A robust elastic net-ℓ1ℓ2 reconstruction method for x-ray luminescence computed tomography
A robust elastic net-ℓ1ℓ2 reconstruction method for x-ray luminescence computed tomography
复制标题
X射线发光计算机断层扫描的鲁棒弹性网-1-2重建方法
DOI：
10.1088/1361-6560/ac246f
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
He Xiaowei
中科院分区：
文献类型：
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作者：
Zhao Jingwen;Hongbo Guo;Jingjing Yu;Huangjian Yi;Yuqing Hou;He Xiaowei
Objective. X射线发光计算机断层扫描（XLCT）在临床前研究和疾病的有效诊断中起着至关重要的作用。然而，由于XLCT反问题的不适定性，重建方法的推广和适当的正则化参数的选择在实际应用中仍然是具有挑战性的。针对这一挑战，本文提出了一种鲁棒的弹性网络-首先，我们的方法包括101和102正则化，以提高稀疏性和抑制光滑性。其次，通过对优化问题的最优逼近，采用Landweber算法的双重修正来求解弹性网-第三，借鉴监督学习的思想，提出了多参数K折交叉验证策略，自适应地确定最优参数。为了评估弹性网-弹性体1 - 2方法的性能，进行了数值模拟、体模和体内实验。在这些实验中，弹性网络-DICE 1-DICE 2方法在所有方法中实现了最小的重建误差（具有最小的位置误差、荧光产量相对误差、归一化均方根误差）和最好的图像重建质量（具有最大的对比度噪声比和Dice相似性）。结果表明，Elastic net-101 - 102在定位精度、双源分辨率、鲁棒性和活体实用性等方面均具有良好的上级重建性能。相信本研究将进一步有利于临床前应用，以期为XLCT的后续研究提供更可靠的参考。
Objective. X-ray luminescence computed tomography (XLCT) has played a crucial role in pre-clinical research and effective diagnosis of disease. However, due to the ill-posed of the XLCT inverse problem, the generalization of reconstruction methods and the selection of appropriate regularization parameters are still challenging in practical applications. In this research, an robust Elastic net-ℓ1ℓ2reconstruction method is proposed aiming to the challenge.Approach. Firstly, our approach consists of ℓ1and ℓ2regularization to enhance the sparsity and suppress the smoothness. Secondly, through optimal approximation of the optimization problem, double modification of Landweber algorithm is adopted to solve the Elastic net-ℓ1ℓ2regulazation. Thirdly, drawing on the ideal of supervised learning, multi-parameter K-fold cross validation strategy is proposed to determin the optimal parameters adaptively.Main results. To evaluate the performance of the Elastic net-ℓ1ℓ2method, numerical simulations, phantom and in vivo experiments were conducted. In these experiments, the Elastic net-ℓ1ℓ2method achieved the minimum reconstruction error (with smallest location error, fluorescent yield relative error, normalized root-mean-square error) and the best image reconstruction quality (with largest contrast-to-noise ratio and Dice similarity) among all methods. The results demonstrated that Elastic net-ℓ1ℓ2can obtain superior reconstruction performance in terms of location accuracy, dual source resolution, robustness and in vivo practicability.Significance. It is believed that this study will further benefit preclinical applications with a view to provide a more reliable reference for the later researches on XLCT.