Abnormal alterations of tissue microcirculation are often associated with early stage of tissue pathology. Detection and characterization of these early microvascular abnormalities can greatly benefit clinical diagnosis and treatment monitoring as well as facilitating the creation of new therapies to counter disease development. For decades, there has been a longstanding quest for the development of a clinical imaging modality that can noninvasively and directly image such tissue microvascular variations. To date, however, such an imaging method remains elusive due to the fundamental compromise between imaging spatial resolution and depth penetration. Therefore, the long-term objective of this project is to fulfill this unmet clinical need by developing the next-generation ultrasound localization microscopy (ULM), which is an ultrasound-based imaging technique that can directly assess structural and functional tissue microvasculature in vivo in humans at a clinically relevant depth. Different from other imaging modalities, ULM is not limited by the resolution-penetration compromise: ULM can noninvasively image capillary-scale microvessels at several centimeters depth and quantitatively measure their blood flow speed (as low as 1 mm/s). Such combination of deep imaging penetration and exquisite spatial resolution and the unique functionality of measuring small vessel blood flow speed make ULM a promising technique for many clinical applications including cancer and cardiovascular diseases. At present, however, ULM is not ready for clinical use due to several key technical limitations: 1) ULM data acquisition is very slow (tens of seconds with breath holding); 2) ULM post-processing is very expensive computationally (several hours to generate a single 2D ULM image); 3) ULM is difficult to be extended to 3D imaging (which is important for comprehensive evaluation of tissue microvasculature such as in cancer applications). These limitations largely forbids ULM from being effectively used in the clinic to provide useful microvascular biomarkers. In this proposal, we will concentrate on addressing these technical barriers and transform ULM to a truly useful clinical imaging tool. Our approach synergistically combines deep learning (DL), parallel computing, and ultrafast 3D ultrasound imaging to fundamentally shorten ULM data acquisition time, substantially accelerate ULM post-processing, and enhance ULM to 3D imaging.
Our first aim will develop and validate DL-based ULM data processing algorithms that would enable real-time 4D morphometric ULM and fast 3D quantitative ULM. Our method uniquely collects real labeled optical imaging data on a chicken embryo microvessel model for DL training.
Our second aim will focus on realizing 3D-ULM on a 2D row-column-addressing transducer with ultrafast 3D plane wave imaging. We will develop a DL-based beamforming technique to enable high-fidelity 3D microbubble imaging for robust 3D-ULM.
Our final aim will focus on validating the in vivo performance of the newly developed 3D-ULM imaging techniques on a mouse tumor model. We will be collaborating with world-renowned experts in deep learning, optical imaging, and comparative medicine at the University of Illinois to accomplish these aims of the proposal.
Imaging-based detection and characterization of abnormal tissue microvascular variations is clinically significant for diagnosis, treatment evaluation, and therapy development in many pathologies such as cancer, cardiovascular diseases, inflammation, and neurodegenerative diseases. At present, there is no viable noninvasive imaging tool that can fulfill this important clinical need. To fill this gap, we propose to develop a new ultrasound-based super-resolution microvessel imaging technique that can directly and quantitatively assess the structure and the function of tissue microcirculation in vivo in humans.
