Hans Knutsson

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Eric S . Kim (UBC)

University of British Columbia

Ghent University

Stanford University

Universidad De Talca

Università degli Studi di Padova

Tulay Kansu

Hacettepe University

Jeffrey L Saver

University of California, Los Angeles

Kenneth Pakenham

The University of Queensland, Australia

Gary rolfe

Swansea University

Rommy von Bernhardi

Pontificia Universidad Catolica de Chile

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Papers by Hans Knutsson

Reinforcement Learning Trees

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Linköping University Post Print Non-ring Filters for Robust Detection of Linear Structures

Many applications in image analysis include the problem of linear structure detection, e.g. segme... more Many applications in image analysis include the problem of linear structure detection, e.g. segmentation of blood vessels in medical images, roads in satellite images, etc. A simple and efficient solution is to apply linear filters tuned to the structures of interest and extract line and edge positions from the filter output. However, if the filter is not carefully designed, artifacts such as ringing can distort the results and hinder a robust detection. In this paper, we study the ringing effects using a common Gabor filter for linear structure detection, and suggest a method for generating nonring filters in 2D and 3D. The benefits of the non-ring design are motivated by results on both synthetic and natural images.

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Enhancement of micro-channels within the human mastoid bone based on local structure tensor analysis

Numerous micro-channels have recently been discovered in the human temporal bone by x-ray micro-C... more Numerous micro-channels have recently been discovered in the human temporal bone by x-ray micro-CTscanning. After a preliminary study suggesting that these microchannels form a separate blood supply for the mucosa of the mastoid air cells, a structural analysis of the micro-channels using a local structure tensor was carried out. Despite the highresolution of the micro-CT scan, presence of noise within the air cells along with missing information in some micro-channels suggested the need of image enhancement. This paper proposes an adaptive enhancement of the micro-channels based on a local structure analysis while minimizing the impact of noise on the overall data. Comparison with an anisotropic diffusion PDE based scheme was also performed.

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Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging

We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at ... more We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in qspace. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on nonuniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.

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Using the residual bootstrap to quantify uncertainty in mean apparent propagator MRI

Estimation of noise-induced variability in MAP-MRI is needed to properly characterize the amount ... more Estimation of noise-induced variability in MAP-MRI is needed to properly characterize the amount of uncertainty in quantities derived from the estimated MAP-MRI coefficients. Bootstrap metrics, such as the standard deviation, provides additional valuable diffusion information in addition to common MAP-MRI parameters, and can be incorporated in MAP-MRI studies to provide more extensive insight. To the best of our knowledge, this is the first paper to study the uncertainty of MAP-MRI derived metrics. The noise variability of quantities of MAP-MRI have been quantified using the residual bootstrap, in which the residuals are resampled using two sampling schemes. The residual bootstrap method can provide empirical distributions for MAP-MRI derived quantities, even when the exact distributions are not easily derived. The residual bootstrap methods are applied to SPARC data and HCP-MGH data, and empirical distributions are obtained for the zero-displacement probabilities. Here, we compare and contrast the residual bootstrap schemes using all shells and within the same shell. We show that residual resampling within each shell generates larger uncertainty than using all shells for the HCP-MGH data. Standard deviation and quartile coefficient maps of the estimated variability are provided.

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Using the Wild Bootstrap to Quantify Uncertainty in Mean Apparent Propagator MRI

Frontiers in Neuroinformatics, Jun 12, 2019

Purpose: Estimation of uncertainty of MAP-MRI metrics is an important topic, for several reasons.... more Purpose: Estimation of uncertainty of MAP-MRI metrics is an important topic, for several reasons. Bootstrap derived uncertainty, such as the standard deviation, provides valuable information, and can be incorporated in MAP-MRI studies to provide more extensive insight. Methods: In this paper, the uncertainty of different MAP-MRI metrics was quantified by estimating the empirical distributions using the wild bootstrap. We applied the wild bootstrap to both phantom data and human brain data, and obtain empirical distributions for the MAP-MRI metrics return-to-origin probability (RTOP), non-Gaussianity (NG), and propagator anisotropy (PA). We demonstrated the impact of diffusion acquisition scheme (number of shells and number of measurements per shell) on the uncertainty of MAP-MRI metrics. We demonstrated how the uncertainty of these metrics can be used to improve group analyses, and to compare different preprocessing pipelines. We demonstrated that with uncertainty considered, the results for a group analysis can be different. Bootstrap derived uncertain measures provide additional information to the MAP-MRI derived metrics, and should be incorporated in ongoing and future MAP-MRI studies to provide more extensive insight.

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<title>Motion-compensated digital subraction angiography</title>

Medical Imaging 1999: Image Processing, May 21, 1999

Digital subtraction angiography, whether based on traditional X-ray or MR, su ers from patient mo... more Digital subtraction angiography, whether based on traditional X-ray or MR, su ers from patient motion artifacts. Until now, the usual remedy is to pixel shift by hand, or in some cases performing a global pixel shift semi-automatically. This is time consuming, and cannot handle rotations or local varying deformations over the image. We h a ve developed a fully automatic algorithm that provides for motion compensation in the presence of large local deformations. Our motion compensation is very accurate for ordinary motions, including large rotations and deformations. It does not matter if the motions are irregular over time. For most images, it takes about a second per image to get adequate accuracy. The method is based on using the phase from lter banks of quadrature lters tuned in di erent directions and frequencies. Unlike traditional methods for optical ow and correlation, our method is more accurate and less susceptible to disturbing changes in the image, e.g. a m o ving contrast bolus. The implications for common practice are that radiologists' time can be signi cantly reduced in ordinary peripheral angiographies and that the numberof retakes due to large or local motion artifacts will be much reduced.

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Anisotropic Nonstationary Image Estimation and Its Applications: Part I--Restoration of Noisy Images

IRE transactions on communications systems, Mar 1, 1983

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Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates

bioRxiv (Cold Spring Harbor Laboratory), Apr 6, 2018

Methodological research rarely generates a broad interest, yet our work on the validity of cluste... more Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with onesample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (P = 0.01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

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Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks

Lecture Notes in Computer Science, 2019

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment ... more Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.

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Reply to Slotnick (2017), "Resting-state fMRI data reflects default network activity rather than null data: A defense of commonly employed methods to correct for multiple comparisons

arXiv (Cornell University), Jan 11, 2017

A recent Editorial by Slotnick (2017) reconsiders the findings of our paper on the accuracy of fa... more A recent Editorial by Slotnick (2017) reconsiders the findings of our paper on the accuracy of false positive rate control with cluster inference in fMRI , in particular criticising our use of resting state fMRI data as a source for null data in the evaluation of task fMRI methods. We defend this use of resting fMRI data, as while there is much structure in this data, we argue it is representative of task data noise and as such analysis software should be able to accommodate this noise. We also discuss a potential problem with Slotnick's own method.

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2ND Scandinavian Conference on Image Analysis

IEEE acoustics, speech, and signal processing newsletter, Jun 1, 1980

The related problems of enhancing and restoring noisy images have received a considerable amount ... more The related problems of enhancing and restoring noisy images have received a considerable amount of attention in recent years. Restoration methods have generally been based on minimum mean-squared error operations, such as Wiener filtering or recursive filtering. The rather vague title of enhancement has been given to a wide variety of more or less ad-hoc methods, such as median filtering, which have nonetheless been found useful. In mast cases, however, the aim is the same: an improvement of the subjective quality of the image.

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A defense of using resting state fMRI as null data for estimating false positive rates

arXiv (Cornell University), Jan 11, 2017

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Bayesian Diffusion Tensor Estimation with Spatial Priors

Springer eBooks, 2017

Spatial regularization is a technique that exploits the dependence between nearby regions to loca... more Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

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Linköping University Post Print Fast Phase Based Registration for Robust

INTRODUCTION: Quantitative magnetic resonance imaging has the major advantage that it handles abs... more INTRODUCTION: Quantitative magnetic resonance imaging has the major advantage that it handles absolute measurements of physical parameters. Quantitative MRI can for example be used to estimate the amount of different tissue types in the brain, but other applications are possible. Parameters such as relaxation rates R1 and R2 and proton density (PD) are independent of MR scanner settings and imperfections and hence are directly representative of the underlying tissue characteristics. Brain tissue quantification is an important aid for diagnosis of neurological diseases, such as multiple sclerosis (MS) and dementia. It is applied to estimate the volume of each tissue type, such as white tissue, grey tissue, myelin and cerebrospinal fluid (CSF). Tissue that deviates from normal values can be found automatically using computer aided diagnosis. In order for the quantification to have a clinical value, both the time in the MR scanner and the time for the data analysis have to be minimized. A challenge in MR quantification is to keep the scan time within clinically acceptable limits. The quantification method that we have used is based on the work by Warntjes et al. [1].

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Seeing Things [1]

Towards Coherent Sampling In Volume Rendering Using Iso Surfaces

We present a new method for sampling the volume rendering integral in volume raycasting where sam... more We present a new method for sampling the volume rendering integral in volume raycasting where samples are correlated based on transfer function content and data set values. This has two major advantages. First, visual artifacts stemming from structured noise, such as wood grain, can be reduced. Second, we will show that the volume data does no longer need to be available during the rendering phase; a surface representation is used instead, which opens up ample opportunities for rendering of large data. We will show that the proposed sampling method gives higher quality renderings with fewer samples when compared to regular sampling in the spatial domain.

The meaninglessness ofSit-and-stare'--How Vision-Action-Understanding is inseparable

Apparatus for determining the degree of variation of a feature in a region of an image that is divided into discrete picture elements