X-ray imaging is a fundamental clinical tool for screening and diagnosing various diseases. However, the spatial resolution of radiographs is often limited, making it challenging to diagnose small image details and leading to difficulties in identifying vertebrae anomalies at an early stage in chest radiographs. To address this limitation, we propose a novel and robust approach to significantly improve the quality of X-ray images by iteratively training a deep neural network. Our framework includes an embedded internal guidance layer that enhances the fine structures of spinal vertebrae in chest X-ray images through fully unsupervised training, utilizing an iterative procedure that employs the same network architecture in each enhancement phase. Additionally, we have designed an optimized loss function that accurately identifies object boundaries and enhances spinal features, thereby further enhancing the quality of the images. Experimental results demonstrate that our proposed method surpasses existing detail enhancement methods in terms of BRISQUE scores, and is comparable in terms of LPC-SI. Furthermore, our approach exhibits superior performance in restoring hidden fine structures, as evidenced by our qualitative results. This innovative approach has the potential to significantly enhance the diagnostic accuracy and early detection of diseases, making it a promising advancement in X-ray imaging technology.

本文介绍了一种通过迭代训练深度神经网络，采用内部指导层优化脊柱 X 光图像的方法，在准确识别物体边界的同时提高图像质量，从而显著提高 X 光成像技术的诊断准确性和疾病早期检测能力。

基于无监督迭代U-Net和内部引导层的胸部X射线图像脊柱对比度增强