###### 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 \(\pm\) 0.01), and a peak signal-to-noise ratio (36.0 \(\pm\) 1.3 dB), as well as three motion feature evaluation metrics, maximum opening distance error between endpoints (0.7 \(\pm\) 0.6 mm), maximum-swept area velocity error between adjacent images (393.3 \(\pm\) 423.3 mm\({}^{2}\)/s), and opening time error (5.5 \(\pm\) 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. m +
Footnote †: journal: AppliedThree main types of imaging techniques are available to assess aortic valves: echocardiography, cardiac magnetic resonance imaging (MRI), and cardiac computed tomography (CT). However, no matter which method is used, dynamic imaging of the aortic valve is still difficult because of its rapid movement [6]. Echocardiography is limited by its low spatial resolution and low frame rate (<20 fps) [7]. MRI and CT have a higher spatial resolution than echocardiography. Even with ECG-gated technology, MRI (<25 fps) and CT (<38 fps) [8] cannot meet the needs of dynamic imaging of aortic valves (>100 fps). Among these three imaging techniques, CT has emerged as a routinely used tool in the diagnosis of aortic valve diseases because of its high spatial resolution [9]. The temporal resolution of CT is limited by the scan time and angular range required for reconstruction. Therefore, when conducting cardiac CT, the rapid movements of aortic valves can cause motion blur. Due to the motion blur, the current CT diagnosis of aortic valves is limited to the analysis of the structure in the diastolic phase and does not use motion information. Motion blur remains a major challenge in cardiac CT diagnosis [10]. Many studies have been conducted to determine how to improve the quality of motion-blurred CT images. These techniques involve three main types: (1) physical methods, (2) image processing methods, and (3) machine learning methods. Physical methods are based on understanding the physics of motion blur and improving image quality using physical theory, such as decreasing the motion, increasing the number of X-rays, and ECG-gating [11]. These methods can slightly improve image quality, but they are limited by physical conditions and cannot handle fast motions. Image processing methods include image denoising and motion compensation [12]. Image denoising uses spatial or frequency domain filtering to remove noise, such as linear filters, nonlinear filters [13], total variation methods [14], and wavelet-based thresholding [15]. Motion compensation uses knowledge of motion to achieve image reconstruction [16], such as an anatomy-guided registration algorithm [17] and a second pass approach for aortic valve motion compensation [18]. Image processing methods perform better than physical methods, but they are not suitable for situations where the feature of motion blur is not obvious in the spatial or frequency domains or where the motion cannot be estimated in advance.