Compressed Sensing-Based Super-Resolution Ultrasound Imaging for Faster Acquisition and High Quality Images.
Compressed Sensing-Based Super-Resolution Ultrasound Imaging for Faster Acquisition and High Quality Images.
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DOI：
10.1109/tbme.2021.3070487
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发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Yoon S
中科院分区：
文献类型：
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作者：
Kim J;Wang Q;Zhang S;Yoon S
利用超声微泡信号与系统点扩散函数之间的归一化二维互相关(2DCC)对注入血管的超声微泡进行定位，重建出典型的SRUS图像。然而，由于对密集的MBS定位不准确，目前的技术需要在受限区域中隔离MBS。为了克服这一局限性，我们开发了基于ℓ1同伦压缩传感(L1H-CS)的SRU成像技术，该技术定位密集的MB以可视化体内的微血管系统。为了评估L1H-CS的性能，我们比较了2DCC、基于内点方法的压缩感知(CVX-CS)和L1H-CS算法的性能。使用已知距离的轴向和横向对准的点目标(PTS)和仿真生成的随机分布的点目标(PTS)来比较定位效率。我们开发了包括杂波抑制、噪声均衡、运动补偿和时空噪声滤波在内的用于活体成像的后处理技术。然后，我们在小鼠肿瘤模型、肾脏、斑马鱼背部干和脑中验证了基于L1H-CS的SRUS成像技术与高密度MBS的能力。与2DCC和CVX-CS算法相比，L1H-CS算法获得了更快的数据采集时间和相当大的SRU图像质量改善。这些结果表明，基于L1H-CS的SRUS成像技术有可能在减少采集和重建时间的情况下检查微血管，以获得增强的SRUS图像质量，这可能是将其转化为临床所必需的。
Typical SRUS images are reconstructed by localizing ultrasound microbubbles (MBs) injected in a vessel using normalized 2-dimensional cross-correlation (2DCC) between MBs signals and the point spread function of the system. However, current techniques require isolated MBs in a confined area due to inaccurate localization of densely populated MBs. To overcome this limitation, we developed the ℓ1-homotopy based compressed sensing (L1H-CS) based SRUS imaging technique which localizes densely populated MBs to visualize microvasculature in vivo. To evaluate the performance of L1H-CS, we compared the performance of 2DCC, interior-point method based compressed sensing (CVX-CS), and L1H-CS algorithms. Localization efficiency was compared using axially and laterally aligned point targets (PTs) with known distances and randomly distributed PTs generated by simulation. We developed post-processing techniques including clutter reduction, noise equalization, motion compensation, and spatiotemporal noise filtering for in vivo imaging. We then validated the capabilities of L1H-CS based SRUS imaging technique with high-density MBs in a mouse tumor model, kidney, and zebrafish dorsal trunk, and brain. Compared to 2DCC and CVX-CS algorithms, L1H-CS achieved faster data acquisition time and considerable improvement in SRUS image quality. These results demonstrate that the L1H-CS based SRUS imaging technique has the potential to examine microvasculature with reduced acquisition and reconstruction time to acquire enhanced SRUS image quality, which may be necessary to translate it into clinics.