| 1. |
- Abramian, David, 1992-, et al.
(författare)
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Anatomically Informed Bayesian Spatial Priors for FMRI Analysis
- 2020
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Ingår i: ISBI 2020. - : IEEE. - 9781538693308
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Konferensbidrag (refereegranskat)abstract
- Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.
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| 2. |
- Afzali, Maryam, et al.
(författare)
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Computing the orientational-average of diffusion-weighted MRI signals : a comparison of different techniques
- 2021
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or shell), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its isotropic part. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.
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| 3. |
- Andersson, Kenneth, 1970-, et al.
(författare)
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Prediction from off-grid samples using continuous normalized convolution
- 2007
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 87:3, s. 353-365
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel method for performing fast estimation of data samples on a desired output grid from samples on an irregularly sampled grid. The output signal is estimated using integration of signals over a neighbourhood employing a local model of the signal using discrete filters. The strength of the method is demonstrated in motion compensation examples by comparing to traditional techniques.
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| 4. |
- Andersson, Mats, et al.
(författare)
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Adaptive Spatio-temporal Filtering of 4D CT-Heart
- 2013
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Ingår i: Image Analyses. - Berlin Heidelberg : Springer. - 9783642388859 - 9783642388866 ; , s. 246-255
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Andersson, Mats, et al.
(författare)
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Global search strategies for solving multilinear least-squares problems
- 2012
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Ingår i: Sultan Qaboos University Journal for Science. - : Sultan Qaboos University. - 1027-524X. ; 17:1, s. 12-21
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Tidskriftsartikel (refereegranskat)abstract
- The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows for moving from one local minimizer to a better one. The efficiency of this strategy is illustrated by results of numerical experiments performed for some problems related to the design of filter networks.
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| 6. |
- Andersson, Mats, et al.
(författare)
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Global Search Strategies for Solving Multilinear Least-squares Problems
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinearoperator is used in place of a matrix-vector product. The MLLS istypically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows formoving from one local minimizer to a better one. The efficiencyof this strategy isillustrated by results of numerical experiments performed forsome problems related to the design of filter networks.
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| 7. |
- Andersson, Mats, et al.
(författare)
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Sparsity Optimization in Design of Multidimensional Filter Networks
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Filter networks is a powerful tool used for reducing the image processing time, while maintaining its reasonably high quality.They are composed of sparse sub-filters whose low sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. If to disregard the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers. Each of the local minimizers is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.
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| 8. |
- Andersson, Mats, et al.
(författare)
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Velocity Adaptive Filtered Angiography
- 1999
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Patent (populärvet., debatt m.m.)abstract
- A method of imaging a blood vessel in a body using X-rays and an injectable contrast medium is described. The contrast medium is injected into the body, and signals constituted by an X-ray image sequence depicting X-ray attenuation values is recorded. The X-ray attenuated values in each spaced-time neighborhood are combined in a way that is dependent on the processed image sequence and separately established for each neighborhood, and separating, from background and vessel signals, flow signals having energy contributions mainly in an area of frequency domain bounded by surfaces corresponding to threshold velocities separately established for each neighborhood, which surfaces are shifted a specified amount along a temporal frequency axis.
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| 9. |
- Borga, Magnus, et al.
(författare)
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An Adaptive Stereo Algorithm Based on Canonial Correlation Analysis
- 1998
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Ingår i: Proceedings of the Second IEEE International Conference on Intelligent Processing Systems. ; , s. 177-182
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel algorithm that uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semitransparent images.
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| 10. |
- Brun, Anders, 1976-
(författare)
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Manifolds in Image Science and Visualization
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A Riemannian manifold is a mathematical concept that generalizes curved surfaces to higher dimensions, giving a precise meaning to concepts like angle, length, area, volume and curvature. A glimpse of the consequences of a non-flat geometry is given on the sphere, where the shortest path between two points – a geodesic – is along a great circle. Different from Euclidean space, the angle sum of geodesic triangles on the sphere is always larger than 180 degrees.Signals and data found in applied research are sometimes naturally described by such curved spaces. This dissertation presents basic research and tools for the analysis, processing and visualization of such manifold-valued data, with a particular emphasis on future applications in medical imaging and visualization.Two-dimensional manifolds, i.e. surfaces, enter naturally into the geometric modelling of anatomical entities, such as the human brain cortex and the colon. In advanced algorithms for processing of images obtained from computed tomography (CT) and ultrasound imaging (US), images themselves and derived local structure tensor fields may be interpreted as two- or three-dimensional manifolds. In diffusion tensor magnetic resonance imaging (DT-MRI), the natural description of diffusion in the human body is a second-order tensor field, which can be related to the metric of a manifold. A final example is the analysis of shape variations of anatomical entities, e.g. the lateral ventricles in the brain, within a population by describing the set of all possible shapes as a manifold.Work presented in this dissertation include: Probabilistic interpretation of intrinsic and extrinsic means in manifolds. A Bayesian approach to filtering of vector data, removing noise from sampled manifolds and signals. Principles for the storage of tensor field data and learning a natural metric for empirical data.The main contribution is a novel class of algorithms called LogMaps, for the numerical estimation of logp (x) from empirical data sampled from a low-dimensional manifold or geometric model embedded in Euclidean space. The logp (x) function has been used extensively in the literature for processing data in manifolds, including applications in medical imaging such as shape analysis. However, previous approaches have been limited to manifolds where closed form expressions of logp (x) have been known. The introduction of the LogMap framework allows for a generalization of the previous methods. The application of LogMaps to texture mapping, tensor field visualization, medial locus estimation and exploratory data analysis is also presented.
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