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Constrained optimization of gradient waveforms for generalized diffusion encoding
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. Elekta Instrument AB, Sweden.
MR Physics, Lund University, Sweden.
Physical Chemistry, Lund University, Sweden.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Show others and affiliations
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. Vol. 261, p. 157-168
Keyword [en]
Diffusion MR; Generalized gradient waveforms; Q-space trajectory imaging; Optimization; Hardware constraints
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-115795DOI: 10.1016/j.jmr.2015.10.012ISI: 000367212100021OAI: oai:DiVA.org:liu-115795DiVA, id: diva2:796686
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
In thesis
1. MRI based radiotherapy planning and pulse sequence optimization
Open this publication in new window or tab >>MRI based radiotherapy planning and pulse sequence optimization
2015 (English)Licentiate 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 algorithm 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 dierent structure from normal tissue. This affects molecular diusion, which can be measured using MRI. The prototypical diusion encoding sequence has recently been challenged with the introduction of more general 

waveforms. To take full advantage of their capabilities 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 45
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1713
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-115796 (URN)10.3384/lic.diva-115796 (DOI)978-91-7519-105-8 (ISBN)
Presentation
2015-04-13, IMT, Campus US, Linköpings universitet, Linköping, 13:15 (Swedish)
Opponent
Supervisors
Funder
Swedish Research Council
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2015-03-20Bibliographically approved
2. 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, JensWestin, Carl-FredrikKnutsson, Hans

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