19 March 2014 Estimation of sparse null space functions for compressed sensing in SPECT
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Abstract
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.
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Joyeeta Mitra Mukherjee, Emil Sidky, 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); doi: 10.1117/12.2043611; https://doi.org/10.1117/12.2043611
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