Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization
| Autor: | Dimitri Ackermann, Stefanie Dencks, Tatjana Opacic, Georg Schmitz, Stefan Delorme, Benjamin Theek, Fabian Kiessling, Anne Rix, Elmar Stickeler, Marion Piepenbrock, Twan Lammers |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
Science General Physics and Astronomy Contrast Media Context (language use) Image processing Triple Negative Breast Neoplasms 01 natural sciences General Biochemistry Genetics and Molecular Biology Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Mice Motion 0302 clinical medicine Cell Line Tumor Neoplasms 0103 physical sciences Microscopy Image Processing Computer-Assisted Animals Humans In patient lcsh:Science 010301 acoustics Ultrasonography Multidisciplinary Microbubbles business.industry Ultrasound General Chemistry Middle Aged Characterization (materials science) Phenotype Feature (computer vision) A549 Cells lcsh:Q Female ddc:500 business Algorithms Neoplasm Transplantation Biomedical engineering |
| Zdroj: | Nature Communications Nature Communications, Vol 9, Iss 1, Pp 1-13 (2018) Nature Communications 9, 1527 (2018). doi:10.1038/s41467-018-03973-8 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-018-03973-8 |
| Popis: | 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. The vascular structure of tumors impacts diagnosis, prognosis and drug response; however, imaging methods to analyse this important feature have been hindered by spatial resolution limitations. Here the authors present a tool called motion model ultrasound localization microscopy to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. |
| Databáze: | OpenAIRE |
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