Abstract
BackgroundImproving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research. Plane wave imaging (PWI) has been deemed as a potential method for UCAI due to its’ high frame rate and low mechanical index. High frame rate can improve the temporal resolution of UCAI. Meanwhile, low mechanical index is essential to UCAI since microbubbles can be easily broken under high mechanical index conditions. However, the clinical practice of ultrasound contrast agent plane wave imaging (UCPWI) is still limited by poor imaging quality for lack of transmit focus. The purpose of this study was to propose and validate a new post-processing method that combined with deep learning to improve the imaging quality of UCPWI. The proposed method consists of three stages: (1) first, a deep learning approach based on U-net was trained to differentiate the microbubble and tissue radio frequency (RF) signals; (2) then, to eliminate the remaining tissue RF signals, the bubble approximated wavelet transform (BAWT) combined with maximum eigenvalue threshold was employed. BAWT can enhance the UCA area brightness, and eigenvalue threshold can be set to eliminate the interference areas due to the large difference of maximum eigenvalue between UCA and tissue areas; (3) finally, the accurate microbubble imaging were obtained through eigenspace-based minimum variance (ESBMV).ResultsThe proposed method was validated by both phantom and in vivo rabbit experiment results. Compared with UCPWI based on delay and sum (DAS), the imaging contrast-to-tissue ratio (CTR) and contrast-to-noise ratio (CNR) was improved by 21.3 dB and 10.4 dB in the phantom experiment, and the corresponding improvements were 22.3 dB and 42.8 dB in the rabbit experiment.ConclusionsOur method illustrates superior imaging performance and high reproducibility, and thus is promising in improving the contrast image quality and the clinical value of UCPWI.
Highlights
Improving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research
Ultrasound contrast agent imaging (UCAI) has become more widely used in clinical diagnosis. Conditions such as low mechanical index which are essential to UCAI have been highly emphasized in clinical examination [5, 6] since microbubbles can be broken under high mechanical index conditions
We extended our previous work [20] and proposed a new post-processing method for ultrasound contrast agent plane wave imaging (UCPWI), Table 1 shows the key differences between the previous method and the proposed
Summary
Improving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research. Plane wave imaging (PWI) has been deemed as a potential method for UCAI due to its’ high frame rate and low mechanical index. Ultrasound contrast agent imaging (UCAI) has become more widely used in clinical diagnosis Conditions such as low mechanical index which are essential to UCAI have been highly emphasized in clinical examination [5, 6] since microbubbles can be broken under high mechanical index conditions. Pulse inversion [11], amplitude modulation [12], chirp-encoded excitation [13], golay-encoded excitation [14], second harmonic imaging [15], sub-harmonic imaging [16], super-harmonic imaging [17] and bubble approximated wavelet transform (BAWT) [18] are the representatives of methods that have significant effect Most of these methods improve the imaging contrast-to-tissue ratio (CTR) based on the time–frequency difference between microbubbles and tissues. When facing strong scattering points, the previous work still showed its deficiencies in the recognition of tissue signals
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