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Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient
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 Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
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, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
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2007 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sebastien Ourselin and Anthony Maeder, Springer Berlin/Heidelberg, 2007, 210-218 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007. 210-218 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 4791
Keyword [en]
MRI
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-12661DOI: 10.1007/978-3-540-75757-3_26ISI: 000250916000026ISBN: 978-3-540-75756-6 (print)ISBN: e-978-3-540-75757-3 OAI: oai:DiVA.org:liu-12661DiVA: diva2:16791
Conference
MICCAI 2007, The 10th International Conference on Medical Image Computing and Computer Assisted Interventio, October 29-November 2, Brisbane, Australia
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2015-10-08
In thesis
1. Advanced MRI Data Processing
Open this publication in new window or tab >>Advanced MRI Data Processing
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging (MRI) is a very versatile imaging modality which can be used to acquire several different types of images. Some examples include anatomical images, images showing local brain activation and images depicting different types of pathologies. Brain activation is detected by means of functional magnetic resonance imaging (fMRI). This is useful e.g. in planning of neurosurgical procedures and in neurological research. To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing a task. The variations in image intensity over time are then compared to a model of the variations expected to be found in active parts of the brain. Locations with high correlation between the intensity variations and the model are considered to be activated by the task.

Since the images are very noisy, spatial filtering is needed before the activation can be detected. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive spatial filtering of fMRI data. One of these is a modification of a previously proposed method, which at each position maximizes the similarity between the filter response and the model. A novel feature of the presented method is rotational invariance, i.e. equal sensitivity to activated regions in different orientations. The other method is based on bilateral filtering. At each position, this method averages pixels which are located in the same type of brain tissue and have similar intensity variation over time.

A method for robust correlation estimation is also presented. This method automatically detects local bursts of noise in a signal and disregards the corresponding signal segments when the correlation is estimated. Hence, the correlation estimate is not affected by the noise bursts. This method is useful not only in analysis of fMRI data, but also in other applications where correlation is used to determine the similarity between signals.

Finally, a method for correcting artifacts in complex MR images is presented. Complex images are used e.g. in the Dixon technique for separate imaging of water and fat. The phase of these images is often affected by artifacts and therefore need correction before the actual water and fat images can be calculated. The presented method for phase correction is based on an image integration technique known as the inverse gradient. The method is shown to provide good results even when applied to images with severe artifacts.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2007. 91 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1140
Keyword
Magnetic resonance imaging (MRI), functional Magnetic Resonance Imaging (fMRI), spatial filtering, robust correlation estimation, correcting artifacts
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-10038 (URN)978-91-85895-59-5 (ISBN)
Public defence
2007-11-30, Linden, Hus 421, Campus US, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2007-11-07 Created: 2007-11-07 Last updated: 2014-10-08

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Publisher's full textLink to Ph.D. thesisfind book at a swedish library/hitta boken i ett svenskt bibliotek

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Rydell, JoakimKnutsson, HansPettersson, JohannaFarnebäck, GunnarDahlqvist Leinhard, OlofLundberg, PeterNyström, FredrikBorga, Magnus

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Rydell, JoakimKnutsson, HansPettersson, JohannaFarnebäck, GunnarDahlqvist Leinhard, OlofLundberg, PeterNyström, FredrikBorga, Magnus
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Center for Medical Image Science and Visualization (CMIV)Medical InformaticsThe Institute of TechnologyDepartment of Biomedical EngineeringRadiation PhysicsFaculty of Health SciencesRadiation PhysicsMedical RadiologyDepartment of Radiology in LinköpingDepartment of Radiation PhysicsInternal Medicine
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