基于 K-解析逼近和正交 Procrustes 分析的字典学习法用于生物发光断层摄影术的重建

Journal of biophotonics Pub Date : 2024-11-01 Epub Date: 2024-10-07 DOI:10.1002/jbio.202400308
Linzhi Su, Limin Chen, Wenlong Tang, Huimin Gao, Yi Chen, Chengyi Gao, Huangjian Yi, Xin Cao
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引用次数: 0

摘要

生物发光层析技术(BLT)是一种无创光学分子成像技术,广泛应用于研究活体动物体内的分子活动和疾病进展。通过结合光传播模型和反演算法,BLT 可以对生物体内的光源进行三维成像和定量分析。然而,光在组织中的散射和吸收以及生物结构的复杂性等挑战极大地影响了 BLT 重建的准确性。在此,我们提出了一种基于 K-稀疏逼近和正交 Procrustes 分析(KSAOPA)的字典学习方法。KSAOPA 采用迭代交替优化策略,在稀疏编码阶段使用 K 系数 Lipschitzian 映射稀疏性(K-LIMAPS)增强解稀疏性,在字典更新阶段使用正交 Procrustes 分析减少误差,从而实现稳定精确的重建。我们通过模拟和活体实验评估了该方法的性能,结果表明,与其他方法相比,KSAOPA 在定位精度、形态恢复和活体适用性方面都非常出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dictionary Learning Method Based on K-Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography.

Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

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