Objective assessment of penalized maximum likelihood reconstruction with sparsity-promoting penalty for myocardial perfusion SPECT imaging

Joyeeta Mitra Mukherjee, University of Massachusetts Medical School
Joyoni Dey, College of Holy Cross
Michael A. King, College of Holy Cross
Souleymane Konate, College of Holy Cross


Novel methods of reconstructing the tracer distribution in myocardial perfusion images are being considered for low count and sparse sampling scenarios. Few examples of low count scenarios are when the amount of radioisotope administered or the acquisition time is lowered, in gated studies where individual gates are reconstructed. Examples of sparse angular sampling scenarios are patient motion correction in traditional SPECT where few angles are acquired at any given pose and in multi-pinhole SPECT where the geometry is sparse and truncated by design. The reconstruction method is based on the assumption that the tracer distribution is sparse in the transform domain, which is enforced by a sparsity-promoting penalty on the transform coefficients. In this work we investigated the curvelet transform as the sparse basis for myocardial perfusion SPECT. The objective is to determine if myocardial perfusion images can be efficiently represented in this transform domain, which can then be exploited in a penalized maximum likelihood (PML) reconstruction scheme for improving defect detection in low-count/ sparse sampling scenarios. The performance of this algorithm is compared to standard OSEM with 3D Gaussian post-filtering using bias-variance plots and numerical observer studies. The Channelized Non-prewhitening Observer (CNPW) was used for defect detection task in a "signalknown- statistically" LROC study. Preliminary investigations indicate better bias-variance characteristics and superior CNPW performance with the proposed curvelet basis. However, further assessment using more defect locations and human observer evaluation is needed for clinical significance. © 2013 SPIE.