Functional MRI denoising Using Data-Driven Multi-Step Deep Neural Network

In this study a novel method for sampling the active and noisy areas is proposed by using the purification of gray and non-gray matter areas of fMRI data. Also, a data-driven network is proposed in a parallel, multi-step and integrated manner for optimal noise reduction of t-fMRI data. Besides, the proposed method reduces substantially physiological noise without considering the specific noise source and only by using the ROI of noise and activity. Based on the results, the proposed method provides a more accurate and improved activity map than previous methods, which increases the power of activity analysis in fMRI data.