Quantification of defect contrast in microSPECT imaging of a myocardial phantom

Abstract

Ischemic heart disease remains a significant public health concern, accentuating the importance of basic research and therapeutic studies of small animals in which myocardial changes can be reproducibly detected and quantified. Few or no studies have investigated the performance of microSPECT in quantifying myocardial lesions. We utilized three versions of a multi-compartment phantom containing two left ventricular myocardial compartments (one uniform and one with a transmural ‘cold’ defect), a ventricular blood pool, and a background compartment, where each version had a different myocardial wall thickness (0.75, 1.0 and 1.25 mm). Each compartment was imaged separately while acquiring list-mode data. The separate compartment data were manipulated into a single data set with a known defect contrast, blood-pool and background activity. Data were processed with background-free defect-contrast values of 0 (no defect), −0.25, −0.5, −0.75, and −1.0 (all defect), three ratios of blood-pool to myocardial activity, 0 (no blood pool activity), 0.1, and 0.2 (20% of the activity in the healthy myocardial compartment), and three ratios of uniform background 0 (no background activity), 0.1 and 0.2, relative to the healthy myocardial compartment. For each wall thickness, defect contrast, blood-pool, and background activity combination, 25 list-mode noise realizations were generated and reconstructed. Volumes of interest were drawn and used to determine mean contrast recovery coefficients (CRCs) over the noise ensembles. We developed a slope-analysis procedure to estimate a single CRC over all contrast levels, with resulting CRC values (for no blood-pool and no background) of 0.848, 0.946, and 0.834 for the 0.75, 1.0, and 1.25 mm wall thicknesses, respectively. We also determined and validated a reprocessing method to calculate an ideal CRC. This work demonstrates the quantitative abilities of microSPECT for myocardial-defect imaging utilizing CRC and establishes a framework for evaluating defect-imaging capabilities in other systems.