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The paper presents a deep learning super‑resolution framework for 4D flow MRI of carotid arteries, using convolutional block attention modules to focus on clinically relevant spatial features and reduce noise. Trained on 120 patients with 240 stenosed carotid arteries, the model leverages patient‑specific CFD simulations as high‑resolution ground truth. Results show a significant reduction in RMSE and improved reconstruction of complex flow patterns compared to a baseline without attention.
arXiv:2610.35396v1 Announce Type: new Abstract: We evaluated synthetic pretraining and multi-cohort transfer learning for deep learning-based super-resolution of intracranial 4D Flow MRI across heter...
FlowMoDL is an unrolled neural network designed for highly accelerated 4D flow MRI reconstruction, optimizing both anatomical magnitude and phase-derived velocity accuracy. It alternates a learned (3+1)D spatiotemporal denoiser with conjugate‑gradient data‑consistency updates, using a dual‑pathway conditioning scheme to handle acceleration factors from 10× to 50×. Trained with a deep‑supervision composite loss that penalizes velocity magnitude and angular errors, FlowMoDL outperforms classical and deep‑learning baselines on the multi‑center CMRx4DFlow dataset, achieving superior gradient‑step efficiency and robust convergence across all acceleration factors.
We evaluated synthetic pretraining and multi-cohort transfer learning for deep learning-based super-resolution of intracranial 4D Flow MRI across heterogeneous neurologic cohorts. This mixed prospecti...
The study introduces a physics‑guided, self‑supervised deep learning framework that estimates macromolecular pool fraction (MPF) maps directly from conventional clinical brain MRI sequences (T1‑, T2‑, and FLAIR) without requiring specialized quantitative magnetization transfer scans. By embedding an analytical two‑pool magnetization transfer model and conditioning on acquisition parameters, the method was trained on 7,705 multisite, multivendor MRI sessions and achieved consistent MPF values across vendors and protocols, with a 35.6% reduction in lesions of demyelinating disease compared to normal‑appearing white matter. Cross‑protocol validation in a healthy volunteer showed a mean voxel‑wise absolute difference of 7.7% and a concordance correlation coefficient of 0.888, demonstrating robust cross‑protocol performance.
The study presents a multimodal BrainAGE framework that fuses structural T1-weighted MRI with DeepCBV maps—vascular information synthesized from non‑contrast MRI—to estimate brain age more accurately. Using two separate 3D convolutional neural networks, the combined model achieved a mean absolute error of 3.95 years on cognitively normal controls, outperforming single‑modality models. Saliency analyses showed MRI highlighted white matter and cortical atrophy, while DeepCBV emphasized vascular‑rich regions, and the integrated approach revealed stronger differentiation between stable and progressive MCI, indicating sensitivity to early vascular changes.