Kidney
Tracking Project
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 purpose 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:
Project Aims
To advance and further develop computer-aided iterative method for
image registration which will enable segmentation of dynamic 3D MR renography.
The approach will deal with contrast changes and movements between
different temporal sequences.
Investigate use of rigid vs. non-rigid registration on these data
sets