Single volume lung biomechanics from chest computed tomography using a
mode preserving generative adversarial network
- URL: http://arxiv.org/abs/2110.07878v1
- Date: Fri, 15 Oct 2021 06:17:52 GMT
- Title: Single volume lung biomechanics from chest computed tomography using a
mode preserving generative adversarial network
- Authors: Muhammad F. A. Chaudhary, Sarah E. Gerard, Di Wang, Gary E.
Christensen, Christopher B. Cooper, Joyce D. Schroeder, Eric A. Hoffman,
Joseph M. Reinhardt
- Abstract summary: 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.
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.
- Score: 10.406580531987418
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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