  Purpose: To accelerate radially sampled diffusion weighted spin-echo
(Rad-DW-SE) acquisition method for generating high quality apparent diffusion
coefficient (ADC) maps. Methods: A deep learning method was developed to
generate accurate ADC maps from accelerated DWI data acquired with the
Rad-DW-SE method. The deep learning method integrates convolutional neural
networks (CNNs) with vision transformers to generate high quality ADC maps from
accelerated DWI data, regularized by a monoexponential ADC model fitting term.
A model was trained on DWI data of 147 mice and evaluated on DWI data of 36
mice, with acceleration factors of 4x and 8x compared to the original
acquisition parameters. We have made our code publicly available at GitHub:
https://github.com/ymli39/DeepADC-Net-Learning-Apparent-Diffusion-Coefficient-Maps,
and our dataset can be downloaded at
https://pennpancreaticcancerimagingresource.github.io/data.html. Results:
Ablation studies and experimental results have demonstrated that the proposed
deep learning model generates higher quality ADC maps from accelerated DWI data
than alternative deep learning methods under comparison when their performance
is quantified in whole images as well as in regions of interest, including
tumors, kidneys, and muscles. Conclusions: The deep learning method with
integrated CNNs and transformers provides an effective means to accurately
compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method.
