Compressed Sensing and MRI Reconstruction
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 4458
Special Issue Editor
Special Issue Information
Dear Colleagues,
Compressed sensing (CS) is a promising approach that employs the sparsity property as a precondition for signal recovery. The sparsity as the main premise in designing CS algorithms for signal compression or reconstruction is characterized by a few nonzero coefficients in one of the transformation domains. Therefore, the sparse signals can be fully reconstructed from a reduced set of incoherent measurements. The developed CS frameworks for the sparse signals’ reconstruction span a wide range of techniques that can be largely divided into the following categories: matching pursuit, constrained convex optimization, and the Bayesian approach. CS-based techniques have been increasingly applied to improve the time efficiency of various biomedical imaging modalities, such as computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). More recently, inspired by the success in the field of computer vision, deep-learning technique has emerged as one of the most prominent approaches for the reconstruction of CS-based MRI. In this special issue, the most up-to-date original research papers and reviews are invited in the areas of CS applications to biomedical signal recovery and image reconstruction, while a greater focus will be given to recent advances in deep-learning based CS-MRI reconstruction.
Prof. Dr. Tie-Qiang Li
Guest Editor
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