Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconve...

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Bibliographic Details
Main Authors: Ge Ren, Sai-kit Lam, Jiang Zhang, Haonan Xiao, Andy Lai-yin Cheung, Wai-yin Ho, Jing Qin, Jing Cai
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-03-01
Series:Frontiers in Oncology
Subjects:
perfusion imaging
lung function imaging
deep learning
perfusion synthesis
CT based image analysis
functional lung avoidance radiation therapy
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.644703/full

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https://www.frontiersin.org/articles/10.3389/fonc.2021.644703/full

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