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Three-dimensional adaptive filtering in magnetic resonance angiography
Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medicine and Care, Radio Physics. Linköping University, Department of Medicine and Care, Radiology. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medicine and Care, Radio Physics. Linköping University, Department of Medicine and Care, Radiology. Linköping University, Faculty of Health Sciences.
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2001 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 14, no 1, 63-71 p.Article in journal (Refereed) Published
Abstract [en]

In order to enhance 3D image data from magnetic resonance angiography (MRA), a novel method based on the theory of multidimensional adaptive filtering has been developed. The purpose of the technique is to suppress image noise while enhancing important structures. The method is based on local structure estimation using six 3D orientation selective filters, followed by an adaptive filtering step controlled by the local structure information. The complete filtering procedure requires approximately 3 minutes of computational time on a standard workstation for a 256 × 256 × 64 data set. The method has been evaluated using a mathematical vessel model and in vivo MRA data (both phase contrast and time of flight (TOF)). 3D adaptive filtering results in a better delineation of small blood vessels and efficiently reduces the high-frequency noise. Depending on the data acquisition and the original data type, contrast-to-noise ratio (CNR) improvements of up to 179% (8.9 dB) were observed. 3D adaptive filtering may provide an alternative to prolonging the scan time or using contrast agents in MRA when the CNR is low.

Place, publisher, year, edition, pages
2001. Vol. 14, no 1, 63-71 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-26713DOI: 10.1002/jmri.1152Local ID: 11307OAI: oai:DiVA.org:liu-26713DiVA: diva2:247263
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
In thesis
1. Multidimensional magnetic resonance imaging: new methods for analysis of cardiovascular dynamics
Open this publication in new window or tab >>Multidimensional magnetic resonance imaging: new methods for analysis of cardiovascular dynamics
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cardiovascular flow and motion occur in three-dimensional (3D) space and vary dynamically over the cardiac cycle. The description of these complicated patterns using non-invasive imaging requires new tools for data acquisition, processing and visualization. In this thesis, a number of techniques are presented, all of which aim at improving the description of multidimensional cardiovascular flow and motion.

For the study of cardiac motion, a new M-mode method was developed that uses time-resolved image data to retrospectively calculate an M-mode image along an arbitrary line. This reduces the dimensionality of the acquired image data to one dimension plus time, which facilitates the analysis of the motion of cardiac structures. In order to describe flow patterns within the heart and great vessels, phase contrast magnetic resonance imaging (MRI) can be used to accurately measure velocities. Existing techniques for the acquisition of phase contrast data were extended in order to acquire time-resolved 3D image data that contain information about all three velocity components in each voxel. A number of possible approaches for reducing the scan time required were applied. Reducing the scan time in MRI often results in images with a poor signal-to-noise ratio (SNR). Image processing techniques were therefore investigated that utilize adaptive filtering in order to reduce the noise level, while still preserving the details of small structures. Once multidimensional image data are acquired, there is an immediate need to visualize the data in a comprehensible way. Particle trace visualization of velocity vector data was applied in order to study flow patterns in the human heart. Using these methods, completely new insights into the patterns of blood flow within the left atrium were achieved. This and future applications are made possible by the powerful combination of massive multidimensional data sets and tools developed specifically for the complicated conditions of cardiovascular flow.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 74 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 807
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-29438 (URN)14784 (Local ID)91-7373-616-3 (ISBN)14784 (Archive number)14784 (OAI)
Public defence
2003-04-29, Föreläsningssal Conrad, Universitetssjukhuset, Linköping, 13:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-01-04

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Wigström, LarsSjöqvist, LarsKnutsson, Hans

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