Dose reduction in radiotherapy treatment planning CT via deep learning-based reconstruction: a single‑institution study.
Authors
Affiliations (5)
Affiliations (5)
- Division of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. [email protected].
- Department of Radiology, Fujita Health University Hospital, Toyoake, Japan.
- Faculty of Radiological Technology, School of Medical Sciences, Fujita Health University, Toyoake, Japan.
- Division of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
- Department of Radiation Oncology, Fujita Health University School of Medicine, Toyoake, Japan.
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 CTDI<sub>vol</sub> and DLP (CTDI<sub>IR</sub>, DLP<sub>IR</sub> vs. CTDI<sub>DLR</sub>, DLP<sub>DLR</sub>) were determined per site. Dose reduction rates were calculated as (CTDI<sub>DLR</sub> - CTDI<sub>IR</sub>)/CTDI<sub>IR</sub> × 100% and similarly for DLP. Statistical significance was assessed by the Mann-Whitney U-test. DLR yielded CTDI<sub>vol</sub> 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 (CTDI<sub>vol</sub>: 75.4%; DLP: 73.5%). Variability also narrowed. Compared with published national DRLs, DLR achieved 34.8 mGy and 18.8 mGy lower CTDI<sub>vol</sub> 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.