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Motion artifact is a major challenge in cardiac CT which hampers accurate delineation of key anatomic (e.g. coronary lumen) and pathological features (e.g. stenosis). Conventional motion correction techniques are limited on patients with high / irregular heart rate, due to simplified modeling of CT systems and cardiac motion. Emerging deep learning based cardiac motion correction techniques have demonstrated the potential of further quality improvement. Yet, many methods require CT projection data or advanced motion simulation tools that are not readily available. We aim to develop an image-domain motion-correction method, using convolutional neural network (CNN) integrated with customized attention and spatial transformer techniques. Forty cardiac CT exams acquired from a clinical dual-source CT system were retrospectively collected to generate training (n=26) and testing (n=14) sets. Dual-source data uniquely allow image reconstruction with different temporal resolutions from the same patient scan. Slow temporal resolution (140ms; equivalent to single-source CT (SSCT) half scan) and fast temporal resolution (75ms; dual source) images were reconstructed to generate paired samples of motion-corrupted and reference images. The combinations of 2 training inference strategies and 3 CNNs were evaluated: strategy #1 – whole-heart images in training / inference; strategy #2 – vessel patches in training / inference; CNN #1 – attention only; CNN #2 – spatial-transformer (STN) only; CNN #3 – attention and STN synergy. Testing data showed that CNN #3 with strategy #2 provided relatively better performance: improving vessel delineation, increasing structural similarity index from 0.85 to 0.91, and reducing mean CT number error of lumen by 71.0%. Our method could improve the image quality in cardiac exams with SSCT.
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