The following is a list of ongoing research projects in the Laboratory for Enabling Technologies in Medical Ultrasound。
Management of chronic illnesses, such as Type 2 diabetics, is challenging for both patients and the health care system. Due to poor circulation, often develop chronic wounds that are slow to heal and that require daily care. Given the cost and inconvenience of regular visits to wound clinics, we are developing image analysis tools that can complement to services rendered by wound clinics. Specifically, we ware developing a smart phone app (Android), which tracks the wound size and wound healing process based on images captured with a smart phone. But weight management, exercise, diet and glucose monitoring are also important in order for the wound to heal properly, and thus the smart phone app is set up stop also acquire such data and to provide constructive feedback.
This research effort deals with extending the capabilities and applications of tissue elasticity imaging. Elasticity imaging is of interest, in part because soft tissues, from normal to pathological, exhibit a greater variation in elastic parameters than in acoustic parameters. We have developed a much faster alternative to the conventional cross-correlation method are interested in evaluating both 2D and 3D elasticity imaging and to investigate the use of force sensors for converting strain images into stress images. In addition, we have developed a much faster alternative to the conventional cross-correlation method, termed Analytical Phase Tracking.
A key element of an ultrasound training simulator is the ultrasound image material that is available for the training. For a realistic scanning experience, the image volume needs to be physically larger than what can be captured with a single 3D sweep. Therefore, such a large image volume can only be created by ‘fusing’ individually acquired, overlapping image volumes into one large composite image volume. Due to body movements and non-uniform transducer pressure, rigid registration is inadequate when joining individual 3D volumes. With obstetrics image volumes, fetal movements become an additional factor. We have developed an elegant and complex process of morphing individually acquired 3D image volumes that reduces misalignments to a negligible minimum.
In collaboration with iPixel, an Oklahoma City based company, we are developing image streaming capabilities over wireless networks, as part of the effort towards making ultrasound video (as well as exam camera video) available anywhere. Specifically, we are implementing this to work on a variety of point-of-care ultrasound systems, to operate on wireless networks, such as wireless LANs (Wi-Fi or 802.11), 3G/4G wireless networks and satellite networks. The challenge is the dynamically changing network conditions, in terms of available bitrate and transmission delays, requiring agile image compression.
Ultrasound is the least costly imaging modality by far and is the modality of choice in obstetrics, due to absence of radiation exposure. Unfortunately, a significant amount of training is required to physically perform ultrasound scanning and to make diagnostic decisions based on ultrasound images. Furthermore, training opportunities are relative costly. Thus, ultrasound training simulators have the potential for providing valuable alternative training opportunities. This has led to the development of a low cost training system that a student or health care professional can personally own, carry and use with a laptop/PC.
The goal of this research is to develop and evaluate a non-invasive ultrasound-based technique for in vivo classification of atherosclerotic plaque. The ultimate goal is to develop a screening tool for stroke risk. The technique measures the absolute integrated backscatter (IBS) of arterial wall structures through an intervening inhomogeneous soft tissue layer. The aberrating effect of this tissue layer is minimized by normalizing the measured IBS from the wall region of interest with the IBS from an adjacent range cell in blood.