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

Guerraty, Marie; Johnson, Lindsay C.; Blankemeyer, Eric; Rader, Daniel J.; Moore, Stephen; Metzler, S.

doi:10.1007/s12350-020-02082-8

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…To simulate the dynamic data, the equations above were averaged over the 10 s of each dynamic frame in 1 s steps, with λ chosen such that the myocardial concentration was approximately that obtained from mice rest/ stress data (Guerraty et al 2017). The appropriate probability function was used for each part of the phantom, applying a half-life correction based on each part's acquisition start time.…”

Section: Dynamic End Diastole and End Systole Image Ensemble Generationmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Dynamic End Diastole and End Systole Image Ensemble Generationmentioning

confidence: 99%

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

et al. 2019

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“…It may also be of interest for future research to determine V in dynamic imaging studies of mice to aid in evaluation of cardiac conditions. Additionally, we have shown in previous work utilizing SPECT imaging of red blood cells in a small group of healthy mice that V increased in response to hyperemia (Guerraty et al 2020). Thus, V may be an important metric to help quantify microvascular disease states in mice.…”

Section: Introductionmentioning

confidence: 71%

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

et al. 2023

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Objective:There is great interest in better understanding coronary microvascular disease using mouse models. Typical quantification requires dynamic imaging to estimate the rate constant K1 of the tracer moving from the blood into the myocardium. In addition to K1 , it is also desirable to determine blood volume fraction V, which if known allows for more accurate fitting of K1 . Our previously published kinetic modeling software did not consider the effect of V. To ensure a better fit of experimental data to the model for myocardial μSPECT imaging, in this work we updated our kinetic modeling software to include a blood volume fraction V, 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-1 g-1), frame length (1, 2, 5, 10, 15, 20 seconds), FWHM of the input Gaussian (10, 20, 40 secs), 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 and K1 increase. 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.

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“…Fourth, as we move to improved quantification for SPECT MBF, it will also be important to account for scatter and attenuation for more accurate SPECT reconstructions and to model accurately the mixing between the myocardium and the blood pool. While careful phantom and patient studies will be needed to do this well, we know intuitively and from animal work 15 that the mixing from the blood pool into the myocardium is not zero; underestimating this parameter will tend to reduce the results of MBF measurement. To go further, in clinical SPECT where the resolution can vary substantially over the heart and where the patient anatomy can vary significantly as well, it may be important to consider methods for region-specific and patient-specific mixing parameters.…”

Section: Introductionmentioning

confidence: 99%

SPECT quantification of myocardial blood flow: Another step toward widespread availability

Guerraty

Metzler

Bravo

2021

Journal of Nuclear Cardiology

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

Cited by 6 publications

References 28 publications

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

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

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

SPECT quantification of myocardial blood flow: Another step toward widespread availability

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