Individualized 3D dose distribution prediction using deep learning

Jianhui Ma, Ti Bai, Dan Nguyen, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, Steve Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In cancer radiotherapy, inverse treatment planning is a multi-objective optimization problem. There exists a set of plans with various trade-offs on Pareto surface which are referred as Pareto optimal plans. Currently exploring such trade-offs, i.e., physician preference is a trial and error process and often time-consuming. Therefore, it is desirable to predict desired Pareto optimal plans in an efficient way before treatment planning. The predicted plans can be used as references for dosimetrists to rapidly achieve a clinically acceptable plan. Clinically the dose volume histogram (DVH) is a useful tool that can visually indicate the specific dose received by each certain volume percentage which is supposed to describe different trade-offs. Consequently, we have proposed a deep learning method based on patient’s anatomy and DVH information to predict the individualized 3D dose distribution. Qualitative measurements have showed analogous dose distributions and DVH curves compared to the true dose distribution. Quantitative measurements have demonstrated that our model can precisely predict the dose distribution with various trade-offs for different patients, with the largest mean and max dose differences between true dose and predicted dose for all critical structures no more than 1.7% of the prescription dose.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsDan Nguyen, Steve Jiang, Lei Xing
PublisherSpringer
Pages110-118
Number of pages9
ISBN (Print)9783030324858
DOIs
StatePublished - 2019
Event1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11850 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Deep learning
  • Dose prediction
  • Trade-offs
  • Treatment planning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ma, J., Bai, T., Nguyen, D., Folkerts, M., Jia, X., Lu, W., Zhou, L., & Jiang, S. (2019). Individualized 3D dose distribution prediction using deep learning. In D. Nguyen, S. Jiang, & L. Xing (Eds.), Artificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 110-118). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11850 LNCS). Springer. https://doi.org/10.1007/978-3-030-32486-5_14