University of Texas at Dallas, Richardson, TX 75080-3021, USA.
IEEE Trans Biomed Eng. 2011 Dec;58(12):3303-9. doi: 10.1109/TBME.2010.2096423. Epub 2010 Dec 3.
Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.
功能磁共振成像（fMRI）的声噪声由于梯度线圈中电流的重复性而呈现出近乎周期性的性质（准周期性）。由于背景噪声和将梯度线圈波形映射到测量声波形的电声传递函数的缓慢漂移，连续周期中波形会发生小的变化。当使用回波平面成像（EPI）测序时，周期取决于每秒的切片数。fMRI 声噪声的线性可预测性对针对消除声噪声的有源噪声控制（ANC）系统的性能有直接影响。结果表明，通过包含前一个周期的一些样本，可以用非常低的阶预测器达到非常高的线性预测精度。这对反馈 ANC 系统有直接影响，因为它们的性能取决于要消除的声噪声的可预测性。本文提出的 fMRI 声噪声的低复杂度线性预测用于推导一种有效且低成本的反馈 ANC 系统。
