Micro X-ray computed tomography (micro-CT) is an imaging modality with significantly higher spatial resolution compared to medical CT. This modality enables nondestructively observing the internal structure of small objects and has been mainly used for nondestructive inspection in industrial fields. In recent years, the scope of micro-CT research has been expanded to the medical field, particularly in the analysis of the three-dimensional structure of pathological specimens and biological tissues. Although micro-CT has the potential to provide information to medical and research fields, many issues are still needed to be addressed for medical applications, such as low-quality images and longer scan time. My research aims to tackle these issues in order to accelerate medical applications of micro-CT by developing algorithms and reconstruction techniques for improving image quality. My research mainly focuses on software development, but we also investigate stating and contrast-enhanced methods. We aim to solve issues from both hardware and software improvement, with the goal of social implementation in the future.

We are interested in developing image processing algorithms for medical images. More specifically, in addition to the micro-CT imaging technique described above, we have been developing various technologies using machine learning and deep learning for practical use such as noise reduction for medical CT, retinal layer extraction of OCT (optical coherence tomography) images, and bleeding detection of laparoscopic images.