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Data-efficient unsupervised deep learning deformable SPECT/CT registration framework for voxel-level radionuclide therapy dosimetry: validation using clinical 131I DTC therapy data

Crossref DOI link: https://doi.org/10.1186/s40658-026-00911-2

Published Online: 2026-06-27

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Liu, Junyi

Wang, Xuanhe

Zhang, Hui

Xu, Ziqiu

Wang, Xuemei

Xu, Xie George
Funding

Funding for this research was provided by:

Double First-Class Interdisciplinary Platform of Medical Physics and Biomedical Engineering of the University of Science and Technology of China (YD2140000601)

Anhui Provincial Key Research and Development Plan (2023s07020020)

National Natural Science Foundation of China (12275372)

State Administration for Science, Technology and Industry for National Defense (HJSYF2025(22))
License Information

Text and Data Mining valid from 2026-06-27

Accepted Manuscript valid from 2026-06-27
More Information

Article History

Received: 31 December 2025

Accepted: 12 June 2026

First Online: 27 June 2026

Declarations

:

: This study involved a retrospective analysis of anonymized imaging data obtained from a previously approved prospective clinical study (Approval No. 2024-ky069). Written informed consent was obtained from all participants prior to data acquisition.

: All patients are consent for publication.

: The authors declare that they have no competing interests.

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