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Improving Patient Comfort in MRI with Predictive Acoustic Noise Cancelling
Abstract:
With sound pressure levels reaching up to 130 dB, acoustic noise in Magnetic Resonance Imaging (MRI) is one of the main sources of patient discomfort in otherwise one of the safest medical imaging modalities. In this work, a noise prediction-based approach, termed predictive noise cancelling (PNC), is applied, for the first time, to suppress noise in MRI. In PN C the noise from the scanner gradient coils is predicted based on linear time-invariant models, which relate the individual gradient coil (X, Y and Z) input to the acoustic noise output. A model setup was constructed of a custom speaker box and MRI -compatible microphone to demonstrate live noise reduction. Additional tuning steps, including output channel equalization and clock mismatch correction, were performed to maximize noise reduction. A calibration sequence was designed to determine the model and tuning parameters. Analysis of actual scanner noise shows an upper limit of 21 dB noise reduction with the proposed linear model. For the components of a clinical example sequence, the setup demonstrated in-bore live noise reduction of up to 10 dB (7.01 ± 0.31 dB, 6.42 ± 2.04 dB and 9.28 ± 0.26 dB for X, Y and Z gradient coils respectively) in the presence of system imperfections. Clinical relevance - The results indicate promising noise attenuation without the need to modify scanner hardware or compromises in acquisition speed or quality. This has potential to substantially and cost effectively improve patient comfort in clinical MRI.
Insights
Predictive noise cancelling (PNC) reduces Magnetic Resonance Imaging (MRI) noise by predicting scanner sounds. This novel approach offers significant noise attenuation, enhancing patient comfort without affecting image quality or scan speed.
Area of Science:
- Medical Imaging
- Acoustics
- Biomedical Engineering
Background:
- Acoustic noise in Magnetic Resonance Imaging (MRI) reaches up to 130 dB, causing significant patient discomfort.
- This noise is a primary limitation in an otherwise safe and valuable diagnostic imaging modality.
- Current methods for noise reduction are often hardware-dependent or compromise image quality.
Purpose of the Study:
- To introduce and evaluate a novel noise prediction-based approach, Predictive Noise Cancelling (PNC), for acoustic noise suppression in MRI.
- To demonstrate the feasibility of live, in-bore noise reduction using PNC without altering MRI hardware.
- To assess the effectiveness of PNC in reducing noise levels across different gradient coil components (X, Y, Z).
Main Methods:
- Developed linear time-invariant models to predict acoustic noise generated by individual MRI gradient coils (X, Y, Z).
- Constructed a custom experimental setup with an MRI-compatible speaker box and microphone for live noise reduction demonstration.
- Implemented tuning steps including output channel equalization and clock mismatch correction, alongside a calibration sequence for parameter determination.
Main Results:
- The proposed linear model achieved an upper limit of 21 dB noise reduction for actual scanner noise.
- In-bore live noise reduction demonstrated up to 10 dB for clinical example sequences (7.01 ± 0.31 dB for X, 6.42 ± 2.04 dB for Y, and 9.28 ± 0.26 dB for Z gradient coils).
- Noise reduction was achieved despite the presence of system imperfections.
Conclusions:
- Predictive Noise Cancelling (PNC) shows significant promise for substantial and cost-effective noise attenuation in clinical MRI.
- This method improves patient comfort by reducing acoustic noise without compromising scanner hardware, acquisition speed, or image quality.
- PNC represents a clinically relevant advancement for enhancing the patient experience during MRI procedures.
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