Presentation + Paper
9 September 2021 Simultaneous electron density and attenuation coefficient reconstruction: a feasibility study
Author Affiliations +
Abstract
Creating a viable reconstruction method for Compton scatter tomography remains challenging. Accounting for scatter attenuation when the underlying attenuation map is not known is particularly difficult, and current mathematical approaches to this vary widely. This work explores a novel approach to joint scatter and attenuation image reconstruction, which leverages the underlying structural similarity between the two images and incorporates a deep learning model in an alternating iterative reconstruction scheme. A single-view CT imaging procedure for recording Compton scatter is first described. A joint reconstruction model, which iterates between algebraically reconstructing scatter images and estimating the attenuation via deep learning is then proposed. This model is tested on both a generated dataset of 2D phantom images designed to mimic human tissues as well as a realistically simulated dataset based on real CT images. Testing results yield convergence of the model and decent reconstruction quality, demonstrating the potential principled utilities of this configuration and deep learning approach. The model achieved a structural similarity index measure of at least 0.84 for scatter and 0.89 for attenuation reconstructions with the realistically simulated dataset. The iterative, deep learning approach outlined in this work shows potential for future efficient medical imaging procedures, reconstructing images with limited scatter information.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher Wiedeman, Wenxiang Cong, and Ge Wang "Simultaneous electron density and attenuation coefficient reconstruction: a feasibility study", Proc. SPIE 11840, Developments in X-Ray Tomography XIII, 118400D (9 September 2021); https://doi.org/10.1117/12.2596280
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KEYWORDS
Signal attenuation

Photons

Sensors

Medical imaging

CT reconstruction

Computed tomography

Image restoration

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