Dynamic respiratory motion estimation using patch-based kernel-pca priors for lung cancer Radiotherapy

Tiancheng He, Ramiro Pino, Bin Teh, Stephen Wong, Zhong Xue

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

Abstract

In traditional radiation therapy of lung cancer, the planned target volume (PTV) is delineated from the average or a single phase of the planning-4D-CT, which is then registered to the intra-procedural 3D-CT for delivery of radiation dose. Because of respiratory motion, the radiation needs to be gated so that the PTV covers the tumor. 4D planning deals with multiple breathing phases, however, since the breathing patterns during treatment can change, there are matching discrepancies between the planned 4D volumes and the actual tumor shape and position. Recent works showed that it is promising to dynamically estimate the lung motion from chest motion. In this paper, we propose a patch-based Kernel-PCA model for estimating lung motion from the chest and upper abdomen motion. First, a statistical model is established from the 4D motion fields of a population. Then, the lung motion of a patient is estimated dynamically based on the patient’s 4D-CT image and chest and upper abdomen motion, using population’s statistical model as prior knowledge. This lung motion estimation algorithm aims to adapt the patient’s planning 4D-CT to his/her current breathing status dynamically during treatment so that the location and shape of the lung tumor can be precisely tracked. Thus, it reduces possible damage to surrounding normal tissue, reduces side-effects, and improves the efficiency of radiation therapy. In experiments, we used the leave-one-out method to evaluate the estimation accuracy from images of 51 male subjects and compared the linear and nonlinear estimation scenarios. The results showed smaller lung field matching errors for the proposed patch-based nonlinear estimation.

Original languageEnglish (US)
Title of host publicationMolecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment - 5th International Workshop, CMMI 2017 2nd International Workshop, RAMBO 2017 and 1st International Workshop, SWITCH 2017 Held in Conjunction with MICCAI 2017, Proceedings
EditorsM. Jorge Cardoso, Tal Arbel
PublisherSpringer-Verlag
Pages55-65
Number of pages11
ISBN (Print)9783319675633
DOIs
StatePublished - 2017
Event5th International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 14 2017Sep 14 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10555 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, 2nd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017 and 1st International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/14/179/14/17

Keywords

  • Breathing pattern shift
  • Dynamic image-guided radiotherapy
  • Statistical model-based motion estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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