Combining Slow Flow Techniques With Adaptive Demodulation for Improved Perfusion Ultrasound Imaging Without Contrast.
Combining Slow Flow Techniques With Adaptive Demodulation for Improved Perfusion Ultrasound Imaging Without Contrast.
复制标题
将慢流技术与自适应解调相结合，以改进无对比灌注超声成像。
DOI：
10.1109/tuffc.2019.2898127
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Byram,Brett
中科院分区：
文献类型：
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作者：
Tierney,Jaime;Walsh,Kristy;Griffith,Helen;Baker,Jennifer;Brown,DanielB;Byram,Brett
由于患者和超声医师手部运动引起的组织杂波信号的频谱展宽，非造影灌注超声成像仍然具有挑战性。为了解决这个问题，我们之前引入了一种自适应解调方案，以在高通滤波之前抑制组织的带宽。我们最初的实现使用单平面波功率多普勒成像和传统的组织滤波器。波束形成和组织滤波方面的最新进展已被提出用于改进慢流成像，包括相干流功率多普勒（CFPD）成像和奇异值分解（SVD）滤波。在这里，我们的目标是使用模拟、单血管模型和体内肝脏肿瘤栓塞研究来评估自适应解调以及波束形成和滤波的改进。我们表明，当使用 CFPD 和 100 ms 整体的自适应解调时，模拟的血液与背景对比度噪声比最高，与单独使用基本 IIR 滤波相比，这导致对比度噪声比平均增加 13.6 dB。我们还表明，对于平均流量为 1 毫米/秒和 5 毫米/秒的体模数据，将自适应解调与 SVD 以及 CFPD + SVD 相结合，与单独使用 IIR 滤波相比，在 700 毫秒和 500 毫秒的集合中，对比噪声比分别增加了 9.3 分贝和 19 分贝。一般来说，组合技术会在模拟和模型中产生更高的信噪比、对比度和广义对比度噪声比。最后，SVD 自适应解调导致栓塞后肿瘤与背景对比度的最大定性和定量变化。
Noncontrast perfusion ultrasound imaging remains challenging due to spectral broadening of the tissue clutter signal caused by patient and sonographer hand motion. To address this problem, we previously introduced an adaptive demodulation scheme to suppress the bandwidth of tissue prior to high-pass filtering. Our initial implementation used single plane wave power Doppler imaging and a conventional tissue filter. Recent advancements in beamforming and tissue filtering have been proposed for improved slow flow imaging, including coherent flow power Doppler (CFPD) imaging and singular value decomposition (SVD) filtering. Here, we aim to evaluate adaptive demodulation in conjunction with improvements in beamforming and filtering using simulations, single-vessel phantoms, and an in vivo liver tumor embolization study. We show that simulated blood-to-background contrast-to-noise ratios are highest when using adaptive demodulation with CFPD and a 100-ms ensemble, which resulted in a 13.6-dB average increase in contrast-to-noise ratio compared to basic IIR filtering alone. We also show that combining adaptive demodulation with SVD and with CFPD + SVD results in 9.3- and 19-dB increases in contrast-to-noise ratios compared to IIR filtering alone at 700- and 500-ms ensembles for phantom data with 1- and 5-mm/s average flows, respectively. In general, combining techniques resulted in higher signal-to-noise, contrast-to-noise, and generalized contrast-to-noise ratios in both simulations and phantoms. Finally, adaptive demodulation with SVD resulted in the largest qualitative and quantitative changes in tumor-to-background contrast postembolization.