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Adaptive Spatio-temporal Filtering of 4D CT-Heart
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-9091-4724
2013 (engelsk)Inngår i: Image Analyses: Image Processing, Computer Vision, Pattern Recognition, and Graphics / [ed] Joni-Kristian Kämäräinen, Markus Koskela, Berlin Heidelberg: Springer, 2013, s. 246-255Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The aim of this project is to keep the x-ray exposure of the patient as low as reasonably achievable while improving the diagnostic image quality for the radiologist. The means to achieve these goals is to develop and evaluate an efficient adaptive filtering (denoising/image enhancement) method that fully explores true 4D image acquisition modes.

The proposed prototype system uses a novel filter set having directional filter responses being monomials. The monomial filter concept is used both for estimation of local structure and for the anisotropic adaptive filtering. Initial tests on clinical 4D CT-heart data with ECG-gated exposure has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. Another promising feature is that the reconstruction induced streak artifacts which generally occur in low dose CT are remarkably reduced in 4D.

sted, utgiver, år, opplag, sider
Berlin Heidelberg: Springer, 2013. s. 246-255
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 7944
Emneord [en]
Computer science, Artificial intelligence., Computer graphics, Computer vision, Optical pattern recognition, Computer Science, Image Processing and Computer Vision, Pattern Recognition, Artificial Intelligence
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-92725DOI: 10.1007/978-3-642-38886-6_24ISI: 000342988500024ISBN: 978-3-642-38885-9 (tryckt)ISBN: 978-3-642-38886-6 (tryckt)OAI: oai:DiVA.org:liu-92725DiVA, id: diva2:621906
Konferanse
18th Scandinavian Conference on Image Analysis, SCIA 2013; Espoo; Finland
Tilgjengelig fra: 2013-05-17 Laget: 2013-05-17 Sist oppdatert: 2018-02-07bibliografisk kontrollert

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