High fidelity, distortion-free brain diffusion-weighed imaging through Compressed SENSE combined Deep Learning reconstruction

Motivation: Distortion-free brain diffusion-weighted imaging (DWI) remains a challenge due to susceptibility artifacts and low signal-to-noise ratio (SNR). Goal(s): Assess the effectiveness of Compressed SENSE (CS) combined with deep learning (CS-DL) in improving brain DWI image quality. Approach: We compared various acceleration schemes and reconstruction methods on TSE-DWI brain images. Results: CS-DL with a factor of 4 improved image quality and SNR, while reducing scan time by 22%. Impact: Implementation of CS-DL in TSE-DWI holds promise for high-fidelity, distortion-free imaging, facilitating detailed analysis of small brain abnormalities in regions affected by magnetic field inhomogeneity and susceptibility.