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Shimao D, Sunaguchi N, Yuasa T, Ando M, Mori K, Gupta R, Ichihara S. X-ray Dark-Field Imaging (XDFI)-a Promising Tool for 3D Virtual Histopathology. Mol Imaging Biol 2021; 23:481-94. [PMID: 33624229 DOI: 10.1007/s11307-020-01577-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 12/20/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
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.
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Ando M, Gupta R, Iwakoshi A, Kim JK, Shimao D, Sugiyama H, Sunaguchi N, Yuasa T, Ichihara S. X-ray dark-field phase-contrast imaging: Origins of the concept to practical implementation and applications. Phys Med 2020; 79:188-208. [PMID: 33342666 DOI: 10.1016/j.ejmp.2020.11.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 11/13/2020] [Accepted: 11/26/2020] [Indexed: 12/18/2022] Open
Abstract
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.
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Oda H, Roth HR, Sugino T, Sunaguchi N, Usami N, Oda M, Shimao D, Ichihara S, Yuasa T, Ando M, Akita T, Narita Y, Mori K. Cardiac fiber tracking on super high-resolution CT images: a comparative study. J Med Imaging (Bellingham) 2020; 7:026001. [PMID: 32206685 PMCID: PMC7064862 DOI: 10.1117/1.jmi.7.2.026001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 02/11/2020] [Indexed: 01/14/2023] Open
Abstract
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.
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Affiliation(s)
- Hirohisa Oda
- Nagoya University, Graduate School of Informatics, Nagoya, Japan
| | - Holger R Roth
- Nagoya University, Graduate School of Informatics, Nagoya, Japan
| | - Takaaki Sugino
- Nagoya University, Graduate School of Informatics, Nagoya, Japan
| | - Naoki Sunaguchi
- Nagoya University Graduate School of Medicine, Department of Radiological and Medical Laboratory Sciences, Nagoya, Japan
| | - Noriko Usami
- Nagoya University School of Medicine, Department of Tissue Engineering, Nagoya, Japan
| | - Masahiro Oda
- Nagoya University, Graduate School of Informatics, Nagoya, Japan
| | - Daisuke Shimao
- Hokkaido University of Science, Department of Radiological Technology, Sapporo, Japan
| | - Shu Ichihara
- Nagoya Medical Center, Clinical Research Center, Department of Pathology, Nagoya, Japan
| | - Tetsuya Yuasa
- Yamagata University, Graduate School of Engineering and Science, Yamagata, Japan
| | - Masami Ando
- Tokyo University of Science, Research Institute of Science and Technology, Tokyo, Japan
| | - Toshiaki Akita
- Nagoya University School of Medicine, Department of Tissue Engineering, Nagoya, Japan
| | - Yuji Narita
- Nagoya University School of Medicine, Department of Tissue Engineering, Nagoya, Japan
| | - Kensaku Mori
- Nagoya University, Graduate School of Informatics, Nagoya, Japan.,Nagoya University, Information Technology Center, Nagoya, Japan.,National Institute of Informatics, Research Center for Medical Bigdata, Tokyo, Japan
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