Simultaneous BOLD detection and incomplete fMRI data reconstruction

The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation. At the same time, BOLD should be detected. We propose a new cost function comprising both BOLD detection and data reconstruction terms. A solution based on singular value thresholding and sparsity-inducing approach is proposed. Due to the low-rank nature of the fMRI data, it is expected that the related techniques to be very effective for reconstruction. Extensive experiments are conducted on different datasets in noisy conditions. The achieved results, both in terms of data quality and data analysis accuracy, are promising and confirm that the proposed method can be effective for recovery of compressed/incomplete fMRI data. Several state-of-the art image reconstruction techniques are compared with the proposed method. In addition, the results of applying general linear model (GLM) using statistical parameter mapping (SPM) toolbox are compared with those of the proposed method.