Redevelopment, System Refinement, and Evaluation of Predictive Noise Cancellation for Acoustic Noise Reduction in MRI
Febricetta Febricetta Zahraketzia Sarwono (TU Delft - Mechanical Engineering)
Kadir Berat Yildirim – Mentor
S.D. Weingärtner – Mentor (TU Delft - ImPhys/Computational Imaging)
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Abstract
Magnetic resonance imaging (MRI) produces high acoustic noise levels that can reduce patient comfort, make the scanning experience intimidating, raise hearing safety concerns, and interfere with auditory functional MRI experiments. Predictive noise cancellation (PNC) aims to reduce this noise by estimating the MRI gradient-to-acoustic transfer function and generating sequence-specific anti-noise. This thesis redeveloped an existing LabVIEW/MATLAB-based PNC pipeline into a Python-based framework and evaluated redesigned calibration strategies for phantom-based MRI acoustic noise reduction.
The Python framework rebuilt the main PNC workflow interface, digital signal processing, calibration, regular sequence processing, audio recording, and arbitrary function generator control. Validation against the original implementation showed that the rebuilt system preserved the main DSP and workflow behavior, with full-workflow errors below 5% for the evaluated blocks except for one flagged regular sequence alignment case. The Python implementation also reduced computation time by approximately 73% and improved workflow usability and stability.
Calibration redesign was evaluated using chirp-based gradients, transfer function stability metrics, live calibration-stage reduction, and phantom measurements using fast field echo (FFE) and echo planar imaging (EPI) sequences. Compared with the original sl014 calibration, the chirp-based gradients provided stronger excitation and generally improved transfer function stability and live reduction. Chirp 1 and chirp 4 were the most reliable broadband calibration candidates across calibration and FFE measurements. For EPI-focused testing, combined chirp+EPI-Y transfer functions improved EPI-band cancellation relative to chirp-only calibration and reduced residual amplitudes near the dominant EPI component.
The achieved cancellation remained limited by channel imbalance, frequency-dependent reduction, low-frequency playback constraints, and acoustic behavior that was not fully described by a fixed linear time-invariant model, especially for the more complex EPI sequence. In vivo evaluation was also not performed, so the results remain limited to phantom-based testing. Overall, this thesis validated a faster and more usable Python-based PNC framework and showed that redesigned broadband and EPI-targeted calibrations can improve sequence-specific MRI acoustic noise reduction.
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