Ding Xiao Fan, Duan Xiaoman, Li Naitao, Khoz Zahra, Wu Fang-Xiang, Chen Xiongbiao, Zhu Ning
Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada.
J Microsc. 2025 Aug;299(2):139-154. doi: 10.1111/jmi.13419. Epub 2025 May 13.
Propagation-based imaging (one method of X-ray phase contrast imaging) with microcomputed tomography (PBI-µCT) offers the potential to visualise low-density materials, such as soft tissues and hydrogel constructs, which are difficult to be identified by conventional absorption-based contrast µCT. Conventional µCT reconstruction produces edge-enhanced contrast (EEC) images which preserve sharp boundaries but are susceptible to noise and do not provide consistent grey value representation for the same material. Meanwhile, phase retrieval (PR) algorithms can convert edge enhanced contrast to area contrast to improve signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) but usually results to over-smoothing, thus creating inaccuracies in quantitative analysis. To alleviate these problems, this study developed a deep learning-based method called edge view enhanced phase retrieval (EVEPR), by strategically integrating the complementary spatial features of denoised EEC and PR images, and further applied this method to segment the hydrogel constructs in vivo and ex vivo. EVEPR used paired denoised EEC and PR images to train a deep convolutional neural network (CNN) on a dataset-to-dataset basis. The CNN had been trained on important high-frequency details, for example, edges and boundaries from the EEC image and area contrast from PR images. The CNN predicted result showed enhanced area contrast beyond conventional PR algorithms while improving SNR and CNR. The enhanced CNR especially allowed for the image to be segmented with greater efficiency. EVEPR was applied to in vitro and ex vivo PBI-µCT images of low-density hydrogel constructs. The enhanced visibility and consistency of hydrogel constructs was essential for segmenting such material which usually exhibit extremely poor contrast. The EVEPR images allowed for more accurate segmentation with reduced manual adjustments. The efficiency in segmentation allowed for the generation of a sizeable database of segmented hydrogel scaffolds which were used in conventional data-driven segmentation applications. EVEPR was demonstrated to be a robust post-image processing method capable of significantly enhancing image quality by training a CNN on paired denoised EEC and PR images. This method not only addressed the common issues of over-smoothing and noise susceptibility in conventional PBI-µCT image processing but also allowed for efficient and accurate in vitro and ex vivo image processing applications of low-density materials.
基于传播的成像（X射线相衬成像的一种方法）结合微型计算机断层扫描（PBI-µCT）能够可视化低密度材料，如软组织和水凝胶构建体，而传统的基于吸收的对比µCT很难识别这些材料。传统的µCT重建会生成边缘增强对比（EEC）图像，该图像保留了清晰的边界，但容易受到噪声影响，并且对于相同材料不能提供一致的灰度值表示。同时，相位恢复（PR）算法可以将边缘增强对比转换为面积对比，以提高信噪比（SNR）和对比噪声比（CNR），但通常会导致过度平滑，从而在定量分析中产生不准确结果。为了缓解这些问题，本研究开发了一种基于深度学习的方法，称为边缘视图增强相位恢复（EVEPR），通过策略性地整合去噪后的EEC图像和PR图像的互补空间特征，并进一步将该方法应用于体内和体外水凝胶构建体的分割。EVEPR使用成对的去噪EEC图像和PR图像，在数据集对数据集的基础上训练深度卷积神经网络（CNN）。该CNN针对重要的高频细节进行训练，例如，来自EEC图像的边缘和边界以及来自PR图像的面积对比。CNN的预测结果显示，与传统PR算法相比，面积对比得到增强，同时提高了SNR和CNR。增强的CNR尤其使得图像能够更高效地进行分割。EVEPR应用于低密度水凝胶构建体的体外和体内PBI-µCT图像。水凝胶构建体增强的可见性和一致性对于分割这种通常对比度极差的材料至关重要。EVEPR图像允许在减少手动调整的情况下进行更准确的分割。分割效率使得能够生成大量用于传统数据驱动分割应用的分割水凝胶支架数据库。通过在成对的去噪EEC图像和PR图像上训练CNN，EVEPR被证明是一种强大的图像后处理方法，能够显著提高图像质量。该方法不仅解决了传统PBI-µCT图像处理中过度平滑和易受噪声影响的常见问题，还允许对低密度材料进行高效准确的体外和体内图像处理应用。
