Registration of time-series contrast enhanced magnetic resonance images for renography

• Main Authors: Yim, P.J., Marcos, H.B., McAuliffe, M., McGarry, D., Heaton, I., Choyke, P.L.
• Format: Conference Proceeding
• Language: English
• Subjects: Computational Intelligence Society; Cost function; Diseases; Heart; Hypertension; Image registration; Large-scale systems; Magnetic resonance; Mutual information; Sensor systems
• ISSN: 1063-7125
• DOI: 10.1109/CBMS.2001.941771

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 the characterization of the renal tissue. However, the analysis of MR renography is hampered by tissue motion. We investigate two automated image registration methods for minimizing 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 seven 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.