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Investigation of photoacoustic tomography reconstruction with a limited view from linear array
Journal of Biomedical Optics ( IF 3.0 ) Pub Date : 2021-09-01 , DOI: 10.1117/1.jbo.26.9.096009
Min Ai 1 , Jiayi Cheng 1 , Davood Karimi 1 , Septimiu E Salcudean 1 , Robert Rohling 1, 2 , Purang Abolmaesumi 1 , Shuo Tang 1
Affiliation  

Significance: As linear array transducers are widely used in clinical ultrasound imaging, photoacoustic tomography (PAT) with linear arrays is similarly suitable for clinical applications. However, due to the limited-view problem, a linear array has limited performance and leads to artifacts and blurring, which has hindered its broader application. There is a need to address the limited-view problem in PAT imaging with linear arrays. Aim: We investigate potential approaches for improving PAT reconstruction from linear array, by optimizing the detection geometry and implementing iterative reconstruction. Approach: PAT imaging with a single-array, dual-probe configurations in parallel-shape and L-shape, and square-shape configuration are compared in simulations and phantom experiments. An iterative model-based algorithm based on the variance-reduced stochastic gradient descent (VR-SGD) method is implemented. The optimum configuration found in simulation is validated on phantom experiments. Results: PAT imaging with dual-probe detection and VR-SGD algorithm is found to improve the limited-view problem compared to a single probe and provide comparable performance as full-view geometry in simulation. This configuration is validated in experiments where more complete structure is obtained with reduced artifacts compared with a single array. Conclusions: PAT with dual-probe detection and iterative reconstruction is a promising solution to the limited-view problem of linear arrays.

中文翻译:


线性阵列有限视野光声断层扫描重建研究



意义:由于线性阵列换能器广泛应用于临床超声成像,线性阵列光声断层扫描(PAT)同样适合临床应用。然而,由于有限视角问题,线性阵列的性能有限,并导致伪影和模糊,这阻碍了其更广泛的应用。需要解决线性阵列 PAT 成像中的有限视角问题。目标:我们通过优化检测几何结构和实施迭代重建来研究改善线性阵列 PAT 重建的潜在方法。方法:在模拟和体模实验中对单阵列、平行和 L 形双探头配置以及方形配置的 PAT 成像进行比较。实现了基于方差减少随机梯度下降 (VR-SGD) 方法的基于迭代模型的算法。仿真中找到的最佳配置在模型实验中得到了验证。结果:与单探头相比,采用双探头检测和 VR-SGD 算法的 PAT 成像可以改善有限视图问题,并在仿真中提供与全视图几何结构相当的性能。这种配置在实验中得到了验证,与单个阵列相比,可以获得更完整的结构,同时减少了伪影。结论:具有双探头检测和迭代重建的 PAT 是解决线性阵列有限视图问题的有前途的解决方案。
更新日期:2021-09-28
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