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四种稀疏视角光声成像全变差正则化重建算法的比较研究。

A Comparative Study of Four Total Variational Regularization Reconstruction Algorithms for Sparse-View Photoacoustic Imaging.

机构信息

Department of Mathematics Science, Liaocheng University, Shandong 252000, China.

出版信息

Comput Math Methods Med. 2021 Oct 18;2021:6622255. doi: 10.1155/2021/6622255. eCollection 2021.

DOI:10.1155/2021/6622255
PMID:34707684
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8545520/
Abstract

Photoacoustic imaging (PAI) is a new nonionizing, noninvasive biomedical imaging technology that has been employed to reconstruct the light absorption characteristics of biological tissues. The latest developments in compressed sensing (CS) technology have shown that it is possible to accurately reconstruct PAI images from sparse data, which can greatly reduce scanning time. This study focuses on the comparative analysis of different CS-based total variation regularization reconstruction algorithms, aimed at finding a method suitable for PAI image reconstruction. The performance of four total variation regularization algorithms is evaluated through the reconstruction experiment of sparse numerical simulation signal and agar phantom signal data. The evaluation parameters include the signal-to-noise ratio and normalized mean absolute error of the PAI image and the CPU time. The comparative results demonstrate that the TVAL3 algorithm can well balance the quality and efficiency of the reconstruction. The results of this study can provide some useful guidance for the development of the PAI sparse reconstruction algorithm.

摘要

光声成像是一种新的非电离、非侵入性的生物医学成像技术，已被用于重建生物组织的光吸收特性。压缩感知（CS）技术的最新发展表明，从稀疏数据中准确重建光声成像是可能的，这可以大大减少扫描时间。本研究专注于不同基于 CS 的全变差正则化重建算法的比较分析，旨在寻找一种适合光声成像重建的方法。通过稀疏数值模拟信号和琼脂体模信号数据的重建实验，评估了四种全变差正则化算法的性能。评估参数包括光声图像的信噪比和归一化平均绝对误差以及 CPU 时间。比较结果表明，TVAL3 算法可以很好地平衡重建的质量和效率。本研究的结果可为光声稀疏重建算法的发展提供一些有益的指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/c35f50878d99/CMMM2021-6622255.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/88d5779f378c/CMMM2021-6622255.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/ff7207838df9/CMMM2021-6622255.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/74bfba3717ff/CMMM2021-6622255.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/c35f50878d99/CMMM2021-6622255.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/88d5779f378c/CMMM2021-6622255.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/3bee9ddb4d69/CMMM2021-6622255.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/f582380b0515/CMMM2021-6622255.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/92088777a2e8/CMMM2021-6622255.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/ef71c9a39d6f/CMMM2021-6622255.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/7d209cdf1feb/CMMM2021-6622255.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/ff7207838df9/CMMM2021-6622255.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/74bfba3717ff/CMMM2021-6622255.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1a/8545520/c35f50878d99/CMMM2021-6622255.010.jpg

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