Sensor-Embedded Tissue Phantom for Magnetic Resonance Elastography Mechanical Failure Testing

Abstract

Abstract Magnetic Resonance Elastography (MRE) is an imaging technique capable of quantifying the stiffness of in vivo tissue by applying and imaging shear waves produced by an MRE actuator. Poor image acquisition may result from the MRE procedure if there is insufficient contact between the MRE actuator and the patient. An experimental test setup outside of the clinic will aid in reducing the number of failed acquisitions by enabling the development of advanced actuators and actuator systems. This work presents the development and testing of a sensor-embedded tissue phantom setup paired with a support vector machine (SVM) classifier to automate the MRE actuator testing process. MRE actuation of soft tissue is simulated by utilizing a voice coil positioning stage that interfaces with a phantom. To capture the resulting vibrations, accelerometers are embedded inside the phantom. Subsequent characterization experiments verify the functionality of the developed phantoms to capture wave propagation. A secondary investigation was performed by utilizing the developed setup to collect acceleration measurements at varying contact distances. We provide an overview of feature analysis and selection to develop SVM models for contact detection. Multiple SVM models are reported, and the best-performing model displayed almost perfect validation (94.53%) and test (90.91%) accuracy. The pairing of sensor-embedded phantom with an SVM for detection demonstrates potential improvements to the MRE actuator developmental process by automatically assessing contact-related issues prior to clinical testing.