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Image denoising with convolutional neural networks for percutaneous transluminal coronary angioplasty
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- Pavoni, Marco (author)
- KTH,Medicinsk bildbehandling och visualisering,Politecnico di Torino, Italy
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- Chang, Yongjun (author)
- KTH,Medicinsk bildbehandling och visualisering
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- Smedby, Örjan, 1956- (author)
- KTH,Medicinsk bildbehandling och visualisering
- (creator_code:org_t)
- 2017-10-13
- 2018
- English.
- In: VipIMAGE 2017. - Cham : Springer Netherlands. - 9783319681948 ; , s. 255-265
- Conference paper (peer-reviewed)
Abstract
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
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- kon (subject category)
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