Development and feasibility of quantitative dynamic cardiac imaging for mice using μSPECT

Determining the effect of cardiac blood volume on accuracy of uptake rate constants by simulation

Objective. There is great interest in better understanding coronary microvascular disease using mouse models. Typical quantification requires dynamic imaging to estimate the rate constantK1of the tracer moving from the blood into the myocardium. In addition toK1, it is also desirable to determine blood volume fractionV, which if known allows for more accurate fitting ofK1. Our previously published kinetic modeling software did not consider the effect ofV. To ensure a better fit of experimental data to the model for myocardialμSPECT imaging, in this work we updated our kinetic modeling software to include a blood volume fractionV, which adds a fraction of the arterial activity concentration into the tissue concentration.Approach. The tissue and blood time-activity curves (TACs) used for fit input were generated using ideal equations with known values in MATLAB. This allowed post-fit results to be compared to known values to determine fit errors. Parameters that were varied in generating the TACs included blood volume fraction (0, 0.05, 0.1, 0.2 and 0.3),K1(0.5, 1.5, 2.5 ml min-1g-1), frame length (1, 2, 5, 10, 15, 20 s), FWHM of the input Gaussian (10, 20, 40 s), and time of the injection peak relative to frame duration. Blood volume-fraction results have low error when blood volume is lowest, but results worsen as frame length andK1increase.Main results. We demonstrated that blood volume can be accurately determined, and also show how fit accuracy varies across TACs with different input properties.Significance. This information allows for robust use of the fitting algorithm and aids in understanding fit performance when used in animal studies.

Microvascular function measurement in mice: From large to small

There is a growing body of interest in cardiac microvascular function because it plays an important role in various cardiac disease conditions. Therefore, efforts have been made to develop non-invasive techniques to measure microvascular function. In this issue of the journal, Guerraty et al developed a micro-SPECT-based approach to assess microvascular function in in vivo mice. They applied two independent approaches to measure microvascular function: (1) myocardial blood flow measurement using 99mTc-sestamibi as a flow tracer and (2) intramyocardial blood volume measurement using 99mTc-red blood cells. Although there are issues to be addressed, they provided an important framework for non-invasive assessment of microvascular function in mice, where a number of disease models are readily available. Thus, their approaches are encouraging for facilitating better understanding of pathophysiology underlying microvascular disease models, and thereby the development of therapeutic options in future.

Quantification of defect contrast in microSPECT imaging of a myocardial phantom

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.