Robust Short-Lag Spatial Coherence Imaging.
Robust Short-Lag Spatial Coherence Imaging.
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DOI：
10.1109/tuffc.2017.2780084
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发表时间：
2018-03
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通讯作者：
Bell MAL
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作者：
Nair AA;Tran TD;Bell MAL
短滞后空间相干（SLSC）成像显示后向散射超声回波之间的空间相干性，而不是它们的信号幅度，并且与传统的延迟求和（DAS）B模式成像相比，对噪声和杂波伪影更具鲁棒性。然而，SLSC成像不考虑以不同滞后形成的图像的内容，并且因此不利用每个短滞后值处的组织纹理的差异。我们提出的方法通过对滞后值的相加进行加权来改进SLSC成像（即，M-加权）和应用鲁棒主成分分析（RPCA）来搜索用于投影用不同滞后值创建的相干图像的低维子空间。基于RPCA的投影被认为是原始图像的去噪版本，然后对原始图像进行加权并在滞后上相加，以产生最终的鲁棒短滞后空间相干（R-SLSC）图像。我们的方法进行了测试模拟，幻影，在体内肝脏数据。相对于DAS B模式图像，当对模拟、体模和体内数据以及考虑的所有滞后求平均值时，R-SLSC图像的平均对比度、信噪比（SNR）和对比度噪声比（CNR）改善分别为21.22 dB、2.54和2.36，分别对应于96.4%、121.2%和120.5%的平均改善。与SLSC图像相比，R-SLSC图像的相应平均改善分别为7.38 dB、1.52和1.30（即，平均改善率分别为14.5%、50.5%和43.2%）。结果表明，平滑SLSC图像的组织纹理和增强无回声或低回声目标的可见性，在较高的滞后值，这可能是有用的临床任务，如乳腺囊肿可视化，肝血管跟踪，和肥胖患者成像的巨大承诺。
Short-lag spatial coherence (SLSC) imaging displays the spatial coherence between backscattered ultrasound echoes instead of their signal amplitudes and is more robust to noise and clutter artifacts when compared to traditional delay-and-sum (DAS) B-mode imaging. However, SLSC imaging does not consider the content of images formed with different lags, and thus does not exploit the differences in tissue texture at each short lag value. Our proposed method improves SLSC imaging by weighting the addition of lag values (i.e., M-weighting) and by applying Robust Principal Component Analysis (RPCA) to search for a low dimensional subspace for projecting coherence images created with different lag values. The RPCA-based projections are considered to be de-noised versions of the originals that are then weighted and added across lags to yield a final Robust Short-Lag Spatial Coherence (R-SLSC) image. Our approach was tested on simulation, phantom, and in vivo liver data. Relative to DAS B-mode images, the mean contrast, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) improvements with R-SLSC images are 21.22 dB, 2.54 and 2.36 respectively, when averaged over simulated, phantom, and in vivo data and over all lags considered which corresponds to mean improvements of 96.4%, 121.2% and 120.5% respectively. When compared to SLSC images, the corresponding mean improvements with R-SLSC images were 7.38 dB, 1.52 and 1.30, respectively, (i.e., mean improvements of 14.5%, 50.5% and 43.2%, respectively). Results show great promise for smoothing out the tissue texture of SLSC images and enhancing anechoic or hypoechoic target visibility at higher lag values which could be useful in clinical tasks such as breast cyst visualization, liver vessel tracking, and obese patient imaging.