Cardiac CT is the first line imaging modality for diagnosis of cardiovascular diseases. A major challenge of cardiac CT remains motion artifacts due to fast and/or irregular cardiac dynamics. The existing motion artifact suppression algorithms can be improved based on distribution shifts due to anatomical and pathological variations in patients, protocol and technical changes of scanners, and other factors. In this paper, we construct a diversified dataset consisting of over 1,000 cardiac CT images of diverse features. Also, we provide a pipeline for source-agnostic vessel segmentation and motion artifact scoring. Our results demonstrate the merits of the approach and suggest a guideline for ensuring source-agnostic representativeness of anatomical and pathological imaging biomarkers in cardiac CT applications and beyond.
This study proposes a method of using one test bolus to monitor peak bolus arrival time at two locations, the aorta and the knee, in CT angiography lower-extremity runoff scans. The resulting aortopopliteal transition time will facilitate determining appropriate CT scan parameters to match the bolus speed. The proposed method first monitors the test bolus peak at the aorta. When the contrast enhancement peak is measured, the table is moved to monitor the test bolus at the knee. Instead of cross-sectional images, the proposed method exploits projection (single view) scans for monitoring the bolus to reduce X-ray exposure and to enable real-time peak identification. The feasibility on scan timing of the proposed method was verified by simulations. The medium and high mean blood velocities used in this study were simulated by Monte Carlo methods. Blood velocity at each location inside the arteries were obtained by a three-segment blood velocity simulation. Table motion specifications of a clinical CT scanner were also simulated. Results shown that for medium aortopopliteal distance (690 mm) and medium blood velocity (65.8 mm/sec), the table arrived at the knee position 9.99 seconds ahead of the test bolus peak, which is enough time to monitor the bolus peak at the second location. For the most challenging case, i.e. shortest aortopopliteal distance (624 mm) and high blood velocity (179.5 mm/sec), the time difference between table and bolus peak arrival to the second location was 1.87 seconds, which allows a small window of monitor scans to detect the bolus peak.
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