Biomedical images represent spatial maps of signals collected from the interaction of energy with the body at a specific time. The signals themselves represent a property of the tissue. There are two strategies to infer functional status of tissue and whole organisms from biomedical images. The first relies on the signals from which the image is collected to target properties that directly relate to function. A common example of this approach is the use of FDG-PET to measure glucose uptake, providing a direct window into tissue glucose metabolism. A single image collected after the injection of radiolabled FDG provides a spatial distribution of the rate of glucose uptake. As the functional signals often target events that occur in cells at a molecular level, this approach to functional imaging is often referred to as molecular imaging. In molecular imaging the key information is included in the spatial distribution of a signal at a single time point.

A second approach to inferring tissue function from biomedical images relies on the evolution of the image as a function of time. With this approach, images from a single time point do not contain functional information. However, tracking a tissue property over time allows information regarding function to be extracted. The time axis contains the critical information in this approach to imaging tissue function. We will define the measurement of tissue function by detecting time changes in images as physiological imaging.