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一种新型定向算法实现的声学分辨率光声显微镜中的图像增强。

Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm.

作者信息

Feng Fei, Liang Siqi, Chen Sung-Liang

机构信息

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China.

These authors contributed equally to this work.

出版信息

Biomed Opt Express. 2022 Jan 28;13(2):1026-1044. doi: 10.1364/BOE.452017. eCollection 2022 Feb 1.

DOI:10.1364/BOE.452017
PMID:35284174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8884221/
Abstract

By considering the line pattern of acoustic-resolution photoacoustic microscopy (AR-PAM) vessel images, we develop modified algorithms for synthetic aperture focusing technique (SAFT) and deconvolution based on a directional approach to enhance images. The modified algorithms consist of Fourier accumulation SAFT (FA-SAFT) and directional model-based (D-MB) deconvolution. To evaluate the performance of our algorithms, we conduct a series of imaging experiments and apply our algorithms, and existing SAFT and deconvolution algorithms are also applied for side-by-side comparison. By imaging tungsten wire phantom, our algorithms enable full width at half maximum of 26 - 31 µm over depth of focus of 1.8 mm and minimum resolvable distance of 46 - 49 µm, besting existing SAFT and deconvolution algorithms. Imaging of leaf skeleton phantom and imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-signal-to-noise ratio, and good-fidelity results for complex structures and for applications, especially for the images with the line pattern. The proposed directional approach can not only be used in AR-PAM but also in other imaging modalities to deal with the line pattern, such as FA-SAFT for ultrasound imaging and D-MB deconvolution for optical coherence tomography angiography.

摘要

通过考虑声学分辨率光声显微镜(AR-PAM)血管图像的线条模式,我们基于一种定向方法开发了用于合成孔径聚焦技术(SAFT)和去卷积的改进算法,以增强图像。改进算法包括傅里叶累积SAFT(FA-SAFT)和基于定向模型(D-MB)的去卷积。为了评估我们算法的性能,我们进行了一系列成像实验并应用我们的算法,同时也应用现有的SAFT和去卷积算法进行并列比较。通过对钨丝模型成像,我们的算法在1.8毫米的焦深范围内实现了半高全宽为26 - 31微米,最小可分辨距离为46 - 49微米,优于现有的SAFT和去卷积算法。对叶片骨架模型的成像以及对小鼠血管的成像也证明,我们的算法能够为复杂结构和应用提供高分辨率、高信噪比和高保真度的结果,特别是对于具有线条模式的图像。所提出的定向方法不仅可以用于AR-PAM,还可以用于其他成像模态来处理线条模式,例如用于超声成像的FA-SAFT和用于光学相干断层扫描血管造影的D-MB去卷积。

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