Ultrasound can provide real-time, safe (no radiation exposure), three dimensional imaging. However, due to the low signal to noise ratio, imaging artifacts and bone surfaces appearing several millimeters in thickness have reduces the application of ultrasound as a standard of care imaging modality. This project involves the enhancement and/or segmentation of bone features from 2D/3D ultrasound data. As a second aim we are also interested in developing robust intra-operative registration methods where a pre-operative plan, developed from computed tomography (CT) or magnetic resonance imaging (MRI), is registered to the ultrasound data for additional augmentation.
Total hospital expenses for 3.6 million spinal fusion surgeries performed between 2001-2010 in United States were more than $287 billion. In most of the spinal fusion surgeries fixation devices called pedicle screws are used to provide stability. Screw placement accuracy impacts fusion rate and it also impacts adjacent level disease. For this project we are aiming to develop a 3D ultrasound-based image guided surgery system. Our aim is to provide a non-radiation based imaging platform with an accuracy acceptable for all levels of spine surgery.
Minimally invasive surgical procedures (percutaneous biopsy, spinal anesthesia and/or intracardiac beating heart procedures) often involve insertion of a thin surgical instruments, such as needles, into the surgical area of interest. Due to its real-time imaging capabilities US imaging is being used as one of the standard imaging modalities during these procedures. However, simultaneous and reliable visualization of these surgical instruments and underlying anatomical structures still continues to be a very challenging task. For this project we are developing fully automated and real-time image processing methods for accurate segmentation of surgical instruments from 3D ultrasound data.
Preterm neonates born earlier at low birth weight are at high risk for developing bleeding inside of the cerebral ventricles known as intraventricular hemorrhage (IVH). Neonates weighing 500-750 g are at 60-70% risk of having IVH. Due to this high risk preterm neonates, born less than 1500g, are routinely imaged using 2D cranial ultrasound (US). For this project we are developing computational methods in order to improve the diagnostic potential of medical US for improved IVH identification and monitoring.
The main objective of this project is the development and validation of new signal and image processing methods an integration of these developed algorithms into the imaging system for the design of a new US imaging platform for liver and kidney cancer diagnosis and monitoring.