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

doi:10.1109/ISBI52829.2022.9761490

Conference proceeding

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

022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp.1-5

IEEE International Symposium on Biomedical Imaging (ISBI), 19th (Kolkata, India, 03/28/2022–03/31/2022)

01/01/2022

DOI: 10.1109/ISBI52829.2022.9761490

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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 1mm3.

Biomechanics

Computed Tomography

Medical Imaging

Generative adversarial networks

Lungs

Performance evaluation

Radiation dosage

Spatial resolution

Tomography

Details

Title: Subtitle: Single Volume Lung Biomechanics from Chest Computed Tomography Using a Mode Preserving Generative Adversarial Network
Creators: Muhammad F. A Chaudhary - University of Iowa
Sarah E. Gerard
Di Wang
Gary E. Christensen
Christopher B. Cooper
Joyce D. Schroeder
Eric A. Hoffman
Joseph M. Reinhardt
Resource Type: Conference proceeding
Publication Details: 022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp.1-5
Conference: IEEE International Symposium on Biomedical Imaging (ISBI), 19th (Kolkata, India, 03/28/2022–03/31/2022)
DOI: 10.1109/ISBI52829.2022.9761490
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Grant note: DOI: 10.13039/100001024, name: Roy J. Carver Charitable Trust
Language: English
Date published: 01/01/2022
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Technology Institute; Radiation Oncology; Radiation Research Laboratory; The Iowa Institute for Biomedical Imaging; Advanced Pulmonary Physiomic Imaging Laboratory; Holden Comprehensive Cancer Center; Internal Medicine
Record Identifier: 9984252345802771

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