Wei Xiao, Guo Hongbo, Yu Jingjing, He Xuelei, Yi Huangjian, Hou Yuqing, He Xiaowei
School of Information and Technology, Northwest University, Xi'an, China.
Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China.
Front Oncol. 2021 Oct 22;11:751055. doi: 10.3389/fonc.2021.751055. eCollection 2021.
Cerenkov luminescence tomography (CLT) is a promising non-invasive optical imaging method with three-dimensional semiquantitative imaging capability. However, CLT itself relies on Cerenkov radiation, a low-intensity radiation, making CLT reconstruction more challenging than other imaging modalities. In order to solve the ill-posed inverse problem of CLT imaging, some numerical optimization or regularization methods need to be applied. However, in commonly used methods for solving inverse problems, parameter selection significantly influences the results. Therefore, this paper proposed a probabilistic energy distribution density region scaling (P-EDDRS) framework. In this framework, multiple reconstruction iterations are performed, and the Cerenkov source distribution of each reconstruction is treated as random variables. According to the spatial energy distribution density, the new region of interest (ROI) is solved. The size of the region required for the next operation was determined dynamically by combining the intensity characteristics. In addition, each reconstruction source distribution is given a probability weight value, and the prior probability in the subsequent reconstruction is refreshed. Last, all the reconstruction source distributions are weighted with the corresponding probability weights to get the final Cerenkov source distribution. To evaluate the performance of the P-EDDRS framework in CLT, this article performed numerical simulation, pseudotumor model mouse experiment, and breast cancer mouse experiment. Experimental results show that this reconstruction framework has better positioning accuracy and shape recovery ability and can optimize the reconstruction effect of multiple algorithms on CLT.
切伦科夫发光断层扫描（CLT）是一种很有前景的非侵入性光学成像方法，具有三维半定量成像能力。然而，CLT本身依赖于切伦科夫辐射，这是一种低强度辐射，使得CLT重建比其他成像方式更具挑战性。为了解决CLT成像的不适定逆问题，需要应用一些数值优化或正则化方法。然而，在常用的求解逆问题的方法中，参数选择对结果有显著影响。因此，本文提出了一种概率能量分布密度区域缩放（P-EDDRS）框架。在该框架中，进行多次重建迭代，并将每次重建的切伦科夫源分布视为随机变量。根据空间能量分布密度求解新的感兴趣区域（ROI）。通过结合强度特征动态确定下一次操作所需区域的大小。此外，给每次重建的源分布赋予一个概率权重值，并刷新后续重建中的先验概率。最后，用相应的概率权重对所有重建源分布进行加权，得到最终的切伦科夫源分布。为了评估P-EDDRS框架在CLT中的性能，本文进行了数值模拟、假肿瘤模型小鼠实验和乳腺癌小鼠实验。实验结果表明，该重建框架具有更好的定位精度和形状恢复能力，能够优化多种算法对CLT的重建效果。
