Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients

Ziyuan Wang*, Marco Virgolin, Peter A.N. Bosman, Brian V. Balgobind, Arjan Bel, Tanja Alderliesten

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

3D dose reconstruction for radiotherapy (RT) is the estimation of the 3D radiation dose distribution patients received during RT. Big dose reconstruction data is needed to accurately model the relationship between the dose and onset of adverse effects, to ultimately gain insights and improve today's treatments. Dose reconstruction is often performed by emulating the original RT plan on a surrogate anatomy for dose estimation. This is especially essential for historically treated patients with long-Term follow-up, as solely 2D radiographs were used for RT planning, and no 3D imaging was acquired for these patients. Performing dose reconstruction for a large group of patients requires a large amount of manual work, where the geometry of the original RT plan is emulated on the surrogate anatomy, by visually comparing the latter with the original 2D radiograph of the patient. This is a labor-intensive process that for practical use needs to be automated. This work presents an image-processing pipeline to automatically emulate plans on surrogate computational tomography (CT) scans. The pipeline was designed for childhood cancer survivors that historically received abdominal RT with anterior-To-posterior and posterior-To-Anterior RT field set-up. First, anatomical landmarks are automatically identified on 2D radiographs. Next, these landmarks are used to derive parameters needed to finally emulate the plan on a surrogate CT. Validation was performed by an experienced RT planner, visually assessing 12 cases of automatic plan emulations. Automatic emulations were approved 11 out of 12 times. This work paves the way to effortless scaling of dose reconstruction data generation.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsPeter R. Bak, Po-Hao Chen
PublisherSPIE
Pages1-9
Number of pages9
Volume10954
ISBN (Electronic)9781510625556
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventMedical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications - San Diego, United States
Duration: 17 Feb 201918 Feb 2019

Publication series

NameProceedings of SPIE

Conference

ConferenceMedical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Country/TerritoryUnited States
CitySan Diego
Period17/02/1918/02/19

Keywords

  • Digitally reconstructed radiograph
  • Dose reconstruction
  • Landmark detection
  • Plan emulation
  • Radiotherapy

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