Exploration of Time-Varying Data for Medical Diagnosis

Dynamic imaging of volume data is used in medicine to detect abnormalities in tissue perfusion, for example in the brain to diagnose an acute stroke or in the female breast for tumor detection and classification. We describe the exploration of such time-varying data for medical diagnosis. As a prerequisite several preprocessing steps are required which have largely been automated to support clinical applicability. Thereafter, the dynamic data are parameterized appropriately and projection methods are applied to convey the spatial relations. We discuss color-mapping schemes to characterize perfusion abnormalities. With these methods, crucial dynamic information can be extracted out of a 4D data volume allowing the simultaneous presentation of dynamic temporal and 3D spatial information.

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