Efficient MR Image Compression using Deep Learning Models for Multi-contrast MRI

As more and more medical imaging dataset is created, efficient and high-rate data compression is in demand for applications such as data transfer, storage and cloud based MR image analysis. However, conventional compression options do not provide the efficiency and compression performance needed for real-time applications such as image query and computer-aided diagnosis. In this work we demonstrated the applicability of the DL based compression algorithm for MRI to improve the compression performance and efficiency. Trained on natural images and fine-tuned on multi-contrast brain MRI, the proposed method provide significantly (~2x) higher compression rate compared with conventional method. Additionally, the end-to-end deep learning compression/de-compression is also several magnitude's faster than conventional methods. This technique can directly benefit industrial and clinical applications, and can provide new model in applications such as multi-contrast fusion and reconstruction.