Very recently, the revolutionary technology of contrast enhanced super-resolution ultrasound imaging has been developed. This novel technique images microvessels at resolutions as small as ten micrometers, over an order of magnitude smaller than the ultrasound diffraction limit, and at depths much greater than traditional limits imposed by the frequency. To achieve advances in all of these seeming paradoxical dimensions super-resolution contrast imaging requires that tens of thousands of frames of data be analyzed, making this technique much more computationally and algorithmically intensive than standard ultrasound imaging. Furthermore the skull presents a unique challenge because it aberrates the wave profile, which reduces the resolution and it generates reverberations that reduce the detectability of contrast agents. Consequently, transcranial super-resolution imaging would be difficult if not impossible to translate to the brain in its current form with current clinical hardware, especially if 3-D imaging is desired (which it is for microvascular morphological analysis). However, there is a solution to this, which our group proposes to achieve in this project. Time reversal, in conjunction with a highly accurate acoustic simulation tool that we have developed, can correct for the aberrations induced by the skull morphology accurately focus ultrasound and improve detectability. New software and implementation approaches designed at UNC, including our innovative adaptive multi-focus beamforming approach, will further increase sensitivity and resolution at clinically relevant depths, and enable full 3-D volume acquisitions at volume frame rates over 5000 FPS, suitable for fast 3-D super-resolution imaging in humans. Recent advances in ultrasound hardware will enable ultra-high frame rate processing. Our academic and clinical teams at UNC Chapel Hill are partnering with Verasonics, Inc, a world leading industrial partner in next-generation ultrasound systems, to develop the first high-frame rate 3-D super resolution imaging system optimized for through-skull imaging. We will do this by first designing and constructing a 256 channel ultra-high frame rate ultrasound system, designed to operate with a custom 16x16 matrix transducer at 1 MHz. Ultra-fast processors, large RAM buffers, GPUs, and high-bandwidth data transfer hardware will be utilized to handle challenging adaptive beamforming tasks and massive data acquisition. Processing will be performed with custom code designed for GPU and cluster computing. Our approach will be validated in phantoms, rodent models of human disease that are placed behind a human skull, and in healthy juvenile pigs. Our motivation is to develop super-resolution imaging as a new approach for imaging blood vessels and blood flow in the brain, with a non-ionizing low-cost imaging technology that could be used bedside. The advancement of the proposed technology will be disruptive for ultrasound imaging ? and could eventually enable real-time functional imaging of the human brain, something previously only possible with much more expensive and lower temporal resolution modalities such as fMRI.

Public Health Relevance

Ultrasound has tremendous potential for transcranial imaging and tumor diagnosis because of its portability, safety, and low cost; however, it is often not considered to be a feasible brain-imaging modality because of its low image quality caused by aberration and reverberation generated by skull morphology. In this project, we propose to work with an industrial partner to develop a new ultrasound imaging technique that addresses these limitations. We will exploit advances in computational hardware, advanced acoustic simulation tools, and data processing to create these super-resolution images of microvessels in-vivo. Our proposed approach builds on recent discoveries with innovative technological advancement to develop an ultrasound imaging technique that can image microvessels deep within the brain.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
3R01EB025149-02S1
Application #
9606663
Study Section
Program Officer
Liu, Guoying
Project Start
2017-09-30
Project End
2019-07-31
Budget Start
2018-08-15
Budget End
2019-07-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biomedical Engineering
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599