Abstract

Super-resolution imaging methods promote tissue characterization beyond the spatial resolution limits of the devices and bridge the gap between histopathological analysis and non-invasive imaging. Here, we introduce motion model ultrasound localization microscopy (mULM) as an easily applicable and robust new tool to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. In tumor-bearing mice and for the first time in patients, we demonstrate that within less than 1 min scan time mULM can be realized using conventional preclinical and clinical ultrasound devices. In this context, next to highly detailed images of tumor microvascularization and the reliable quantification of relative blood volume and perfusion, mULM provides multiple new functional and morphological parameters that discriminate tumors with different vascular phenotypes. Furthermore, our initial patient data indicate that mULM can be applied in a clinical ultrasound setting opening avenues for the multiparametric characterization of tumors and the assessment of therapy response.

Details

Title
Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization
Author
Opacic, Tatjana 1 ; Dencks, Stefanie 2   VIAFID ORCID Logo  ; Theek, Benjamin 1 ; Piepenbrock, Marion 2 ; Ackermann, Dimitri 2 ; Rix, Anne 1 ; Lammers, Twan 1 ; Stickeler, Elmar 3 ; Delorme, Stefan 4 ; Schmitz, Georg 2   VIAFID ORCID Logo  ; Kiessling, Fabian 1 

 Institute for Experimental Molecular Imaging, University Clinic Aachen, RWTH Aachen University, CMBS, Aachen, Germany 
 Chair for Medical Engineering, Department of Electrical Engineering and Information Technology, Ruhr University Bochum, Bochum, Germany 
 Department of Obstetrics and Gynecology, University Clinic Aachen, RWTH Aachen University, Aachen, Germany 
 Department of Radiology, German Cancer Research Center, Heidelberg, Germany 
Pages
1-13
Publication year
2018
Publication date
Apr 2018
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2027020060
Copyright
© 2018. 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.