The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT

Abstract

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1 > RPM2 > Fermi > SCM > LSVD > MUP [Formula: see text] other methods. In terms of bias, the models ranked best-to-worst are: SCM > RPM2 > RPM1 > PTU > LSVD [Formula: see text] other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.

Quantitative imaging: systematic review of perfusion/flow phantoms

“…Model outcomes include computation of absolute blood flow (BF), blood volume (BV), and/or mean transit times (MTTs) [1]. Multiple BF models of tissue perfusion exist, including model-based deconvolution, modelindependent singular value decomposition and maximum upslope models [2]. These BF models are increasingly used in addition to standard semiquantitative analysis, as these show potential towards better accuracy and standardised assessment of perfusion measures [3][4][5].…”

Impact of Abnormal Remote Stress Myocardial Blood Flow by Dynamic CT Perfusion on Clinical Outcomes

“…The mean radiation dose could be reduced to 2 to 3 mSv using these protocols. However, low sampling rate might reduce the MBF value 30 and furthermore, MBF values might differ between various calculation methods 31 . Further study is necessary to normalize the value of MBF derived from dynamic CTP.…”

Comparison of automated beam hardening correction (ABHC) algorithms for myocardial perfusion imaging using computed tomography

“…First, computational methods can give inaccurate, imprecise results leading to varying results. [14][15][16][17][18][19][20] However, our robust physiologic model 21 and super-voxel SLICR method 22 have gone a long way towards making more accurate myocardial blood flow assessment more accurate and precise. Second, BH artifacts can reduce the accuracy of quantitative blood flow estimation and create false positives in qualitative assessment of myocardial ischemia.…”

“…[32][33][34] Some methods use an iterative aproproach. 1,4,8,9,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] As raw projection data are unavailable in many medical imaging applications, others, including us, have postprocessed images, requiring image-based methods for BH correction. Typically, the image will be segmented to high and low attenuating materials and forward projections of the segmented images will be obtained to estimate contributions of different materials to projections.…”

“…Since ABHC-NH, ABHC-2, ABHC-3 have additional parameters as compared to ABHC-1, we are particularly concerned about robustness of corrections in noisy images. 21 In this regard, we test methods on images acquired at different mAs values.…”