An efficient feedback active noise control algorithm based on reduced-order linear predictive modeling of FMRI acoustic noise

Gradient and shim technologies for ultra high field MRI

Ultra High Field (UHF) MRI requires improved gradient and shim performance to fully realize the promised gains (SNR as well as spatial, spectral, diffusion resolution) that higher main magnetic fields offer. Both the more challenging UHF environment by itself, as well as the higher currents used in high performance coils, require a deeper understanding combined with sophisticated engineering modeling and construction, to optimize gradient and shim hardware for safe operation and for highest image quality. This review summarizes the basics of gradient and shim technologies, and outlines a number of UHF-related challenges and solutions. In particular, Lorentz forces, vibroacoustics, eddy currents, and peripheral nerve stimulation are discussed. Several promising UHF-relevant gradient concepts are described, including insertable gradient coils aimed at higher performance neuroimaging.

Smartphone-based real-time speech enhancement for improving hearing aids speech perception

In this paper, the development of a speech processing pipeline on smartphones for hearing aid devices (HADs) is presented. This pipeline is used for noise suppression and speech enhancement (SE) to improve speech quality and intelligibility. The proposed method is implemented to run in real-time on Android smartphones. The results of the testing conducted indicate that the proposed method suppresses the noise and improves the perceptual quality of speech in terms of three objective measures of perceptual evaluation of speech quality (PESQ), noise attenuation level (NAL), and the coherent speech intelligibility index (CSII).

Neuroimaging paradigms for tonotopic mapping (II): the influence of acquisition protocol

Numerous studies on the tonotopic organisation of auditory cortex in humans have employed a wide range of neuroimaging protocols to assess cortical frequency tuning. In the present functional magnetic resonance imaging (fMRI) study, we made a systematic comparison between acquisition protocols with variable levels of interference from acoustic scanner noise. Using sweep stimuli to evoke travelling waves of activation, we measured sound-evoked response signals using sparse, clustered, and continuous imaging protocols that were characterised by inter-scan intervals of 8.8, 2.2, or 0.0 s, respectively. With regard to sensitivity to sound-evoked activation, the sparse and clustered protocols performed similarly, and both detected more activation than the continuous method. Qualitatively, tonotopic maps in activated areas proved highly similar, in the sense that the overall pattern of tonotopic gradients was reproducible across all three protocols. However, quantitatively, we observed substantial reductions in response amplitudes to moderately low stimulus frequencies that coincided with regions of strong energy in the scanner noise spectrum for the clustered and continuous protocols compared to the sparse protocol. At the same time, extreme frequencies became over-represented for these two protocols, and high best frequencies became relatively more abundant. Our results indicate that although all three scanning protocols are suitable to determine the layout of tonotopic fields, an exact quantitative assessment of the representation of various sound frequencies is substantially confounded by the presence of scanner noise. In addition, we noticed anomalous signal dynamics in response to our travelling wave paradigm that suggest that the assessment of frequency-dependent tuning is non-trivially influenced by time-dependent (hemo)dynamics when using sweep stimuli.

Parallel feedback active noise control of MRI acoustic noise with signal decomposition using hybrid RLS-NLMS adaptive algorithms

This paper presents a cost-effective adaptive feedback Active Noise Control (FANC) method for controlling functional Magnetic Resonance Imaging (fMRI) acoustic noise by decomposing it into dominant periodic components and residual random components. Periodicity of fMRI acoustic noise is exploited by using linear prediction (LP) filtering to achieve signal decomposition. A hybrid combination of adaptive filters-Recursive Least Squares (RLS) and Normalized Least Mean Squares (NLMS) are then used to effectively control each component separately. Performance of the proposed FANC system is analyzed and Noise attenuation levels (NAL) up to 32.27 dB obtained by simulation are presented which confirm the effectiveness of the proposed FANC method.