A Linear Correction for Principal Component Analysis of Dynamic Fluorescence Diffuse Optical Tomography Images

The analysis of dynamic fluorescence diffuse optical tomography (D-FDOT) is important both for drug delivery research and for medical diagnosis and treatment. The low spatial resolution and complex kinetics, however, limit the ability of FDOT in resolving drug distributions within small animals. Principal component analysis (PCA) provides the capability of detecting and visualizing functional structures with different kinetic patterns from D-FDOT images. A particular challenge in using PCA is to reduce the level of noise in D-FDOT images. This is particularly relevant in drug study, where the time-varying fluorophore concentration (drug concentration) will result in the reconstructed images containing more noise and, therefore, affect the performance of PCA. In this paper, a new linear corrected method is proposed for modeling these time-varying fluorescence measurements before performing PCA. To evaluate the performance of the new method in resolving drug biodistribution, the metabolic processes of indocyanine green within mouse is dynamically simulated and used as the input data of PCA. Simulation results suggest that the principal component (PC) images generated using the new method improve SNR and discrimination capability, compared to the PC images generated using the uncorrected D-FDOT images.