Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity. Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI. Approach:In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations. Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance. Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

中文翻译：

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使用时空奇异值分解增强体内心脏光声信号特异性

意义：光声成像 (PAI) 可用于在超声空间分辨率下使用光激发在体内非侵入性地推断有关心肌健康的分子信息。对于临床和临床前线性阵列成像系统，通常使用传统的延迟求和 (DAS) 波束成形。然而，DAS 心脏 PA 图像容易出现伪影，例如具有随时间变化的降噪心肌信号特异性的弥散准静态杂波。通常，多帧平均方案用于提高心脏 PAI 的质量，这会影响空间和时间分辨率并降低对细微 PA 信号变化的敏感性。此外，由于自然心壁运动的存在，帧平均可能会破坏心肌氧饱和度的量化。在本文中，提出了一种时空奇异值分解 (SVD) 处理算法，以减少 DAS PAI 伪影并随后增强心肌信号的特异性。目的：使用时空 SVD 处理与心电图 (ECG) 和呼吸信号 (ECG-R) 门控在体内鼠心脏 PAI 相比，与周围组织和左心室 (LV) 腔内的血液相比，展示来自心肌组织的 PA 信号的增强。方法：通过收集 8 只健康小鼠的单波长 (850 nm) 光声通道数据进行体内鼠心脏 PAI。使用自定义 ECG-R 门控算法和 DAS 波束成形重建了一个心动周期内复杂 PAI 数据的三维 (3D) 体积。时空 SVD 应用于使用 3D 体积 PAI 数据生成的二维卡索拉蒂矩阵。然后对奇异值谱 (SVS) 进行滤波，以去除来自扩散准静态杂波和随机噪声的影响。最后，使用过滤的 SVS 和逆 SVD 计算导出 SVD 处理的波束成形图像。结果：与 DAS 和最小方差 (MV) 波束形成的定性比较表明，SVD 处理的图像具有更好的心肌信号特异性、对比度和目标可检测性。通过计算对比度 (CR)、广义对比度噪声比 (gCNR) 和信噪比 (SNR) 对 DAS、MV 和 SVD 图像进行定量评估。在从共同配准的超声 M 模式图像确定的三个心脏时间点（收缩期、收缩末期 (ES) 和舒张期）进行定量评估。ES 的 SVD 图像的平均 CR、gCNR 和 SNR 值为 245、115.15 和 258。比 DAS 图像高 17 倍，通过单向方差分析评估具有统计显着性。结论：我们的结果表明，使用时空 SVD 处理可以实现显着更好质量的图像，用于体内鼠心脏 PAI。