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MRI based radiotherapy planning and pulse sequence optimization
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
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. , 45 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1713
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-115796DOI: 10.3384/lic.diva-115796ISBN: 978-91-7519-105-8 (print)OAI: oai:DiVA.org:liu-115796DiVA: diva2:796693
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
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, 3274-3279 p.Conference 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
Keyword
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: 2015-09-14Bibliographically 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, 825-839 p.Article 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: 2017-12-05Bibliographically 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, 157-168 p.Article 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
Keyword
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: 2017-12-04Bibliographically approved

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