Jonathan Langdon wins Outstanding Dissertation Award for Engineering
Thursday, May 5, 2016
Congratulations to graduate student Jonathan Langdon of the McAleavey Lab who received the prestigious Outstanding Dissertation Award for Engineering this year. This honor attests to Jonathan’s exceptional work in the field of biomedical ultrasound and comes with a monetary award of $1,000. His thesis was titled, “Development of Single Track Location Shear Wave Viscoelasticity Imaging for Real-Time Characterization of Biological Tissues.”
"Jonathan was an outstanding PhD student in our biomedical engineering program,” said Diane Dalecki, director of the Rochester Center for Biomedical Ultrasound, and professor of biomedical engineering. “His dissertation research made significant advances in new ultrasound elastography technologies for measuring and visualizing viscoleastic properties of tissues. I am delighted to see Jonathan's work recognized with this award.”
The summary of Jonathan’s research project is below:
Development of Single Track Location Shear Wave Viscoelasticity Imaging for Real-Time Characterization of Biological Tissues In response to chronic inflammation, many tissues undergo a transformation known as fibrosis that results in increased stiffness of the tissue. The parenchyma of the liver is one such tissue and may undergo fibrotic change as a result of a number of chronic diseases. The gold standard for monitoring the progression of chronic liver disease is biopsy. However, it is associated with a non-trivial morbidity. Therefore, non-invasive methods of assessing disease state are being sought. Elastography is a set of measurement methods that allow for the non-invasive estimation of tissue stiffness. Unfortunately, distinguishing between early stages of fibrosis has proven to be a challenge since a high level of measurement precision is required. Single Tracking Location Shear Wave Elasticity Imaging (STL-SWEI) is an elastography method that has been shown to improve measurement precision by compensating for speckle-induced bias. This method was previously investigated in the setting of liver fibrosis using a rat model. However, the precision of the measurements proved to be inadequate to distinguish the very earliest fibrosis stages. Additionally, it is unclear from the previous work that the Single Tracking Location per se is responsible for any measurement improvement. In this work, the STL-SWEI method is improved upon by introducing a real-time imaging software suite with matched implementations of both STL and Multiple Tracking Location (MTL) SWEI.
Additionally, a novel viscoelastic estimator is implemented based on Maximum Likelihood Estimation theory. Third, a suite of graphic processing unit (GPU) accelerated simulation tools are introduced that allow for the simulation of SWEI images and exploration of the effects of boundary conditions on SWEI estimates. Finally, the ability of STL-SWEI to distinguish between stages of fibrosis in rat liver is re-evaluated using these new tools and directly compared to MTL-SWEI.
Steve McAleavey Receives 9th Percentile Score for Grant Supporting Ultrasound Imaging Research
Monday, March 7, 2016
BME Professor Stephen McAleavey recently received a 9th percentile score for his R21 grant entitled, "Quantification of Shear Wave Strain Dependence in Breast Tissues.” Many women presently undergo breast biopsy due to lesions detected with x-ray and ultrasound imaging. The great majority of these biopsies are negative, resulting in needless expense and worry. The goal of this project is to improve the power of ultrasound imaging to predict if a breast lesion is benign or malignant. This will be achieved by a novel, high resolution technique to non-invasively map the non-linear mechanical properties of breast tissue. These properties are determined by the microstructure of the tissue and show marked differences between benign and malignant tissues.
This project will combine the efforts of faculty in HSEAS (Stephen McAleavey, PhD in BME, Marvin Doyley, PhD in ECE) and the URMC (Linda M. Schiffhauer, MD in Pathology, and Avice O’Connell, MD in Imaging Sciences).