Skip to main content
Presentation Time: 9:00-9:20
Home University: UNC-Chapel Hill
Research Mentor: Kennita Johnson, Biomedical engineering
Program: SMART
Research Title: Towards Early Detection of Diabetic Kidney Disease Using Contrast Enhanced Ultrasound Perfusion Parameters

Diabetes is the leading cause of kidney disease and 40% of type 2 diabetic patients will go on to develop end stage kidney disease. However, current clinical markers lag behind disease progression. In contrast to blood and urine markers, renal perfusion may help to detect diabetic kidney disease quicker. Earlier detection will allow clinicians to identify patients susceptible to developing kidney disease and take steps to mitigate and possibly prevent disease progression. Compared to other imaging modalities, ultrasound is cost effective, portable, widely accessible, does not involve ionizing radiation, and ultrasound contrast agents (microbubbles) are safe for use in compromised kidneys. Microbubbles are highly echogenic micron-sized gas particles surrounded by a lipid shell that enable detection of microvascular flow. Microbubble transit through the kidney was observed to capture the wash in and wash out phases, in order to assess changes in renal perfusion between healthy, insulin-resistant, and diabetic populations. Time-intensity curve data from the bolus injection was fit with three different perfusion models (log-normal, lagged normal, and gamma variate) to determine the best fit model for each data set. Perfusion parameters such as mean transit time (MTT), time to peak enhancement (TTP), and area under the curve were extracted to identify parameters with the potential to distinguish healthy and diseased kidneys.