Purpose: Earlier work showed that IVIM‐NET_orig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved version, IVIM‐NET_optim, and characterizes its superior performance in pancreatic cancer patients.Method: In simulations (signal‐to‐noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM‐NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root‐mean‐square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CV_NET), respectively. The best performing network, IVIM‐NET_optim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM‐NET_optim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within‐subject standard deviations (wSD) and treatment‐induced changes were assessed.Results: In simulations (SNR = 20), IVIM‐NET_optim outperformed IVIM‐NET_orig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CV_NET(D) = 0.013 vs 0.104; CV_NET(f) = 0.020 vs 0.054; CV_NET(D*) = 0.036 vs 0.110). IVIM‐NET_optim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM‐NET_optim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM‐NET_optim detected the most individual patients with significant parameter changes compared to day‐to‐day variations.Conclusion: IVIM‐NET_optim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent. However, the tissue microenvironment is typically homogeneous in a local environment, where neighboring voxels may contain correlated information. To harness the potential benefits of exploiting correlations among signals in adjacent voxels, this study introduces a novel approach to deep learning parameter estimation that effectively incorporates relevant spatial information. This is achieved by training neural networks on patches of synthetic data encompassing plausible combinations of direct correlations between neighboring voxels. We evaluated the approach on the intravoxel incoherent motion (IVIM) model in DWI. We explored the potential of several deep learning architectures to incorporate spatial information using self-supervised and supervised learning. We assessed performance quantitatively using novel fractal-noise-based synthetic data, which provide ground truths possessing spatial correlations. Additionally, we present results of the approach applied to in vivo DWI data consisting of twelve repetitions from a healthy volunteer. We demonstrate that supervised training on larger patch sizes using attention models leads to substantial performance improvements over both conventional voxelwise model fitting and convolution-based approaches.

Accurate fetal brain tissue segmentation and biometric measurement are essential for monitoring neurodevelopment and detecting abnormalities in utero. The Fetal Tissue Annotation (FeTA) Challenges have established robust multi-center benchmarks for evaluating state-of-the-art segmentation methods. This paper presents the results of the 2024 challenge edition, which introduced three key innovations. First, we introduced a topology-aware metric based on the Euler characteristic difference (ED) to overcome the performance plateau observed with traditional metrics like Dice or Hausdorff distance (HD), as the performance of the best models in segmentation surpassed the inter-rater variability. While the best teams reached similar scores in Dice (0.81-0.82) and HD95 (2.1-2.3 mm), ED provided greater discriminative power: the winning method achieved an ED of 20.9, representing roughly a 50% improvement over the second- and third-ranked teams despite comparable Dice scores. Second, we introduced a new 0.55T low-field MRI test set, which, when paired with high-quality super-resolution reconstruction, achieved the highest segmentation performance across all test cohorts (Dice=0.86, HD95=1.69, ED=6.26). This provides the first quantitative evidence that low-cost, low-field MRI can match or surpass high-field systems in automated fetal brain segmentation. Third, the new biometry estimation task exposed a clear performance gap: although the best model reached a mean average percentage error (MAPE) of 7.72%, most submissions failed to outperform a simple gestational-age-based linear regression model (MAPE=9.56%), and all remained above inter-rater variability with a MAPE of 5.38%. Finally, by analyzing the top-performing models from FeTA 2024 alongside those from previous challenge editions, we identify ensembles of 3D nnU-Net trained on both real and synthetic data with both image- and anatomy-level augmentations as the most effective approaches for fetal brain segmentation. Our quantitative analysis reveals that acquisition site, super-resolution strategy, and image quality are the primary sources of domain shift, informing recommendations to enhance the robustness and generalizability of automated fetal brain analysis methods.

Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis of the developing human brain, with Deep Learning providing an automated alternative for this otherwise tedious manual process. However, segmentation performances of Convolutional Neural Networks often suffer from domain shift, where the network fails when applied to subjects that deviate from the distribution with which it is trained on. In this work, we aim to train networks capable of automatically segmenting fetal brain MRIs with a wide range of domain shifts pertaining to differences in subject physiology and acquisition environments, in particular shape-based differences commonly observed in pathological cases. We introduce a novel data-driven train-time sampling strategy that seeks to fully exploit the diversity of a given training dataset to enhance the domain generalizability of the trained networks. We adapted our sampler, together with other existing data augmentation techniques, to the SynthSeg framework, a generator that utilizes domain randomization to generate diverse training data. We ran thorough experimentations and ablation studies on a wide range of training/testing data to test the validity of the approaches. Our networks achieved notable improvements in the segmentation quality on testing subjects with intense anatomical abnormalities (p < 1e-4), though at the cost of a slighter decrease in performance in cases with fewer abnormalities. Our work also lays the foundation for future works on creating and adapting data-driven sampling strategies for other training pipelines.

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.