Adaptive and Robust Vessel Quantification in Contrast-Free Ultrafast Ultrasound Microvessel Imaging.
Adaptive and Robust Vessel Quantification in Contrast-Free Ultrafast Ultrasound Microvessel Imaging.
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DOI：
10.1016/j.ultrasmedbio.2022.05.034
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发表时间：
2022-10
影响因子：
2.9
通讯作者：
Chen, Shigao
中科院分区：
文献类型：
--
作者：
Tang, Shanshan;Huang, Hengwu;Gong, Ping;Lok, U-wai;Zhou, Chenyun;Yang, Lulu;Knoll, Kate M.;Robinson, Kathryn A.;Sheedy, Shannon P.;Fletcher, Joel G.;Bruining, David H.;Knudsen, John M.;Chen, Shigao
病变组织中血管的形态特征与正常组织中的血管形态特征显著不同。因此，血管量化对于疾病诊断和分期至关重要。超声微血管成像（UMI）与超快速超声采集已被证明其在临床应用中的潜力，因为它在血流检测方面具有上级灵敏度。然而，由于低成像信噪比和运动高回声散射引起的不相干杂波伪影导致的空间相关噪声的存在降低了UMI的性能和血管量化的可靠性。为了解决这些问题，我们提出了一种改进的UMI技术沿着自适应血管分割工作流程，用于稳健的血管识别和血管特征量化。一个先前提出的子孔径互相关技术和归一化互相关技术被施加到均衡的空间相关的噪声水平和抑制非相干杂波伪影。然后使用平方算子和非局部均值滤波器来更好地将血流信号与残余背景噪声分离。在去噪后的UMI图像上，根据血流信号和背景噪声的不同空间模式，开发了一种自动自适应的血管分割方法。将所提出的工作流程应用于CIRS体模和多普勒血流体模，以及包括炎症性肠、肾脏和肝脏在内的体内应用，以验证其可行性。结果表明，自动，自适应，和强大的血管识别性能，可以实现使用所提出的方法，而没有由放射科医生/操作员造成的主观性。
The morphological features of vasculature in diseased tissue differ significantly from those in normal tissue. Therefore, vasculature quantification is crucial for disease diagnosis and staging. Ultrasound microvessel imaging (UMI) with ultrafast ultrasound acquisitions has demonstrated its potential in clinical applications given its superior sensitivity in blood flow detection. However, the presence of spatial-dependent noise due to low imaging signal-to-noise ratio and incoherent clutter artifacts caused by moving hyperechoic scatters degrade the performance of UMI and reliability of vascular quantification. To tackle these issues, we proposed an improved UMI technique along with an adaptive vessel segmentation workflow for robust vessel identification and vascular feature quantification. A previously proposed sub-aperture cross-correlation technique and a normalized cross-correlation technique were applied to equalize the spatially dependent noise level and suppress the incoherent clutter artifact. A square operator and non-local means filter were then used to better separate the blood flow signal from residual background noise. On the de-noised UMI image, an automatic and adaptive vessel segmentation method was developed, based on the different spatial patterns of blood flow signal and background noise. The proposed workflow was applied to a CIRS phantom, and a Doppler flow phantom, and in vivo applications including an inflammatory bowel, a kidney and a liver, to validate its feasibility. Results demonstrated that automatic, adaptive, and robust vessel identification performance can be achieved using the proposed method without the subjectivity caused by radiologists/operators.
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DOI：
10.1109/tuffc.2017.2778941
发表时间：
2018-03
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
--
作者：
Song P;Trzasko JD;Manduca A;Huang R;Kadirvel R;Kallmes DF;Chen S
通讯作者：
Chen S
影响因子：
10.6
作者：
Baranger, Jerome;Arnal, Bastien;Demene, Charlie
通讯作者：
Demene, Charlie
影响因子：
10.6
作者：
Demene, Charlie;Deffieux, Thomas;Tanter, Mickael
通讯作者：
Tanter, Mickael
影响因子：
10.6
作者：
Coupe, Pierrick;Hellier, Pierre;Barillot, Christian
通讯作者：
Barillot, Christian
影响因子：
4.6
作者：
Huang, Chengwu;Lowerison, Matthew R.;Chen, Shigao
通讯作者：
Chen, Shigao