Abstract

We have demonstrated the capability of laboratory propagation-based microtomography (miroCT) in non-destructive 3D virtual histopathology of human blood clots without any contrast agent. The volumetric information are valuable to understand the mechanical properties of clots which are crucial in selecting the most efficient mechanical thrombectomy method for clot extraction. Different clot types retrieved by mechanical thrombectomy from patient victims of acute ischemic stroke were evaluated through propagation-based microCT. The results were correlated with high-resolution scanning electron microscopy (SEM) images, confirming detected cellular and fibrillary structures. Calcifications appeared as glassy opacity areas with relatively intense signal on microCT images, also proved by energy-dispersive spectroscopy and X-ray diffraction. Hyperintense regions on the microCT corresponded to individual or compact aggregates of red blood cells, whereas fibrin dominated volumes appeared at consistently moderate to low normalized microCT values. Red blood cell shapes and sizes are consistent with the SEM observations. Together with other potential parameters, 3D porosity distribution and volume fraction of structures can be easily measured by microCT data. Further development of automated post-processing techniques for X-ray propagation-based micro/nanoCT, also based on machine learning algorithms, can enable high throughput analysis of blood clot composition and their 3D histological features on large sample cohorts.

Details

Title
Non contrast enhanced volumetric histology of blood clots through high resolution propagation-based X-ray microtomography
Author
Saghamanesh Somayeh 1 ; Dumitriu LaGrange Daniela 2 ; Reymond, Philippe 2 ; Wanke, Isabel 3 ; Karl-Olof, Lövblad 4 ; Neels Antonia 1 ; Zboray, Robert 1 

 Swiss Federal Laboratories for Materials Science and Technology (Empa), Center for X-Ray Analytics, Dübendorf, Switzerland (GRID:grid.7354.5) (ISNI:0000 0001 2331 3059) 
 University of Geneva, Neuroradiology Division, Diagnostic Department, Geneva, Switzerland (GRID:grid.8591.5) (ISNI:0000 0001 2322 4988) 
 Klinik Hirslanden, Neuroradiology Division, Zurich, Switzerland (GRID:grid.417546.5) (ISNI:0000 0004 0510 2882) 
 University of Geneva, Neuroradiology Division, Diagnostic Department, Geneva, Switzerland (GRID:grid.8591.5) (ISNI:0000 0001 2322 4988); University of Geneva, Faculty of Medicine, Geneva, Switzerland (GRID:grid.8591.5) (ISNI:0000 0001 2322 4988) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2629528565
Copyright
© The Author(s) 2022. corrected publication 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.