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Algorithms for magnetic resonance imaging in radiotherapy
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Elekta Instrument AB.ORCID iD: 0000-0002-9099-3522
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: urn:nbn:se:liu:diva-144351DOI: 10.3384/diss.diva-144351ISBN: 9789176853634 (print)OAI: oai:DiVA.org:liu-144351DiVA, id: diva2:1174599
Public defence
2018-03-23, Eken, hus 421, ingång 65, plan 9, Campus US, Linköping, 09:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2012-4281Available from: 2018-02-20 Created: 2018-01-16 Last updated: 2018-05-09Bibliographically approved
List of papers
1. Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features
Open this publication in new window or tab >>Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features
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2014 (English)In: 22nd International Conference on Pattern Recognition (ICPR), 2014, IEEE , 2014, p. 3274-3279Conference paper, Published paper (Refereed)
Abstract [en]

Magnetic resonance (MR) images lack information about radiation transport-a fact which is problematic in applications such as radiotherapy planning and attenuation correction in combined PET/MR imaging. To remedy this, a crude but common approach is to approximate all tissue properties as equivalent to those of water. We improve upon this using an algorithm that automatically identifies bone tissue in MR. More specifically, we focus on segmenting the skull prior to stereotactic neurosurgery, where it is common that only MR images are available. In the proposed approach, a machine learning algorithm known as a support vector machine is trained on patients for which both a CT and an MR scan are available. As input, a combination of local and global information is used. The latter is needed to distinguish between bone and air as this is not possible based only on the local image intensity. A whole skull segmentation is achievable in minutes. In a comparison with two other methods, one based on mathematical morphology and the other on deformable registration, the proposed method was found to yield consistently better segmentations.

Place, publisher, year, edition, pages
IEEE, 2014
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keywords
Bones; Computed tomography; Image segmentation; Magnetic resonance imaging; Positron emission tomography; Support vector machines; Training
National Category
Physical Sciences
Identifiers
urn:nbn:se:liu:diva-113296 (URN)10.1109/ICPR.2014.564 (DOI)000359818003068 ()
Conference
22nd International Conference on Pattern Recognition (ICPR), 2014, 24-28 August, Stockholm, Sweden
Available from: 2015-01-15 Created: 2015-01-15 Last updated: 2018-01-16Bibliographically approved
2. Generating patient specific pseudo-CT of the head from MR using atlas-based regression
Open this publication in new window or tab >>Generating patient specific pseudo-CT of the head from MR using atlas-based regression
2015 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 60, no 2, p. 825-839Article in journal (Refereed) Published
Abstract [en]

Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation. Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT. We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-113297 (URN)10.1088/0031-9155/60/2/825 (DOI)000347675100023 ()25565133 (PubMedID)
Available from: 2015-01-15 Created: 2015-01-15 Last updated: 2018-01-16Bibliographically approved
3. Constrained optimization of gradient waveforms for generalized diffusion encoding
Open this publication in new window or tab >>Constrained optimization of gradient waveforms for generalized diffusion encoding
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2015 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 261, p. 157-168Article in journal (Refereed) Published
Abstract [en]

Diffusion MRI is a useful probe of tissue structure. The prototypical diffusion encoding sequence, the single pulsed field gradient, has recently been challenged with the introduction of more general gradient waveforms. Out of these, we focus on q-space trajecory imaging, which generalizes the scalar b-value to a tensor valued property. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We formulate this as a constrained optimization problem that accomodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radiofrequency pulses. The power of this approach is demonstrated by a comparison with previous work on optimization of isotropic diffusion sequences, showing possible gains in diffusion weighting or in heat dissipation, which in turn means increased signal or reduced scan-times.

Place, publisher, year, edition, pages
Elsevier, 2015
Keywords
Diffusion MR; Generalized gradient waveforms; Q-space trajectory imaging; Optimization; Hardware constraints
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-115795 (URN)10.1016/j.jmr.2015.10.012 (DOI)000367212100021 ()
Note

On the day of the defence date the status of this article was Manuscript.

Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2018-01-16Bibliographically approved
4. Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
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
5. Bayesian uncertainty quantification in linear models for diffusion MRI
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

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