Reconstruction of Motion Images from Single Two-Dimensional Motion-Blurred Computed Tomographic Image of Aortic Valves Using In Silico Deep Learning: Proof of Concept

Abstract

The visualization of motion is important in the diagnosis and treatment of aortic valve disease. It is difficult to perform using computed tomography (CT) because of motion blur. Existing research focuses on suppressing or removing motion blur. The purpose of this study is to prove the feasibility of inferring motion images using motion information from a motion-blurred CT image. An in silico learning method is proposed, to infer 60 motion images from a two-dimensional (2D) motion-blurred CT image, to verify the concept. A dataset of motion-blurred CT images and motion images was generated using motion and CT simulators to train a deep neural network. The trained model was evaluated using two image similarity evaluation metrics, a structural similarity index measure (0.97 ± 0.01), and a peak signal-to-noise ratio (36.0 ± 1.3 dB), as well as three motion feature evaluation metrics, maximum opening distance error between endpoints (0.7 ± 0.6 mm), maximum-swept area velocity error between adjacent images (393.3 ± 423.3 mm2/s), and opening time error (5.5 ± 5.5 ms). According to the results, the trained model can successfully infer 60 motion images from a motion-blurred CT image. This study demonstrates the feasibility of inferring motion images from a motion-blurred CT image under simulated conditions.