声分辨率光声显微镜(AR-PAM)图像分辨率由成像系统的点扩散函数(PSF)确定。以前的算法,包括理查森-露西(R-L)反卷积和基于模型(MB)的反卷积,通过利用PSF作为先验知识来提高空间分辨率。然而,这些方法遇到了反卷积不准确的问题,意味着去卷积的特征大小和原始特征大小不一致(例如,前者可以小于后者)。我们提出了一种新颖的基于深度卷积神经网络(CNN)的算法,该算法具有多尺度特征大小的高保真恢复功能,以提高AR-PAM的横向分辨率。CNN是用模拟图像对线图案训练的,就是模仿血管.研究合适的CNN模型结构,阐述CNN方法与非学习方法相比的有效性,我们选择了五种不同的CNN模型,而R-L和方向MB方法也用于比较。除了模拟数据,实验数据包括钨丝,叶脉,和体内血管也被评估。使用自定义的相对尺寸误差(RSE)度量来量化不同方法的多尺度特征恢复能力。与其他方法相比,增强的深度超分辨率(EDSR)网络和残差密集块网络(RRDBNet)模型在直径为30μm至120μm的钨丝的RSE方面显示出更好的恢复。此外,测试了叶脉的AR-PAM图像,以证明优化的CNN方法(通过EDSR和RRDBNet)对复杂模式的有效性。最后,获取小鼠耳血管和大鼠耳血管的体内图像,然后进行反卷积,结果表明,提出的CNN方法(特别是RRDBNet)可以对多尺度特征大小进行精确的反卷积,从而实现良好的保真度。
Acoustic-resolution photoacoustic microscopy (AR-PAM) image resolution is determined by the point spread function (PSF) of the imaging system. Previous algorithms, including Richardson-Lucy (R-L) deconvolution and model-based (MB) deconvolution, improve spatial resolution by taking advantage of the PSF as prior knowledge. However, these methods encounter the problems of inaccurate deconvolution, meaning the deconvolved feature size and the original one are not consistent (e.g., the former can be smaller than the latter). We present a novel deep convolution neural network (CNN)-based algorithm featuring high-fidelity recovery of multiscale feature size to improve lateral resolution of AR-PAM. The CNN is trained with simulated image pairs of line patterns, which is to mimic blood vessels. To investigate the suitable CNN model structure and elaborate on the effectiveness of CNN methods compared with non-learning methods, we select five different CNN models, while R-L and directional MB methods are also applied for comparison. Besides simulated data, experimental data including tungsten wires, leaf veins, and in vivo blood vessels are also evaluated. A custom-defined metric of relative size error (RSE) is used to quantify the multiscale feature recovery ability of different methods. Compared to other methods, enhanced deep super resolution (EDSR) network and residual in residual dense block network (RRDBNet) model show better recovery in terms of RSE for tungsten wires with diameters ranging from 30 μ m to 120 μ m . Moreover, AR-PAM images of leaf veins are tested to demonstrate the effectiveness of the optimized CNN methods (by EDSR and RRDBNet) for complex patterns. Finally, in vivo images of mouse ear blood vessels and rat ear blood vessels are acquired and then deconvolved, and the results show that the proposed CNN method (notably RRDBNet) enables accurate deconvolution of multiscale feature size and thus good fidelity.