Health AI Central
Peer-Reviewed Publication
J Med Imaging Radiat Sci2026;57(6):102604.September 25, 2026Journal Article

Feasibility and image quality evaluation of accelerated T2-weighted and time-of-flight MRI using AI-assisted compressed sensing and deep reconstruction.

Minghui Tang1, Noriyuki Fujima2, Hiroyuki Kameda3, Yohei Ikebe4, Yan Tong5, Tsubasa Kaji6, Kenji Hirata7, Kohsuke Kudo8
1Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, 060-8638, Japan; Medical AI Research and Development Center, Hokkaido University Hospital, Sapporo, 060-8648, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 060-8648, Japan. Electronic address: toumeiki@hs.hokudai.ac.jp.
2Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 060-8648, Japan; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, 060-8648, Japan.
3Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 060-8648, Japan; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, 060-8648, Japan; Faculty of Dental Medicine, Department of Radiology, Hokkaido University, Sapporo, 060-8586, Japan.
4Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, 060-8648, Japan; Center for Cause of Death investigation, Faculty of Medicine, Hokkaido University, Sapporo, 060-8638, Japan.
5Clinical Marketing and Application, International Business, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China.
6Department of Marketing & Business, United Imaging Healthcare Japan K.K., Tokyo, 102-0083, Japan.
7Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, 060-8638, Japan; Medical AI Research and Development Center, Hokkaido University Hospital, Sapporo, 060-8648, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 060-8648, Japan; Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, 060-8648, Japan.
8Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, 060-8638, Japan; Medical AI Research and Development Center, Hokkaido University Hospital, Sapporo, 060-8648, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 060-8648, Japan; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, 060-8648, Japan.
PubMed DOI

Abstract

BACKGROUND: Magnetic Resonance Imaging (MRI) provides high-resolution, non-invasive diagnostic images, but long acquisition times increase patient burden and motion artefacts. Compressed sensing helps reduce scan duration, yet further image quality improvements remain necessary. Recent advances in artificial intelligence (AI) have shown promise in enhancing image quality and accelerating MRI acqui…

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