Super-resolution ultrasound imaging has the capacity to distinguish and map structures that are smaller than the classical limit, typically a fraction of the wavelength. For ultrasound imaging, this means exploring features, such as blood vessels, in the micrometric range deep inside tissue. At the end of this course, students should be able to understand and reproduce super-resolution ultrasound imaging experiments, from data acquisition to image reconstruction, and apply such knowledge in their specific fields.
We first explore the fundamental aspects of imaging resolution in ultrasound. Various approaches to bypass the diffraction-limit with microbubbles and other agents are presented. Particularly, we discuss ultrasound localization microscopy, which has recently improved the resolution for vascular imaging by more than 10-fold. We present its various steps, including separation, localization and tracking, and compare different approaches. Specific elements such as temporal resolution, motion correction or volumetric imaging are considered. We then detail the applications of super-resolution ultrasound for brain, cancerous tumor, kidney, liver, lymph node and peripheral vessel imaging, along with future perspectives in the clinical and preclinical context.
We will share tips and tricks for setting up and conducting in vivo super-resolution imaging experiments on chicken embryos, mice, rats, rabbits, pigs, and humans. Hands-on advice will be shared and discussed (e.g., tail vein and jugular vein catheterization, chicken embryo microbubble injections, microbubble bolus versus steady-state infusion, craniotomy and transcranial imaging preparations, respiratory gating considerations, microbubble concentration/sparsity checking and manipulations before data acquisition, data size and processing requirements/considerations, etc.).
The last part of the course will include hands-on image processing of open data (in-silico and in- vivo) with provided ultrasound localization microscopy algorithms. Deep learning-based microbubble localization and tracking methods will also be introduced in this course. We will provide practical experience for microbubble and flow model simulations to generate data for training and testing the neural network.
