for pediatric cerebrovascular imaging.

BACKGROUND: The posterior fossa undergoes rapid development in utero, yet normative volumetric data are limited, especially during mid-gestation when most fetal MRIs are performed.

OBJECTIVE: This study aimed to develop a segmentation method tailored to the fetal posterior fossa and establish normative growth trajectories for key infratentorial structures between 22 weeks and 32 weeks of gestation.

MATERIALS AND METHODS: Eighty-five pregnant women were prospectively recruited at a single institution. Inclusion criteria were normal singleton pregnancies between 19-40 weeks' gestation and maternal age 18-45 years. Exclusion criteria included fetal abnormalities, MRI contraindications, or significant maternal comorbidities. Eighteen fetuses were excluded, resulting in 67 normal fetuses (43 male, 24 female). Imaging was performed at 3 T using multiplanar T2-weighted sequences, reconstructed into isotropic volumes after motion correction and brain extraction. Segmentation labels were created using age-specific atlases from the Developing Human Connectome Project. Expert annotations defined the midbrain, pons, medulla, vermis, and cerebellar hemispheres. Images were registered to the atlases using the Symmetric Normalization (SyN) algorithm, with labels manually verified by a pediatric neuroradiologist. Volumes were normalized to total brain volume and analyzed using linear regression with gestational age and sex as predictors.

RESULTS: Absolute volumes of all posterior fossa structures significantly increased with gestational age (P<0.001), showing strong linear associations (R2=0.80-0.87). The cerebellum exhibited the steepest growth (β=1,056.01, R2=0.87, P<0.001). Growth was symmetric with no significant left-right differences. Relative volumetric trends varied: the cerebellum increased proportionally (β=0.007, P<0.001), while the midbrain, pons, and medulla decreased relative to total brain volume. The vermis showed no significant association with gestational age but had sex-specific effects; males had smaller relative vermian volumes than females (β=-0.43, P=0.04), despite larger absolute posterior fossa volumes overall.

CONCLUSIONS: This study provides normative volumetric references for fetal posterior fossa structures between 22-32 weeks' gestation using a novel segmentation method. Absolute growth followed linear patterns, while relative measures revealed sex-specific variations in the vermis and cerebellum. These benchmarks may enhance diagnostic accuracy for detecting posterior fossa anomalies in clinical fetal MRI.

Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model. IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion. To promote physically plausible image registration, we introduce a biophysically informed loss function that effectively balances registration and model-fitting quality. We validated the efficacy of IVIM-morph by establishing a correlation between the predicted IVIM model parameters of the lung and gestational age (GA) using fetal DWI data of 39 subjects. Our approach was compared against six baseline methods: 1) no motion compensation, 2) affine registration of all DWI images to the initial image, 3) deformable registration of all DWI images to the initial image, 4) deformable registration of each DWI image to its preceding image in the sequence, 5) iterative deformable motion compensation combined with IVIM model parameter estimation, and 6) self-supervised deep-learning-based deformable registration. IVIM-morph exhibited a notably improved correlation with gestational age (GA) when performing in-vivo quantitative analysis of fetal lung DWI data during the canalicular phase. Specifically, over 2 test groups of cases, it achieved an R f 2 of 0.44 and 0.52, outperforming the values of 0.27 and 0.25, 0.25 and 0.00, 0.00 and 0.00, 0.38 and 0.00, and 0.07 and 0.14 obtained by other methods. IVIM-morph shows potential in developing valuable biomarkers for non-invasive assessment of fetal lung maturity with DWI data. Moreover, its adaptability opens the door to potential applications in other clinical contexts where motion compensation is essential for quantitative DWI analysis. The IVIM-morph code is readily available at:https://github.com/TechnionComputationalMRILab/qDWI-Morph.

PURPOSE: To develop a rapid, high-resolution and distortion-free technique for simultaneous water-fat separation, R 2 * and B 0 mapping of the fetal brain at 3T.

METHODS: A 2D multi-echo radial FLASH sequence with blip gradients is adapted for data acquisition during maternal free breathing. A calibrationless model-based reconstruction with sparsity constraints is developed to jointly estimate water, fat, R 2 * and B 0 field maps directly from k-space. This approach was validated and compared to reference methods using numerical and NIST phantoms and data from nine fetuses between 26 and 36 weeks of gestation age.

RESULTS: Both numerical and experimental phantom studies confirm good accuracy and precision. In fetal studies, model-based reconstruction yields quantitative R 2 * values in close agreement with those from a parallel imaging compressed sensing (PICS) technique using Graph Cut (intra-class correlation coefficient [ICC] = 0.9601), while providing enhanced image detail. Repeated scans confirm good reproducibility (ICC = 0.9213). Compared to multi-echo EPI, the proposed radial technique produces higher-resolution (1.1 × 1.1 × 3 mm3 vs. 2-3 × 2-3 × 3 mm3) R 2 * maps with reduced distortion. Despite of differences in motion, resolution and distortion, R 2 * values are comparable between the two acquisition strategies (ICC = 0.8049). Additionally, the proposed approach enables synthesis of high-resolution and distortion-free R 2 * -weighted images.

CONCLUSION: This study demonstrates the feasibility of using multi-echo radial FLASH combined with calibrationless model-based reconstruction for motion-robust, distortion-free R 2 * mapping of the fetal brain at 3T, achieving a nominal resolution of 1.1 × 1.1 × 3 mm3 within 2 seconds per slice.

BACKGROUND AND OBJECTIVES: Succinic semialdehyde dehydrogenase deficiency (SSADHD) is an inherited metabolic disorder resulting in hyper-physiologic concentrations of the neurotransmitter γ-aminobutyrate (GABA). This study aims to provide the most comprehensive description, to date, of the neuropsychological profile of individuals with SSADHD and assess whether neuroimaging, neurophysiologic, and biochemical indices of cortical inhibition correlate with those of standardized behavioral tests.

METHODS: Participants enrolled in the SSADHD Natural History Study underwent medical and neurological examinations, magnetic resonance imaging (MRI) and spectroscopy (MRS), biochemical tests of GABA and its related metabolites, transcranial magnetic stimulation (TMS), and gene expression quantification, as well as complete neuropsychological assessment including standardized measures for cognition, adaptive skills, motor function, receptive and expressive language, autism spectrum disorder, and behavior problems.

RESULTS: The neuropsychological profile of the study's 65 enrollees [54 % females, median (interquartile range) age 9.6 (5.4-14.7)] consisted almost universally of intellectual disability, delays in adaptive skills, and deficits in expressive more than receptive language. Autism Spectrum Disorder was noted in ∼50 %, and behavioral problems in ∼70 %, predominated by obsessive-compulsive behaviors and attention problems but also including affective problems, anxiety, and, rarely, aggression and possible psychosis. Correlation analyses showed that increased internalizing, externalizing, and overall psychiatric morbidity significantly correlated with increasing age (R = 0.391, p = 0.033), as well as age-independent indices representing decreased cortical inhibition such as lower MRS-derived GABA (R = -0.530, p = 0.029) and TMS-derived resting motor threshold (R = -0.418, p = 0.053).

DISCUSSION: The natural history study of SSADHD indicates that intellectual disability, delayed adaptive skills, and expressive>receptive language deficits are nearly universal, with behavior problems in the vast majority. Increased psychiatric morbidity in SSADHD with age-independent decreased cortical inhibition may serve as the basis for establishing disorder-specific biomarkers for behavioral and psychiatric outcomes in SSADHD and other non-syndromic psychiatric disorders.

The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated segmentation model for hip cartilage and labrum from MRI. Direct hip MR arthrographies (01/2020-10/2021) were selected from 100 symptomatic patients. Institutional routine protocol included a 3D T1 mapping sequence, which was used for manual segmentation of hip cartilage and labrum. 80 hips were used for training and the remaining 20 for testing. Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. Inter-rater agreement of manual segmentation served as benchmark for automatic segmentation performance. 100 patients were included (mean age 30 ± 10 years, 64% female patients). Mean DSC for cartilage was 0.92 ± 0.02 (95% confidence interval [CI] 0.92-0.93) and 0.83 ± 0.04 (0.81-0.85) for labrum and comparable (p = 0.232 and 0.297, respectively) to inter-rater agreement of manual segmentation: DSC cartilage 0.93 ± 0.04 (0.92-0.95); DSC labrum 0.82 ± 0.05 (0.80-0.85). When tested on the external dataset, the DSC was 0.89 ± 0.02 (0.88-0.90) and 0.71 ± 0.04 (0.69-0.73) for cartilage and labrum, respectively.The presented deep learning approach accurately segments hip cartilage and labrum from 3D MRI sequences and can potentially be used in clinical practice to provide rapid and accurate 3D MRI models.