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

Linning Ye and Zujun Hou

Mathematical Problems in Engineering, 2018, vol. 2018, 1-10

Abstract:

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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2018/5670165.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2018/5670165.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5670165

DOI: 10.1155/2018/5670165

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().