For evaluation of perfusion information from PPGI sequences, motion artifacts are generally disturbing. On one hand, a clear spatial assignment of detected signals to certain body regions is no longer possible. On the other hand, the detected signal itself is corrupted by coupled frequency components of the movement artifact. Depending on the movements, the detected signal may be totally disrupted. To avoid this, depending on the type of expected movement (e.g., slight, strong, translational, or rotatory), the computational time available, and required accuracy of the result, various compensatory techniques can be applied.15 In our research, we mainly use block-matching algorithms, hierarchical motion approximation with the modified Newton algorithm, or calculation of motion constraints. The temporal effort for these methods depends on the image resolution, the size and number of tracked ROIs as well as the required accuracy. On modern PCs, the movement compensation of a single ROI with our experimental setup can be computed in quasi-real-time. For IRTI, these motion detection and compensation methods are applied in an analogous way.