Dose reduction in radiotherapy treatment planning CT via deep learning-based reconstruction: a single‑institution
Keisuke Yasui1, Yuri Kasugai2, Maho Morishita3
1Division of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. k-yasui@fujita-hu.ac.jp.
Abstract:
To quantify radiation dose reduction in radiotherapy treatment-planning CT (RTCT) using a deep learning-based reconstruction (DLR; AiCE) algorithm compared with adaptive iterative dose reduction (IR; AIDR). To evaluate its potential to inform RTCT-specific diagnostic reference levels (DRLs). In this single-institution retrospective study, 4-part RTCT scans (head, head and neck, lung, and pelvis) were acquired on a large-bore CT. Scans reconstructed with IR (n = 820) and DLR (n = 854) were compared. The 75th-percentile CTDIvol and DLP (CTDIIR, DLPIR vs. CTDIDLR, DLPDLR) were determined per site. Dose reduction rates were calculated as (CTDIDLR - CTDIIR)/CTDIIR × 100% and similarly for DLP. Statistical significance was assessed by the Mann-Whitney U-test. DLR yielded CTDIvol reductions of 30.4-75.4% and DLP reductions of 23.1-73.5% across sites (p < 0.001), with the greatest reductions in head and neck RTCT (CTDIvol: 75.4%; DLP: 73.5%). Variability also narrowed. Compared with published national DRLs, DLR achieved 34.8 mGy and 18.8 mGy lower CTDIvol for head and neck versus UK-DRLs and Japanese multi-institutional data, respectively. DLR substantially lowers RTCT dose indices, providing quantitative data to guide RTCT-specific DRLs and optimize clinical workflows.
