Autoregressive moving average (ARMA) model applied to quantification of cerebral blood flow using dynamic susceptibility contrast-enhanced magnetic resonance imaging.
OBJECTIVE
To investigate the feasibility of the autoregressive moving average (ARMA) model for quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) in comparison with deconvolution analysis based on singular value decomposition (DA-SVD).


METHODS
Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various CBFs, cerebral blood volumes and signal-to-noise ratios (SNRs) for three different types of residue function (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The ARMA model and DA-SVD were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIFs, and the estimated values were compared with the assumed values.


RESULTS
We found that the CBF value estimated by the ARMA model was more sensitive to the SNR and the delay in AIF than that obtained by DA-SVD. Although the ARMA model considerably overestimated CBF at low SNRs, it estimated the CBF more accurately than did DA-SVD at high SNRs for the exponential or triangular residue function.


CONCLUSIONS
We believe this study will contribute to an understanding of the usefulness and limitations of the ARMA model when applied to quantification of CBF with DSC-MRI.
Murase
Kenya
K
Department of Medical Engineering, Division of Allied Health Sciences, Osaka University Medical School, Japan. murase@sahs.med.osaka-u.ac.jp
Yamazaki
Youichi
Y
Shinohara
Masaaki
M
D001783:Blood Flow Velocity / Q000502:physiology; D001810:Blood Volume; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D003287:Contrast Media; D005240:Feasibility Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009010:Monte Carlo Method; D012039:Regional Blood Flow / Q000502:physiology; D012680:Sensitivity and Specificity; D013997:Time Factors
D003287:Contrast Media