Enhancing voxel-based dosimetry accuracy with an unsupervised deep learning approach for hybrid medical image registration.

Journal: Medical physics
PMID: 38772037

Abstract

BACKGROUND: Deformable registration is required to generate a time-integrated activity (TIA) map which is essential for voxel-based dosimetry. The conventional iterative registration algorithm using anatomical images (e.g., computed tomography (CT)) could result in registration errors in functional images (e.g., single photon emission computed tomography (SPECT) or positron emission tomography (PET)). Various deep learning-based registration tools have been proposed, but studies specifically focused on the registration of serial hybrid images were not found.

Authors

  • Keon Min Kim
    Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, Republic of Korea.
  • Minseok Suh
    Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Haniff Shazwan Muhd Safwan Selvam
    Nuclear Medicine Centre, Sunway Medical Centre, Subang Jaya, Selangor, Malaysia.
  • Teik Hin Tan
    Nuclear Medicine Centre, Sunway Medical Centre, Subang Jaya, Selangor, Malaysia.
  • Gi Jeong Cheon
    Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • Keon Wook Kang
    Seoul National University Hospital, Seoul, Republic of Korea.
  • Jae Sung Lee
    Department of Biomedical Sciences, Seoul National University, Seoul, Korea jaes@snu.ac.kr.

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