An automated three-dimensional plus time registration framework for dynamic MR renography
Journal of Visual Communication and Image Representation
Integrated four dimensional registration and segmentation of dynamic renal MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Abstract: Renovascular disease is an important cause of hypertension. For assessing treatment options for renovascular disease such as angioplasty or nephrectomy, it is important to characterize the renal tissue. Magnetic resonance (MR) renography is becoming a viable method for characterization of the renal tissue. However, analysis of MR renography is hampered by tissue motion. We investigate two automated image registration methods for minimization of the effects of tissue motion The first is semi-automated registration using contours. The second is an adaptation of the Automated Image Registration (AIR) algorithm that accommodates large-scale motion and tissue enhancement from a contrast agent. We compared the results of these methods with manual registration using image overlays. Semi- automated registration using contours accurately registered a 2D MR renography data set of 140 time frames with obvious errors in only 7 slices. With correction in those slices, semi- automatic registration had equivalent quality to manual registration. The adaptation of the AIR algorithm produced better results on 3D MR renography in healthy kidneys than manual registration but worse results in a diseased kidney. We conclude that automated registration of 2D and 3D MR renography is feasible.