Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization

Tatjana Opacic, Stefanie Dencks, Benjamin Theek, Marion Piepenbrock, Dimitri Ackermann, Anne Rix, Twan Lammers, Elmar Stickeler, Stefan Delorme, Georg Schmitz () and Fabian Kiessling ()
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Tatjana Opacic: University Clinic Aachen, RWTH Aachen University, CMBS
Stefanie Dencks: Ruhr University Bochum
Benjamin Theek: University Clinic Aachen, RWTH Aachen University, CMBS
Marion Piepenbrock: Ruhr University Bochum
Dimitri Ackermann: Ruhr University Bochum
Anne Rix: University Clinic Aachen, RWTH Aachen University, CMBS
Twan Lammers: University Clinic Aachen, RWTH Aachen University, CMBS
Elmar Stickeler: University Clinic Aachen, RWTH Aachen University
Stefan Delorme: German Cancer Research Center
Georg Schmitz: Ruhr University Bochum
Fabian Kiessling: University Clinic Aachen, RWTH Aachen University, CMBS

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-03973-8

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