一种基于学习的3D光电声成像中空间脉冲响应补偿框架
A Learning-based Framework for Spatial Impulse Response Compensation in 3D Photoacoustic Computed Tomography
AI总结：
本文提出了一种基于学习的3D光电声成像中空间脉冲响应补偿框架，通过数据域中的学习方法提升图像重建的分辨率和鲁棒性。
摘要：
光电声成像（PACT）是一种结合光学对比优势与超声检测优点的有前景的成像模态。使用具有更大表面积的超声换能器可以提高检测灵敏度。然而，当使用计算效率高的解析重建方法，这些方法忽略了换能器的空间脉冲响应（SIR）时，重建图像的空间分辨率会受到损害。尽管基于优化的重建方法可以显式考虑SIR效应，但其计算成本通常较高，尤其是在三维（3D）应用中。为了解决对准确但快速的3D PACT图像重建的需求，本研究提出了一种框架，用于建立一个在数据域中操作的学习SIR补偿方法。该学习补偿方法将受SIR污染的PACT测量数据映射到由理想点状换能器记录的补偿数据。随后，补偿数据可以与忽略SIR效应的计算高效重建方法结合使用。研究了两种学习补偿模型的变体，分别采用U-Net模型和专门设计的受物理启发的模型，称为Deconv-Net。此外，快速且分析性的训练数据生成过程也是所提框架的一部分。该框架在虚拟成像研究中得到了严格验证，证明了分辨率的提高以及对噪声变化、物体复杂性和声速异质性的鲁棒性。当应用于活体乳腺成像数据时，学习的补偿模型揭示了被SIR引起的伪影所掩盖的细结构。据我们所知，这是首次在3D PACT成像中展示学习SIR补偿。
英文摘要：
Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity. However, when computationally efficient analytic reconstruction methods that neglect the spatial impulse responses (SIRs) of the transducer are employed, the spatial resolution of the reconstructed images will be compromised. Although optimization-based reconstruction methods can explicitly account for SIR effects, their computational cost is generally high, particularly in three-dimensional (3D) applications. To address the need for accurate but rapid 3D PACT image reconstruction, this study presents a framework for establishing a learned SIR compensation method that operates in the data domain. The learned compensation method maps SIR-corrupted PACT measurement data to compensated data that would have been recorded by idealized point-like transducers. Subsequently, the compensated data can be used with a computationally efficient reconstruction method that neglects SIR effects. Two variants of the learned compensation model are investigated that employ a U-Net model and a specifically designed, physics-inspired model, referred to as Deconv-Net. A fast and analytical training data generation procedure is also a component of the presented framework. The framework is rigorously validated in virtual imaging studies, demonstrating resolution improvement and robustness to noise variations, object complexity, and sound speed heterogeneity. When applied to in-vivo breast imaging data, the learned compensation models revealed fine structures that had been obscured by SIR-induced artifacts. To our knowledge, this is the first demonstration of learned SIR compensation in 3D PACT imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
