Translator Disclaimer
19 March 2014 Estimation of sparse null space functions for compressed sensing in SPECT
Author Affiliations +
Compressed sensing (CS) [1] is a novel sensing (acquisition) paradigm that applies to discrete-to-discrete system models and asserts exact recovery of a sparse signal from far fewer measurements than the number of unknowns [1- 2]. Successful applications of CS may be found in MRI [3, 4] and optical imaging [5]. Sparse reconstruction methods exploiting CS principles have been investigated for CT [6-8] to reduce radiation dose, and to gain imaging speed and image quality in optical imaging [9]. In this work the objective is to investigate the applicability of compressed sensing principles for a faster brain imaging protocol on a hybrid collimator SPECT system. As a proofof- principle we study the null space of the fan-beam collimator component of our system with regards to a particular imaging object. We illustrate the impact of object sparsity on the null space using pixel and Haar wavelet basis functions to represent a piecewise smooth phantom chosen as our object of interest.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joyeeta Mitra Mukherjee, Emil Sidky, and Michael A. King "Estimation of sparse null space functions for compressed sensing in SPECT", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90330X (19 March 2014);

Back to Top