Motion estimation and correction in cardiac CT angiography images using convolutional neural networks. (September 2019)

• Record Type: Journal Article
• Authors: Lossau (née Elss), T.; Nickisch, H.; Wissel, T.; Bippus, R.; Schmitt, H.; Morlock, M.; Grass, M.
• Is Part Of: Computerized medical imaging and graphics. Volume 76(2019)
• Publication Date: 2019-09
• Subjects: Coronary computed tomography angiography -- Motion compensation -- Deep learning; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2019.06.001
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Coronary Motion Forward Artifact model for CT data (CoMoFACT) generates motion perturbed image patches along with underlying motion vectors. Supervised learning for Coronary Motion estimation by Patch Analysis in CT data (CoMPACT). First iterative motion compensation pipeline based on CoMPACT. Either global artifact reduction or fast local processing of few coronary centerline points. Significantly reduced motion artifact levels, especially in image data with severe artifacts. Abstract: Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel motion compensation approach dealing with Coronary Motion estimation by Patch Analysis in CT data (CoMPACT) is presented. First, the required data for supervised learning is generated by the Coronary Motion Forward Artifact model for CT data (CoMoFACT) which introduces simulated motion to 19 artifact-free clinical CT cases with step-and-shoot acquisition protocol. Second, convolutional neural networks (CNNs) are trained to estimate underlying 2D motion vectors from 2.5D image patches based on the coronary artifact appearance. In a phantom study with computer-simulated vessels, CNNs predict the motion direction and the motion magnitude with average test accuracies of 13.37°±1.21° and 0.77 ± 0.09 mm, respectively. On clinical data with simulated motion, average test accuracies of 34.85°±2.09° and 1.86 ± 0.11 mm are achieved, whereby the precision of the motion direction prediction increases with the motion magnitude. The trained CNNs are integrated into an iterative motion compensation pipeline which includes distance-weighted motion vector extrapolation. Alternating motion estimation and compensation in twelve clinical cases with real cardiac motion artifacts leads to significantly reduced artifact levels, especially in image data with severe artifacts. In four observer studies, mean artifact levels of 3.08 ± 0.24 without MC and 2.28 ± 0.29 with CoMPACT MC are rated in a five point Likert scale.