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Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93 Stockholm, Sweden.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
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. p. 778-782
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-8452
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-138632DOI: 10.1109/ISBI.2017.7950634ISI: 000414283200181ISBN: 978-1-5090-1172-8 (electronic)ISBN: 978-1-5090-1173-5 (print)OAI: oai:DiVA.org:liu-138632DiVA, id: diva2:1112410
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
In thesis
1. Algorithms for magnetic resonance imaging in radiotherapy
Open this publication in new window or tab >>Algorithms for magnetic resonance imaging in radiotherapy
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential.

Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning approach to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs.

Cancerous tissue has a different structure from normal tissue. This affects molecular diffusion, which can be measured using MRI. The prototypical diffusion encoding sequence has recently been challenged with the introduction of more general gradient waveforms. One such example is diffusional variance decomposition (DIVIDE), which allows non-invasive mapping of parameters that reflect variable cell eccentricity and density in brain tumors. To take full advantage of such more general gradient waveforms it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints. We demonstrate that, by using the optimized gradient waveforms, it is technically feasible to perform whole-brain diffusional variance decomposition at clinical MRI systems with varying performance.

The last part of the thesis is devoted to estimation of diffusion MRI models from measurements. We show that, by using a machine learning framework called Gaussian processes, it is possible to perform diffusion spectrum imaging using far fewer measurements than ordinarily required. This has the potential of making diffusion spectrum imaging feasible even though the acquisition time is limited. A key property of Gaussian processes, which is a probabilistic model, is that it comes with a rigorous way of reasoning about uncertainty. This is pursued further in the last paper, in which we propose a Bayesian reinterpretation of several of the most popular models for diffusion MRI. Thanks to the Bayesian interpretation it possible to quantify the uncertainty in any property derived from these models. We expect this will be broadly useful, in particular in group analyses and in cases when the uncertainty is large.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 63
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1905
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-144351 (URN)10.3384/diss.diva-144351 (DOI)9789176853634 (ISBN)
Public defence
2018-03-23, Eken, hus 421, ingång 65, plan 9, Campus US, Linköping, 09:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2012-4281
Available from: 2018-02-20 Created: 2018-01-16 Last updated: 2018-05-09Bibliographically approved

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Sjölund, JensEklund, AndersÖzarslan, EvrenKnutsson, Hans
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