AI 中文总结
该研究分析采用随机像差掩模的单次快照压缩超声成像的极限，优化掩模参数，评估L1正则化最小二乘、LSQR等算法在粒子定位和B型成像中的表现，明确系统性能上限。
AI 中文摘要
压缩感知作为一种范式转变，可利用配备像差掩模的单元素换能器实现实时容积超声成像。这种编码孔径将视野（FoV）中的每个散射体编码为特定回波信号，将每个容积压缩为时间序列，再通过计算方法重建为图像。压缩成像（CI）可大幅降低数据速率并简化电子设备。尽管具有这种潜力，但单次快照CI的实际性能仍不明确，尤其是其如何受掩模设计、成像任务复杂度和重建策略的影响。为此，我们将掩模提供的信息预算与算法依赖的编码信息提取相分离。首先，校准了不同像素尺寸和时延范围的随机掩模的空间脉冲响应，并使用其相似矩阵的基于熵的有效秩来量化可用编码容量。像素尺寸约为半波长、时延范围为两个载波周期的掩模提供了更高的编码容量，达到总采样体素的1.1%。进一步针对受超声微血流成像（ULM）启发的粒子定位和B型成像，评估了算法依赖的信息提取。对于粒子定位，L1范数正则化最小二乘方法重建的粒子对应约10%的可用编码容量，优于匹配滤波。当粒子数量超过该值时，观察到从成功重建到失败重建的转变，揭示了采用随机掩模的现有CI系统的上限。对于B型成像，最小二乘正交化（LSQR）在评估的方法中达到最高结构相似性（SSIM），高达0.12，尽管当前随机掩模的编码容量仍不足以实现高保真重建。
英文摘要
Compressive sensing emerges as a paradigm shift to realize real-time volumetric ultrasound imaging using a single element transducer equipped with an aberrative mask. Such a coded aperture encodes each scatterer in the FoV as a specific echo signal, compressing each volumetric into a time sequence that are reconstructed into image via computational methods. Compressive imaging (CI) can greatly reduce the data rate and simplify the electronics. Despite this potential, the practical performance of single-snapshot CI remains unclear, especially how it is influenced by mask design, imaging task complexity, and reconstruction strategy. For this, we separate the information budget provided by the mask from the algorithm-dependent extraction of the encoded information. First, the spatial impulse responses of random masks with different pixel sizes and time-delay ranges were calibrated, and the entropy-based effective rank of their similarity matrix was used to quantify the available encoding capacity. Masks with pixel size of approx. half wavelength and a time-delay range of two carrier periods provided higher encoding capacities, reaching up to 1.1% of the total sampled voxels. Algorithm-dependent information extraction was further evaluated for both ULM-motivated particle localization and B-mode imaging. For particle localization, L1-norm regularized least-squares method reconstructed particles corresponding to approx. 10% of the available encoding capacity, outperforming the matched filter. A transition from successful to failed reconstruction was observed when the number of particles exceeded this value, revealing the upper limit of the present CI systems using random masks. For B-mode imaging, LSQR achieved the highest SSIM among the evaluated methods, of up to 0.12, although the encoding capacity of the current random masks remained insufficient for high-fidelity reconstruction.
Comments15 pages, 13 figures
