Deep Learning Image Enhancement for Point of Care Ultrasound

项目总结

Project Summary Ultrasound has many clinical applications due to it’s non-invasive, non-ionizing, and real-time imaging properties. However, ultrasound still relies heavily on operator skills for image acquisition and interpretation. Operator skill is especially challenged in overweight and obese patient populations where imaging artifacts such as acoustic clutter are more prominent and decrease anatomical conspicuity. To decrease the interpretation burden faced by operators, we aim to develop a deep learning framework for real-time acoustic clutter artifact suppression. We generate preliminary in silico training data using a configurable cloud-compute tool that scales to an 8000 CPU cluster. This tool is ideal for deep learning methods as it significantly speeds up the turnaround time for simulating unique ultrasound acquisition configurations enabling data generation in days as opposed to months. In this project, we will open-source our cloud-compute simulations tools, improve our current in silico data model of acoustic clutter by incorporating human abdominal wall tissue information from medical CT scans, and assess our clutter correction model’s performance on in vivo data. To translate our model’s results for medical provider interpretation, image post-processing is necessary. In our recently published work, MimickNet, we use deep learning methods to successfully approximate post-processing algorithms found on some of the best clinical-grade ultrasound scanners. We propose extending MimickNet to incorporate post-processing approximations for anatomy-specific use cases such as cardiac and vascular imaging. This will provide more off-the-shelf tooling for researchers to translate their algorithmic research into image forms familiar to providers, thus easing clinical translation. Lastly, portable ultrasound hardware has significantly decreased in cost, enabling the widespread use of mobile point-of-care ultrasound (POCUS). Since many consumer devices contain hardware accelerators specific for deep learning applications, there is an opportunity to correct ultrasound artifacts in real-time, even while constrained to mobile hardware. Our preliminary data show that beamforming operations and MimickNet can run at > 100 frames-per-second on an NVIDIA P100 GPU. We propose developing a framework to transfer our image processing pipeline completely onto mobile hardware accelerators. This work will enable translating novel image processing algorithms as easy as downloading software. Our work in developing a deep learning framework for POCUS systems covers the full image reconstruction pipeline from simulated data to producing a clinical-grade image familiar to providers. This framework will provide a rapid translational path for improving ultrasound imaging quality on cheap and widely available mobile hardware.