Joint estimation of hemodynamic response function and voxel activation in functional MRI data

Abstract

This paper proposes a method of voxel-wise hemodynamic response function (HRF) estimation using sparsity and smoothing constraints on the HRF. The slow varying baseline drift at the voxel time-series is initially estimated via empirical mode decomposition (EMD). This estimation is refined by two-stage optimization that estimates HRF and slow-varying noise iteratively. In addition, this paper proposes a novel method of finding voxel activation via projection of voxel time- series on signal subspace constructed using the prior estimates of HRF. The performance of the proposed method is demonstrated on both synthetic and real fMRI data.