Limited view reconstruction for differential phase-contrast computed tomography

Ring artifact removal for differential phase-contrast X-ray computed tomography using a conditional generative adversarial network

PURPOSE

The integration process used as a pre-processing step in the reconstruction of differential phase-contrast X-ray CT (d-PCCT) causes the measurement noise to propagate throughout the projection image, which is leading to increased ring artifacts (RA) in the reconstructed image. It is difficult to eliminate the RA using conventional RA removal methods that were developed for the absorption-based CT field. We propose an effective method that can remove RA of d-PCCT images.

METHODS

The proposed method uses Laplacian images reconstructed from second-derivative projections of d-PCCT. This method is based on a conditional generative adversarial network (cGAN), whose loss function is designed by adding the L1- and L2-norm to the original cGAN. The training data were taken from a numerical phantom generated by a d-PCCT imaging simulator. To validate the applicability of the trained network, we tested its RA removal effect on test data from numerical phantoms generated randomly and actual experimental data.

RESULTS

The results of numerical validation using numerical phantoms showed that the proposed method improved the RA removal effect compared to conventional methods. In addition, image comparison by visual evaluation showed that only the proposed method was able to remove RA while preserving original structures in the actual biological d-PCCT images.

CONCLUSION

We proposed a cGAN-based method for RA removal that exploits the physical properties of d-PCCT. The proposed method was able to completely remove RA from d-PCCT images on both simulated data and biological data. We believe that this method is useful for the observation of various types of biological soft tissue.

X-ray Dark-Field Imaging (XDFI)-a Promising Tool for 3D Virtual Histopathology

X-ray dark-field imaging (XDFI) utilizing a thin silicon crystal under Laue case enables visualizing three-dimensional (3D) morphological alterations of human tissue. XDFI uses refraction-contrast derived from phase shift rather than absorption as the main X-ray image contrast source to render 2D and 3D images of tissue specimens in unprecedented detail. The unique features of XDFI are its extremely high sensitivity (approximately 1000:1 compared to absorption for soft tissues under X-ray energy of around 20 keV, theoretically) and excellent resolution (8.5 μm) without requiring contrast medium or staining. Thus, XDFI-computed tomography can generate 3D virtual histological images equivalent to those of stained histological sections pathologists observe under low-power light microscopy as far as organs and tissues selected as samples in preliminary studies. This paper reviews the fundamental principles and the potential of XDFI, describes two optical setups for XDFI with examples, illustrates features of XDFI that are salient for histopathology, and presents XDFI examples of refraction-contrast images of atherosclerotic plaques, musculoskeletal tissue, neuronal tissue, and breast cancer specimens. Availability of this X-ray imaging in routine histopathological evaluations of tissue specimens would help guide clinical decision making by highlighting suspicious areas in unstained, thick sections for further sampling and analysis using conventional histopathological techniques. XDFI is a promising tool for 3D virtual histopathology.

X-ray dark-field phase-contrast imaging: Origins of the concept to practical implementation and applications

The basic idea of X-ray dark-field imaging (XDFI), first presented in 2000, was based on the concepts used in an X-ray interferometer. In this article, we review 20 years of developments in our theoretical understanding, scientific instrumentation, and experimental demonstration of XDFI and its applications to medical imaging. We first describe the concepts underlying XDFI that are responsible for imparting phase contrast information in projection X-ray images. We then review the algorithms that can convert these projection phase images into three-dimensional tomographic slices. Various implementations of computed tomography reconstructions algorithms for XDFI data are discussed. The next four sections describe and illustrate potential applications of XDFI in pathology, musculoskeletal imaging, oncologic imaging, and neuroimaging. The sample applications that are presented illustrate potential use scenarios for XDFI in histopathology and other clinical applications. Finally, the last section presents future perspectives and potential technical developments that can make XDFI an even more powerful tool.