Generalizing the Enhanced-Deep-Super-Resolution neural network to brain MR images: a retrospective study on the Cam-CAN dataset.

Abstract

The Enhanced-Deep-Super-Resolution (EDSR) model is a state-of-art convolutional neural network suitable for improving image spatial resolution. It was previously trained with general-purpose pictures and then, in this work, tested on biomedical Magnetic Resonance (MR) images, comparing the network outcomes with traditional up-sampling techniques. We explored possible changes in the model response when different MR sequences were analyzed. T1w and T2w MR brain images of 70 human healthy subjects (F:M 40:30) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository were down-sampled and then up-sampled using EDSR model and BiCubic (BC) interpolation. Several reference metrics were used to quantitatively assess the performance of up-sampling operations (RMSE, pSNR, SSIM and HFEN). Two-dimensional and three-dimensional reconstructions were evaluated. Different brain tissues were analyzed individually. The EDSR model was superior to BC interpolation on the selected metrics, both for two- and three- dimensional reconstructions. The reference metrics showed higher quality of EDSR over BC reconstructions for all the analyzed images, with a significant difference of all the criteria in T1w images and of the perception-based SSIM and HFEN in T2w images. The analysis per tissue highlights differences in EDSR performance related to the gray-level values, showing a relative lack of out-performance in reconstructing hyper-intense areas. The EDSR model, trained on general-purpose images, better reconstructs MR T1w and T2w images than BC, without any re-training or fine-tuning. These results highlight the excellent generalization ability of the network and lead to possible applications on other MR measurements.Significance Statement Super resolution applications in biomedical images may help in reducing acquisition scan time and concurrently improving the quality of the exam. Neural networks have been shown to work better than traditional up-sampling techniques, even though ad hoc training experiments need to be performed for specific kind of data. In this work, we used a model previously trained with general-purpose images and we directly applied to magnetic resonance human brain ones; we verified its ability of reconstructing new kind of images, comparing the results with traditional up-sampling techniques. Our analysis highlights the excellent generalization capabilities of the model over the images tested, without need of specific re-training, suggesting that such results might be reproduced on images from other acquisition systems.