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Özarslan, Evren
Alternative names
Publications (10 of 11) Show all publications
Tampu, I. E., Yolcu, C., Knutsson, H., Koay, C. G. & Özarslan, E. (2019). Estimation of the orientationally-averaged magnetic resonance (MR) signal for characterizing neurite morphology. In: : . Paper presented at Medicinteknikdagarna.
Open this publication in new window or tab >>Estimation of the orientationally-averaged magnetic resonance (MR) signal for characterizing neurite morphology
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2019 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The orientationally-averaged diffusion magnetic resonance (MR) signal acquired at high diffusion weighting shows great potential for answering fundamental questions about neural tissue microstructure[1]. The noise-induced bias in the magnitude-valued signal and angular resolution limitations in diffusion encoding are among the challenges in obtaining an accurate estimate. Here, we present a data processing framework for computing the orientationally-averaged diffusion signal that corrects the noise induced bias and accounts for the low angular resolution of the acquisition. Noise correction is performed using a statistical transformation framework [2] that converts the noisy MR signal from a noncentralChi distribution to a noisy Gaussian one. Weights for each of the probing directions are computed to improve the rotationally invariant representation of the sample. Synthetic data, generated to mimic diffusion acquisitions with different noise levels and number of acquisition directions, were used to test the data processing framework. The performance of the framework was evaluated by comparing the processed data with the analytical solution of the orientationally-averaged signal. Results show that the computation of the orientationally-averaged signal benefits from both the noise correction and the weighted averaging, especially in the low signal regime. This work provides a tool for processing high diffusion-weighted MR signals whose interpretation could improve our knowledge about neural tissue microstructure.

[1] Özarslan E, Yolcu C, Herberthson M, Knutsson H, Westin CF. Influence of the size and curvedness ofneural projections on the orientationally averaged diffusion MR signal. Front Phys, 2018; 6:17.

[2] Koay CG, Özarslan E, Basser PJ. A signal transformational framework for breaking the noise floorand its applications in MRI. J Magn Reson 2009; 197(2):108–119.

National Category
Other Medical Engineering
Identifiers
urn:nbn:se:liu:diva-160815 (URN)
Conference
Medicinteknikdagarna
Available from: 2019-10-09 Created: 2019-10-09 Last updated: 2019-10-09
Liu, C. & Özarslan, E. (2019). Multimodal integration of diffusion MRI for better characterization of tissue biology. NMR in Biomedicine, 32(4)
Open this publication in new window or tab >>Multimodal integration of diffusion MRI for better characterization of tissue biology
2019 (English)In: NMR in Biomedicine, ISSN 0952-3480, E-ISSN 1099-1492, Vol. 32, no 4Article in journal (Refereed) Published
Abstract [en]

The contrast in diffusion-weighted MR images is due to variations of diffusion properties within the examined specimen. Certain microstructural information on the underlying tissues can be inferred through quantitative analyses of the diffusion-sensitized MR signals. In the first part of the paper, we review two types of approach for characterizing diffusion MRI signals: Blochs equations with diffusion terms, and statistical descriptions. Specifically, we discuss expansions in terms of cumulants and orthogonal basis functions, the confinement tensor formalism and tensor distribution models. Further insights into the tissue properties may be obtained by integrating diffusion MRI with other techniques, which is the subject of the second part of the paper. We review examples involving magnetic susceptibility, structural tensors, internal field gradients, transverse relaxation and functional MRI. Integrating information provided by other imaging modalities (MR based or otherwise) could be a key to improve our understanding of how diffusion MRI relates to physiology and biology.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
Keywords
confinement tensor; cumulants; diffusion fMRI; diffusion-relaxation correlation; mean apparent propagator; structure tensor; susceptibility tensor; tensor distribution
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-155993 (URN)10.1002/nbm.3939 (DOI)30011138 (PubMedID)
Available from: 2019-04-01 Created: 2019-04-01 Last updated: 2019-09-24
Gu, X., Eklund, A., Özarslan, E. & Knutsson, H. (2019). Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI. Frontiers in Neuroinformatics, 13, Article ID 43.
Open this publication in new window or tab >>Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
2019 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed) Published
Abstract [en]

Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019
Keywords
Bootstrap, diffusion MRI, MAP-MRI, uncertainty, RtoP, NG, PA
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-157089 (URN)10.3389/fninf.2019.00043 (DOI)000471589200001 ()31244637 (PubMedID)
Note

Funding agencies:  Swedish Research Council [2015-05356]; Linkoping University Center for Industrial Information Technology (CENIIT); Knut and Alice Wallenberg Foundation project Seeing Organ Function; National Institute of Dental and Craniofacial Research (NIDCR); National

Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-11-19
Sjölund, J., Eklund, A., Özarslan, E., Herberthson, M., Bånkestad, M. & Knutsson, H. (2018). Bayesian uncertainty quantification in linear models for diffusion MRI. NeuroImage, 175, 272-285
Open this publication in new window or tab >>Bayesian uncertainty quantification in linear models for diffusion MRI
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2018 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 175, p. 272-285Article in journal (Refereed) Published
Abstract [en]

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.

Place, publisher, year, edition, pages
Academic Press, 2018
Keywords
Diffusion MRI, Uncertainty quantification, Signal estimation
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-147245 (URN)10.1016/j.neuroimage.2018.03.059 (DOI)000432949000023 ()29604453 (PubMedID)
Note

Funding agencies: Swedish Foundation for Strategic Research [AM13-0090]; Swedish Research Council CADICS Linneaus research environment; Swedish Research Council [2012-4281, 2013-5229, 2015-05356, 2016-04482]; Linkoping University Center for Industrial Information Technolog

Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-06-28
Özarslan, E., Yolcu, C., Herberthson, M., Knutsson, H. & Westin, C.-F. (2018). Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal. Frontiers in Physics, 6, 1-10, Article ID 17.
Open this publication in new window or tab >>Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal
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2018 (English)In: Frontiers in Physics, E-ISSN 2296-424X, Vol. 6, p. 1-10, article id 17Article in journal (Refereed) Published
Abstract [en]

Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, Ē(q) ∝ q−1, where q is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon et al. [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the q−1 decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of q-values as the true asymptotic behavior of the signal decay is Ē(q) ∝ q−4 for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
National Category
Biomedical Laboratory Science/Technology Radiology, Nuclear Medicine and Medical Imaging Atom and Molecular Physics and Optics Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-145426 (URN)10.3389/fphy.2018.00017 (DOI)
Available from: 2018-03-02 Created: 2018-03-02 Last updated: 2018-03-27Bibliographically approved
Özarslan, E., Yolcu, C., Herberthson, M., Westin, C.-F. & Knutsson, H. (2017). Effective Potential for Magnetic Resonance Measurements of Restricted Diffusion. Frontiers in Physics, 5, Article ID 68.
Open this publication in new window or tab >>Effective Potential for Magnetic Resonance Measurements of Restricted Diffusion
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2017 (English)In: Frontiers in Physics, E-ISSN 2296-424X, Vol. 5, article id 68Article in journal (Refereed) Published
Abstract [en]

The signature of diffusive motion on the NMR signal has been exploited to characterize the mesoscopic structure of specimens in numerous applications. For compartmentalized specimens comprising isolated subdomains, a representation of individual pores is necessary for describing restricted diffusion within them. When gradient waveforms with long pulse durations are employed, a quadratic potential profile is identified as an effective energy landscape for restricted diffusion. The dependence of the stochastic effective force on the center-of-mass position is indeed found to be approximately linear (Hookean) for restricted diffusion even when the walls are sticky. We outline the theoretical basis and practical advantages of our picture involving effective potentials.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2017
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-143866 (URN)10.3389/fphy.2017.00068 (DOI)
Available from: 2017-12-22 Created: 2017-12-22 Last updated: 2019-11-11Bibliographically approved
Sjölund, J., Eklund, A., Özarslan, E. & Knutsson, H. (2017). Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017: . Paper presented at 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, 18-21 April 2017 (pp. 778-782). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
2017 (English)In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 778-782Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-8452
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-138632 (URN)10.1109/ISBI.2017.7950634 (DOI)000414283200181 ()978-1-5090-1172-8 (ISBN)978-1-5090-1173-5 (ISBN)
Conference
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, 18-21 April 2017
Note

Funding agencies: Swedish Research Council (VR) [2012-4281, 2013-5229, 2015-05356]; Swedish Foundation for Strategic Research (SSF) [AM13-0090]; EUREKA ITEA BENEFIT [2014-00593]; Linneaus center CADICS; NIDCR; NIMH; NINDS

Available from: 2017-06-20 Created: 2017-06-20 Last updated: 2018-01-16Bibliographically approved
Schultz, T., Özarslan, E. & Hotz, I. (Eds.). (2017). Modeling, Analysis, and Visualization of Anisotropy. Cham: Springer
Open this publication in new window or tab >>Modeling, Analysis, and Visualization of Anisotropy
2017 (English)Collection (editor) (Refereed)
Abstract [en]

This book focuses on the modeling, processing and visualization of anisotropy, irrespective of the context in which it emerges, using state-of-the-art mathematical tools. As such, it differs substantially from conventional reference works, which are centered on a particular application. It covers the following topics: (i) the geometric structure of tensors, (ii) statistical methods for tensor field processing, (iii) challenges in mapping neural connectivity and structural mechanics, (iv) processing of uncertainty, and (v) visualizing higher-order representations. In addition to original research contributions, it provides insightful reviews.This multidisciplinary book is the sixth in a series that aims to foster scientific exchange between communities employing tensors and other higher-order representations of directionally dependent data. A significant number of the chapters were co-authored by the participants of the workshop titled Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis, which was held in Dagstuhl, Germany in April 2016.

It offers a valuable resource for those working in the field of multi-directional data, vital inspirations for the development of new models, and essential analysis and visualization techniques, thus furthering the state-of-the-art in studies involving anisotropy.

Place, publisher, year, edition, pages
Cham: Springer, 2017. p. 407
Series
Mathematics and Visualization, ISSN 1612-3786, E-ISSN 2197-666X ; 2017
National Category
Telecommunications Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:liu:diva-152348 (URN)10.1007/978-3-319-61358-1 (DOI)9783319613574 (ISBN)9783319613581 (ISBN)
Available from: 2018-10-29 Created: 2018-10-29 Last updated: 2019-09-12Bibliographically approved
Shakya, S., Gu, X., Batool, N., Özarslan, E. & Knutsson, H. (2017). Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions. In: VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine: . Paper presented at Eurographics Workshop on Visual Computing for Biology and Medicine, September 7-8, 2017, Bremen, Germany (pp. 1-5). The Eurographics Association
Open this publication in new window or tab >>Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions
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2017 (English)In: VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine, The Eurographics Association , 2017, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.

Place, publisher, year, edition, pages
The Eurographics Association, 2017
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778, E-ISSN 2070-5786 ; 2017
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-140739 (URN)10.2312/vcbm.20171244 (DOI)9783038680369 (ISBN)
Conference
Eurographics Workshop on Visual Computing for Biology and Medicine, September 7-8, 2017, Bremen, Germany
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2019-11-19Bibliographically approved
Shakya, S., Batool, N., Özarslan, E. & Knutsson, H. (2017). Multi-Fiber Reconstruction Using Probabilistic Mixture Models for Diffusion MRI Examinations of the Brain. In: Thomas Schultz, Evren Özarslan, Ingrid Hotz (Ed.), Thomas Schultz; Evren Özarslan; Ingrid Hotz (Ed.), Modeling, Analysis, and Visualization of Anisotropy: . Paper presented at Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis, Dagstuhl, Germany in April 2016 (pp. 283-308). Paper presented at Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis, Dagstuhl, Germany in April 2016. Springer
Open this publication in new window or tab >>Multi-Fiber Reconstruction Using Probabilistic Mixture Models for Diffusion MRI Examinations of the Brain
2017 (English)In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer, 2017, p. 283-308Chapter in book (Other academic)
Abstract [en]

In the field of MRI brain image analysis, Diffusion tensor imaging (DTI) provides a description of the diffusion of water through tissue and makes it possible to trace fiber connectivity in the brain, yielding a map of how the brain is wired. DTI employs a second order diffusion tensor model based on the assumption of Gaussian diffusion. The Gaussian assumption, however, limits the use ofDTI in solving intra-voxel fiber heterogeneity as the diffusion can be non-Gaussian in several biological tissues including human brain. Several approaches to modeling the non-Gaussian diffusion and intra-voxel fiber heterogeneity reconstruction have been proposed in the last decades. Among such approaches are the multi-compartmental probabilistic mixture models. These models include the discrete or continuous mixtures of probability distributions such as Gaussian, Wishart or von Mises-Fisher distributions. Given the diffusion weighted MRI data, the problem of resolving multiple fibers within a single voxel boils down to estimating the parameters of such models.

In this chapter, we focus on such multi-compartmental probabilistic mixture models. First we present a review including mathematical formulations of the most commonly applied mixture models. Then, we present a novel method based on the mixture of non-central Wishart distributions. A mixture model of central Wishart distributions has already been proposed earlier to resolve intra-voxel heterogeneity. However, we show with detailed experiments that our proposed model outperforms the previously proposed probabilistic models specifically for the challenging scenario when the separation angles between crossing fibers (two or three) are small. We compare our results with the recently proposed probabilistic models of mixture of central Wishart distributions and mixture of hyper-spherical von Mises-Fisher distributions. We validate our approach with several simulations including fiber orientations in two and three directions and with real data. Resistivity to noise is also demonstrated by increasing levels of Rician noise in simulated data. The experiments demonstrate the superior performance of our proposed model over the prior probabilistic mixture models.

Place, publisher, year, edition, pages
Springer, 2017
Series
Mathematics and Visualization (MATHVISUAL), ISSN 1612-3786
Keywords
Crossing fibers, Diffusion MRI, DTI review, Intra-voxel fiber orientation, Mixture models, Mixture of hyper-spherical von Mises-Fisher distributions, Mixture of Wishart distributions, Wishart non-centrality parameter, Rician noise
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-142048 (URN)10.1007/978-3-319-61358-1_12 (DOI)978-3-319-61357-4 (ISBN)978-3-319-61358-1 (ISBN)
Conference
Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis, Dagstuhl, Germany in April 2016
Available from: 2017-10-19 Created: 2017-10-19 Last updated: 2019-11-11
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