biostudies-literatureLi YNIBIB NIH HHSNIA NIH HHSNIMH NIH HHSNCI NIH HHSNational Institutes of Health105-117https://www.ebi.ac.uk/biostudies/studies/S-EPMC10691280biostudies-literatureUnknown91(1)<h4>Purpose</h4>To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality ADC maps.<h4>Methods</h4>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 4× and 8× compared to the original acquisition parameters.<h4>Results</h4>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.<h4>Conclusions</h4>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.Magnetic resonance in medicineLearning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model.PMC10691280R01 AG066650EB022573R01 MH120811AG066650R01 EB022573U24 CA231858MH120811Li YSong HKFan YZhou RJoaquim MRPickup SfalseLearning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model.<h4>Purpose</h4>To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality ADC maps.<h4>Methods</h4>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 4× and 8× compared to the original acquisition parameters.<h4>Results</h4>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.<h4>Conclusions</h4>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.2024-01-01T00:00:00Z2024 Jan2025-04-18T13:24:21.265Z2025-04-06T23:03:42.029ZS-EPMC106912803759839810.1002/mrm.29833