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Related Concept Videos

Active Filters01:25

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
Passive Filters01:27

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff frequency...

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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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LMS-based active noise cancellation methods for fMRI using sub-band filtering.

Ali A Milani1, Issa Panahi, Richard Briggs

  • 1Dept. of Electrical Engineering, University of Texas, Dallas, USA. ali.a.milani@student.utdallas.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary

This study introduces an adaptive method to reduce acoustic noise in 3T-fMRI scans using sub-band filtering. The findings demonstrate effective noise reduction for clearer brain imaging.

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Area of Science:

  • Neuroimaging
  • Acoustic Engineering
  • Signal Processing

Background:

  • Acoustic noise from 3T-fMRI scanners can degrade image quality and participant comfort.
  • Effective noise reduction is crucial for accurate functional magnetic resonance imaging (fMRI) data acquisition.
  • Adaptive filtering techniques offer potential for real-time noise cancellation.

Purpose of the Study:

  • To apply and evaluate an adaptive Least Mean Squares (LMS)-based method for active acoustic noise reduction in 3T-fMRI.
  • To investigate the efficacy of two distinct sub-band filtering techniques within the adaptive framework.
  • To analyze the impact of varying the number of sub-band filters on noise reduction performance.

Main Methods:

  • Development and application of an adaptive LMS-based filtering algorithm.
  • Design and implementation of two different sub-band filtering strategies tailored to fMRI noise characteristics.
  • Performance analysis using acoustic noise data from a 3T fMRI scanner.
  • Comparative evaluation of noise reduction across different sub-band filter configurations.

Main Results:

  • The adaptive LMS-based method with sub-band filtering effectively reduces 3T-fMRI acoustic noise.
  • Performance varied based on the specific sub-band filtering technique employed.
  • An increase in the number of sub-band filters generally improved noise reduction efficacy.
  • The proposed methods show promise for enhancing the signal-to-noise ratio in fMRI.

Conclusions:

  • Adaptive sub-band filtering presents a viable approach for mitigating acoustic noise in 3T-fMRI.
  • The choice of sub-band filter design and quantity significantly influences noise cancellation effectiveness.
  • This technique can contribute to improved data quality and patient experience during fMRI examinations.