Deep neural network (DNN)–based image reconstruction
Commercially available deep neural network (DNN)–based MR image reconstruction software was used to reconstruct the accelerated acquisition images (SwiftMR, v2.0.1.0. AIRS medical, Seoul, Korea). The software algorithm was based on the popular 2D U-net structureSchematic diagram of FSE-DNN architecture. Input data is DICOM image acquired by accelerated MRI sequence, and output data is enhanced image with denoising.The algorithm includes a deep convolutional neural network (CNN) component that removes noise in the image domain and estimates the truncated high-frequency image data. This pipeline can be applied to 2D and 3D acquisitions in multiple anatomic regions and for various pulse sequences, contrast weightings, field strengths, and coil configurations. The amount of noise reduction could be controlled, owing to the model’s training process incorporating varying levels of noise amount on the input side. For this study, noise reduction level of low (51% reduction) was used because this level had been found to yield the most similar perceived image quality when compared to the standard images.