Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi-modal morphological and functional fetal MRI data from 333 control cases and 93 preterm birth cases. The GA at birth predictions were classified into term and preterm categories and their accuracy, sensitivity, and specificity were reported. An ablation study was performed to further validate the design of the pipeline. Performance was evaluated using stratified 10-fold cross-validation. The pipeline achieves an R2 score of 0.13 and a mean absolute error of 2.74 weeks. It also achieves a 0.77 accuracy, 0.59 sensitivity, and 0.82 specificity across folds. The predominant features selected by the pipeline include cervical length and statistics derived from placental T2* values. The confluence of fast, motion-robust and multi-modal fetal MRI techniques and machine learning prediction allowed the prediction of the gestation at birth. This information is essential for any pregnancy. To the best of our knowledge, preterm birth had only been addressed as a classification problem in the literature. Therefore, this work provides a proof of concept. Future work will increase the cohort size to allow for finer stratification within the preterm birth cohort. Our code is available at https://github.com/dfajardorojas/ml-for-preterm-birth-.

OBJECTIVES:To assess if maternal stress is higher in pregnancies with congenital heart disease (CHD) compared to low-risk pregnancies and if maternal stress is associated with placental microstructure and function. To explore if CHD alters the relationship between maternal stress and placental measures. METHODS:In this prospective observational study, 27 participants carrying a fetus with CHD and 42 participants with typical low-risk pregnancies underwent 1-2 combined diffusion-T2∗ relaxation placental MRIs from 20 weeks gestation (GA) and completed the Edinburgh Postnatal Depression Scale and State Trait Anxiety Inventory [43 male fetuses, median (IQR) GA at assessment 30.86 weeks (27.43-34.00), interval between assessments 6.00 weeks (4.86-7.14)]. 98 complete placental MRI and maternal stress datasets were available. Generalized Estimating Equations were used for analyses. RESULTS:Higher trait anxiety was associated with higher placental apparent diffusion coefficient (p = 0.023) adjusting for CHD, sex, GA at assessment, GA at assessment, state anxiety, depressive symptoms and previous mental health treatment. Maternal state anxiety (p = 0.005) and depressive symptoms (p = 0.046) were higher in pregnancies with CHD adjusting for GA at assessment and previous mental health treatment. CHD did not alter these relationships (p > 0.119). CONCLUSIONS:Maternal proneness to anxiety, measured with the trait anxiety inventory, is associated with increased diffusivity in the placenta, which may reflect altered microstructural maturation. Mothers with fetal CHD show more depressive symptoms and feelings of anxiety and may benefit from screening for elevated maternal stress. The findings contribute to a growing body of research regarding the influence of prenatal stress on placental development.

Pelvic diseases in women of reproductive age represent a major global health burden, with diagnosis frequently delayed due to high anatomical variability, complicating MRI interpretation. Existing AI approaches are largely disease-specific and lack real-time compatibility, limiting generalizability and clinical integration. To address these challenges, we establish a benchmark framework for disease- and parameter-agnostic, real-time-compatible unsupervised anomaly detection in pelvic MRI. The method uses a residual variational autoencoder trained exclusively on healthy sagittal T2-weighted scans acquired across diverse imaging protocols to model normal pelvic anatomy. During inference, reconstruction error heatmaps indicate deviations from learned healthy structure, enabling detection of pathological regions without labeled abnormal data. The model is trained on 294 healthy scans and augmented with diffusion-generated synthetic data to improve robustness. Quantitative evaluation on the publicly available Uterine Myoma MRI Dataset yields an average area-under-the-curve (AUC) value of 0.736, with 0.828 sensitivity and 0.692 specificity. Additional inter-observer clinical evaluation extends analysis to endometrial cancer, endometriosis, and adenomyosis, revealing the influence of anatomical heterogeneity and inter-observer variability on performance interpretation. With a reconstruction time of approximately 92.6 frames per second, the proposed framework establishes a baseline for unsupervised anomaly detection in the female pelvis and supports future integration into real-time MRI. Code is available upon request (https://github.com/AniKnu/UADPelvis), prospective data sets are available for academic collaboration.

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.

Female pelvic diseases remain an under researched area characterized by often delayed diagnosis. While pelvic MRI offers superior soft-tissue contrast for diagnosis and image-guided procedures, real-time anomaly detection remains challenging due to physiological motion, tissue deformation, and instrument artifacts. Existing supervised approaches are impractical, as adverse events are rare, heterogeneous, and difficult to annotate. We present a Dinomaly-based unsupervised anomaly detection framework adapted for pelvic MRI that learns normative representations from healthy cases and flags deviations without requiring labels. Our approach leverages a frozen DINOv3 Vision Transformer encoder combined with a noisy MLP bottleneck and Linear Attention decoder to prevent identity mapping while maintaining computational efficiency. Anomalies are localized via per-token cosine distance between encoder and decoder representations, yielding spatial anomaly maps that provide immediate feedback at the scanner to support radiologist decision-making and adaptive protocol adjustment. Evaluated on a curated subset of the Uterine Myoma Dataset, the framework achieves a pixel-level AUROC of 88.06% and high specificity (95.45%) at frame level at 40.5 slices/s, meeting real-time clinical deployment requirements. The spatial anomaly maps and frame-level scores provide immediate, localized feedback at the scanner to support radiologist decision-making and adaptive protocol adjustment during active procedures.

PURPOSE:Altered placental capillary blood flow is closely linked to obstetric complications, yet quantifying capillary-scale blood velocity remains challenging with existing imaging methods. This is partially because capillary networks form disordered microvascular beds at the voxel scale, rather than coherent, directional vessels. Here, we combine Monte Carlo (MC) simulations with machine learning to estimate placental capillary blood velocity directly from diffusion MRI (dMRI) data. METHODS:MC simulations incorporating perfusion and diffusion were performed to generate signal dictionaries for supervised machine learning regressors (random forest [RF] and multilayer perceptron [MLP]) to estimate velocity, perfusion fraction and diffusivity. The trained regressors were applied to simulated and in vivo dMRI data, with intravoxel incoherent motion (IVIM)-based estimates as baselines for comparison. RESULTS:Our approach outperformed IVIM-based methods on simulated test data in recovering ground-truth parameters. Among the evaluated models, the MC-based MLP approach produced physiologically reasonable estimates of velocity in in vivo placentas, in agreement with previously reported measures and exhibited greater sensitivity than IVIM-generated methods. CONCLUSION:This study presents an approach to characterize capillary perfusion that alternates conventional IVIM models, particularly in organs such as the placenta, where pseudo-diffusion coefficient can be similar to tissue diffusion coefficient, limiting reliable IVIM parameter separation. Our method could enable detection of capillary blood-flow alterations, which could help identify pathological placental blood-flow conditions and potentially diagnosis relevant diseases, such as preeclampsia and fetal growth restriction. More broadly, the approach may translate to other organs where microscale blood-flow changes are key indicators of underlying pathologies.

Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid motion, and limited spatial resolution. However, while rtMRI acquisitions may provide synchronized acoustic signals, existing methods discard this information, and the few multimodal approaches that incorporate audio cannot be deployed when audio is unavailable. We propose a three-stage framework that leverages acoustic and phonological supervision during training while requiring only the rtMRI image at inference: phonological representations are converted into spatial bounding-box priors for articulator localization, visual and acoustic encoders are aligned via dual-level cross-modal contrastive pretraining, and the learned representations are fused through a cross-attention decoder, effectively transferring multimodal knowledge into a single-modality inference pipeline. Evaluated on 75-Speaker~Annot-16 and USC-TIMIT datasets, our method outperforms existing unimodal and multimodal methods, demonstrating that multimodal supervision provides transferable benefits for precise and clinically deployable vocal tract segmentation.

Magnetic resonance imaging (MRI)-based volumetry of the fetus, placenta, and amniotic fluid is clinically valuable but rarely used due to labor-intensive manual segmentation of motion-corrupted two-dimensional (2-D) stacks. Existing deep learning approaches are typically limited to single structures and 2-D data, while no robust automated solution exists for whole-uterus volumetry in reconstructed three-dimensional (3-D) MRI, and normative reference ranges are lacking. To develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and derive normative growth models for fetal, placental, and amniotic fluid volumes. Motion-corrupted T2-weighted stacks (0.55–3-T field strength) were reconstructed into 3-D isotropic images using deformable slice-to-volume reconstruction, followed by automated segmentation with a 3-D U-Net. The method was applied to 357 normal-control datasets with confirmed term birth (16–41 weeks gestational age range) to derive quadratic normative growth curves. Performance and clinical utility were further evaluated on 43 independent datasets. Segmentation was highly accurate (Dice: fetus 0.997, placenta 0.995, amniotic fluid 0.998) with low volume errors (<1

Background:Volumetric assessment of the fetus, placenta and amniotic fluid is clinically valuable, but MRI volumetry is rarely performed in clinical practice because of the required labour-intensive manual segmentation of motion-corrupted 2-dimensional (2-D) stacks. Existing deep-learning approaches typically segment single structures in 2-D motion-corrupted stacks, are, however limited in accuracy by slice misalignment. No current method provides a reliable automated solution for whole-uterus volumetry in 3-D reconstructed MRI. Furthermore, normative ranges for computation of centiles are currently missing. Objective:To develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and to generate normative growth models for fetal, placental and amniotic fluid volumes in healthy pregnancies with confirmed delivery at term. Materials and methods:Deformable slice-to-volume 3-D reconstruction was applied to motion-corrupted T2-weighted (T2W) stacks from 0.55T-3T MRI, and a 3-D UNet was trained to segment fetus, placenta and amniotic fluid on the resulting reconstructed 3-D images. A reporting tool generates centiles, z-scores and structured HTML outputs. Automated segmentation was performed in 357 healthy control datasets from 16-41 weeks gestational age (GA) range with confirmed delivery at term. After visual checks of segmeted labels and minor refinements, GA-based quadratic normative volumetry models were derived and correlations with maternal and fetal characteristics assessed. The utility of the pipeline for clinical research was further evaluated using 95 longitudinal scans from 42 fetuses and 86 preterm (≤ 32 weeks at delivery) pregnancies. Results:Automated segmentation produced accurate 3-D labels, with only small local corrections (< 1% volume difference) required in the control cohort(< 25% of the datasets). Fetal and placental volumes increased across gestation, while amniotic fluid volume peaked mid-pregnancy and declined toward term. Volumes and centiles correlated with maternal size and birth weight. Longitudinal scans showed individual fetal and placental trajectories closely following the normative curves, with greater variability in amniotic fluid. Preterm pregnancies showed significantly lower fetal, placental and amniotic fluid volumes and centiles than the controls with confirmed delivery at term. Conclusion:This study introduces an automated whole-uterus volumetry pipeline and corresponding normative 3-D MRI growth models. The method provides robust, standardised volumetric assessment of fetal, placental and amniotic fluid development and offers a practical tool for evaluating growth patterns in both normal and high-risk pregnancies.

Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treatment and rehabilitation strategies. Toward this goal, we introduce an audio-to-video generation framework for creating Real Time/cine-Magnetic Resonance Imaging (RT-/cine-MRI) visuals of the vocal tract from speech signals. Our framework first preprocesses RT-/cine-MRI sequences and speech samples to achieve temporal alignment, ensuring synchronization between visual and audio data. We then employ a modified stable diffusion model, integrating structural and temporal blocks, to effectively capture movement characteristics and temporal dynamics in the synchronized data. This process enables the generation of MRI sequences from new speech inputs, improving the conversion of audio into visual data. We evaluated our framework on healthy controls and tongue cancer patients by analyzing and comparing the vocal tract movements in synthesized videos. Our framework demonstrated adaptability to new speech inputs and effective generalization. In addition, positive human evaluations confirmed its effectiveness, with realistic and accurate visualizations, suggesting its potential for outpatient therapy and personalized simulation of vocal tract visualizations.

Objectives:To assess if maternal stress is higher in pregnancies with congenital heart disease (CHD) compared to low-risk pregnancies and if maternal stress is associated with placental microstructure and function. To explore if CHD alters the relationship between maternal stress and placental measures. Methods:In this prospective observational study, 27 participants carrying a fetus with CHD and 42 participants with typical low-risk pregnancies underwent 1-2 combined diffusion□T2* relaxation placental MRIs from 20 weeks gestation (GA) and completed the Edinburgh Postnatal Depression Scale and State Trait Anxiety Inventory [43 male fetuses, median (IQR) GA at assessment 30.86 weeks (27.43-34.00), interval between assessments 6.00 weeks (4.86-7.14)]. 98 complete placental MRI and maternal stress datasets were available. Generalized Estimating Equations were used for analyses. Results:Higher trait anxiety was associated with higher placental apparent diffusion coefficient (p=0.023) adjusting for CHD, sex, GA at assessment, GA at assessment 2 , state anxiety, depressive symptoms and previous mental health treatment. Maternal state anxiety (p=0.005) and depressive symptoms (p=0.046) were higher in pregnancies with CHD adjusting for GA at assessment and previous mental health treatment. CHD did not alter these relationships (p>0.119). Conclusions:Maternal proneness to anxiety, measured with the trait anxiety inventory, is associated with increased diffusivity in the placenta, which may reflect altered microstructural maturation. Mothers with fetal CHD show more depressive symptoms and feelings of anxiety and may benefit from screening for elevated maternal stress. The findings contribute to a growing body of research regarding the influence of prenatal stress on placental development. Highlights:Maternal stress and placental MRI data acquired in pregnancies with and without CHDMaternal trait anxiety is associated with increased placental diffusivityMaternal state anxiety and depressive symptoms are higher in fetal CHDState anxiety and depressive symptoms not associated with placental MRI measuresCHD did not moderate relationships between placental MRI measures and stress.

PURPOSE:To develop and evaluate a real-time framework for targeted slice-level reacquisition in fetal diffusion MRI. METHODS:A DW-SE-EPI sequence was modified to support independent diffusion preparation per slice. A pathology-robust nnU-Net segmentation network, updated from a previously published baseline (V1) using pathological fetal cases and physics-inspired augmentation (V2), and intensity-based artifact detection drive automated reacquisition prioritization. Retrospective validation was performed on 60 cases across three cohorts at two field strengths and prospective deployment in 14 cases at two clinical sites. RESULTS:Segmentation V2 significantly outperformed V1 across all cohorts ( p < 0 . 001 ), with largest gains at b > 0 s mm - 2 (median DSC gain + 0.87 at 1.5 T). Blackout detection achieved F1 = 0.81 ± 0.29 at T = 0.35. Prospective real-time deployment confirmed clinical feasibility at both field strengths. CONCLUSION:Real-time slice-level fetal diffusion reacquisition is demonstrated for the first time, opening a pathway toward routine quantitative fetal diffusion MRI in research and clinical settings.

Purpose: To develop and validate a practical, open-source framework to overcome common issues in inline deployment of established offline MR reconstruction, including (1) scan disruption from lengthy reconstructions, (2) limited support for multi-scan input reconstructions, (3) needs to adapt scripts for different raw-data formats, and (4) limited guidance and experience in retaining scanner reconstructions and applying scanner-based post-processing to custom-reconstructed images. Methods: The framework builds upon the Gadgetron platform as implemented on Siemens scanners and includes: (1) a general input converter to convert Gadgetron-used ISMRMRD format raw into a Siemens format raw structure, facilitating reuse of code; (2) an asynchronous trigger-and-retrieve mechanism enabling long custom reconstructions without delaying scanner processes; (3) resource-aware scheduling for parallel execution of reconstructions; (4) integrated file management to support multi-scan inputs; and (5) preservation of scanner-based reconstructions and post-processing. The framework was validated on 2 Siemens scanners for SENSE, AlignedSENSE, and NUFFT reconstructions, and in a large-cohort study. Results: Minimum code modification for inline deployment was demonstrated, and all reconstructions were successfully executed inline without disrupting scanner workflows. Images were retrieved automatically via retrieval scans or manually via retro-reconstruction, with scanner-based post-processing applied to custom outputs. Multi-sequence reconstructions were executed using GPU-aware scheduling, confirming feasibility for large-scale applications. In 480 examinations, inline reconstructions were retrieved in 99% of cases without disruptions. Conclusion: The framework lowers the technical barrier to inline deployment of offline reconstructions, enabling robust, scalable, and post-processing-compatible integration. It is openly available with documentation and demonstration cases to support reproducibility and community adoption.

Real-time magnetic resonance imaging (rtMRI) of speech production enables non-invasive visualization of dynamic vocal-tract motion and is valuable for speech science and clinical assessment. However, rtMRI is fundamentally constrained by trade-offs among spatial resolution, temporal resolution, and acquisition speed, often leading to undersampled k-space measurements and degraded reconstructions. We propose SIREM, a speech-informed MRI reconstruction framework that uses synchronized speech as a cross-modal prior. The central idea is that vocal-tract configurations during speech are correlated with the produced acoustics, making part of the image content predictable from audio. SIREM models each frame as a fusion of an audio-driven component and an MRI-driven component through a spatial weighting map. The audio branch predicts articulator-related structure from speech, while the MRI branch reconstructs complementary content from measured k-space data. We further introduce a learnable soft weighting profile over spiral arms, enabling a differentiable study of how k-space arm usage interacts with speech-informed fusion. This yields a unified multimodal formulation that combines audio-driven prediction, MRI reconstruction, and sampling adaptation. We evaluate SIREM on the USC speech rtMRI benchmark against standard baselines, including gridding, wavelet-based compressed sensing, and total variation. SIREM introduces a speech-informed reconstruction paradigm that operates in a substantially higher-throughput regime than iterative methods while preserving anatomically plausible vocal-tract structure. These results establish an initial benchmark for multimodal speech-informed rtMRI reconstruction and highlight the potential of synchronized speech as an auxiliary prior for fast reconstruction. The source code is available at https://github.com/mdhasanai/SIREM

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.

Abstract Regional volumetric assessment of perinatal brain development is currently limited by the lack of consistent high quality multi-regional segmentation methods applicable to both fetal and neonatal MRI. We present Multi-BOUNTI, a deep learning pipeline for automated multi-lobe segmentation of fetal and neonatal T2w brain MRI. The method is based on a dedicated 43-label parcellation protocol and a 3D Attention U-Net trained on brain MRI datasets of subjects spanning 21–44 weeks gestational/postmenstrual age. The pipeline integrates preprocessing, segmentation and volumetric analysis, and was evaluated on independent datasets, demonstrating fast (< 10 min/case) and accurate performance with high agreement to manually refined labels. We demonstrate the application of the framework with 267 fetal and 593 neonatal MRI datasets from the developing Human Connectome Project without reported clinically significant brain anomalies to derive normative volumetric growth models across 21–44 weeks GA/PMA. These models were used to characterise developmental trajectories, assess differences between fetal and preterm neonatal cohorts, and analyse longitudinal changes. The resulting normative models were integrated into an automated reporting framework enabling subject-specific volumetric assessment via centiles and z-scores. Multi-BOUNTI provides a unified and scalable approach for perinatal brain segmentation and volumetry, supporting large-scale studies and facilitating future clinical translation. The full pipeline is publicly available at https://github.com/SVRTK/perinatal-brain-mri-analysis .

Uterine peristalsis is a key physiological phenomenon responsible for various functions across the menstrual cycle, intimately linked to uterine wall microstructure. Alterations in uterine motion and tissue properties are implicated in the etiology of gynecological diseases, yet these processes have been studied in isolation. We introduce a dynamic multi-echo gradient echo EPI framework for simultaneous characterization and correlation of uterine peristaltic activity and time-resolved T2* changes at 0.55T. Inherent susceptibility artifacts, reduced resolution, and burden of manual uterine layer annotation are addressed by an unsupervised adversarial domain adaptation framework, transferring segmentation knowledge from labeled cine MRI to unlabeled dynamic EPI. We implemented Unet-LSTM with multi-scale domain discriminators that exploits temporal layer dynamics. A Dice score of 0.88 and Jaccard index of 0.80 was achieved. Mean T2* values were 108ms, 76ms, and 124ms for the myometrium, junctional zone, and endometrium. A negative correlation between junctional zone area and T2* was observed in 14/39 cases, providing first insights into oxygenation patterns associated with junctional zone contraction and motion, demonstrating feasibility of assessing the interplay between contractility and dynamic T2* changes.

Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their use at non-clinical TEs and making them incompatible with quantitative T2 mapping, a protocol- and center-independent biomarker of fetal brain maturation requiring HR reconstructions across multiple TEs. We present PRIME-SVR, the first implicit neural representation (INR) framework for joint HR reconstruction from multi-echo MRI. A single fully connected network models a continuous function from spatial coordinates to signal intensities across TEs, while a second network estimates slice-specific acquisition degradations. Cross-TE coherence is enforced via a Bloch equation-derived regularization penalizing deviations from expected T2 decay, with adaptive weighting that strengthens coupling for degraded stacks. The method is fully self-supervised. We validate PRIME-SVR on 39 in vivo fetal acquisitions (13 subjects x 3 TEs) from two centers, two vendors, and two field strengths (1.5 T and 0.55 T). Compared to state-of-the-art SVR, PRIME-SVR improves reconstruction sharpness by 47

Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools. This work presents Female-RHINO: (R)eproductive (H)ealth (I)maging A(N)alysis T(O)ol, a real-time AI-assisted framework for automated quantitative uterine MRI analysis and structured reporting during image acquisition. We present an end-to-end system that integrates inline communication with the MRI scanner and deep learning-based analysis to derive quantitative uterine biomarkers from sagittal T2-weighted pelvic MRI. The framework combines segmentation and anatomical landmark detection models trained and evaluated on more than 500 multi-center datasets spanning diverse protocols, vendors, and patient populations. It performs volumetry, detects and quantifies common incidental findings such as fibroids and Nabothian cysts, and extracts six anatomical landmarks for biometric assessment. Results are compiled into a structured clinician-oriented report with integrated visualizations, without manual interaction. Evaluation on independent retrospective and prospective cohorts demonstrated robust performance across varying acquisition settings. Mean Dice similarity coefficients were 0.82 for the uterus and 0.80 for fibroids, with lower but consistent agreement for Nabothian cysts. Landmark detection achieved a mean radial error of 3.7 mm. End-to-end processing was completed in under 70 seconds, enabling availability of results during the ongoing scan. Prospective deployment yielded immediate, standardized, and reproducible analyses supported by inter-observer agreement. The proposed system enables real-time scanner-integrated AI for automated uterine MRI analysis and reporting, with potential to improve standardization, efficiency, and clinical workflow in pelvic imaging.

Fetal diffusion MRI (dMRI) provides fascinating and clinically crucial insights into the microstructure of the human brain during development, but is highly sensitive to motion artifacts of fetal movement and maternal breathing, which impact data quality and limit diagnostic accuracy. This study introduces HERON, a robust, real-time, automatic pipeline designed to enhance fetal brain dMRI by performing motion assessment and re-acquisition. HERON leverages AI-driven brain localization, segmentation, and motion assessment on a clinical 0.55T scanner to automatically plan, quality check, and reacquire motion-affected dMRI volumes. Remaining inter-volume motion is corrected during post-processing. Tested in 20 cases, the pipeline effectively improved image quality, reduced intra- and inter-volume motion, and enabled more reliable quantitative analysis even in challenging cases. Excellent agreement with human observers (specificity 97%, sensitivity 92%) was shown and the mean Apparent Diffusion Coefficient and Intravoxel Incoherent Motion dropped in the majority of cases after correction. Improving fetal dMRI through an automatic AI-driven pipeline enables higher diagnostic quality and thus potentially wider use in both research and clinical applications.