A Fundamental Study on the Removal of Vascular Pulsation Artifacts Using U-Net-Based Deep Neural Network

Introduction Artifacts caused by vascular pulsation manifest as periodically high signals in the phase direction, often overlapping the target area and hindering accurate observation. Traditionally, these artifacts have been mitigated using flow compensation and presaturation pulses. However, complete removal remains challenging owing to extended imaging times and the need to consider the specific absorption rate. Therefore, we aimed to propose a deep learning network for postprocessing to reduce these artifacts. Materials and methods Following approval from the institutional ethics committee, magnetic resonance imaging scans were conducted on 15 adult volunteers to create an image dataset. Short tau inversion recovery (STIR) images of the lower leg, where artifacts were prevalent, were acquired. The same cross-section was imaged under conditions likely to produce artifacts and conditions designed to minimize artifacts. We propose an artifact reduction network that combines a batch normalization layer and a dropout layer based on the U-Net architecture. The network performance was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics on the test images. Visual evaluations were conducted using a five-point scale to assess artifact reduction and image resolution. Statistical analyses were performed for each evaluation metric. Profiles of the artifact-prone areas were obtained and assessed before and after artifact reduction. Results The average PSNR was 27.83 and 28.57 for the artifact-laden and corrected image groups, respectively. The average SSIM values were 0.869 and 0.882 for the artifact-laden and corrected image groups, respectively. No significant differences were observed between the artifact-laden and corrected image groups for either PSNR (p = 0.315) or SSIM (p = 0.436). The average visual assessment scores for artifact presence were 4.68, 3.52, and 4.34 for the reference, artifact-laden, and corrected image groups, respectively. The average visual assessment scores for image resolution were 4.34, 4.30, and 3.86 for the reference, artifact-laden, and corrected image groups, respectively. No significant differences were observed between the reference and corrected image groups in the presence of artifacts (p = 0.456), although significant differences were noted between these groups and the artifact-laden image group. Furthermore, no significant differences were observed among the three groups regarding resolution evaluation. Conclusion  To our knowledge, this is the first study to apply deep learning to reduce flow artifacts caused by vascular pulsation using STIR images. We proposed a U-Net-based pulsation artifact reduction network and demonstrated its potential utility. Further detailed evaluation is required to develop an approach suitable for clinical application.