Computational Methods for MRI-Guided and Powered Ferric Applicators: Modeling and Image Processing

dc.contributor.advisorTsekos, Nikolaos V.
dc.contributor.committeeMemberShi, Weidong
dc.contributor.committeeMemberEick, Christoph F.
dc.contributor.committeeMemberBecker, Aaron T.
dc.creatorChu, Wenhui 2021 2021
dc.description.abstractCardiac 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.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.identifier.citationPortions 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.
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dc.subjectCardiac Segmentation
dc.titleComputational Methods for MRI-Guided and Powered Ferric Applicators: Modeling and Image Processing
dcterms.accessRightsThe 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.terms2023-12-01 of Natural Sciences and Mathematics Science, Department of Science of Houston of Philosophy


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