Chen Yi, Du Mengfei, Zhang Gege, Zhang Jun, Li Kang, Su Linzhi, Zhao Fengjun, Yi Huangjian, Cao Xin
Opt Express. 2023 Jul 17;31(15):24845-24861. doi: 10.1364/OE.493797.
As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.
作为近年来出现的一种双模态成像技术，锥束X射线发光计算机断层扫描（CB-XLCT）已展现出作为早期三维检测小动物肿瘤工具的潜力。然而，由于组织中光的低吸收和高散射带来的挑战，CB-XLCT重建问题是一个严重病态的逆问题，难以获得令人满意的重建结果。在本研究中，提出了一种利用字典学习和组结构（DLGS）的策略，以实现令人满意的CB-XLCT重建性能。组结构用于考虑生物体内特定区域中纳米磷光体的聚类，这可以增强同一组中元素的相互关系。此外，实施字典学习策略以有效捕获稀疏特征。通过数值模拟和体内实验对所提方法的性能进行了评估。实验结果表明，所提方法在定位精度、目标形状、鲁棒性、双源分辨率和体内实用性方面均实现了卓越的重建性能。
