Super‐resolution of brain tumor MRI images based on deep learning

INTRODUCTION: To explore and evaluate the performance of MRI‐based brain tumor super‐resolution generative adversarial network (MRBT‐SR‐GAN) for improving the MRI image resolution in brain tumors. METHODS: A total of 237 patients from December 2018 and April 2020 with T2‐fluid attenuated inversion recovery (FLAIR) MR images (one image per patient) were included in the present research to form the super‐resolution MR dataset. The MRBT‐SR‐GAN was modified from the enhanced super‐resolution generative adversarial networks (ESRGAN) architecture, which could effectively recover high‐resolution MRI images while retaining the quality of the images. The T2‐FLAIR images from the brain tumor segmentation (BRATS) dataset were used to evaluate the performance of MRBT‐SR‐GAN contributed to the BRATS task. RESULTS: The super‐resolution T2‐FLAIR images yielded a 0.062 dice ratio improvement from 0.724 to 0.786 compared with the original low‐resolution T2‐FLAIR images, indicating the robustness of MRBT‐SR‐GAN in providing more substantial supervision for intensity consistency and texture recovery of the MRI images. The MRBT‐SR‐GAN was also modified and generalized to perform slice interpolation and other tasks. CONCLUSIONS: MRBT‐SR‐GAN exhibited great potential in the early detection and accurate evaluation of the recurrence and prognosis of brain tumors, which could be employed in brain tumor surgery planning and navigation. In addition, this technique renders precise radiotherapy possible. The design paradigm of the MRBT‐SR‐GAN neural network may be applied for medical image super‐resolution in other diseases with different modalities as well.