Comparisons of datasets obtained from individual subjects between imaging modalities are very important for the evaluation of the normal physiologic responses of the anatomic structure or the pathophysiological changes that accompany disease states. Likewise, it is also critical to compare data between individuals both within and across different imaging modalities. Unfortunately, many structures of interest, particularly in the brain, are often smaller than the spatial resolution of the imaging devices and corrections aided by anatomical imaging modalities such as CT and MR are often required [53,54].
Anatomic structures, particularly those in the brain, can also be identified using a standardized reference coordinate system or functional image data can be fitted to a standard anatomical atlas (e.g., Talairach space) with the aid of anatomical landmarks or contours [55-58]. This idea is somewhat similar to the model-based approaches where analytically or parametrically defined models are used to segment the organ boundaries. The difference lies in the definition of the model, which is described by a computerized anatomy atlas or a stereotaxic coordinate system—a reference that the functional images are mapped onto by either linear or nonlinear transformation. A number of transformation techniques have been developed for this process . The ROIs defined on the template are then available to the functional image data.
Similarly, functional (PET and SPECT) images and structural (CT and MR) images obtained from individual subjects can be fused (coregistered), allowing precise anatomical localization of activity on the functional images [60, 61]. Precise alignment between the anatomic/template and PET images is necessary for these methods. Importantly, methods that use registration to a standard coordinate system are problematic when patients with pathological processes (e.g., tumors, infarction, and atrophy) are studied.
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