Lachambre Celestine, Basarab Adrian, Bera Jean-Christophe, Nicolas Barbara, Varray Francois, Gilles Bruno
IEEE Trans Ultrason Ferroelectr Freq Control. 2024 Aug;71(8):995-1005. doi: 10.1109/TUFFC.2024.3416813. Epub 2024 Aug 19.
High-intensity focused ultrasound (HIFU) can produce cavitation, which requires monitoring for specific applications such as sonoporation, targeted drug delivery, or histotripsy. Passive acoustic mapping has been proposed in the literature as a method for monitoring cavitation, but it lacks spatial resolution, primarily in the axial direction, due to the absence of a time reference. This is a common issue with passive imaging compared to standard pulse-echo ultrasound. In order to improve the axial resolution, we propose an adaptation of the cross spectral matrix fitting (CMF) method for passive cavitation imaging, which is based on the resolution of an inverse problem with different regularizations that promote sparsity in the reconstructed cavitation maps: Elastic Net (CMF-ElNet) and sparse Total Variation (CMF-spTV). The results from both simulated and experimental data are presented and compared to state-of-the-art approaches, such as the frequential delay-and-sum (DAS) and the frequential robust capon beamformer (RCB). We show the interest of the method for improving the axial resolution, with an axial full width half maximum (FWHM) divided by 3 and 5 compared to RCB and DAS, respectively. Moreover, CMF-based methods improve contrast-to-noise ratio (CNR) by more than 15 dB in experimental conditions compared to RCB. We also show the advantage of the sparse Total Variation (spTV) prior over Elastic Net (ElNet) when dealing with cloud-shaped cavitation sources, that can be assumed as sparse grouped sources.
高强度聚焦超声（HIFU）会产生空化现象，对于诸如声孔效应、靶向给药或组织粉碎等特定应用而言，需要对其进行监测。文献中已提出将被动声学成像作为一种监测空化的方法，但由于缺乏时间参考，它在空间分辨率方面存在不足，主要是在轴向方向上。与标准脉冲回波超声相比，这是被动成像中一个常见的问题。为了提高轴向分辨率，我们提出了一种适用于被动空化成像的互谱矩阵拟合（CMF）方法，该方法基于通过不同正则化解决逆问题，以促进重建空化图中的稀疏性：弹性网络（CMF-ElNet）和稀疏全变差（CMF-spTV）。文中展示了模拟数据和实验数据的结果，并与诸如频率延迟求和（DAS）和频率稳健卡彭波束形成器（RCB）等现有技术方法进行了比较。我们证明了该方法在提高轴向分辨率方面的优势，与RCB和DAS相比，轴向半高宽（FWHM）分别缩小为原来的三分之一和五分之一。此外，在实验条件下，基于CMF的方法与RCB相比，对比度噪声比（CNR）提高了超过15 dB。我们还展示了在处理可视为稀疏分组源的云状空化源时，稀疏全变差（spTV）先验相对于弹性网络（ElNet）的优势。
