Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network

Muhammad F. A Chaudhary; Sarah E Gerard; Di Wang; Gary E Christensen; Christopher B Cooper; Joyce D Schroeder; Eric A Hoffman; Joseph M Reinhardt

Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network

Preprint

Open access

Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network

Muhammad F. A Chaudhary, Sarah E Gerard, Di Wang, Gary E Christensen, Christopher B Cooper, Joyce D Schroeder, Eric A Hoffman and Joseph M Reinhardt

ArXiv.org

10/15/2021

Files and links (1)

url

https://arxiv.org/abs/2110.07878View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registration-free method for predicting local tissue expansion. We evaluated model performance across varying degrees of disease severity and compared its performance with two image-to-image translation frameworks - UNet and Pix2Pix. Our model achieved an overall PSNR of 18.95 decibels, SSIM of 0.840, and Spearman's correlation of 0.61 at a high spatial resolution of 1 mm3.

Details

Title: Subtitle: Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network
Creators: Muhammad F. A Chaudhary
Sarah E Gerard
Di Wang
Gary E Christensen
Christopher B Cooper
Joyce D Schroeder
Eric A Hoffman
Joseph M Reinhardt
Resource Type: Preprint
Publication Details: ArXiv.org
ISSN: 2331-8422
Number of pages: 5 pages
Language: English
Date posted: 10/15/2021
Academic Unit: Radiation Oncology; Electrical and Computer Engineering; Roy J. Carver Department of Biomedical Engineering; Advanced Pulmonary Physiomic Imaging Laboratory; Internal Medicine; Radiology; Iowa Technology Institute; The Iowa Institute for Biomedical Imaging; Radiation Research Laboratory; Holden Comprehensive Cancer Center
Record Identifier: 9984204106802771

Metrics

9 Record Views