Evidence map›Paper›PMID 40143696›Full record

ArticleCurrent medical imaging2025

Sparse-View CT Joint Reconstruction Strategy with Sparse Sampling Encoding Layer

Hu Guo, Minghan Yang, Ziheng Zhang, Haibo Yu, Shuai Chen, Jianye Wang, Minghao Li

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Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hu GuoUniversity of Science and Technology of China, Hefei, 230026, Anhui, China.
Minghan YangInstitute of Nuclear Energy Safety Technology, Chinese Academy of Sciences Hefei Institutes of Physical Science, Hefei, 230031, Anhui, China.
Ziheng ZhangInstitute of Nuclear Energy Safety Technology, Chinese Academy of Sciences Hefei Institutes of Physical Science, Hefei, 230031, Anhui, China.
Haibo YuUniversity of Science and Technology of China, Hefei, 230026, Anhui, China.
Shuai ChenUniversity of Science and Technology of China, Hefei, 230026, Anhui, China.
Jianye WangInstitute of Nuclear Energy Safety Technology, Chinese Academy of Sciences Hefei Institutes of Physical Science, Hefei, 230031, Anhui, China.
Minghao LiInstitute of Nuclear Energy Safety Technology, Chinese Academy of Sciences Hefei Institutes of Physical Science, Hefei, 230031, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSparse angular projection is an important way to reduce CT dose. It consists of two processes, sparse sampling, and image reconstruction based on sparse projection. Under the traditional reconstruction framework, the sparseness of the projection angle may cause a degradation effect in the reconstructed image. A series of machine learning methods for sparse angle CT reconstruction developed in recent years, especially deep learning methods, can effectively improve the reconstruction quality, however, these methods can only reconstruct CT images based on a certain sparse sampling scheme.

objectiveOn the other words, they cannot search for an efficient sparse sampling scheme under a certain dose constraint automatically, which became the motivation to develop an end-to-end sparse angular CT reconstruction method.

methodsIn this work, we propose a sampling encoding layer for searching sparse sampling schemes and integrate it into a sparse reconstruction neural network model based on projection data. Meanwhile, a joint reconstruction strategy based on both the radon domain and image domain painting is also developed.

resultsExperiments based on public CT datasets demonstrate the effectiveness of the method.

conclusionThe results show that the joint reconstruction network based on a sparse sampling coding layer has great application potential.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray ComputedAlgorithmsDeep LearningHumansMachine LearningNeural Networks, ComputerRadiation DosageCTDeep learning.Image reconstructionImaging processingSparse angular projection

Identifiers

PMID40143696
PMCPMC13181201

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