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<p>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 (<i>n</i> = 820) and DLR (<i>n</i> = 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 <i>U</i>-test. DLR yielded CTDI<sub>vol</sub> reductions of 30.4–75.4% and DLP reductions of 23.1–73.5% across sites (<i>p</i> &lt; 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&#xa0;mGy and 18.8&#xa0;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.</p>

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Dose reduction in radiotherapy treatment planning CT via deep learning-based reconstruction: a single‑institution study

  • 期刊论文

  • 发表日期：

    2025年9月24日

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  • Keisuke Yasui,
  • Yuri Kasugai,
  • Maho Morishita,
  • Yasunori Saito,
  • Hidetoshi Shimizu,
  • Haruka Uezono,
  • Naoki Hayashi

摘要

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.