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超越合成孔径聚焦:基于模拟点扩散函数的反卷积用于基于线性阵列的三维光声成像的仰角分辨率增强

Beyond synthetic aperture focusing: deconvolution-based elevation resolution enhancement using simulated point spread function for linear array-based three-dimensional photoacoustic imaging.

作者信息

Tang Yichuan, Lesniak Wojciech G, Gao Shang, Wu Yixuan, Pomper Martin G, Zhang Haichong K

机构信息

Department of Robotics Engineering, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609, USA.

Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA.

出版信息

Biomed Opt Express. 2024 Feb 23;15(3):1847-1860. doi: 10.1364/BOE.517423. eCollection 2024 Mar 1.

Abstract

This paper introduces a deconvolution-based method to enhance the elevation resolution of a linear array-based three-dimensional (3D) photoacoustic (PA) imaging system. PA imaging combines the high contrast of optical imaging with the deep, multi-centimeter spatial resolution of ultrasound (US) imaging, providing structural and functional information about biological tissues. Linear array-based 3D PA imaging is easily accessible and applicable for ex vivo studies, small animal research, and clinical applications in humans. However, its elevation resolution is limited by the acoustic lens geometry, which establishes a single elevation focus. Previous work used synthetic aperture focusing (SAF) to enhance elevation resolution, but the resolution achievable by SAF is constrained by the size of the elevation focus. Here, we introduce the application of Richardson-Lucy deconvolution, grounded in simulated point-spread-functions, to surpass the elevation resolution attainable with SAF alone. We validated this approach using both simulation and experimental data, demonstrating that the full-width-at-half-maximum of point targets on the elevation plane was reduced compared to using SAF only, suggesting resolution improvement. This method shows promise for improving 3D image quality of existing linear array-based PA imaging systems, offering potential benefits for disease diagnosis and monitoring.

摘要

本文介绍了一种基于反卷积的方法,以提高基于线性阵列的三维(3D)光声(PA)成像系统的仰角分辨率。PA成像将光学成像的高对比度与超声(US)成像的深度、多厘米空间分辨率相结合,提供有关生物组织的结构和功能信息。基于线性阵列的3D PA成像易于实现,适用于离体研究、小动物研究以及人体临床应用。然而,其仰角分辨率受声学透镜几何结构的限制,该结构建立了单个仰角焦点。先前的工作使用合成孔径聚焦(SAF)来提高仰角分辨率,但SAF可实现的分辨率受仰角焦点大小的限制。在此,我们介绍基于模拟点扩散函数的理查森-露西反卷积的应用,以超越仅使用SAF所能达到的仰角分辨率。我们使用模拟和实验数据验证了这种方法,结果表明与仅使用SAF相比,仰角平面上点目标的半高全宽减小,这表明分辨率有所提高。该方法有望改善现有基于线性阵列的PA成像系统的3D图像质量,为疾病诊断和监测带来潜在益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfb2/10942676/9c1a6773431c/boe-15-3-1847-g001.jpg

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