Semi–Synthetic Dataset for the Evaluation of Motion Compensation Approaches for Voltage Sensitive Dye Imaging

Intracranial, functional optical imaging (OI) of intrinsic signals (like blood oxygenation coupled reflection changes) and of extrinsic properties of voltage sensitive probes (like voltage-sensitive dyes) belongs to a group of invasive neuroimaging techniques with very high temporal and spatial resolutions on a meso-to macroscopic scale. Voltage sensitive dye imaging (VSDI) images brain activity with low temporal delays, but the raw signal has a poor signal to noise ratio. An important pre-processing step for many biomedical imaging techniques is image registration and motion compensation. We can apply motion compensation successfully for optical imaging of intrinsic signals but VSDI recordings have low spatial contrast and often do not contain fine grained texture details which are crucial for successful image based motion compensation. In this work, we design a semi-synthetic dataset based on real recordings and a dummy voltage sensitive dye response for the evaluation of advanced motion compensation strategies for VSDI. This dataset aims to be used as a benchmark for the development of novel motion compensation strategies for VSDI and to derive error bounds of the methodologies with respect to motion.