Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images Using Structure-constrained Super-Resolution Network

Abstract

Medical images routinely acquired in clinical facilities are mostly low resolution (LR), in consideration of acquisition time and efficiency. This renders challenging for clinical diagnosis of hippocampal sclerosis where additional sequences for hippocampus need to be acquired. In contrast, high-resolution (HR) images provide more detailed information for disease investigation. Recently, image super-resolution (SR) methods were proposed to reconstruct HR images from LR inputs. However, current SR methods generally use simulated LR images and intensity constraints, which limit their applications in clinical practice. To solve this problem, we utilized real paired LR and HR images and trained a Structure-Constrained Super Resolution (SCSR) network. First, we proposed a single image super-resolution framework where mixed loss functions were introduced to enhance the reconstruction of brain tissue boundaries besides intensity constraints; Second, since the structure hippocampus is relatively small, we further proposed a weight map to enhance the reconstruction of subcortical regions. Experimental results using 642 real paired cases showed that the proposed method outperformed the the-state-of-the-art methods in terms of image quality with a PSNR of 27.0405 and an SSIM of 0.9958. Also, experiments using Radiomics features extracted from hippocampus on SR images obtained through the proposed method achieved the best accuracy of 95% for differentiating subjects with left and right hippocampal sclerosis from normal controls. The proposed method shows its potential for disease screening using clinical routine images.