An Efficient Method for Detection of Outliers in Tracer Curves Derived from Dynamic Contrast-Enhanced Imaging

Presence of outliers in tracer concentration-time curves derived from dynamic contrast-enhanced imaging can adversely affect the analysis of the tracer curves by model-fitting. A computationally efficient method for detecting outliers in tracer concentration-time curves is presented in this study. The proposed method is based on a piecewise linear model and implemented using a robust clustering algorithm. The method is noniterative and all the parameters are automatically estimated. To compare the proposed method with existing Gaussian model based and robust regression-based methods, simulation studies were performed by simulating tracer concentration-time curves using the generalized Tofts model and kinetic parameters derived from different tissue types. Results show that the proposed method and the robust regression-based method achieve better detection performance than the Gaussian model based method. Compared with the robust regression-based method, the proposed method can achieve similar detection performance with much faster computation speed

Accepted

I would like to express my sincere appreciation to Dr. Sunanda Mitra. Her guidance, encouragement, and support are invaluable. I would also like to gratefully thank Dr. Brian Nutter for his illumination, inspiration, and discussion. I appreciate Dr. Tanja Karp for her precious advice and help. I am grateful to Dr. Padmanabhan Seshaiyer for being on my committee and for his generous assistance. I would like to acknowledge Dr. Jiangling Guo for his knowledge, discussion, and invaluable suggestions. I would also like to appreciate my parents, and all my friends for their unremitting support