Hiroyuki Akai, Koichiro Yasaka, Haruto Sugawara, Taku Tajima, Masaru Kamitani, Toshihiro Furuta, Masaaki Akahane, Naoki Yoshioka, Kuni Ohtomo, Osamu Abe, et al. Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study. BMC medical imaging. 2023. 23. 1. 5-5
T Tajima, H Akai, K Yasaka, A Kunimatsu, Y Yamashita, M Akahane, N Yoshioka, O Abe, K Ohtomo, S Kiryu. Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clinical radiology. 2022
Koichiro Yasaka, Tomoya Tanishima, Yuta Ohtake, Taku Tajima, Hiroyuki Akai, Kuni Ohtomo, Osamu Abe, Shigeru Kiryu. Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction. Neuroradiology. 2022. 64. 10. 2077-2083
Hiroyuki Akai, Koichiro Yasaka, Haruto Sugawara, Taku Tajima, Masaaki Akahane, Naoki Yoshioka, Kuni Ohtomo, Osamu Abe, Shigeru Kiryu. Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study. Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine. 2022
Taku Tajima, Hiroyuki Akai, Haruto Sugawara, Toshihiro Furuta, Koichiro Yasaka, Akira Kunimatsu, Naoki Yoshioka, Masaaki Akahane, Osamu Abe, Kuni Ohtomo, et al. Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer. Magnetic resonance imaging. 2022. 92. 169-179