Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of
cardiovascular disease. Deep learning (DL) has recently revolutionized the field through …

Image reconstruction is an inverse problem that solves for a computational image based on
sampled sensor measurement. Sparsely sampled image reconstruction poses additional …

Abstract Magnetic Resonance Imaging (MRI) is now a widely used modality for providing
multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but …

Diffusion models with continuous stochastic differential equations (SDEs) have shown
superior performances in image generation. It can serve as a deep generative prior to …

Deep learning (DL) networks have achieved remarkable performance in infrared small
target detection (ISTD). However, these structures exhibit a deficiency in interpretability and …

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of
the heart's structure, function, and tissue characteristics with high-resolution spatial …

Recently, score-based diffusion models have shown satisfactory performance in MRI
reconstruction. Most of these methods require a large amount of fully sampled MRI data as a …

Accurate navigation and targeting are critical for neurological interventions including biopsy
and deep brain stimulation. Real-time image guidance further improves surgical planning …

In this work, we propose a novel image reconstruction framework that directly learns a
neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic …

The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction.
Tensor low-rank methods preserve the inherent high-dimensional structure of data, allowing …