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Generating patient specific pseudo-CT of the head from MR using atlas-based regression
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.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. Sectra, Sweden.ORCID iD: 0000-0003-0908-9470
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
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.

Place, publisher, year, edition, pages
2015. Vol. 60, no 2, p. 825-839
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-113297DOI: 10.1088/0031-9155/60/2/825ISI: 000347675100023PubMedID: 25565133OAI: oai:DiVA.org:liu-113297DiVA, id: diva2:780843
Available from: 2015-01-15 Created: 2015-01-15 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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