MRI RF-induced Heating Prediction for Complex-shaped Passive Implantable Devices using Neural Network Methods

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dc.contributor.advisorChen, Ji
dc.contributor.committeeMemberBenhaddou, Driss
dc.contributor.committeeMemberChen, Jiefu
dc.contributor.committeeMemberJackson, David R.
dc.contributor.committeeMemberKainz, Wolfgang
dc.creatorChang, Jiajun
dc.creator.orcid0000-0002-1726-1798
dc.date.accessioned2023-05-26T16:07:47Z
dc.date.createdMay 2022
dc.date.issued2022-05-12
dc.date.updated2023-05-26T16:07:49Z
dc.description.abstractMagnetic Resonance Imaging (MRI) Radiofrequency (RF) -induced heating are one of the major safety concerns for patients with Passive Implantable Medical Devices (PIMDs) implanted inside the body. Evaluation of RF-induced heating includes experimental measurements and full-wave Electromagnetic (EM) simulations which will cost a significant amount of time and computational resources. Neural Network (NN) methods are introduced as a data-driven regression model which trains the parameters using device features to implement the predictions of RF-induced heating. While the previous NN models cannot predict the RF-induced heating of complex-shaped PIMDs as the device structure cannot be characterized by several parameters. Also, no discussions have been made on the strategy of training dataset selection. In this study, mesh-based Convolutional Neural Network (CNN) models are introduced to implement the heating prediction for complex-shaped PIMDs. Tibia Plating System and Spinal Fixation System device models are developed with variations on geometrical features. In-vitro and in-vivo EM simulations are performed with device mesh information and peak SAR values extracted. Incident Electric field information and mesh information are combined to form the input of the CNN models. After training and testing, CNN model convergence and data correlations are observed as a metric of CNN general prediction efficacy. The distribution of absolute errors and absolute percentage errors are shown to further investigate the prediction performance for different data. For the selection of training dataset, naïve strategy is initially introduced which uses different sizes of training dataset to find the training dataset with good prediction performance and least data possible. Principal Component Analysis (PCA) is performed on the input matrices of CNN model, which provides a threshold as the best prediction performance a training dataset can achieve with the dataset size same with the number of top-ranking Principle Components (PCs). Overall, mesh-based CNN models can predict the RF-induced heating of some complex-shaped PIMDs with acceptable performance. With the guidance of PCA analysis, the optimal size of training dataset can be determined before the simulations are performed which can save a lot of time used by obtaining excessive simulation results.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Chang, Jiajun, Jianfeng Zheng, Ran Guo, Qianlong Lan, Mayur Thakore, Wolfgang Kainz, and Ji Chen. "A mesh-based CNN for the evaluation of MR RF-induced heating of complex-shaped passive implants"; and in: Chang, Jiajun, Jianfeng Zheng, Mahir Foysal, Ran Guo, and Ji Chen. "Prediction of Radiofrequency-induced Heating of Spinal Fixation System using Mesh-based Convolutional Neural Network." In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), pp. 337-338. IEEE, 2022.
dc.identifier.urihttps://hdl.handle.net/10657/14280
dc.language.isoeng
dc.rightsThe 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.subjectMagnetic Resonance Imaging
dc.subjectNeural networks
dc.titleMRI RF-induced Heating Prediction for Complex-shaped Passive Implantable Devices using Neural Network Methods
dc.type.dcmiText
dc.type.genreThesis
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.lift2024-05-01
local.embargo.terms2024-05-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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