4 papers
Cone-Beam CT Image Quality Enhancement Using A Latent Diffusion Model Trained with Simulated CBCT Artifacts
Naruki Murahashi, Mitsuhiro Nakamura, Megumi Nakao
Cone-beam computed tomography (CBCT) images are problematic in clinical medicine because of their low contrast and high artifact content compared with conventional CT images. Altho…
Limited-Angle CT Reconstruction Using Multi-Volume Latent Consistency Model
Hinako Isogai, Naruki Murahashi, Mitsuhiro Nakamura +1
Limited-angle computed tomography (LACT) reconstruction is an inverse problem with severe ill-posedness arising from missing projection angles, and it is difficult to restore high-…
Construction of an Organ Shape Atlas Using a Hierarchical Mesh Variational Autoencoder
Zijie Wang, Ryuichi Umehara, Mitsuhiro Nakamura +1
An organ shape atlas, which represents the shape and position of the organs and skeleton of a living body using a small number of parameters, is expected to have a wide range of cl…
Feedback Assisted Adversarial Learning to Improve the Quality of Cone-beam CT Images
Takumi Hase, Megumi Nakao, Mitsuhiro Nakamura +1
Unsupervised image translation using adversarial learning has been attracting attention to improve the image quality of medical images. However, adversarial training based on the g…