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.
Refraction-contrast X-ray computed tomography (RCT), which provides a high-contrast image of biological soft tissues, has recently been used in pathology and micro anatomy research. One of the characteristics of RCT is that the projection obtained by RCT system represents the first differentiation of the object's radon transformation. Therefore, the reconstruction of RCT involves an integration process of the projection before conventional back-projection. However, this process causes noise generated by the detector or refraction angle estimation errors to propagate throughout the projection image, which leads to increase ring artifacts (RA) on the reconstructed image. Although various methods for RA removal have been developed in the absorption-based CT (ACT) field, it is difficult to eliminate surface widespread RA on RCT image. In this research, we propose a RA removal method based on conditional generative adversarial network (cGAN) for RCT. This method incorporates a sparse property of Laplacian RCT image which is easily obtained by reconstructing the differential projection. To demonstrate the effectiveness of this method, we applied the proposed and conventional ACT-based methods to simulation data of numerical phantoms and compared them by the root mean square error and the structural similarity. In the result, we demonstrated the proposed method can remove RA better than conventional methods.
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.
Purpose: High-resolution cardiac imaging and fiber analysis methods are required to understand cardiac anatomy. Although refraction-contrast x-ray CT (RCT) has high soft tissue contrast, it cannot be commonly used because it requires a synchrotron system. Microfocus x-ray CT (μCT) is another commercially available imaging modality.
Approach: We evaluate the usefulness of μCT for analyzing fibers by quantitatively and objectively comparing the results with RCT. To do so, we scanned a rabbit heart by both modalities with our original protocol of prepared materials and compared their image-based analysis results, including fiber orientation estimation and fiber tracking.
Results: Fiber orientations estimated by two modalities were closely resembled under the correlation coefficient of 0.63. Tracked fibers from both modalities matched well the anatomical knowledge that fiber orientations are different inside and outside of the left ventricle. However, the μCT volume caused incorrect tracking around the boundaries caused by stitching scanning.
Conclusions: Our experimental results demonstrated that μCT scanning can be used for cardiac fiber analysis, although further investigation is required in the differences of fiber analysis results on RCT and μCT.
High-resolution cardiac imaging and fiber analysis methods are desired for deeper understanding cardiac anatomy. Although refraction-contrast X-ray CT (RCT) has high contrast for soft tissues, its scanning cost is very high. On the other hand, micro-focus X-ray CT (μCT) is a modality that is commercially available with lower cost, but its contrast for soft tissue is not as high as RCT. To investigate the efficacy of μCT for fiber analysis, we scanned a common rabbit heart with both modalities with our original protocol of preparing materials, and compared their image-based analysis results. Their results were very similar, with correlation coefficient of 0.95. We confirmed that µCT volumes prepared by our protocol are useful for fiber analysis as well as RCT.
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