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Contrast enhancement on cardiac CT provides valuable information about myocardial perfusion and methods have been
proposed to assess perfusion with static and dynamic acquisitions. There is a lack of knowledge and consensus on the
appropriate approach to ensure 1) sufficient diagnostic accuracy for clinical decisions and 2) low radiation doses for
patient safety. This work developed a thorough dynamic CT simulation and several accepted blood flow estimation
techniques to evaluate the performance of perfusion assessment across a range of acquisition and estimation scenarios.
Cardiac CT acquisitions were simulated for a range of flow states (Flow = 0.5, 1, 2, 3 ml/g/min, cardiac output = 3,5,8
L/min). CT acquisitions were simulated with a validated CT simulator incorporating polyenergetic data acquisition and
realistic x-ray flux levels for dynamic acquisitions with a range of scenarios including 1, 2, 3 sec sampling for 30 sec
with 25, 70, 140 mAs. Images were generated using conventional image reconstruction with additional image-based
beam hardening correction to account for iodine content. Time attenuation curves were extracted for multiple regions
around the myocardium and used to estimate flow. In total, 2,700 independent realizations of dynamic sequences were
generated and multiple MBF estimation methods were applied to each of these. Evaluation of quantitative kinetic
modeling yielded blood flow estimates with an root mean square error (RMSE) of ~0.6 ml/g/min averaged across
multiple scenarios. Semi-quantitative modeling and qualitative static imaging resulted in significantly more error
(RMSE = ~1.2 and ~1.2 ml/min/g respectively). For quantitative methods, dose reduction through reduced temporal
sampling or reduced tube current had comparable impact on the MBF estimate fidelity. On average, half dose
acquisitions increased the RMSE of estimates by only 18% suggesting that substantial dose reductions can be employed
in the context of quantitative myocardial blood flow estimation. In conclusion, quantitative model-based dynamic
cardiac CT perfusion assessment is capable of accurately estimating MBF across a range of cardiac outputs and tissue
perfusion states, outperforms comparable static perfusion estimates, and is relatively robust to noise and temporal
subsampling.
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