Abstract

Magnetic resonance imaging (MRI) tomography is often used for noninvasive scanning of various parts of a human body without undesirable effects present in X-ray computed tomography. In MRI devices, slices of a tested subject are selected in 3D coordinates by a system of gradient coils. The current flowing through these coils changes rapidly, which results in mechanical vibration. This vibration is significant also in the equipment working with a low magnetic field, and it causes image blurring of thin layer samples and acoustic noise significantly degrading a speech signal recorded simultaneously during MR scanning of the vocal tract. There are always negative physiological and psychological effects on a person exposed to vibration and acoustic noise. In order to minimize these negative impacts depending on intensity and time duration of exposition, we mapped relationship between energy of vibration and noise signals measured in the MRI scanning area and its vicinity.

Keywords: magnetic resonance imaging, acoustic noise, mechanical vibration, statistical analysis, low magnetic field environment

## 1. Introduction

The magnetic resonance imaging (MRI) method is successfully used for monitoring progress in therapy after vocal fold cancer surgery or for monitoring of the implanted cartilage in legs or arms, and/or the process of regeneration in different tissues, etc. In the case of the open-air MRI device, a weak magnetic field (up to 0.2 T) is usually generated by a pair of permanent magnets. Between these magnets, the gradient system consisting of 2 3 planar coils is situated together with the RF receiving/transmitting coils surrounding the tested object [1]. Slices of a tested object are selected in 3D coordinates by a gradient system consisting of planar coils parallel to the magnets. A rapidly changing current flowing through the gradient coils produces significant mechanical vibration [2, 3] causing blurring of images of thin layer samples and acoustic noise significantly degrading the speech signal recorded simultaneously during MR scanning of the human vocal tract [4, 5]. Acoustic noise has always negative physiological and psychological consequences on the exposed person depending on the noise intensity and time duration of noise

exposure [6]. In order to minimize these negative factors, this work is focused on mapping of energy relationship between vibration and noise signals measured in the MRI scanning area and its vicinity with the final aim to choose the proper scan sequence and its parameters—repetition time (TR), echo time (TE), orientation of scan slices, etc. Apart from real-time recording of the vibration and noise signals, the sound pressure level (SPL) was measured by a sound level meter using frequency weighting to match human perception of noise. The measured data and recorded signals were further processed off-line—the determined energetic features were statistically analyzed and the results were compared visually and numerically. ρ θð Þ¼ A þ B � cos θ, A þ B ¼ 1, (1)

where A = 1, B = 0 for omnidirectional, A = 0, B = 1 for figure-of-eight, and

Analysis of Energy Relations between Noise and Vibration Produced by a Low-Field MRI Device

H sðÞ¼ <sup>G</sup> � <sup>2</sup>π<sup>f</sup> <sup>2</sup>

s þ 2πf <sup>1</sup>

where f<sup>1</sup> = 20.6 Hz, f<sup>2</sup> = 12,194 Hz, and 20 log G = 0.062 dB [9]. To get the transfer function of the digital IIR filter, the frequency scale is warped by the

s ! 2 �

The noise distribution in the scanning area of the MRI equipment and its neighborhood has to be mapped prior to the selection of the proper recording microphone location. C-weighting was used for SPL measurement to accommodate the objective noise intensity to the subjective loudness at high sound levels. The Cweighting filter frequency response in s-domain is given by the equation

<sup>2</sup> � <sup>s</sup>

<sup>2</sup> � <sup>s</sup> <sup>þ</sup> <sup>2</sup>π<sup>f</sup> <sup>2</sup>

<sup>1</sup> � <sup>z</sup>�<sup>1</sup>

The sensors measuring vibration signals are placed inside the MRI scanning area where the basic stationary magnetic field of the MRI device is present together with the superimposed pulse magnetic field generated by the gradient system as well as the high voltage field originated during activation of the excitation RF coil. These fields would disturb a signal picked up by the sensor from ferromagnetic material or damage electronics integrated with the sensor [10, 11], which can be avoided using the vibration sensor with a piezoelectric transducer. The sensor must have good sensitivity and maximally flat frequency response with the frequency range covering the vibration and noise harmonic frequencies that fall into the low band due to frequency-limited gradient pulses. As a similar frequency range can be found in basic processing of speech signals, it is very important in the case of 3D scanning of the human vocal tract by MRI with parallel recording of a

The mentioned requirements imposed on the vibration sensor can be met by the sensor for acoustic musical instruments [12]. Its first usage in the magnetic field environment must be preceded by a calibration procedure and a measurement of its sensitivity and frequency response. The measured frequency response is used to determine a correction curve for filtering of the picked-up vibration signal and consecutive linearization operation that has effect on correctness of all analyzed spectral properties determined from the vibration signals—see the block diagram in Figure 1. The correction filter is proposed by a standard procedure of second-order

H zð Þ¼ <sup>b</sup><sup>0</sup> <sup>þ</sup> <sup>b</sup>1z�<sup>1</sup> <sup>þ</sup> <sup>b</sup>2z�<sup>2</sup>

<sup>A</sup> <sup>¼</sup> <sup>10</sup> <sup>G</sup>

For the sampling frequency fs, the polynomial filter coefficients a0,1,2 and b0,1,2 are derived from three input parameters—gain G, mid-point frequency fc, and

<sup>20</sup>,ω<sup>c</sup> ¼ 2π �

f c fs

4

<sup>2</sup> , (2)

<sup>1</sup> <sup>þ</sup> <sup>z</sup>�<sup>1</sup> : (3)

<sup>a</sup><sup>0</sup> <sup>þ</sup> <sup>a</sup>1z�<sup>1</sup> <sup>þ</sup> <sup>a</sup>2z�<sup>2</sup> : (4)

, (5)

A = 0.5, B = 0.5 for cardioid directional patterns.

DOI: http://dx.doi.org/10.5772/intechopen.85275

bilinear transform from s-plane to z-plane

speech signal [5].

shelving filter design [13]:

99

quality factor Q—in the following manner:
