Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction.

Qian Wang, Morteza Salehjahromi, Hengyong Yu

Abstract read

In one paragraph

Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

3 authors.

Qian WangDepartment of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA 01854, USA.ORCID 0000-0001-8988-126X

Morteza SalehjahromiDepartment of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA 01854, USA.ORCID 0000-0002-7036-1683

Hengyong YuDepartment of Electrical and Computer Engineering, University of Massachusetts at Lowell, Lowell, MA 01854, USA.ORCID 0000-0002-5852-0813

Funding

High dose efficiency CT SystemU01EB017140 · NIBIB · STANFORD UNIVERSITY · PI EDIC, PETER, PELC, NORBERT J. · 2014 to 2018

$5.4M

NIBIB NIH HHS U01 EB017140

6 · The paper itself

Abstract

Spectral computed tomography (CT) is extension of the conventional single spectral CT (SSCT) along the energy dimension, which achieves superior energy resolution and material distinguishability. However, for the state-of-the-art photon counting detector (PCD) based spectral CT, because the emitted photons with a fixed total number for each X-ray beam are divided into several energy bins, the noise level is increased in each reconstructed channel image, and it further leads to an inaccurate material decomposition. To improve the reconstructed image quality and decomposition accuracy, in this work, we first employ a refined locally linear transform to convert the structural similarity among two-dimensional (2D) spectral CT images to a spectral-dimension gradient sparsity. By combining the gradient sparsity in the spatial domain, a global three-dimensional (3D) gradient sparsity is constructed, then measured with

Indexed as

constrained optimizationiterative reconstructionmaterial decompositionRefined locally linear transformsparsity constructionspectral CTspectral-dimension gradient sparsitystructural similarity

Identifiers

PMID33996345

DOI10.1109/access.2021.3071492

PMCPMC8118116

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.