声分辨率光声显微镜成像增强:群稀疏性与深度去噪先验的集成

Zhengyuan Zhang;Zuozhou Pan;Zhuoyi Lin;Arunima Sharma;Chia-Wen Lin;Manojit Pramanik;Yuanjin Zheng
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摘要

声分辨率光声显微镜(AR-PAM)是一种新型的医学成像方式,可用于深层生物组织的结构和功能成像。然而,由于其依赖于声聚焦,成像分辨率降低,结构细节丢失,这极大地限制了其在医疗和临床场景中的应用范围。为了解决上述问题,采用了结合传统分析先验术语的基于模型的方法,这使得捕获解剖生物结构的更精细细节变得具有挑战性。本文利用从AR-PAM内部图像中提取的斑块之间的非局部结构相似性,提出了一种创新的同时重建先验——群稀疏先验。改进了局部图像的细节和分辨率,同时引入了伪影。为了减轻基于补丁的重建方法带来的伪影,我们进一步集成了外部图像数据集作为额外的信息提供者,并用深度去噪先验巩固了组稀疏性先验。这样,就可以利用互补信息来改善重建结果。我们进行了大量的实验来增强模拟和体内AR-PAM成像结果。其中,在模拟图像中,平均峰值信噪比(PSNR)和结构相似指数测量(SSIM)值分别从16.36 dB和0.46增加到27.62 dB和0.92。体内重建结果也表明,该方法具有较好的局部和全局感知质量,信噪比(SNR)和噪声对比比(CNR)指标分别从10.59和8.61显著提高到30.83和27.54。此外,以光学分辨率光声显微镜(OR-PAM)数据作为参考图像验证了重建的保真度。
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Acoustic Resolution Photoacoustic Microscopy Imaging Enhancement: Integration of Group Sparsity With Deep Denoiser Prior
Acoustic resolution photoacoustic microscopy (AR-PAM) is a novel medical imaging modality, which can be used for both structural and functional imaging in deep bio-tissue. However, the imaging resolution is degraded and structural details are lost since its dependency on acoustic focusing, which significantly constrains its scope of applications in medical and clinical scenarios. To address the above issue, model-based approaches incorporating traditional analytical prior terms have been employed, making it challenging to capture finer details of anatomical bio-structures. In this paper, we proposed an innovative prior named group sparsity prior for simultaneous reconstruction, which utilizes the non-local structural similarity between patches extracted from internal AR-PAM images. The local image details and resolution are improved while artifacts are also introduced. To mitigate the artifacts introduced by patch-based reconstruction methods, we further integrate an external image dataset as an extra information provider and consolidate the group sparsity prior with a deep denoiser prior. In this way, complementary information can be exploited to improve reconstruction results. Extensive experiments are conducted to enhance the simulated and in vivo AR-PAM imaging results. Specifically, in the simulated images, the mean peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values have increased from 16.36 dB and 0.46 to 27.62 dB and 0.92, respectively. The in vivo reconstructed results also demonstrate the proposed method achieves superior local and global perceptual qualities, the metrics of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) have significantly increased from 10.59 and 8.61 to 30.83 and 27.54, respectively. Additionally, reconstruction fidelity is validated with the optical resolution photoacoustic microscopy (OR-PAM) data as reference image.
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