Deep learning-based fast volumetric image generation for image-guided proton radiotherapy Journal Article

   


Authors: Chang, C. W.; Lei, Y.; Wang, T.; Tian, S.; Roper, J.; Lin, L.; Bradley, J.; Liu, T.; Zhou, J.; Yang, X.
Article Title: Deep learning-based fast volumetric image generation for image-guided proton radiotherapy
Abstract: Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultrahigh dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV X-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a 4-D computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135 degrees and 225 degrees yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were 75 +/- 22 hounsfield unit, 19 +/- 3.7 dB, 0.938 +/- 0.044, and -1.3%+/- 4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.
Keywords: training; protons; anatomy; tumor; therapy; computed tomography; lung-cancer; biomedical imaging; image-guided radiation; therapy (igrt); (ct); water equivalent thickness; image synthesis; x-ray imaging; deep learning (dl); three-dimensional displays; 4-d computed tomography
Journal Title: IEEE Transactions on Radiation and Plasma Medical Sciences
Volume: 8
Issue: 8
ISSN: 2469-7311
Publisher: IEEE  
Date Published: 2024-11-01
Start Page: 973
End Page: 983
Language: English
ACCESSION: WOS:001350735600012
DOI: 10.1109/trpms.2024.3439585
PROVIDER: wos
PMCID: PMC10402267
PUBMED: 37546731
Notes: Article -- Source: Wos
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