The prevalence of obesity in the United States has risen to record levels over the past 40 years,
putting strain on the healthcare system and creating difficult challenges for medical imaging. We
propose to overcome the challenges that obesity poses to ultrasound imaging by (1) developing
novel image-quality improvement techniques, and (2) implementing them on pulse-echo
ultrasound imaging systems to yield high-quality images of the liver.
Ultrasound imaging is uniquely affected by the presence of additional connective tissue and thick
subcutaneous fat layers in overweight and obese patients; these additional subcutaneous layers
greatly exacerbate reverberation and phase-aberration of the acoustic wave, leading to high
levels of clutter, degraded resolution, and overall poor-quality ultrasound images. Our proposed
methods will determine the local speed-of-sound in abdominal tissue layers and use this
information to accomplish distributed phase-aberration correction. We also apply machine
learning techniques to model and suppress the effects of reverberation clutter and speckle noise.
The combination of these techniques is expected to achieve significant improvements in liver
image quality. These image-quality improvement methods will be implemented on a real-time
ultrasound scanner and will be evaluated in clinical imaging tasks of overweight and obese
patients undergoing ultrasound surveillance of hepatocellular carcinoma.
Successful development of the proposed technology will not only enable high-quality ultrasound
imaging of the liver in otherwise difficult-to-image overweight and obese patients, but also
facilitate improved image quality across nearly all ultrasound imaging applications, for all