Deep Learning enhanced joint reconstruction and Nyquist ghost correction in multiband diffusion imaging

Motivation: Multiband imaging in EPI Diffusion sequences can suffer from Nyquist ghosting artifacts and poor slice separation. This affects evaluation of ADC, FA, and kurtosis maps in high performance gradient systems. Goal(s): Reduce ghosting and improve SNR in multiband images so that ADC, FA, and kurtosis maps deviate minimally from single-band imaging. Approach: EPI data is split into odd and even echoes and independently reconstructed with ARC algorithm. Virtual channel combination with phase correction along with a Deep Learning algorithm provides SNR enhancement. Results: There was minimal error in the ADC, FA, and kurtosis maps with the proposed approach compared to single-band images. Impact: Our reconstruction algorithm helps multiband imaging achieve minimal deviation in ADC, FA, orthogonal and parallel kurtoses as in single-band imaging but in a shorter acquisition time.