Skip to main navigation Skip to search Skip to main content

I-Corps: Advanced Ultrasonic Imaging for Medical and Non-Destructive Testing Applications

Project: Research

Abstract & Details

Description

Award ID: 1906883

The broader impact/commercial potential of this I-Corps project is the development of a next-generation, high-resolution ultrasonic imaging technique for medical and non-destructive testing applications. This technique will improve on existing imaging technologies by enabling highly detailed physical and spatial properties of the imaged material to be determined, beyond what is currently capable from ultrasound, X-Rays or MRI. This will enhance the capability of medical and non-destructive imaging technologies, for example, by allowing more accurate identification and diagnoses of tumor tissues in medical scans, or improving the identification of defects in manufactured parts. Combined with the benefits of ultrasound, in particular, safety, speed, portability, patient comfort, and cost, this technology has potential for broad impact. Commercially, the technology will enable medical practitioners to make diagnoses faster and with higher accuracy. This will allow earlier and more precise identification of tumors, reducing the number of false positives and follow-up procedures. This will ultimately increase diagnostic efficiency, reduce costs and improve patient outcomes. In a non-destructive testing application, this technology will enable imaging detail that was previously only available with more expensive and time-consuming techniques such as X-Rays. This I-Corps project combines techniques from the fields of ultrasonic imaging and the geosciences to significantly improve existing mm- to cm-scale imaging technologies. The technology adapts an advanced geophysical imaging method known as full waveform inversion (FWI) for medical and non-destructive testing purposes. Full waveform inversion is conventionally applied for imaging the earth?s structure and to discover oil and gas reservoirs. Instead of applying the technique at seismic scales (m to km), the company applies it at the ultrasonic scale (mm to cm) to generate high-resolution 3-dimensional images with embedded physical properties. Research is focused on both software and hardware development. Software algorithms are being developed which construct images from ultrasonic measurements within seconds to minutes. This is enabled through the use of cutting-edge, highly efficient algorithms and high-performance computing infrastructure. Research and development of prototype hardware is also being undertaken, necessary for generating high-quality ultrasonic data for input into the imaging algorithms. To-date, rapid, high-resolution 2- and 3-dimensional image construction using full waveform inversion techniques has been demonstrated on a range of ?synthetic? ultrasonic-scale objects. This demonstrates that images can be quickly generated, containing a full range of material properties including material density, stiffness and attenuation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman
StatusClosed
Effective start/end date12/01/1805/31/22

Funding

  • I-Corps Teams: $50,000.00

Active Fiscal Year

  • FY2022

Start Fiscal Year

  • FY2019

TIP Programs

  • I-Corps Teams

Key Technology Areas

  • Biotechnology
  • (confidence score: 100%)

Technology Foci

  • Medical Technology
  • (confidence score: 100%)

Congressional District at Award

  • District n. 12 of New Jersey

Current Congressional District

  • District n. 12 of New Jersey

United States

  • New Jersey

Core Based Statistical Area (CBSA)

  • Trenton-Princeton, NJ

County

  • County: Mercer, NJ

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine