Abstract
3D organ property mapping has gained a considerable amount of interest in the recent years because of its diagnostic and clinical significance. Existing methods for 3D property mapping include computed tomography (CT), magnetic resonance imaging (MRI), and 3D ultrasound (3DUS). These methods, while capable of producing 3D maps, suffer from one or more of the following drawbacks: high cost, long scan times, computational complexity, use of ionizing radiation, lack of portability, and the need for bulk equipment. We propose the development of a framework that allows for the creation of 3D property maps at point of care (specifically, structure and speed of sound). A fusion of multiple low-cost sensors in a Bayesian framework localizes a conventional 1D-ultrasound probe with respect to the room or the patient’s body: localizing the probe relative to the body is achieved by using the patient’s superficial vasculature as a natural encoding system. Segmented 2D ultrasound images and quantitative 2D speed of sound maps obtained using numeric inversion are stitched together to create 3D property maps. A further advantage of this framework is that it provides clinicians with dynamic feedback during freehand scans; specifically, it dynamically updates the underlying structural or property map to reflect high and low uncertainty regions. This allows clinicians to repopulate regions within additional scans. Lastly, the method also allows for the registration and comparison of longitudinally acquired 3D property/structural maps.
About Alex Benjamin
Alex is a 5th year PhD student majoring in Mechanical Engineering and Computational Science and Engineering. His work lies at the intersection of acoustics, ultrasound imaging, inverse problems, computer vision and robotics. His current work focuses on the use of freehand ultrasound scans to quantify organ health; specifically, his work focuses on the use of ultrasound biomarkers to diagnose and stage the progression of non-alcoholic fatty liver disease (NAFLD). Prior to attending MIT, he got his BS/MS in Mechanical Engineering from Drexel University during which, the main focus of his work was computational mechanics and fluid dynamics.