UniField++ is a two‑agent framework that enhances MRI images across multiple field‑strength transitions, such as 64 mT‑to‑3 T and 3 T‑to‑7 T. It combines a VLM‑based planner that predicts risk regions and a flow‑matching enhancer that performs loop reasoning to refine high‑risk areas, guided by radiologist‑annotated hallucination‑prone regions. The method achieves significant improvements in PSNR, SSIM, and whole‑brain segmentation performance compared to existing approaches.
arXiv:2609.00960v1 Announce Type: new Abstract: Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data acro...
arXiv:2605.24625v2 Announce Type: replace Abstract: Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution...
arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractogra...
arXiv:2609.39083v1 Announce Type: new Abstract: Super-resolution and quality enhancement of 1.5\,T brain MRI are normally validated with image-fidelity metrics, although their purpose is to improve d...
Multi-parametric MRI is the gold standard for neuro-oncology, yet acquiring complete 4-modality protocols may be unfeasible in real-world clinical practice. When handling missing modalities, existing...
arXiv:2605.05522v3 Announce Type: replace-cross Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...
CAPS is a Clinically‑Verifiable Anatomy‑Prioritized Synthesis framework designed to impute missing MRI modalities for brain tumor segmentation. It uses a hybrid loss (L₁ + L_AFP + L_DualDice) to preserve deep anatomical features and topological boundaries, achieving high synthesis fidelity and robust segmentation (82.38% mean Dice) on the BraTS Adult Glioma dataset. The method also generalizes well to unseen cohorts and has been validated by blinded radiologists, who report improved image quality and clinical utility.
The paper introduces AURA, an nnU-Net-based asymmetric supervision strategy for segmenting ultra‑low‑field (0.064 T) pediatric brain MRI. It treats high‑field‑derived (HF) and low‑field‑edited (LF) annotations as distinct observations, anchoring training to the HF mask while gating LF contributions through a reliability mechanism. On a 16‑case development split, AURA achieved Dice scores comparable to HF‑only training and improved boundary metrics, demonstrating its potential for ULF MRI segmentation.
arXiv:2603. 03710v3 Announce Type: replace-cross Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness.
arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
arXiv:2610.09784v1 Announce Type: new Abstract: Reliable liver segmentation in contrast-enhanced MRI is essential for quantitative hepatic assessment, treatment planning, and longitudinal disease mon...
arXiv:2608.28705v1 Announce Type: cross Abstract: Dose accumulation is increasingly important in adaptive radiation therapy and reirradiation, but its clinical validity depends on the performance of...