A Deep Learning Based Method for Fast Registration of Cardiac Magnetic Resonance Images
| dc.contributor.advisor | Jacob, Christian | |
| dc.contributor.advisor | Alim, Usman | |
| dc.contributor.author | Graham, Benjamin | |
| dc.contributor.committeemember | Zhao, Richard | |
| dc.contributor.committeemember | Garcia Flores, Julio | |
| dc.contributor.committeemember | Hoyer, Peter | |
| dc.date | 2024-05 | |
| dc.date.accessioned | 2024-05-01T17:08:52Z | |
| dc.date.available | 2024-05-01T17:08:52Z | |
| dc.date.issued | 2024-04-29 | |
| dc.description.abstract | Image registration is used in many medical image analysis applications, such as tracking the motion of tissue in cardiac images, where cardiac kinematics can be an indicator of tissue health. Registration is a challenging problem for deep learning algorithms because ground truth transformations are not feasible to create, and because there are potentially multiple transformations that can produce images that appear correlated with the goal. Unsupervised methods have been proposed to learn to predict effective transformations, but these methods take significantly longer to predict than established baseline methods. For a deep learning method to see adoption in wider research and clinical settings, it should be designed to run in a reasonable time on common, mid-level hardware. Fast methods have been proposed for the task of image registration but often use patch-based methods which can affect registration accuracy for a highly dynamic organ such as the heart. In this thesis, a fast, volumetric registration model is proposed for the use of quantifying cardiac strain. The proposed Deep Learning Neural Network (DLNN) is designed to utilize an architecture that can com-pute convolutions incredibly efficiently, allowing the model to achieve registration fidelity similar to other state-of-the-art models while taking a fraction of the time to perform inference. The proposed fast and lightweight registration (FLIR) model is used to predict tissue motion which is then used to quantify the non-uniform strain experienced by the tissue. For acquisitions taken from the same patient at approximately the same time, it would be expected that strain values measured between the acquisitions would have very small differences. Using this metric, strain values computed using the FLIR method are shown to be very consistent. | |
| dc.identifier.citation | Graham, B. (2024). A deep learning based method for fast registration of cardiac magnetic resonance images (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | |
| dc.identifier.uri | https://hdl.handle.net/1880/118640 | |
| dc.language.iso | en | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject.classification | Artificial Intelligence | |
| dc.title | A Deep Learning Based Method for Fast Registration of Cardiac Magnetic Resonance Images | |
| dc.type | master thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.thesis.accesssetbystudent | I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible. |