Large Cone Beam CT SCan Image Quality Improvement Using a Deep Learning U-Net Model

Joel Ruhe; Valeriu  Codreanu; Pascal Wiggers

Large Cone Beam CT SCan Image Quality Improvement Using a Deep Learning U-Net Model

Joel Ruhe, Valeriu Codreanu, Pascal Wiggers

Research output: Contribution to conference › Paper › Academic

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Abstract

Cone beam CT scanners use much less radiation than to normal CT scans. However, compared to normal CT scans the images are noisy, showing several artifacts. The UNet Convolutional Neural Network may provide a way to reconstruct the a CT image from cone beam scans.

Original language	English
Number of pages	2
Publication status	Published - 19 Nov 2020
Event	BNAIC / BeneLearn 2020 - Leiden, Netherlands Duration: 19 Nov 2020 → 20 Nov 2020 https://bnaic.liacs.leidenuniv.nl/

Conference

Conference	BNAIC / BeneLearn 2020
Country/Territory	Netherlands
City	Leiden
Period	19/11/20 → 20/11/20
Internet address	https://bnaic.liacs.leidenuniv.nl/

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title = "Large Cone Beam CT SCan Image Quality Improvement Using a Deep Learning U-Net Model",

abstract = "Cone beam CT scanners use much less radiation than to normal CT scans. However, compared to normal CT scans the images are noisy, showing several artifacts. The UNet Convolutional Neural Network may provide a way to reconstruct the a CT image from cone beam scans.",

keywords = "AI, Responsible IT, Computer Vision, Deep Learning",

author = "Joel Ruhe and Valeriu Codreanu and Pascal Wiggers",

year = "2020",

month = nov,

day = "19",

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T1 - Large Cone Beam CT SCan Image Quality Improvement Using a Deep Learning U-Net Model

AU - Ruhe, Joel

AU - Codreanu, Valeriu

AU - Wiggers, Pascal

PY - 2020/11/19

Y1 - 2020/11/19

N2 - Cone beam CT scanners use much less radiation than to normal CT scans. However, compared to normal CT scans the images are noisy, showing several artifacts. The UNet Convolutional Neural Network may provide a way to reconstruct the a CT image from cone beam scans.

AB - Cone beam CT scanners use much less radiation than to normal CT scans. However, compared to normal CT scans the images are noisy, showing several artifacts. The UNet Convolutional Neural Network may provide a way to reconstruct the a CT image from cone beam scans.

KW - AI

KW - Responsible IT

KW - Computer Vision

KW - Deep Learning

UR - https://bnaic.liacs.leidenuniv.nl/

M3 - Paper

T2 - BNAIC / BeneLearn 2020

Y2 - 19 November 2020 through 20 November 2020

ER -

Large Cone Beam CT SCan Image Quality Improvement Using a Deep Learning U-Net Model

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Conference

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