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Updated: Mar 15, 2026
05:57
Blood Flow Imaging with Ultrafast Doppler
Published on: October 14, 2020
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Research on Denoising Methods for Laser Doppler Blood Flow Signals Based on Time-Domain Noise Perception and DWT.
Quanxin Sun1, Jie Duan1, Hui Guo2
1School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130013, China.
Sensors (Basel, Switzerland)
|March 14, 2026
Summary
This study introduces an adaptive denoising algorithm for laser Doppler flow (LDF) signals, effectively reducing composite noise and improving signal fidelity. The new method enhances vascular hemodynamic monitoring in challenging noise conditions.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Medical Imaging
Background:
- Laser Doppler flow (LDF) signal processing faces challenges from composite noise (speckle, thermal, random pulse interference) and non-stationarity.
- Traditional thresholding methods struggle to balance noise suppression with signal fidelity in LDF analysis.
- Accurate LDF signal processing is crucial for effective vascular hemodynamic monitoring.
Purpose of the Study:
- To develop and validate an adaptive denoising algorithm for LDF signals that overcomes limitations of traditional methods.
- To improve the fidelity of LDF signals in the presence of complex noise environments.
- To provide a robust solution for accurate vascular hemodynamic monitoring.
Main Methods:
- An adaptive denoising algorithm integrating temporal noise perception and discrete wavelet transform (DWT) was proposed.
- A composite noise model was established, followed by a five-level DWT decomposition with local energy detection.
- An SNR-driven dynamic thresholding strategy, combining inter-layer adaptive allocation and local weighting, was employed, followed by improved smoothing.
Main Results:
- Simulations showed a significant improvement from 1 dB input SNR to 15.45 dB output SNR with low RMSE (0.05634), outperforming existing methods.
- Application to a vascular phantom signal (initial SNR -1.04 dB) resulted in a 13.86 dB output SNR and 0.00258 RMSE.
- The algorithm demonstrated superior performance compared to traditional wavelet methods, local variance, and variational mode decomposition (VMD).
Conclusions:
- The proposed adaptive denoising algorithm effectively suppresses composite noise and non-stationarity in LDF signals.
- The method achieves high-fidelity waveform capture, crucial for accurate vascular hemodynamic monitoring.
- This algorithm offers a robust and effective solution for LDF signal processing in complex noise environments.
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