Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study

Abstract

Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for quick diagnosis and risk stratification of coronary artery disease. However, one major drawback of dynamic MPCT imaging is the heavy radiation dose to patients due to its dynamic image acquisition protocol. In this work, to address this issue, we present a robust dynamic MPCT deconvolution algorithm via adaptive-weighted tensor total variation (AwTTV) regularization for accurate residue function estimation with low-mA s data acquisitions. For simplicity, the presented method is termed 'MPD-AwTTV'. More specifically, the gains of the AwTTV regularization over the original tensor total variation regularization are from the anisotropic edge property of the sequential MPCT images. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate in the framework of an iterative shrinkage/thresholding algorithm. We validate and evaluate the presented algorithm using both digital XCAT phantom and preclinical porcine data. The preliminary experimental results have demonstrated that the presented MPD-AwTTV deconvolution algorithm can achieve remarkable gains in noise-induced artifact suppression, edge detail preservation, and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in digital phantom studies, and similar gains can be obtained in the porcine data experiment.

Figure 1

The rRMSE measurements of the present MPD-AwTTV deconvolution algorithm versus the number of iteration in phantom study under a low noise level.

Figure 2

(a) A modified XCAT phantom composed of the left and right ventricular cavities, aorta, healthy and ischemic myocardium. (b) Contrast dynamic of the left ventricle, aorta, healthy myocardium, ischemic myocardium, and right ventricle.

Figure 3

The MBF (column one), MBV (column two) and MTT (column three) maps calculated by three different deconvolution algorithms under a low noise level. The first row was the noise-free MPHM used as ground-truth; the second row was estimated by the sSVD deconvolution algorithm from noisy measurements; the third row was calculated by the MPDTTV deconvolution algorithm from noisy measurements; and the fourth row was calculated by the presented MPD-AwTTV deconvolution algorithm from noisy measurements. MBF in unit of ml/100g/min, MBV in unit of ml/100g, and MTT in unit of sec.

Figure 4

The PSNR, MPSE and MPAE measurements on the ROIs indicated by the yellow squares in Fig. 3 from different deconvolution algorithms: (a) MBF maps; (b) MBV maps; and (c) MTT maps.

Figure 5

The flow-scaled residue function calculated by three different deconvolution algorithms.

Figure 6

The MBF (column one), MBV (column two) and MTT (column three) maps estimated by different algorithms under a higher noise level. The first row was noise-free MPHM used as ground-truth; the second row was estimated by the sSVD deconvolution algorithm; the third row was calculated by the MPD-TTV deconvolution algorithm and the fourth row was obtained by the present MPD-AwTTV deconvolution algorithm.

Figure 7

The UQI measurements of the MPHM estimated by the three deconvolution algorithms.

Figure 8

The MBF (column one), MBV (column two) and MTT (column three) maps estimated by three different algorithms from the low-dose MPCT images, respectively. The first row was calculated from the high-dose MPCT images; the second row was estimated by the sSVD deconvolution algorithm; the third row was estimated by the MPD-TTV deconvolution algorithm; and the fourth row was estimated by the presented MPD-AwTTV deconvolution algorithm. MBF in unit of ml/100g/min, MBV in unit of ml/100g, and MTT in unit of sec.

Figure 9

The zoomed details of ROIs of MPHM in Fig. 8.

Figure 10

The horizontal profiles of the MPHM shown in Fig. 8. The first row denotes the results of the MBF maps; the second row denotes the results of the MBV maps; and the third row denotes the results of the MTT maps. The corresponding algorithms are illustrated in figure legend

Figure 11

The histogram maps of the two ROIs for the high-dose MBF map and low-dose MBF map estimated by two different deconvolution algorithms. The ROI 1 represents the healthy myocardium region. The ROI 2 represents the ischemic myocardium region.

Figure 12

The UQI measurements on the ROI indicated by the yellow rectangle in Fig. 8.

Figure 13

The MBF (column one), MBV (column two) and MTT (column three) maps estimated by different algorithms under a higher noise level in the porcine study.

Figure 14

The RMSE and UQI measurements for the presented MPD-AwTTV deconvolution algorithm with different δ values in the phantom (a) and porcine (b) data studies, respectively. The ‘blue solid line with circle marker’ and ‘red solid line with x-mark marker’ represent the UQI and RMSE measurements, respectively.