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.

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

BACKGROUND: Despite growing interest in coronary microvascular disease (CMVD), there is a dearth of mechanistic understanding. Mouse models offer opportunities to understand molecular processes in CMVD. We have sought to develop quantitative mouse imaging to assess coronary microvascular function. METHODS: We used 99mTc-sestamibi to measure myocardial blood flow in mice with MILabs U-SPECT+ system. We determined recovery and crosstalk coefficients, the influx rate constant from blood to myocardium (K1), and, using microsphere perfusion, constraints on the extraction fraction curve. We used 99mTc and stannous pyrophosphate for red blood cell imaging to measure intramyocardial blood volume (IMBV) as an alternate measure of microvascular function. RESULTS: The recovery coefficients for myocardial tissue (RT) and left ventricular arterial blood (RA) were 0.81 ± 0.16 and 1.07 ± 0.12, respectively. The assumption RT = 1 - FBV (fraction blood volume) does not hold in mice. Using a complete mixing matrix to fit a one-compartment model, we measured K1 of 0.57 ± 0.08 min-1. Constraints on the extraction fraction curve for 99mTc-sestamibi in mice for best-fit Renkin-Crone parameters were α = 0.99 and ß = 0.39. Additionally, we found that wild-type mice increase their IMBV by 22.9 ± 3.3% under hyperemic conditions. CONCLUSIONS: We have developed a framework for measuring K1 and change in IMBV in mice, demonstrating non-invasive µSPECT-based quantitative imaging of mouse microvascular function.