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Magnetic Resonance Imaging (MRI) acoustic noise: estimation, characterization and reduction

12 December 2008

PhD ceremony: C.V. Rizzo Sierra, 14.45 uur, Academiegebouw, Broerstraat 5, Groningen

Thesis: Magnetic Resonance Imaging (MRI) acoustic noise: estimation, characterization and reduction

Promotor(s): prof. H. Duifhuis

Faculty: Mathematics and Natural Sciences


Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noise is a useful step to its reduction. To study acoustic noise the MR scanner is modeled as a linear electro-acoustical system in chapter 2 generating sound pressure signals proportional to the time derivative of the input gradient currents. The transfer function of one MR scanner is determined for two different input specifications. Since fMRI is often based on echo planar imaging (EPI) sequences, a useful validation of the transfer function prediction is obtained by calculating the acoustic output for the EPI sequence. A method to reduce the acoustic noise generated by MRI systems is proposed in chapter 3 based on the linear response theory: destructive interference of resonance frequencies. Sound pressure level (SPL) reduction of 10 dB is found. The study in chapter 4 aims at a quantitative approach of noise reduction; we want to obtain physical and subjective magnitude measures of the acoustic scanner noise. This is achieved by performing a psychophysical matching experiment between different echo planar imaging (EPI) sequences and a third octave band of pink noise. Electro-acoustical characterization of one MRI system in chapter 5 was studied in six different locations inside and outside the scanner bore. We want to obtain qualitative and quantitative effects of MRI bore enclosure in electro-acoustical transfer function estimation.


Last modified:15 September 2017 3.38 p.m.

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