Barbara Dymerska^{1}, Bernard Siow^{2}, and Karin Shmueli^{1}

^{1}Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ^{2}Magnetic Resonance Imaging, The Francis Crick Institute, London, United Kingdom

Microbubbles are a well-established intravascular ultrasound contrast agent. There is increasing interest in MRI-guided microbubble-mediated focused ultrasound treatments such as thermal surgery. MRI magnitude has a non-linear and non-local dependence on microbubble size and volume fraction, making it unsuitable for estimating microbubble concentrations. Quantitative Susceptibility Mapping (QSM) is a strong candidate for tracking microbubble concentration, destruction and clearance because the susceptibility depends linearly on the volumetric bubble concentration. Here, we show the first QSM of microbubbles in a phantom and observe that the measured susceptibility has a high SNR and is directly proportional to the microbubble volumetric concentration.

1. Ferrara K, Pollard R, Borden M. Ultrasound Microbubble Contrast Agents: Fundamentals and Application to Gene and Drug Delivery. Annu. Rev. Biomed. Eng. 2007;9:415–447 doi: 10.1146/annurev.bioeng.8.061505.095852.

2. Wong KK, Huang I, Kim YR, et al. In vivo study of microbubbles as an MR susceptibility contrast agent. Magn. Reson. Med. 2004;52:445–452 doi: 10.1002/mrm.20181.

3. Cheung JS, Chow AM, Guo H, Wu EX. Microbubbles as a novel contrast agent for brain MRI. NeuroImage 2009;46:658–664 doi: 10.1016/j.neuroimage.2009.02.037.

4. Hynynen K. MRI-guided focused ultrasound treatments. Ultrasonics 2010;50:221–229 doi: 10.1016/j.ultras.2009.08.015.

5. Poon C, McMahon D, Hynynen K. Noninvasive and targeted delivery of therapeutics to the brain using focused ultrasound. Neuropharmacology 2017;120:20–37 doi: 10.1016/j.neuropharm.2016.02.014.

6. Dharmakumar R, Plewes DB, Wright GA. On the parameters affecting the sensitivity of MR measures of pressure with microbubbles. Magn. Reson. Med. 2002;47:264–273 doi: 10.1002/mrm.10075.

7. Liu T, Wisnieff C, Lou M, Chen W, Spincemaille P, Wang Y. Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping. Magn. Reson. Med. 2013;69:467–476 doi: 10.1002/mrm.24272.

8. Dymerska B, Eckstein K, Trattnig S, Shmueli K, Robinson SD. Rapid Opensource Minimum Spanning TreE AlgOrithm for Phase Unwrapping (ROMEO). Proc. BCISMRM 2019.

9. Liu T, Khalidov I, Rochefort L de, et al. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed. 2011;24:1129–1136 doi: 10.1002/nbm.1670.

10. Kressler B, de Rochefort L, Liu T, Spincemaille P, Jiang Q, Wang Y. Nonlinear Regularization for Per Voxel Estimation of Magnetic Susceptibility Distributions from MRI Field Maps. IEEE Trans. Med. Imaging 2010;29:273–281 doi: 10.1109/TMI.2009.2023787.

Fig 1. Magnitude, *R*_{2}*
map and QSM results for the phantom with vials of different microbubble
concentrations. Magnitude values decrease whereas *R*_{2}* and susceptibility
values increase with the increasing microbubble concentration. Cylindrical regions
marked in red were used for the plots in Fig 2.

Fig 2. Plots of *R*_{2}*
and susceptibility values from cylindrical ROIs (see Fig 1.) as a function of the
volumetric concentration of Expancel microbubbles. Mean ROI values are plotted
with error bars showing the standard deviation within each ROI. Both *R*_{2}*
and susceptibility increase linearly with the concentration. The blue lines
represent the results of linear regression with the parameters and goodness of
fit (R^{2}) values in the plot legends.