| dc.date.accessioned | 2023-05-03T07:10:14Z | |
| dc.date.available | 2023-05-03T07:10:14Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://hdl.handle.net/10852/102107 | |
| dc.description.abstract | Glioblastoma is a deadly type of brain cancer. It is difficult to treat and has a poor prognosis, with most adult patients surviving only 12-15 months after diagnosis. Magnetic resonance imaging (MRI) scans are essential for diagnosing and treating this disease, but it can be challenging to ensure that the scans provide accurate, clinically useful information.
In this thesis, we investigate methods for improving the accuracy of MRI scans in brain cancer treatment. MRI scans use high-resolution structural images to show doctors the location and size of a tumor, and they can also be used to track how well a person is responding to treatment. In addition, functional MRI techniques, such as measuring cerebral blood volume, can provide additional clinical information.
However, it is not easy to ensure that these imaging modalities, along with advanced postprocessing techniques, correctly present the information that doctors need. For example, it is important to have anatomically correct and consistent values for assessing the blood supply to a tumor, and to be able to track changes in the tumor over time.
In this thesis, we focus on tracking image intensity and displacement, which we call "voxel tracking". This allows us to extract the most anatomically and physiologically correct information from MRI scans. We correct for errors in blood perfusion scans, create prognostic tissue markers, and create a model of how cancer grows and affects the brain.
Our research lays the foundation for more advanced studies of brain cancer, and it has the potential to lead to more personalized treatment plans for patients with glioblastoma. By understanding the details of how the disease progresses, doctors may be able to develop tailored treatment plans that are more effective and have better outcomes for patients. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.haspart | Paper 1. Hovden, I.T., Geier, O.M., Digernes, I., Fuster-Garcia, E., Løvland, G., Vik-Mo, E., Meling, T.R., Emblem, K.E., 2020. The impact of EPI-based distortion correction of dynamic susceptibility contrast MRI on cerebral blood volume estimation in patients with glioblastoma. European Journal of Radiology 132, 109278. DOI: 10.1016/j.ejrad.2020.109278. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.ejrad.2020.109278 | |
| dc.relation.haspart | Paper 2. Fuster-Garcia, E., Thokle Hovden, I., Fløgstad Svensson, S., Larsson, C., Vardal, J., Bjørnerud, A., Emblem, K.E., 2022. Quantification of Tissue Compression Identifies High-Grade Glioma Patients with Reduced Survival. Cancers 14, 1725. DOI: 10.3390/cancers14071725. The article is included in the thesis. Also available at: https://doi.org/10.3390/cancers14071725 | |
| dc.relation.haspart | Paper 3. Thokle Hovden, I., Fuster-Garcia, E., Li, J., Bjørnerud, A., Larsson, C., Fløgstad Svensson, S., Emblem, K.E., 2022. A Parametric Evaluation of Deformable Image Registration Methods for Assessing Glioblastoma Growth. Manuscript. The paper is included in the thesis. | |
| dc.relation.uri | https://doi.org/10.1016/j.ejrad.2020.109278 | |
| dc.relation.uri | https://doi.org/10.3390/cancers14071725 | |
| dc.title | Structural and functional tracking in longitudinal magnetic resonance imaging of glioblastoma | en_US |
| dc.type | Doctoral thesis | en_US |
| dc.creator.author | Hovden, Ivar Thokle | |
| dc.type.document | Doktoravhandling | en_US |