Deep Learning Based Microbubble Localization for Fast and Robust Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) is a recently developed technique that addresses the resolution-penetration trade-off of ultrasound. However, its clinical application was limited by localization performance. In this study, we propose to improve the localization performance of ULM with a deep learning based localization technique that uses Field-II simulation and RF data.

[1]  K. Boone,et al.  Effect of skin impedance on image quality and variability in electrical impedance tomography: a model study , 1996, Medical and Biological Engineering and Computing.

[2]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[3]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[4]  Robert J. Eckersley,et al.  In Vivo Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles , 2015, IEEE Transactions on Medical Imaging.

[5]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Jianwen Luo,et al.  Deep Learning for Ultrasound Localization Microscopy , 2020, IEEE Transactions on Medical Imaging.

[8]  Mickael Tanter,et al.  Resolution limits of ultrafast ultrasound localization microscopy , 2015, Physics in medicine and biology.

[9]  K. Jaqaman,et al.  Robust single particle tracking in live cell time-lapse sequences , 2008, Nature Methods.

[10]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[11]  Matthew R. Lowerison,et al.  Short Acquisition Time Super-Resolution Ultrasound Microvessel Imaging via Microbubble Separation , 2020, Scientific Reports.

[12]  Yonina C. Eldar,et al.  Deep Learning for Super-resolution Vascular Ultrasound Imaging , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  J. Arendt.  Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging: Field: A Program for Simulating Ultrasound Systems , 1996 .

[14]  M. Tanter,et al.  Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging , 2015, Nature.

[15]  Mickael Tanter,et al.  Super-resolution Ultrasound Imaging. , 2020, Ultrasound in medicine & biology.

[16]  J. Jensen,et al.  Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers , 1992, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  C. Dunsby,et al.  3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles , 2015, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.