Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study

Ge Ren, Wai Yin Ho, Jing Qin, Jing Cai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Functional avoidance radiation therapy for lung cancer patients aims to limit dose delivery to highly functional lung. However, the clinical functional imaging suffers from many shortcomings, including the need of exogenous contrasts, longer processing time, etc. In this study, we present a new approach to derive the lung functional images, using a deep convolutional neural network to learn and exploit the underlying functional information in the CT image and generate functional perfusion image. In this study, 99mTc MAA SPECT/CT scans of 30 lung cancer patients were retrospectively analyzed. The CNN model was trained using randomly selected dataset of 25 patients and tested using the remaining 5 subjects. Our study showed that it is feasible to derive perfusion images from CT image. Using the deep neural network with discrete labels, the main defect regions can be predicted. This technique holds the promise to provide lung function images for image guided functional lung avoidance radiation therapy.

Original languageEnglish
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
Pages102-109
Number of pages8
ISBN (Print)9783030324858
DOIs
Publication statusPublished - 10 Oct 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: 17 Oct 201917 Oct 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
Period17/10/1917/10/19

Keywords

  • Deep learning
  • Functional avoidance radiation therapy
  • Perfusion imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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