Literature DB >> 26796948

Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT.

Wei Zhao, Tianye Niu, Lei Xing, Yaoqin Xie, Guanglei Xiong, Kimberly Elmore, Jun Zhu, Luyao Wang, James K Min.   

Abstract

Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local HighlY constrained backPRojection reconstruction (HYPR-LR) framework. With effective use of the non-local mean, the proposed algorithm, which is referred to as HYPR-NLM, reduces the noise in dual-energy decomposition while preserving the accuracy of quantitative measurement and spatial resolution of the material-specific dual-energy images. We demonstrate the noise reduction and resolution preservation of the algorithm with an iodine concentrate numerical phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also show the superior performance of the HYPR-NLM over the existing methods by using two sets of cardiac perfusing imaging data. The DECT material decomposition comparison study shows that all four algorithms yield acceptable quantitative measurements of iodine concentrate. Direct matrix inversion yields the highest noise level, followed by HYPR-LR and Iter-DECT. HYPR-NLM in an iterative formulation significantly reduces image noise and the image noise is comparable to or even lower than that generated using Iter-DECT. For the HYPR-NLM method, there are marginal edge effects in the difference image, suggesting the high-frequency details are well preserved. In addition, when the search window size increases from to , there are no significant changes or marginal edge effects in the HYPR-NLM difference images. The reference drawn from the comparison study includes: (1) HYPR-NLM significantly reduces the DECT material decomposition noise while preserving quantitative measurements and high-frequency edge information, and (2) HYPR-NLM is robust with respect to parameter selection.

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Year:  2016        PMID: 26796948     DOI: 10.1088/0031-9155/61/3/1332

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  8 in total

1.  Adaptive Nonlocal Means Method for Denoising Basis Material Images From Dual-Energy Computed Tomography.

Authors:  Yuan Yuan; Yanbo Zhang; Hengyong Yu
Journal:  J Comput Assist Tomogr       Date:  2018 Nov/Dec       Impact factor: 1.826

2.  Impact of prior information on material decomposition in dual- and multienergy computed tomography.

Authors:  Liqiang Ren; Shengzhen Tao; Kishore Rajendran; Cynthia H McCollough; Lifeng Yu
Journal:  J Med Imaging (Bellingham)       Date:  2019-03-14

3.  Segmentation-free x-ray energy spectrum estimation for computed tomography using dual-energy material decomposition.

Authors:  Wei Zhao; Lei Xing; Qiude Zhang; Qingguo Xie; Tianye Niu
Journal:  J Med Imaging (Bellingham)       Date:  2017-06-30

4.  Optimization of Energy Combination for Gold-based Contrast Agents below K-edges in Dual-energy Micro-CT.

Authors:  Yuan Yuan; Yanbo Zhang; Hengyong Yu
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2017-12-18

Review 5.  Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review.

Authors:  Hao Zhang; Dong Zeng; Hua Zhang; Jing Wang; Zhengrong Liang; Jianhua Ma
Journal:  Med Phys       Date:  2017-03       Impact factor: 4.071

6.  Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.

Authors:  Weiwen Wu; Peijun Chen; Shaoyu Wang; Varut Vardhanabhuti; Fenglin Liu; Hengyong Yu
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2020-05-26

7.  Quantitative analysis of therapeutic response in psoriatic arthritis of digital joints with Dual-energy CT iodine maps.

Authors:  Reina Kayama; Takeshi Fukuda; Sho Ogiwara; Mami Momose; Tadashi Tokashiki; Yoshinori Umezawa; Akihiko Asahina; Kunihiko Fukuda
Journal:  Sci Rep       Date:  2020-01-27       Impact factor: 4.379

8.  Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning.

Authors:  Wei Zhao; Tianling Lv; Rena Lee; Yang Chen; Lei Xing
Journal:  Pac Symp Biocomput       Date:  2020
  8 in total

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