Quantification of myocardial uptake rate constants in dynamic small-animal SPECT using a cardiac phantom

Abstract

Myocardial blood flow and myocardial blood flow reserve (MBFR) measurements are often used clinically to quantify coronary microvascular function. Developing imaging-based methods to measure MBFR for research in mice would be advantageous for evaluating new treatment methods for coronary microvascular disease, yet this is more challenging in mice than in humans. This work investigates microSPECT's quantitative capabilities of cardiac imaging by utilizing a multipart cardiac phantom and applying a known kinetic model to synthesize kinetic data from static data, allowing for assessment of kinetic modeling accuracy. The phantom was designed with four main components: two left-ventricular (LV) myocardial sections and two LV blood-pool sections, sized for end-systole and end-diastole. Each section of the phantom was imaged separately while acquiring list-mode data. These static, separate-compartment data were manipulated into synthetic dynamic data using a kinetic model representing the myocardium and blood-pool activity concentrations over time and then combined into a set of dynamic image frames and reconstructed. Regions of interest were drawn on the resulting images, and kinetic parameters were estimated. This process was performed for three tracer uptake values (K 1), three myocardial wall thicknesses, 10 filter parameters, and 20 iterations for 25 noise ensembles. The degree of filtering and iteration number were optimized to minimize the root mean-squared error (RMSE) of K 1 values, with the largest number of iterations and minimal filtering yielding the lowest error. Using the optimized parameters, K 1 was determined with reasonable error (~3% RMSE) over all wall thicknesses and K 1 input values. This work demonstrates that accurate and precise measurements of K 1 are possible for the U-SPECT+ system used in this study, for several different uptake rates and LV dimensions. Additionally, it allows for future investigation utilizing other imaging systems, including PET studies with any radiotracer, as well as with additional phantom parts containing lesions.

Quantification of defect contrast in microSPECT imaging of a myocardial phantom

“…These plots show that the first few iterations yielded a sharp decrease in bias with an initially small increase in standard deviation. This is consistent with previously published results (Johnson et al 2019), where it was concluded that higher iterations (approximately 20 with 4 subsets) and no smoothing provided better quantitative performance.…”

“…First, the same phantom and protocol could be used in microPET to allow for a direct application-specific image-quality comparison between the two modalities. Also, using the alternative form of the phantom from previous work (Johnson et al 2019), dynamic imaging accuracy can be assessed while utilizing different K 1 and k 2 uptake parameters appropriate for different radiotracers, to allow for evaluation of the utility of different radiotracers for cardiac imaging.…”

“…This work utilized a multi-compartment cardiac phantom that we designed and printed via three-dimensional printing (printed by Solid Technologies, Inc. (Blue Bell, PA) on an MJP ProJet 2500 system using VisiJet M2R-CL Rigid Clear material), and is similar to that used in previous work (Johnson et al 2019). The phantom included two left ventricular myocardial compartments (one uniform and one with a transmural 'cold' defect), a ventricular blood pool, and a background compartment, as shown in figure 1.…”

“…We have previously investigated the quantitative accuracy of rate-constant estimation in dynamic small-animal imaging in order to measure myocardial blood flow (Johnson et al 2019, Guerraty et al 2020.…”

Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

“…Scientists are trying to develop sequences that can operate in an interleaving fashion with other sequences or motion models which can help predict motion at any given time during the acquisition. An example of parallel sequences is given by Johnson et al, who incorporated six degrees of freedom motion tracking spherical three-dimensional navigators into a turbo-FLASH sequence with no detrimental impact to image quality [40]. These models use minimal MRI information to correlate each motion of different parts of the body with a surrogate signal, which can be obtained from an external device or another MRI sequence-and this is why we refer to them as hybrid tracking methods.…”