为了加速径向采样的扩散加权自旋回波（Rad-DW-SE）采集方法生成高质量的ADC图像，我们开发了一种深度学习方法，用于从使用Rad-DW-SE方法加速的DWI数据中生成准确的ADC图像。这种深度学习方法将卷积神经网络（CNN）与视觉变换器相结合，通过一个单指数ADC模型拟合项来生成高质量的ADC图像。我们在147只小鼠的DWI数据上训练了一个模型，并在36只小鼠的DWI数据上进行了评估，相较于原始采集参数，加速因子分别为4×和8×。消融研究和实验结果表明，与其他深度学习方法相比，所提出的深度学习模型能够以整个图像以及包括肿瘤、肾脏和肌肉在内的感兴趣区域的性能量化来生成更高质量的ADC图像。集成了CNN和变换器的深度学习方法为准确计算从我们使用Rad-DW-SE方法加速的DWI数据得到的ADC图像提供了有效手段。© 2023国际磁共振医学学会。

To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality ADC maps.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.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.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.© 2023 International Society for Magnetic Resonance in Medicine.