Recently, diffusion models have shown considerable promise for MRI reconstruction.
However, extensive experimentation has revealed that these models are prone to …

Three-dimensional coronary magnetic resonance angiography (CMRA) requires
reconstruction algorithms that can significantly suppress the artifacts encountered in heavily …

Deep unfolding methods have gained significant popularity in the field of inverse problems
as they have driven the design of deep neural networks (DNNs) using iterative algorithms. In …

In the specialized domain of brain tumor segmentation, supervised segmentation
approaches are hindered by the limited availability of high-quality labeled data, a condition …

Recently, data-driven techniques have demonstrated remarkable effectiveness in
addressing challenges related to MR imaging inverse problems. However, these methods …

In the field of parallel imaging (PI), alongside image-domain regularization methods,
substantial research has been dedicated to exploring $ k $-space interpolation. However …

Long scan time significantly hinders the widespread applications of three-dimensional multi-
contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate …

Early screening for anxiety and the implementation of appropriate interventions are crucial in
preventing self-harm and suicide among patients. While multimodal real-world data provides …

There is a growing interest in model-based deep learning (MBDL) for solving imaging
inverse problems. MBDL networks can be seen as iterative algorithms that estimate the …

Recently, regularization model-driven deep learning (DL) has gained significant attention
due to its ability to leverage the potent representational capabilities of DL while retaining the …