X-ray Dark-Field Imaging (XDFI)-a Promising Tool for 3D Virtual Histopathology.
Absorption contrast
Mesoscale
Phase-contrast
Refraction-contrast
Virtual histopathology
X-ray dark-field imaging
Journal
Molecular imaging and biology
ISSN: 1860-2002
Titre abrégé: Mol Imaging Biol
Pays: United States
ID NLM: 101125610
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
received:
08
07
2020
accepted:
22
12
2020
revised:
20
12
2020
pubmed:
25
2
2021
medline:
15
2
2022
entrez:
24
2
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33624229
doi: 10.1007/s11307-020-01577-7
pii: 10.1007/s11307-020-01577-7
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
481-494Informations de copyright
© 2021. World Molecular Imaging Society.
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