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Title: DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Abstract

Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [3]
  1. Univ. of Michigan, Ann Arbor, MI (United States); Shanghai Jiao Tong Univ. (China)
  2. Michigan State Univ., East Lansing, MI (United States)
  3. Univ. of Michigan, Ann Arbor, MI (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1768537
Report Number(s):
LA-UR-18-31890
Journal ID: ISSN 0278-0062; TRN: US2207108
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Medical Imaging
Additional Journal Information:
Journal Volume: 39; Journal Issue: 4; Journal ID: ISSN 0278-0062
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Computer Science; Mathematics

Citation Formats

Li, Zhipeng, Ravishankar, Saiprasad, Long, Yong, and Fessler, Jeffrey A. DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering. United States: N. p., 2020. Web. doi:10.1109/tmi.2019.2946177.
Li, Zhipeng, Ravishankar, Saiprasad, Long, Yong, & Fessler, Jeffrey A. DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering. United States. https://doi.org/10.1109/tmi.2019.2946177
Li, Zhipeng, Ravishankar, Saiprasad, Long, Yong, and Fessler, Jeffrey A. Wed . "DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering". United States. https://doi.org/10.1109/tmi.2019.2946177. https://www.osti.gov/servlets/purl/1768537.
@article{osti_1768537,
title = {DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering},
author = {Li, Zhipeng and Ravishankar, Saiprasad and Long, Yong and Fessler, Jeffrey A.},
abstractNote = {Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.},
doi = {10.1109/tmi.2019.2946177},
journal = {IEEE Transactions on Medical Imaging},
number = 4,
volume = 39,
place = {United States},
year = {2020},
month = {4}
}

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