| 1. |
- Pavoni, Marco, et al.
(författare)
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Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention
- 2018
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Ingår i: Journal of Medical Imaging. - : Elsevier. - 2329-4302 .- 2329-4310. ; 5:2
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Pavoni, Marco, et al.
(författare)
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Image denoising with convolutional neural networks for percutaneous transluminal coronary angioplasty
- 2018
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Ingår i: VipIMAGE 2017. - Cham : Springer Netherlands. - 9783319681948 ; , s. 255-265
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Konferensbidrag (refereegranskat)abstract
- Percutaneous transluminal coronary angioplasty (PTCA) requires X-ray images employing high radiation dose with high concentration 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 PTCA images. In this paper, convolutional neural networks were used in order to denoise low dose PTCA-like images, built by adding artificial noise to high dose images. MSE and SSIM based loss functions were tested and compared visually and quantitatively for different types and levels of noise. The results showed promising performance for denoising task.
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