QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction

arXiv:2402.17951v1 Announce Type: cross
Abstract: Inverse problems span across diverse fields. In medical contexts, computed tomography (CT) plays a crucial role in reconstructing a patient's internal structure, presenting challenges due to artifacts caused by inherently ill-posed inverse problems. Previous research advanced image quality via post-processing and deep unrolling algorithms but faces challenges, such as extended convergence times with ultra-sparse data. Despite enhancements, resulting images often show significant artifacts, limiting their effectiveness for real-world diagnostic applications. We aim to explore deep …

abstract advanced algorithms arxiv challenges cs.cv diverse eess.iv fields image medical mlp patient post-processing presenting processing quality research role type via view