Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention.
Percutaneous coronary intervention (PCI) uses x-ray images, which may give high radiation dose and high concentrations of contrast media, leading to the risk of radiation-induced injury and nephropathy. These drawbacks can be reduced by using lower doses of x-rays and contrast media, with the disadvantage of noisier PCI images with less contrast. Vessel-edge-preserving convolutional neural networks (CNN) were designed to denoise simulated low x-ray dose PCI images, created by adding artificial noise to high-dose images. Objective functions of the designed CNNs have been optimized to achieve an edge-preserving effect of vessel walls, and the results of the proposed objective functions were evaluated qualitatively and quantitatively. Finally, the proposed CNN-based method was compared with two state-of-the-art denoising methods: K-SVD and block-matching and 3D filtering. The results showed promising performance of the proposed CNN-based method for PCI image enhancement with interesting capabilities of CNNs for real-time denoising and contrast enhancement tasks.
['Pavoni|Marco|M|', 'Chang|Yongjun|Y|https://orcid.org/0000-0001-8953-3986', 'Park|Sang-Ho|SH|', 'Smedby|Orjan|O|https://orcid.org/0000-0002-7750-1917']
convolutional neural networks deep learning edge-preserving image enhancement image denoising loss function