Spectrally grouped total variation reconstruction for scatter imaging using ADMM

Ikenna Odinaka, Yan Kaganovsky, Joel A. Greenberg, Mehadi Hassan, David G. Politte, Joseph A. O'Sullivan, Lawrence Carin, David J. Brady

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We consider X-ray coherent scatter imaging, where the goal is to reconstruct momentum transfer profiles (spectral distributions) at each spatial location from multiplexed measurements of scatter. Each material is characterized by a unique momentum transfer profile (MTP) which can be used to discriminate between different materials. We propose an iterative image reconstruction algorithm based on a Poisson noise model that can account for photon-limited measurements as well as various second order statistics of the data. To improve image quality, previous approaches use edge-preserving regularizers to promote piecewise constancy of the image in the spatial domain while treating each spectral bin separately. Instead, we propose spectrally grouped regularization that promotes piecewise constant images along the spatial directions but also ensures that the MTPs of neighboring spatial bins are similar, if they contain the same material. We demonstrate that this group regularization results in improvement of both spectral and spatial image quality. We pursue an optimization transfer approach where convex decompositions are used to lift the problem such that all hyper-voxels can be updated in parallel and in closed-form. The group penalty introduces a challenge since it is not directly amendable to these decompositions. We use the alternating directions method of multipliers (ADMM) to replace the original problem with an equivalent sequence of sub-problems that are amendable to convex decompositions, leading to a highly parallel algorithm. We demonstrate the performance on real data.

Original languageEnglish
Title of host publication2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467398626
DOIs
StatePublished - Oct 3 2016
Event2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015 - San Diego, United States
Duration: Oct 31 2015Nov 7 2015

Publication series

Name2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015

Conference

Conference2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
CountryUnited States
CitySan Diego
Period10/31/1511/7/15

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    Odinaka, I., Kaganovsky, Y., Greenberg, J. A., Hassan, M., Politte, D. G., O'Sullivan, J. A., Carin, L., & Brady, D. J. (2016). Spectrally grouped total variation reconstruction for scatter imaging using ADMM. In 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015 [7582220] (2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSSMIC.2015.7582220