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Adaptiv filtering of 4D-heart CT for image denoising and patient safety
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.ORCID iD: 0000-0003-3352-8330
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
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2010 (English)Conference paper, Published paper (Other academic)
Abstract [en]

The aim of this medical image science project is to increase patient safety in terms of improved image quality and reduced exposure to ionizing radiation in CT. 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. Four-dimensional (4D) medical image data are captured as a time sequence of image volumes. During 4D image acquisition, a 3D image of the patient is recorded at regular time intervals. The resulting data will consequently have three spatial dimensions and one temporal dimension. Increasing the dimensionality of the data impose a major increase the computational demands. The initial linear filtering which is the cornerstone in all adaptive image enhancement algorithms increase exponentially with the dimensionality. On the other hand the potential gain in Signal to Noise Ratio (SNR) also increase exponentially with the dimensionality. This means that the same gain in noise reduction that can be attained by performing the adaptive filtering in 3D as opposed to 2D can be expected to occur once more by moving from 3D to 4D. The initial tests on on both synthetic and clinical 4D images has resulted in a significant reduction of the noise level and an increased detail compared to 2D and 3D methods. When tuning the parameters for adaptive filtering is extremely important to attain maximal diagnostic value which not necessarily coincide with an an eye pleasing image for a layman. Although this application focus on CT the resulting adaptive filtering methods will be beneficial for a wide range of 3D/4D medical imaging modalities e.g. shorter acquisition time in MRI and improved elimination of noise in 3D or 4D ultrasound datasets.

Place, publisher, year, edition, pages
2010.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-62787OAI: oai:DiVA.org:liu-62787DiVA: diva2:374427
Conference
MEDICINTEKNIKDAGARNA 2010 6-7 oktober 2010, Umeå
Available from: 2010-12-03 Created: 2010-12-03 Last updated: 2013-09-05

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Andersson, MatsSmedby, ÖrjanSandborg, MichaelFarnebäck, GunnarHans, Knutsson

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Andersson, MatsSmedby, ÖrjanSandborg, MichaelFarnebäck, GunnarHans, Knutsson
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Center for Medical Image Science and Visualization (CMIV)Medical InformaticsThe Institute of TechnologyRadiologyFaculty of Health SciencesDepartment of Radiology in LinköpingRadiation PhysicsDepartment of Radiation Physics
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