Superresolution US Breast Cancer Imaging
Development of Motion-Model Ultrasound Localization Microscopy to Support Breast Cancer Diagnosis and Therapy Monitoring in Patients
Ultrasound imaging is routinely applied in breast cancer screening and for the dignity assessment of suspect lesions. Supported by the DFG, we developed a super-resolution contrast-enhanced ultrasound method called motion model ultrasound localization microscopy (mULM), which tracks individual microbubbles in video sequences and generates images of the vasculature at a resolution beyond the diffraction limit.
The mULM method enables an accurate assessment of the vascular architecture in tumors, individual blood vessel velocities and flow directions, and the quantification of relative blood volume and perfusion. Previously, we showed in mouse xenograft tumors that this method allows to extract various morphometric parameters and combined with a radiomics analysis supports the automated discrimination of different tumor types. <\p>
In this project, we aim to adapt and develop mULM and ultrasound-based radiomics for the application in patients and to advance the technique to 3D acquisition and tracking. In a preliminary analysis of clinical datasets, we were already able to extract super-resolution data of vascular tracks. However, several technical challenges for the robust application in a clinical setting emerged: in particular, patient motion and the lower image resolution together with increased slice thickness and non-optimized injection protocols allowed only to use short image sequences and to extract a reduced number of tracks.
Thus, in an initial step, the mULM methodology will be refined and adapted for its application in breast imaging on clinical ultrasound devices. Subsequently, the 2D mULM method will be applied to monitor breast cancers’ responses to neoadjuvant chemotherapy. Its accuracy will be compared to conventional contrast-enhanced ultrasound data analyses. Furthermore, we strive to advance the method from 2D to full 3D acquisition with a real-time volumetric scanner and compare both approaches in a small cohort of patients with breast tumors of different dignity. To exploit the full potential of mULM data analysis, algorithms will be developed and implemented that allow the automated extraction and clustered analysis of the vascular features. Measurements of similarity as well as cluster analysis will be performed to automatically group the patients. Thus, this project will be a first contribution to the clinical translation of multiparametric super-resolution ultrasound and open the way for ultrasound-based radiomics analysis.
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Project Information
Project start
1. July 2020
Project end
30. June 2023
Funded by
German Research Foundation
Project Number
233312120
Publications
[1]
M. Siepmann et al., “Phase shift variance imaging - a new technique for destructive microbubble imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control, vol. 60, no. 5, pp. 909–923, 2013, doi: 10.1109/TUFFC.2013.2648.
[1]
S. Dencks et al., “Relative Blood Volume Estimation from Clinical Super-Resolution US Imaging in Breast Cancer,” Oct. 2018, pp. 1–4. doi: 10.1109/ULTSYM.2018.8580013.
[1]
I. Spivak et al., “Low-Dose Molecular Ultrasound Imaging with E-Selectin-Targeted PBCA Microbubbles,” Mol. Imaging Biol., vol. 18, no. 2, pp. 180–190, Apr. 2016, doi: 10.1007/s11307-015-0894-9.
[1]
T. Opacic et al., “Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization,” Nature Communications, vol. 9, no. 1, p. 1527, Apr. 2018, doi: 10.1038/s41467-018-03973-8.
[1]
M. Piepenbrock, S. Dencks, and G. Schmitz, “Performance of Foreground-Background Separation Algorithms for the Detection of Microbubbles in Super-Resolution Imaging,” Oct. 2018, pp. 1–4. doi: 10.1109/ULTSYM.2018.8579815.
[1]
M. Piepenbrock, S. Dencks, and G. Schmitz, “Microbubble Tracking with a Nonlinear Motion Model,” Sep. 2020, pp. 1–4. doi: 10.1109/IUS46767.2020.9251581.
[1]
K. Christensen-Jeffries et al., “Super-resolution Ultrasound Imaging,” Ultrasound Med Biol, vol. 46, no. 4, pp. 865–891, Apr. 2020, doi: 10.1016/j.ultrasmedbio.2019.11.013.
[1]
S. Dencks, M. Piepenbrock, and G. Schmitz, “Maximum-Likelihood Estimation to Assess the Degree of Reconstruction of Microvasculature from Super-Resolution US Imaging,” in 2019 IEEE International Ultrasonics Symposium (IUS), Oct. 2019, pp. 376–379. doi: 10.1109/ULTSYM.2019.8925640.
[1]
M. Piepenbrock, D. Koretskaia, G. Schmitz, and S. Dencks, “3D Microbubble Localization with a Convolutional Neural Network for Super-Resolution Ultrasound Imaging,” in 2021 IEEE International Ultrasonics Symposium (IUS), Sep. 2021, pp. 1–4. doi: 10.1109/IUS52206.2021.9593473.
[1]
S. Dencks, M. Piepenbrock, and G. Schmitz, “Assessing Vessel Reconstruction in Ultrasound Localization Microscopy by Maximum Likelihood Estimation of a Zero-Inflated Poisson Model,” IEEE Trans Ultrason Ferroelectr Freq Control, vol. 67, no. 8, pp. 1603–1612, Aug. 2020, doi: 10.1109/TUFFC.2020.2980063.
[1]
M. Piepenbrock, S. Dencks, and G. Schmitz, “Tissue Motion Estimation of Contrast Enhanced Ultrasound Images with A Stable Principal Component Pursuit,” in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Apr. 2021, pp. 1642–1645. doi: 10.1109/ISBI48211.2021.9434155.
[1]
S. Dencks, M. Piepenbrock, and G. Schmitz, “Velocity Filtering with a Median Filter Better Preserves Small Vessels for Ultrasound Localization Microscopy,” in 2021 IEEE International Ultrasonics Symposium (IUS), Sep. 2021, pp. 1–4. doi: 10.1109/IUS52206.2021.9593882.
[1]
M. Piepenbrock, S. Dencks, and G. Schmitz, “Reliable Motion Estimation in Super-Resolution US by Reducing the Interference of Microbubble Movement,” in 2019 IEEE International Ultrasonics Symposium (IUS), Oct. 2019, pp. 384–387.
[1]
M. Piepenbrock, A. Brieden, S. Dencks, and G. Schmitz, “Advancing the Feasible Microbubble Concentration in Super-Resolution,” in 2019 IEEE International Ultrasonics Symposium (IUS), Oct. 2019, pp. 388–391.
[1]
S. Dencks, D. Ackermann, and G. Schmitz, “Evaluation of bubble tracking algorithms for super-resolution imaging of microvessels,” presented at the IEEE International Ultrasonics Symposium, Sep. 18, 2016.
Contact
M. Sc. Thomas Lisson
ID 04/221
+49 (0) 234 32 27804
Thomas.Lisson@rub.de
Dr.-Ing. Stefanie Dencks
ID 04/227
+49 (0) 234 32 29508
Stefanie.Dencks@rub.de
Prof. Dr.-Ing. Georg Schmitz
ID 04/233
+49 (0) 234 32 27573
Georg.Schmitz@rub.de