Deconvolution of dynamic dual photon microscopy images of cerebral microvasculature to assess the hemodynamic status of the brain

Assessing the hemodynamic status of the brain and its variations in various situations are required to understand the local cerebral circulatory mechanisms. Dynamic contrast enhanced imaging of cerebral microvasculature provides information that can be used in understanding physiology of cerebral diseases. Bolus tracking is a technique that can be used to extract characteristic parameters that quantify local cerebral blood flow. However, post-processing of the data is needed to segment the FOV and to perform Deconvolution to remove the effects of input bolus profile and the path it travels to reach the imaging window. Finding the arterial input function (AIF) and dealing with the ill-posedness of deconvolution system make this process difficult. We propose using ICA to segment the FOV and to extract a local AIF as well as the venous output function that is required for deconvolution. This also helps to stabilize the system as ICA suppresses noise efficiently. Tikhoniv regularization (with L-curve analysis to find the best regularization parameter) is used to make the system stable and solve the problem. We have acquired dynamic 2PLSM images of a rat brain in two conditions (when the animal is at rest and when it is stimulated) and performed deconvolution. The experimental along with the simulation studies provided promising results that demonstrate the feasibility and importance of performing deconvolution.