Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of cardiovascular diseases but also for prediction of cardiac future events. Current imaging modalities has limitations that could degrade the accuracy of the analysis indices. In this paper, we present a projection-based motion estimation method for x-ray CT that estimates cardiac motion with high spatio-temporal resolution using projection data and a reference 3D volume image. The experiment using a synthesized digital phantom showed promising results for motion analysis.
We have developed a digitally synthesized patient which we call “Zach” (Zero millisecond Adjustable
Clinical Heart) phantom, which allows for an access to the ground truth and assessment of image-based
cardiac functional analysis (CFA) using CT images with clinically realistic settings. The study using Zach
phantom revealed a major problem with image-based CFA: "False dyssynchrony." Even though the true
motion of wall segments is in synchrony, it may appear to be dyssynchrony with the reconstructed cardiac
CT images. It is attributed to how cardiac images are reconstructed and how wall locations are updated
over cardiac phases. The presence and the degree of false dyssynchrony may vary from scan-to-scan,
which could degrade the accuracy and the repeatability (or precision) of image-based CT-CFA exams.
For myocardial perfusion CT exams, beam hardening (BH) artifacts may degrade the accuracy of myocardial perfusion defect detection. Meanwhile, cardiac motion may make BH process inconsistent, which makes conventional BH correction (BHC) methods ineffective. The aims of this study were to assess the severity of BH artifacts and motion artifacts and propose a projection-based iterative BHC method which has a potential to handle the motion-induced inconsistency better than conventional methods. In this study, four sets of forward projection data were first acquired using both cylindrical phantoms and cardiac images as objects: (1) with monochromatic x-rays without motion; (2) with polychromatic x-rays without motion; (3) with monochromatic x-rays with motion; and (4) with polychromatic x-rays with motion. From each dataset, images were reconstructed using filtered back projection; for datasets 2 and 4, one of the following BHC methods was also performed: (A) no BHC; (B) BHC that concerns water only; and (C) BHC that takes both water and iodine into account, which is an iterative method we developed in this work. Biases of images were quantified by the mean absolute difference (MAD). The MAD of images with BH artifacts alone (dataset 2, without BHC) was comparable or larger than that of images with motion artifacts alone (dataset 3): In the study of cardiac image, BH artifacts account for over 80% of the total artifacts. The use of BHC was effective: with dataset 4, MAD values were 170 HU with no BHC, 54 HU with water BHC, and 42 HU with the proposed BHC. Qualitative improvements in image quality were also noticeable in reconstructed images.
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