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.

Keywords: convolutional neural network; deep learning; image processing; microcomputed tomography; phase contrast imaging.

© 2025 The Author(s). Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.

基于传播的成像（X射线相位对比成像的一种方法）与微计算机断层扫描（PBI-μCT）可以可视化低密度材料，如软组织和水凝胶构建体，这些是常规吸收基底对比度μCT难以识别的。传统的μCT重建会产生边缘增强对比度（EEC）图像，这些图像保留了清晰的边界，但容易受噪声影响，并且无法为相同的材料提供一致的灰度值表示。与此同时，相位检索（PR）算法可以将边缘增强对比转换成面积对比以提高信噪比（SNR）和对比度噪声比（CNR），但是通常会导致过度平滑化，从而导致定量分析中的不准确性。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检索（EVEPR），通过战略性整合去噪的EEC图像和PR图像的空间特征，并进一步将此方法应用于体内和体外水凝胶构建体的分割。EVEPR使用配对的去噪EEC和PR图像在数据集之间训练深度卷积神经网络（CNN）。该CNN已在重要的高频细节上进行过训练，例如从EEC图像中的边缘和边界以及从PR图像中的面积对比度。CNN预测结果显示了优于传统相位检索算法的增强面积对比，并提高了SNR和CNR。增强的CNR尤其允许更高效的分割。EVEPR被应用于低密度水凝胶构建体的体内和体外PBI-μCT图像。对于通常表现出极差对比度的材料，水凝胶构建体的可见性和一致性得到了显著提高，这对于此类材料的分割至关重要。EVEPR图像使得分割更加准确，并减少了手动调整的需求。分割效率允许生成大量已分割的水凝胶支架数据库，这些数据可用于传统的基于数据驱动的分割应用。证明了EVEPR是一种稳健的后成像处理方法，通过在去噪EEC和PR图像配对上训练CNN显著提高了图像质量。此方法不仅解决了传统PBI-μCT图像处理中的过度平滑化和噪声敏感性等常见问题，还允许低密度材料的有效而准确的体内和体外图像处理应用。

关键词：卷积神经网络；深度学习；图像处理；微计算机断层扫描；相位对比成像。