Automatic Determination of Arterial Input Function for Dynamic Contrast Enhanced MRI in Tumor Assessment View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Jeremy Chen , Jianhua Yao , David Thomasson

ABSTRACT

Dynamic Contrast Enhanced MRI (DCE-MRI) is today one of the most popular methods for tumor assessment. Several pharmacokinetic models have been proposed to analyze DCE-MRI. Most of them depend on an accurate arterial input function (AIF). We propose an automatic and versatile method to determine the AIF. The method has two stages, detection and segmentation, incorporating knowledge about artery structure, fluid kinetics, and the dynamic temporal property of DCE-MRI. We have applied our method in DCE-MRIs of four different body parts: breast, brain, liver and prostate. The results show that we achieve average 89.5% success rate for 40 cases. The pharmacokinetic parameters computed from the automatic AIF are highly agreeable with those from a manually derived AIF (R2 = 0.89, P (T <=t) = 0.19) and a semiautomatic AIF (R2 = 0.98, P(T <=t) = 0.01). More... »

PAGES

594-601

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-85988-8_71

DOI

http://dx.doi.org/10.1007/978-3-540-85988-8_71

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046690088

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18979795


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