Dynamic MR renography has broad clinical applications and is becoming a viable method for characterization of the renal tissue, but suffers from respiratory motion that limits analysis and interpretation. Since each examination yields at least 10-20 serial 3D images of the abdomen, manual registration is prohibitively labor-intensive.
An effective framework for registration and segmentation is necessary to analyze these data sets. Our aim is to develop and validate a computer-aided iterative framework for registration and segmentation of kidney structures on dynamic contrast-enhanced 3D (4D) MR renography.
Without satisfactory image registration, segmentation algorithms fail. A good registration facilitates tissue segmentation because it allows the algorithm to exploit multidimensional voxel data. On the other hand, a robust segmentation of intrarenal regions (e.g. renal cortex, medulla, and collecting system) for each time series can facilitate accurate image registration.
The same slice of kidney at different times with and without contrast: