TY - JOUR
T1 - Dose reduction in radiotherapy treatment planning CT via deep learning-based reconstruction
T2 - a single‑institution study
AU - Yasui, Keisuke
AU - Kasugai, Yuri
AU - Morishita, Maho
AU - Saito, Yasunori
AU - Shimizu, Hidetoshi
AU - Uezono, Haruka
AU - Hayashi, Naoki
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics 2025.
PY - 2025/12
Y1 - 2025/12
N2 - 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.
AB - 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.
KW - AiCE
KW - CT dose index
KW - CT reconstruction algorithm
KW - Deep learning-based reconstruction
KW - Treatment planning
UR - https://www.scopus.com/pages/publications/105016878953
UR - https://www.scopus.com/pages/publications/105016878953#tab=citedBy
U2 - 10.1007/s12194-025-00967-2
DO - 10.1007/s12194-025-00967-2
M3 - Article
C2 - 40987935
AN - SCOPUS:105016878953
SN - 1865-0333
VL - 18
SP - 1192
EP - 1198
JO - Radiological Physics and Technology
JF - Radiological Physics and Technology
IS - 4
ER -