The use of deep learning based reconstruction in accelerating rectal cancer imaging

Motivation: Clinical routine rectal cancer imaging requires high resolution and hence increased number of excitations to achieve sufficient signal to noise ratio (SNR). Goal(s): The deep learning method is applied to improve the image quality and imaging speed of rectal magnetic resonance imaging. Approach: We investigate the use of deep learning based reconstruction in shortening the scan time of the T2-weighted TSE imaging (T2DL) sequence in rectal cancer imaging. Results: The results show that the DL reconstruction improves the SNR and CNR of the images. Also, the image acquisition time can be reduced by reconstructing images with reduced number of excitations by deep learning. Impact: Deep learning reconstruction may lead to unprecedented improvements in SNR and CNR compared to conventional reconstruction algorithms, which may be used to obtain higher quality images. In addition, deep learning methods can indirectly shorten image acquisition time.