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| 2. |
- Dahlqvist Leinhard, Olof, et al.
(författare)
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Quantification of abdominal fat accumulation during hyperalimentation using MRI
- 2009
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Ingår i: Proceedings of the ISMRM Annual Meeting (ISMRM'09), 2009. - Berkeley, CA, USA : International Society for Magnetic Resonance in Medicine. ; , s. 206-
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- There is an increasing demand for imaging methods that can be used for automatic, accurate and quantitative determination of the amounts of abdominal fat. Such methods are important as they will allow the evaluation of some of the risk factors underlying the ’metabolic syndrome’. The metabolic syndrome is becoming common in large parts of the world, and it appears that a dominant risk factor for developing this syndrome is abdominal obesity. Subjects that are afflicted with the metabolic syndrome are exposed to a high risk for developing a large range of diseases such as type 2 diabetes, cardiac failure, and stroke. The aim of this work
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| 3. |
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| 4. |
- Friman, Ola, et al.
(författare)
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Emphysema Detection in CT Images
- 2002
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper describes a fully automatic approach for detecting emphysema in CT im ages of the lungs. The method combines an image processing step, where potential emphysematous area s are extracted, and a neural network step trained to rec
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| 5. |
- Friman, Ola, et al.
(författare)
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Imaging Brain Function
- 2002
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Knutsson, Hans, et al.
(författare)
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Respiratory Arifact Reduction in MRI using Dynamic Deformation Modelling
- 2002
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a novel magnetic resonance imaging (MRI) reconstruction method that will reconstruct an object correctly despite the presence of respiratory-type motions. The basis for the method is the observation that affine deformations of an object will correspond to a different but unique affine coordinate transform of the Fourier representation (k-space) of the object. The resulting sample points will be irregularly distributed prohibiting the use of standard IFFT to reconstruct the object. The object can however be reconstructed through the use of a weighted regularized pseudo inverse. Short computing times are obtained using a novel fast sequential pseudo inverse algorithm.
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| 7. |
- Rydell, Joakim, 1979-
(författare)
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Advanced MRI Data Processing
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 8. |
- Tibell, Kajsa, et al.
(författare)
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Fast Prototype Based Noise Reduction
- 2009. - 1
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Ingår i: Image Analysis. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642022296 - 9783642022302 ; , s. 159-168
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a novel method for noise reduction in medical images based on concepts of the Non-Local Means algorithm. The main objective has been to develop a method that optimizes the processing speed to achieve practical applicability without compromising the quality of the resulting images. A database consisting of prototypes, composed of pixel neighborhoods originating from several images of similar motif, has been created. By using a dedicated data structure, here Locality Sensitive Hashing (LSH), fast access to appropriate prototypes is granted. Experimental results show that the proposed method can be used to provide noise reduction with high quality results in a fraction of the time required by the Non-local Means algorithm.
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