Principal component analysis of dynamic fluorescence tomography in measurement space.

Challenges remain in resolving metabolic processes of drugs within small animals using a fluorescence tomographic image. In our previous work, using principal component analysis (PCA), we detected functional structures with different kinetic behaviors, where PCA was applied in fluorescence tomographic sequence (i.e. in the image space). As a result, all measurement data had to be reconstructed before performing PCA, which imposed a large computational burden. In this paper, we propose a new approach and apply PCA directly to fluorescence projection sequence (i.e. in the measurement space). Utilizing the compression property of PCA, it is possible to resolve regions with different kinetics by reconstructing only a few principal components. Hence, the computational cost can be significantly reduced. To evaluate the performance of the new method, numerical simulation and a phantom experiment are performed on a hybrid fluorescence and x-ray computed tomography imaging system. The results demonstrate that the proposed method greatly reduces the computational time compared with the previous method, while keeping a similar resolving capability.