Large-factor Micro-CT super-resolution of bone microstructure

Abstract

Background: Bone microstructure is important for evaluating bone strength and requires the support of high-resolution (HR) imaging equipment. Computed tomography (CT) is widely used for medical imaging, but the spatial resolution is not sufficient for bone microstructure. Micro-CT scan data is the gold standard for human bone microstructure or animal experiment. However, Micro-CT has more ionizing radiation and longer scanning time while providing high-quality imaging. It makes sense to reconstruct HR images with less radiation. Image super-resolution (SR) is adapted to the above-mentioned research. The specific objective of this study is to reconstruct HR images of bone microstructure based on low-resolution (LR) images under large-factor condition.Methods: We propose a generative adversarial network (GAN) based on Res2Net and residual channel attention network which is named R2-RCANGAN. We use real high-resolution and low-resolution training data to make the model learn the image corruption of Micro-CT, and we train six super-resolution models such as super-resolution convolutional neural network to evaluate our method performance.Results: In terms of peak signal-to-noise ratio (PSNR), our proposed generator network R2-RCAN sets a new state of the art. Such PSNR-oriented methods have high reconstruction accuracy, but the perceptual index to evaluate perceptual quality is very poor. Thus, we combine the generator network R2-RCAN with the U-Net discriminator and loss function with adjusted weights, and the proposed R2-RCANGAN shows the pleasing results in reconstruction accuracy and perceptual quality as compared to the other methods.Conclusion: The proposed R2-RCANGAN is the first to apply large-factor SR to improve Micro-CT images of bone microstructure. The next steps of the study are to investigate the role of SR in image enhancement during fracture rehabilitation period, which would be of great value in reducing ionizing radiation and promoting recovery.