An efficient and robust algorithm to characterize the brain response from functional Magnetic Resonance Imaging (fMRI) measurements is proposed and validated through both synthetic and real fMRI measurements. The algorithm combines a Newton method with a cubature Kalman filter.
- An efficient solution methodology for estimating the parameters of the brain response model is proposed.
This method distinguishes itself from existing calibrating techniques by employing intelligently (a) the Newton algorithm, (b) a Tikhonov regularization approach, and (c) a Kalman filtering procedure.
- Both synthetic and real fMRI measurements were used to assess the performance of this method.
- The fast convergence, accuracy, and robustness to the noise effect of this method are clearly demonstrated by the reported numerical results.
- This method outperforms existing methods.
The calibration of the hemodynamic model that describes changes in blood flow and blood oxygenation during brain activation is a crucial step for successfully monitoring and possibly predicting brain activity. This, in turn, has the potential to provide diagnosis and treatment of brain diseases in early stages.
We propose an efficient numerical procedure for calibrating the hemodynamic model using some fMRI measurements. The proposed solution methodology is a regularized iterative method equipped with a Kalman filtering-type procedure. The Newton component of the proposed method addresses the nonlinear aspect of the problem. The regularization feature is used to ensure the stability of the algorithm. The Kalman filter procedure is incorporated here to address the noise in the data.
Numerical results obtained with synthetic data as well as with real fMRI measurements are presented to illustrate the accuracy, robustness to the noise, and the cost-effectiveness of the proposed method.