Compressed sense acquisition with artificial intelligence based denoising: evaluation in ultra-high field high resolution resting-state fMRI

Motivation: Compressed Sense (CS) acquisition in combination with novel deep learning-based reconstructions has been shown as a viable acceleration technique that brings about additional artificial intelligence (AI) based denoising. Goal(s): Here, we investigate the impact of CS-AI acceleration and denoising on high-resolution resting-state (rs)-fMRI analysis. Approach: CS was performed, and different reconstruction methods were compared: (i) conventional CS, (ii) CS with moderate SmartSpeed AI based denoising and (iii) CS with strong SmartSpeed AI based denoising. Results: Our preliminary results indicate that the underlying reconstruction CS nets do not introduce “artificial” noise or bias and are capable of generating the expected neuronal networks. Impact: Increasing the rs-fMRI resolution, without sacrificing fidelity in functional connectivity maps, via the application of CS-AI, will lead to higher confidence in human brain mapping, thus reducing the number of participants needed to detect differences between healthy and clinical populations.