Deep learning for artifact correction for CT & MR acquired from imperfect acquisition condition

Abstract

In this paper, we propose various reconstruction methods of medical images through a deep learning method using an artificial neural network. The various medical images discussed here include computed tomography images and magnetic resonance images. The greatest problem with the computed tomography technique is the radiation exposure to the subject. Because the amount of exposure is directly related to the safety of the subject, various studies are actively under way to reduce these levels. Among the various research methodologies, there are sparse-view imaging techniques and interior tomography techniques. The sparse-view imaging technique refers to a means of reducing the amount of exposure by intermittently measuring a reduced number of X-ray images. If a tomography image is reconstructed using this rarely acquired X-ray image, strong streaking artifacts are generated in the reconstructed image. Another method of reducing the exposure dose, the interior tomography technique, requires the localization of the x-ray imaging area to reduce exposure. If a tomography image is reconstructed using an interior X-ray image acquired by this technique, strong cupping artifacts are generated in the reconstructed image. Therefore, here an algorithm is devised to remove these streaking artifacts and cupping artifacts generated by the sparse-view CT technique and the interior tomography technique, respectively, using an artificial neural network.

With other medical images, the most serious problem with magnetic resonance imaging is that the recording time is longer. Subjects should maintain a fixed posture during the recording time in magnetic resonance imaging, which takes a few minutes to a few tens of minutes. If a subject is unable to maintain a fixed posture and moves, motion-induced noise will occur in the MRI image. In order to solve this problem, various studies are underway to develop a robust magnetic resonance imaging method or to shorten the scanning time to minimize the motion of the subject. A robust magnetic resonance imaging technique is radial trajectory imaging. The radial trajectory imaging technique shares the same mathematical theory as the sparse-view CT technique mentioned above. Therefore, the sparse-view reconstruction technique as part of the computed tomography reconstruction method is extended and applied to the reconstruction of the magnetic resonance image along the radial trajectory. A means of shortening the scanning time can be achieved by reducing the data to be acquired. Although acquisition data of magnetic resonance imaging is acquired in the frequency domain, most artificial neural network studies focus on the imaging domain. Therefore, we devised an artificial neural network that directly interpolates the frequency domain by applying an artificial neural network in the actually acquired frequency domain rather than in the existing image domain.