Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography.
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| Title: | Deep denoising approach to improve shear wave phase velocity map reconstruction in ultrasound elastography. |
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| Authors: | Sahshong, Phidakordor1 (AUTHOR), Chandra, Akash2 (AUTHOR), Mercado‐Shekhar, Karla P.2 (AUTHOR), Bhatt, Manish1 (AUTHOR) manishb@iitg.ac.in |
| Source: | Medical Physics. Mar2025, Vol. 52 Issue 3, p1481-1499. 19p. |
| Subjects: | Signal denoising, Phase velocity, Signal-to-noise ratio, Artificial neural networks, Elastography, Image reconstruction, Deep learning, Ultrasonic imaging |
| Abstract: | Background: Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal‐to‐noise ratio (SNR) can be very low. Purpose: The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity image maps. Methods: The study addresses noise by transforming particle velocity data into a time‐frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high‐noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3 to 60 kPa. A total of 1 85 570 samples with 80%–20%$\%$ split were used for training and validation. The approach is tested on experimental phantom and ex‐vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal–Wallis one‐way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann–Whitney U test and Holm–Bonferroni adjustment of p−values$p-{\rm values}$. Results: The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from –2 to 9.9 dB levels to 15.6 to 30.3 dB levels. Kruskal–Wallis one‐way analysis showed statistical significance (p<0.05$p<0.05$). Multiple comparisons with p‐value corrections also showed statistically significant improvement when compared to the bandpass and wavelet filtering scheme (p<0.05$p<0.05$). Smoother phase velocity maps were reconstructed after denoising. The coefficient of variation is less than 5%$5\%$ in CIRS phantom and less than 18%$18\%$ in ex‐vivo goat liver tissue. Conclusions: The proposed approach demonstrates improvement in shear wave phase velocity image map reconstruction and holds promise that deep learning methods can be effectively utilized to extract true shear wave signal from measured noisy data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Background: Measurement noise often leads to inaccurate shear wave phase velocity estimation in ultrasound shear wave elastography. Filtering techniques are commonly used for denoising the shear wavefields. However, these filters are often not sufficient, especially in fatty tissues where the signal‐to‐noise ratio (SNR) can be very low. Purpose: The purpose of this study is to develop a deep learning approach for denoising shear wavefields in ultrasound shear wave elastography. This may lead to improved reconstruction of shear wave phase velocity image maps. Methods: The study addresses noise by transforming particle velocity data into a time‐frequency representation. A neural network with encoder and decoder convolutional blocks effectively decomposes the input and extracts the signal of interest, improving the SNR in high‐noise scenarios. The network is trained on simulated phantoms with elasticity values ranging from 3 to 60 kPa. A total of 1 85 570 samples with 80%–20%$\%$ split were used for training and validation. The approach is tested on experimental phantom and ex‐vivo goat liver tissue data. Performance was compared with the traditional filtering methods such as bandpass, median, and wavelet filtering. Kruskal–Wallis one‐way analysis of variance was performed to check statistical significance. Multiple comparisons were performed using the Mann–Whitney U test and Holm–Bonferroni adjustment of p−values$p-{\rm values}$. Results: The results are evaluated using SNR and the percentage of pixels that can be reconstructed in the phase velocity maps. The SNR levels in experimental data improved from –2 to 9.9 dB levels to 15.6 to 30.3 dB levels. Kruskal–Wallis one‐way analysis showed statistical significance (p<0.05$p<0.05$). Multiple comparisons with p‐value corrections also showed statistically significant improvement when compared to the bandpass and wavelet filtering scheme (p<0.05$p<0.05$). Smoother phase velocity maps were reconstructed after denoising. The coefficient of variation is less than 5%$5\%$ in CIRS phantom and less than 18%$18\%$ in ex‐vivo goat liver tissue. Conclusions: The proposed approach demonstrates improvement in shear wave phase velocity image map reconstruction and holds promise that deep learning methods can be effectively utilized to extract true shear wave signal from measured noisy data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00942405 |
| DOI: | 10.1002/mp.17581 |
