Abstract
Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.
Highlights
Magnetic resonance (MR) images can detect small pathological tissue at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease
Aiming at the problem that it is difficult for large deep learning networks to balance model complexity and training difficulty, this paper proposes a method based on heterogeneous network fusion [27]
This paper aims at solving the three limitations of applying a deep learning network in the super-resolution task of MR images, such as not fully considering the characteristics of MR images’ sequence, difficulty in obtaining the optimal solution of loss function and difficulty in balancing model complexity and training difficulty
Summary
MR images can detect small pathological tissue at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. As. MR images with high-resolution can provide clearer structural details, which can help doctors to analyze and diagnose the disease correctly, it is more desirable to obtain highresolution MR images in a stronger magnetic field [1]. The acquisition of highresolution MR images requires a stronger magnetic field and longer radiation scanning time. The high-resolution refers to the resolution of an image for one organ region. For the same size of the organ region, the low-resolution is 512 × 512, and the high-resolution is 1024 × 1024. When improving the resolution of MR images, the use of software to improve the resolution of MR images in this paper is a cost-effective method compared with the hardware method of upgrading the imaging equipment. Compared with traditional interpolation methods, such as Bicubic Interpolation and Nearest Neighbor
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