Phantom Signals and Analysis for Magnetic Resonance Elastography
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Hong, Charles Phung
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Abstract
Magnetic resonance elastography (MRE) requires low-frequency induced vibrations to properly obtain representative stiffness data of the region of interest (ROI). However, variability in the strength of vibration, mechanical setup, and patient vasculature can lead to poor outcomes or also known as failure cases within MRE. This work investigates the development of a device to detect vibrations without using an MRI machine and classification implementation for actuator quality control. In the first aim, a sensor phantom apparatus is developed and validated to detect shear waves. In the second aim, a supervised machine learning algorithm is paired with the device to detect actuator contact. Ultimately, the availability of home MRE actuator testing will expand the inclusion of the MRE procedure, as multiple MRE actuator setups can be tested prior to clinical use.
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Undergraduate Research Option Thesis