Computational Methods for MRI-Guided and Powered Ferric Applicators: Modeling and Image Processing
| dc.contributor.advisor | Tsekos, Nikolaos V. | |
| dc.contributor.committeeMember | Shi, Weidong | |
| dc.contributor.committeeMember | Eick, Christoph F. | |
| dc.contributor.committeeMember | Becker, Aaron T. | |
| dc.creator | Chu, Wenhui | |
| dc.date.accessioned | 2022-06-17T21:36:36Z | |
| dc.date.created | December 2021 | |
| dc.date.issued | 2021-12 | |
| dc.date.submitted | December 2021 | |
| dc.date.updated | 2022-06-17T21:36:37Z | |
| dc.description.abstract | Cardiac diseases are major causes of global mortality which are a consistent threat to the lives of people. With the development of left ventricle segmentation, the real-time MRI-based control of a ferromagnetic application for endovascular navigation with data sensing and feedback in cardiac was applied in recent years. In this work, we first propose three novel deep learning architectures called BNU-net, LNU-net, and IBU-net for left ventricle segmentation from short-axis cine MRI images. BNU-net is the batch normalized (BN) U-net, LNU-net is the layer normalized (LN) U-net, and IBU-net is the instance-batch normalized (IB) U-net. The architectures of BNU-net, LNU-net, and IBU-net have an encoding path for feature extraction and a decoding path that enables precise localization. BNU-net, LNU-net, and IBU-net have left ventricle segmentation methods: BNU-net employs batch normalization to the results of each convolutional layer, LNU-net applies layer normalization in each convolutional block, while IBU-net incorporates instance and batch normalization together in the first convolutional block. Our method incorporates affine transformations and elastic deformations for image data processing. Our dataset that contains 805 MRI images regarding the left ventricle from 45 patients is used for evaluation. The experimental results reveal that our approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the dice coefficient and the average perpendicular distance. We then simulate a computational platform for preoperative planning and modeling of MRI-powered applicators inside blood vessels. This platform was implemented as a two-way data and command pipeline that links the MRI scanner, the computational core, and the operator. The platform first processes multi-slice MR data to extract the vascular bed and then fits a virtual corridor inside the vessel. This corridor serves as a virtual fixture (VF), a forbidden region for the applicators to avoid vessel perforation or collision. The geometric features of the vessel centerline, the VF, and MRI safety compliance (dB/dt, max available gradient) are then used to generate magnetic field gradient waveforms. Different blood flow profiles can be user-selected, and those parameters are used for modeling the applicator's maneuvering. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Chu, Wenhui, Giovanni Molina, Nikhil V. Navkar, Christoph F. Eick, Aaron T. Becker, Panagiotis Tsiamyrtzis, and Nikolaos V. Tsekos. "BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI." In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 731-736. IEEE, 2019. | |
| dc.identifier.uri | https://hdl.handle.net/10657/9262 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | MRI | |
| dc.subject | Cardiac Segmentation | |
| dc.title | Computational Methods for MRI-Guided and Powered Ferric Applicators: Modeling and Image Processing | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2023-12-01 | |
| local.embargo.terms | 2023-12-01 | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science, Department of | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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