The importance of the three-dimensional (3D) pathological observation of biological soft tissues has increased in recent year, and various visualization tools to obtain 3D information easily and analysis methods focusing on the 3D micro structures have been developed. Refraction-contrast computed tomography based on x-ray dark-field imaging technique (XDFI) is one of the powerful methods with a high contrast and spatial resolution. In this study, in order to apply XDFI as new pathology tool, we will develop the x-ray optics and the x-ray camera, which are important components of the XDFI imaging system, to achieve a spatial resolution of 5 μm and evaluate the spatial resolution by experiments of the x-ray micro chart and the breast tissue specimen.
We visualized the luminal structures generated by DCIS and UDH using a refraction-contrast X-ray CT technique, which provides a contrast close to stained tissue images, and showed that DCIS and UDH forms a bubble-like shape and a tube-like shape, respectively. To express the difference in the three-dimensional structure between these tissue clearly, the number of luminal spaces, luminal volume, luminal density, and path length of the luminal structures were introduced. As a result, it was found that DCIS has many smaller and shorter lumens than UDH, which can contribute to the development of 3D pathology.
Cribriform architecture is a histological pattern reminiscent of Swiss cheese that is commonly recognized in ductal carcinoma in situ (DCIS) of the breast observed by microscope. However, there are only a few three-dimensional studies to elucidate whether each glandular cavities of cribriform pattern are connected or not. The main reason for paucity of three-dimensional studies is that the conventional reconstruction based on histological sections requires laborious and time-consuming works. In this research, we first performed three-dimensional reconstruction of the cribriform pattern using crystal analyzer-based phase contrast technique, X-ray dark field computed tomography (XDFI-CT), which provides high contrast image of biological soft tissue with non-destructive and non-staining approach. Then, we propose a machine-learning-based method to extract the cavity from XDFI-CT images. Finally, we show that the useful information to analyze the cribriform patterns in DCIS such as the density and volume of the cavity can be obtained from the XDFI-CT images.
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