arXiv:2606. 00146v1 Announce Type: cross Abstract: Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability.
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:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.
arXiv:2606. 01293v1 Announce Type: cross Abstract: Accurate segmentation of fetal brain tissues in Magnetic Resonance Imaging (MRI) is critical for early diagnosis of congenital abnormalities and improving prenatal care.
arXiv:2606. 17989v1 Announce Type: cross Abstract: Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis.
arXiv:2608. 18036v1 Announce Type: cross Abstract: MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements.
arXiv:2607. 04069v1 Announce Type: cross Abstract: Cardiac cine Magnetic Resonance Imaging (MRI) is a critical diagnostic tool that provides dynamic insights for radiologists.
arXiv:2603. 05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines.
arXiv:2507. 06764v5 Announce Type: replace-cross Abstract: In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data.
Zero-Shot Self-Supervised Learning (ZS-SSL) has emerged as a promising paradigm for accelerated Magnetic Resonance Imaging (MRI) reconstruction, eliminating the reliance on fully-sampled external datasets. However, learning solely from a single under-sampled scan suffers from supervision scarcity and optimization instability, often leading to overfitting or artifacts.
arXiv:2508. 05321v4 Announce Type: replace-cross Abstract: Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available.
arXiv:2606. 04419v1 Announce Type: cross Abstract: MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam costs and limiting scanner throughput.
arXiv:2607. 18882v1 Announce Type: cross Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.