Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

Journal: Medical physics
PMID: 34061380

Abstract

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC).

Authors

  • Taisuke Murata
    Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan.
  • Hajime Yokota
    Department of Radiology, Graduate School of Medicine, Chiba University, Chiba, Japan.
  • Ryuhei Yamato
    Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan.
  • Takuro Horikoshi
    Department of Radiology, Chiba University Hospital, Chiba, Japan.
  • Masato Tsuneda
    Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Chiba, 260-8670, Japan.
  • Ryuna Kurosawa
    Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan.
  • Takuma Hashimoto
    Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan.
  • Joji Ota
    Department of Radiology, Chiba University Hospital, Chiba, Japan.
  • Koichi Sawada
    Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan.
  • Takashi Iimori
    Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan.
  • Yoshitada Masuda
    Department of Radiology, Chiba University Hospital, Chiba, 260-8677, Japan.
  • Yasukuni Mori
    Department of Applied and Cognitive Informatics, Graduate School of Engineering, Chiba University, Chiba, Japan.
  • Hiroki Suyari
    Department of Applied and Cognitive Informatics, Graduate School of Engineering, Chiba University, Chiba, Japan.
  • Takashi Uno
    Division of Clinical Infection Diseases & Chemotherapy, Tohoku Medical and Pharmaceutical University, Sendai, Japan.

Keywords

External Resources

Popular Topics
Recent Journals