Near-field clutter artifact reduction algorithm based on wavelet thresholding method in echocardiography using 3D printed cardiac phantom

One of the typical sources of noise of echocardiography, near-field clutter (NFC), is an artifact in the near field and causes diagnostic errors with decreased accuracy. The purpose of this study is to apply an algorithm based on the wavelet thresholding method to remove only the NFC area, without affecting the essential regions for accurate diagnosis. Ultrasound images including NFC were obtained using a self-manufactured left ventricle (LV) phantom, after which comparative evaluation was performed after applying a wavelet thresholding method-based algorithm. When the algorithm based on the wavelet thresholding method was applied to the NFC image, the root mean square error (RMSE) value decreased by 71.38%, from 35.54 to 10.17. The correlation coefficient (CC) value increased by 12.64%, from 0.87 to 0.98, and the mean structural similarity (MSSIM) value increased by 23.68%, from 0.76 to 0.94. Finally, the universal quality index (UQI) value increased by 17.28%, from 0.81 to 0.95. In conclusion, the algorithm based on the wavelet thresholding method proved effective for removing significant NFCs, which affect image diagnosis in echocardiography. Furthermore, when this algorithm is used in cardiac ultrasound machines, it is expected to improve the accuracy of diagnosis by removing NFC.