TH-SP.2A.3

Predicting MRI RF Exposure for Passive Implantable Medical Devices Using a Mesh-based Convolutional Neural Network

Qianlong Lan, Jianfeng Zheng, Jiajun Chang, Ran Guo, Ji Chen, University of Houston, United States; Wolfgang Kainz, US Food and Drug Administration, United States
Session:
Artificial Intelligence and Deep Learning: A New Era in Imaging and Inverse Scattering
Track:
Special Sessions (AP-S)
Location:
Peony Junior Ballroom 4512
Presentation Time:
Thu, 9 Dec, 09:00 - 09:20 Singapore Time (UTC +8)
Thu, 9 Dec, 02:00 - 02:20 France Time (UTC +1)
Thu, 9 Dec, 01:00 - 01:20 UTC
Wed, 8 Dec, 20:00 - 20:20 New York Time (UTC -5)
Session Co-Chairs:
Ji Chen, University of Houston and Marco Salucci, ELEDIA@UniTN - University of Trento
Session Manager:
Room R10 Manager
Presentation
Discussion
Resources
Session TH-SP.2A
TH-SP.2A.1: Deep Learning Enhanced Joint Inversion of Multiphysics Data with Nonconforming Discretization
Yanyan Hu, Jiefu Chen, Xuqing Wu, University of Houston, United States; Yueqin Huang, Cyentech Consulting LLC, United States
TH-SP.2A.2: Convolutional Neural Networks for Radio Source Detection
Jayakrishnan Vijayamohanan, Arjun Gupta, Oameed Noakoasteen,, Christos Christodoulou, University of New Mexico, United States
TH-SP.2A.3: Predicting MRI RF Exposure for Passive Implantable Medical Devices Using a Mesh-based Convolutional Neural Network
Qianlong Lan, Jianfeng Zheng, Jiajun Chang, Ran Guo, Ji Chen, University of Houston, United States; Wolfgang Kainz, US Food and Drug Administration, United States
TH-SP.2A.4: Prediction of Active Implantable Medical Device Electromagnetic Models Using a Neural Network
Jiajun Chang, Qianlong Lan, Ran Guo, Jianfeng Zheng, Ji Chen, University of Houston, United States; Wolfgang Kainz, US Food and Drug Administration, United States
TH-SP.2A.5: Deep Surrogate Models for Time-Domain Electromagnetic Analysis using Attention: Going Beyond Recurrent Neural Networks
Oameed Noakoasteen, Jayakrishnan Vijayamohanan, Arjun Gupta, Christos Christodoulou, University of New Mexico, United States
TH-SP.2A.6: Data-Driven Electromagnetic Scalar Field Estimation of a Patch Antenna Using Deep Convolutional Neural Network
Md Rayhan Khan, Constantinos L. Zekios, Shubhendu Bhardwaj, Stavros V. Georgakopoulos, Florida International University, United States
TH-SP.2A.7: Sequential Deep Learning for In-Home Activity Monitoring Using mm-Wave FMCW Radar
Hajar Abedi, Ahmad Ansariyan, Plinio Morita, Jennifer Boger, Alexander Wong, George Shaker, University of Waterloo, Canada
TH-SP.2A.8: A Machine Learning-Based Model for Fast Recognition of Orbital Angular Momentum Modes
Jia-Jing Sun, Sheng Sun, Jun Hu, University of Electronic Science and Technology of China, China
TH-SP.2A.9: A Tailored Semi-Physics-Driven And Semi-Data-Driven Artificial Neural Network For Electromagnetic Full-Wave Inversion
Feng Han, Yanjin Chen, Xiamen University, China
TH-SP.2A.10: Electromagnetic Inverse Scattering Based on Deep Learning
Renzhou Gui, Tianyu Tang, Juan Li, Huilin Zheng, Xiaohong Ji, Jun Zhao, Xiaomeng Zhao, Tongji University, China

Platinum Sponsor

Huawei

Gold Sponsors

Nano Dimension / AME Academy
TMYTEK

Bronze Sponsors

Microwave Vision Group
Spring Technologies
Dassault Systemes