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Dual apodization with cross‐correlation combined with robust Capon beamformer applied to ultrasound passive cavitation mapping
Author(s) -
Lu Shukuan,
Li Renyan,
Zhao Yan,
Yu Xianbo,
Wang Diya,
Wan Mingxi
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14093
Subject(s) - capon , apodization , computer science , beamforming , transducer , cross correlation , energy (signal processing) , algorithm , channel (broadcasting) , signal processing , signal (programming language) , ultrasound , acoustics , mathematics , optics , telecommunications , physics , radar , statistics , programming language
Purpose Passive acoustic mapping (PAM) has received increasing attention in recent years and has an extremely widespread application prospect in real‐time monitoring of ultrasound treatment. When using a diagnostic ultrasound transducer, such as a linear‐array transducer, the initially used time exposure acoustics (TEA) algorithm will produce high‐level artifacts. To address this problem, we recently proposed an enhanced algorithm for linear‐array PAM by introducing dual apodization with the cross‐correlation (DAX) method into TEA. But due to that the delay and sum beamformer used to create RX1 and RX2 is non‐adaptive, the remaining X‐type artifacts cannot be completely suppressed, yielding unsatisfactory image quality. This study aims to propose an improved version by combining DAX and robust Capon beamformer (DAX‐RCB). Methods Different from the delay and sum beamformer in the DAX‐TEA algorithm, in the proposed version, the two sets of channel signals from a pair of complementary receive apodizations are beamformed by the RCB method, which may make passive cavitation images much less sensitive to X‐type artifacts. The performance of the DAX‐RCB algorithm is validated by simulations and in vitro experiments and compared with the initially used TEA algorithm and the previous DAX‐TEA and RCB algorithms. Four indexes, including passive energy beam (PEB) size, image signal‐to‐background ratio (ISBR), energy estimation ratio (EER), and computing time, are used to evaluate the algorithm performance. Results Consider an example of the 8–8 alternating pattern (a pair of complementary apodizations are obtained by extracting eight elements every eight elements), the experimental results show that the A –6dB area (2D PEB size) of the proposed DAX‐RCB is significantly reduced by 11.0 and 6.8 mm 2 when compared with TEA and DAX‐TEA and is not significantly reduced when compared with RCB, the ISBR is significantly improved by 19.6, 10.8, and 5.6 dB compared with TEA, DAX‐TEA, and RCB, and the EER of DAX‐RCB is over 90%. The simulation tests indicate that the DAX‐RCB algorithm is also applicable to the image enhancement in the double‐source scenario and the high‐level noise scenario but at a risk of low energy estimation. The improvement of algorithm performance is accompanied by an increase in the computing time. The proposed DAX‐RCB consumes 113.3%, 29.5%, and 17.8% more time than TEA, DAX‐TEA, and RCB. Conclusions The proposed DAX‐RCB can be considered as an effective reconstruction algorithm for passive cavitation mapping and provide an appropriate monitoring means for ultrasound therapy, especially for cavitation‐mediated applications.