Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention

Marco Pavoni; Yongjun Chang; Sang-ho Park; Örjan Smedby

doi:10.1117/1.JMI.5.2.024006

28 June 2018 Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention

Marco Pavoni, Yongjun Chang, Sang-ho Park, Örjan Smedby

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 2, 024006 (June 2018). https://doi.org/10.1117/1.JMI.5.2.024006

Abstract

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.

Citation Download Citation

Marco Pavoni, Yongjun Chang, Sang-ho Park, and Örjan Smedby "Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention," Journal of Medical Imaging 5(2), 024006 (28 June 2018). https://doi.org/10.1117/1.JMI.5.2.024006

Received: 26 February 2018; Accepted: 4 June 2018; Published: 28 June 2018

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