Objective: To reduce acoustic noise levels in T 1-weighted and proton-density-weighted turbo spin-echo (TSE) sequences, which typically reach acoustic noise levels up to 100 dB(A) in clinical practice.

Materials And Methods: Five acoustic noise reduction strategies were combined: (1) gradient ramps and shapes were changed from trapezoidal to triangular, (2) variable-encoding-time imaging was implemented to relax the phase-encoding gradient timing, (3) RF pulses were adapted to avoid the need for reversing the polarity of the slice-rewinding gradient, (4) readout bandwidth was increased to provide more time for gradient activity on other axes, (5) the number of slices per TR was reduced to limit the total gradient activity per unit time. We evaluated the influence of each measure on the acoustic noise level, and conducted in vivo measurements on a healthy volunteer. Sound recordings were taken for comparison.

Results: An overall acoustic noise reduction of up to 16.8 dB(A) was obtained by the proposed strategies (1-4) and the acquisition of half the number of slices per TR only. Image quality in terms of SNR and CNR was found to be preserved.

Conclusions: The proposed measures in this study allowed a threefold reduction in the acoustic perception of T 1-weighted and proton-density-weighted TSE sequences compared to a standard TSE-acquisition. This could be achieved without visible degradation of image quality, showing the potential to improve patient comfort and scan acceptability.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10334-015-0502-7DOI Listing

Publication Analysis

Top Keywords

acoustic noise
24
noise reduction
12
proton-density-weighted turbo
8
turbo spin-echo
8
noise levels
8
1-weighted proton-density-weighted
8
tse sequences
8
gradient activity
8
number slices
8
image quality
8

Similar Publications

The identification of vibration and reconstruction of sound fields of plate structures are important for understanding the vibroacoustic characteristics of complex structures. This paper presents a data-physics driven (DPD) model integrated with transfer learning (DPDT) for high-precision identification and reconstruction of vibration and noise radiation of plate structures. The model combines the Kirchhoff-Helmholtz integral equation with convolutional neural networks, leveraging physical information to reduce the need for extensive data.

View Article and Find Full Text PDF

The acoustic signals generated during the laser paint removal process contain valuable information that reflects the state of paint removal. However, it is often overshadowed by complex environmental noise, posing significant challenges for real-time monitoring of paint removal based on acoustic signals. This paper introduces a real-time acoustic monitoring method for laser paint removal using deep learning techniques for the first time.

View Article and Find Full Text PDF

Distributed acoustic sensing (DAS) is a technology that uses optical fiber as a sensing unit to detect external vibration signals. Due to the high resolution and high sensitivity of DAS, it has great application potential in the detection of vibration events. However, high detection performance will bring limitations to DAS in multi-source detection.

View Article and Find Full Text PDF

Integrating visual features has been proven effective for deep learning-based speech quality enhancement, particularly in highly noisy environments. However, these models may suffer from redundant information, resulting in performance deterioration when the signal-to-noise ratio (SNR) is relatively high. Real-world noisy scenarios typically exhibit widely varying noise levels.

View Article and Find Full Text PDF

Adaptive focusing for wideband beamforming in multipath environments.

J Acoust Soc Am

January 2025

Electrical and Computer Engineering, Duke University, Durham, North Carolina 27704, USA.

This paper addresses achieving the high time-bandwidth product necessary for low signal-to-noise ratio (SNR) target detection and localization in complex multipath environments. Time bandwidth product is often limited by dynamic environments and platform maneuvers. This paper introduces data-driven wideband focusing methods for passive sonar that optimize parameterized unitary matrices to align signal subspaces across the frequency band without relying on wave propagation models which are subject to mismatch in complex multipath environments.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!