Presentation + Paper
4 April 2022 Prior image-based medical image reconstruction using a style-based generative adversarial network
Varun A. Kelkar, Mark A. Anastasio
Author Affiliations +
Abstract
Computed medical imaging systems require a computational reconstruction procedure for image formation. In order to recover a useful estimate of the object to-be-imaged when the recorded measurements are incomplete, prior knowledge about the nature of object must be utilized. In order to improve the conditioning of an ill-posed imaging inverse problem, deep learning approaches are being actively investigated for better representing object priors and constraints. This work proposes to use a style-based generative adversarial network (StyleGAN) to constrain an image reconstruction problem in the case where additional information in the form of a prior image of the sought-after object is available. An optimization problem is formulated in the intermediate latent-space of a StyleGAN, that is disentangled with respect to meaningful image attributes or “styles”, such as the contrast used in magnetic resonance imaging (MRI). Discrepancy between the sought-after and prior images is measured in the disentangled latent-space, and is used to regularize the inverse problem in the form of constraints on specific styles of the disentangled latent-space. A stylized numerical study inspired by MR imaging is designed, where the sought-after and the prior image are structurally similar, but belong to different contrast mechanisms. The presented numerical studies demonstrate the superiority of the proposed approach as compared to classical approaches in the form of traditional metrics.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Varun A. Kelkar and Mark A. Anastasio "Prior image-based medical image reconstruction using a style-based generative adversarial network", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120310Z (4 April 2022); https://doi.org/10.1117/12.2612287
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image restoration

Magnetic resonance imaging

Inverse problems

Brain

Medical imaging

Neuroimaging

Compressed sensing

Back to Top