Automatic determination of arterial input function for dynamic contrast enhanced MRI in tumor assessment.
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).
Chen
Jeremy
J
Diagnostic Radiology Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.
Yao
Jianhua
J
Thomasson
David
D
D000465:Algorithms; D001158:Arteries / Q000378:metabolism; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media