Scan-rescan variability in perfusion assessment of tumors in MRI using both model and data-derived arterial input functions.
OBJECTIVE
To evaluate the contribution to scan-rescan coefficient of variation (CV) of patient-specific arterial input function (AIF) measurement in dynamic contrast-enhanced MRI (DCE-MRI) data, and to determine whether any advantage or disadvantage to using a data-derived arterial input function is related to the anatomical location of the target lesion.


METHODS
Two methods are presented for the calculation of perfusion parameters from DCE-MRI data using a two-compartment model. The first method makes use of a single-model AIF across all study data sets, while the second uses an automated process to derive an AIF specific to each data set. Both methods are applied to the analysis of a 25-subject scan-rescan study of patients with advanced solid tumors located in either the lungs or the liver. The parameters of interest in this study are the volume transfer constant between arterial plasma and extracellular extravascular space (Ktrans) and the blood-normalized initial area under the tumor enhancement curve over the first 90 seconds postinjection (IAUCBN90).


RESULTS
The use of a data-derived AIF reduces the visit-to-visit CV in both parameters for liver lesions by approximately 70% while the improvement is less than 20% for lung lesions.


CONCLUSIONS
The use of a data-derived AIF in the analysis of DCE-MRI data provides a substantial reduction in scan-rescan CV in the measurement of vascular parameters such as Ktrans and IAUCBN90. These results show a much larger advantage in the liver than in the lungs. However, this difference is largely driven by a small number of outliers, and may be spurious.
Ashton
Edward
E
VirtualScopics, Inc., 500 Linden Oaks, Rochester, New York 14625, USA. ashton@VirtualScopics.com
Raunig
David
D
Ng
Chaan
C
Kelcz
Fredrick
F
McShane
Teresa
T
Evelhoch
Jeffrey
J
D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000175:diagnosis*; D008175:Lung Neoplasms / Q000175:diagnosis*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008954:Models, Biological; D055420:Perfusion Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult