The Computing Core will provide software-, data-, and computing-related services to the participants of the Program Project. The majority of the Core's activities revolve around a software package known as """"""""MIAMI Fuse"""""""" (Mutual Information for Automatic Multimodality Image Fusion). It was developed by the Department of Radiology's Digital Image Processing Laboratory under previous funding and is used to perform multi-modality, 3D, automated registration (or fusion) between medical imaging datasets. In this Program Project, the individual Projects will use registered datasets for a number of different purposes related to cancer management. An innovative method of tracking size and shape changes of lesions in 3D across serial exams for intrahepatic tumors is proposed by one project; this is accomplished by the registration of serial CT exams, followed by analysis of the difference between datasets. In a project on preneurosurgical planning, registration is used to perform motion correction in functional MRI studies by registering 2D slices into a 3D volume. The breast ultrasound project will employ MIAMI Fuse to register 3D studies to assess response to therapy and to improve resolution of 3D compound imaging. Registration will also be used in a new algorithm that improves PET reconstructions by incorporating side information (in this case, attenuation) from CT scans. A number of services will be offered by the Computing Core to support these uses of MIAMI Fuse and to manage the large 3D image datasets on which it operates. These services include the creation of a standardized computing environment; maintaining the MIAMI Fuse computer code on all computing systems; providing collaborators with training and consulting; maintaining a common computing lab; providing a central compute server; maintaining centralized and coordinated retrieval, storage, archiving, and management of imaging data and other information; and enhancement of selected portions of the MIAMI Fuse algorithm.
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| Berisha, Visar; Wisler, Alan; Hero, Alfred O et al. (2016) Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure. IEEE Trans Signal Process 64:580-591 |
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