Document Type

Conference Proceeding

Language

eng

Format of Original

4 p.

Publication Date

10-2011

Publisher

Institute of Electrical and Electronics Engineers

Source Publication

IEEE Nuclear Science Symposium and Medical Imaging Conference Record

Source ISSN

1082-3654

Original Item ID

doi: 10.1109/NSSMIC.2011.6152786

Abstract

Single Photon Emission Computed Tomography (SPECT) systems are being developed with multiple cameras and without gantry rotation to provide rapid dynamic acquisitions. However, the resulting data is angularly undersampled, due to the limited number of views. We propose a novel reconstruction algorithm for sparse-view SPECT based on Compressed Sensing (CS) theory. The algorithm models Poisson noise by modifying the Iterative Hard Thresholding algorithm to minimize the Kullback-Leibler (KL) distance by gradient descent. Because the underlying objects of SPECT images are expected to be smooth, a discrete wavelet transform (DWT) using an orthogonal spline wavelet kernel is used as the sparsifying transform. Preliminary feasibility of the algorithm was tested on simulated data of a phantom consisting of two Gaussian distributions. Single-pinhole projection data with Poisson noise were simulated at 128, 60, 15, 10, and 5 views over 360 degrees. Image quality was assessed using the coefficient of variation and the relative contrast between the two objects in the phantom. Overall, the results demonstrate preliminary feasibility of the proposed CS algorithm for sparse-view SPECT imaging.

Comments

Accepted version. Published as part of the proceedings of the conference, 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011: 2668-2671. DOI. © 2011 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.

Download

Share

COinS