WALTHAM, Mass., July 10 /PRNewswire/ -- Biomedical engineers at the University of Virginia (U.Va.) School of Engineering and Applied Science have developed a new imaging tool that hopes to dramatically improve medical ultrasounds, potentially leading to more accurate and timely diagnoses of breast cancer and other life threatening conditions.
WALTHAM, Mass., July 10 /PRNewswire/ — Biomedical engineers at the University of Virginia (U.Va.) School of Engineering and Applied Science have developed a new imaging tool that hopes to dramatically improve medical ultrasounds, potentially leading to more accurate and timely diagnoses of breast cancer and other life threatening conditions.
Using Star-P(TM) software from Interactive Supercomputing, the University's biomedical engineering research team, led by Associate Professor William F. Walker, created an advanced beamforming algorithm — called the Time-domain Optimized Near-field Estimator (TONE) — which significantly improves the contrast and resolution of ultrasound images.
"The potential applications for this algorithm are almost infinite," said James H. Aylor, dean of U.Va.'s School of Engineering and Applied Science. "Not only can it be used in the medical community to benefit patients nationwide, it will have applications in the fields of radio astronomy, seismology and more."
While conventional beamforming algorithms have been used in ultrasound scanners for nearly a half century, they typically result in degraded images that are blurry or cluttered. The culprit: off-axis signals, or the sound wave reflections coming from undesired locations within the organ or tissue.
The TONE algorithm reduces undesired off-axis signals, resulting in much higher definition images, but at the price of a much greater computational load. The algorithm developed on desktop computers overwhelmed the computer's processing ability. The team solved this problem by automatically parallelizing their algorithms with Star-P to run on a powerful, memory-rich IBM Linux cluster.
"We were not able to generate images with such a fine sampling pitch until we used Star-P," said Research Associate Francesco Viola. "It takes a huge amount of memory and computational resources to execute the algorithm. Typical resolution for ultrasound imaging systems is in the 200-300 micron range. With Star-P, we were able to tap into the University's supercomputing clusters to generate ultra high resolution images of 67 microns, without having to become parallel programming experts."
Star-P is an interactive parallel computing platform that enables the biomedical engineering team to code algorithms and imaging models on their desktops using MATLAB(R), but run them instantly and interactively on a 32- processor Linux cluster with 64 gigabytes of memory. Star-P eliminates the need to re-program the applications in C, Fortran or MPI to run on parallel systems — which typically takes months to complete for large, complex problems.
The U.Va. Engineering School team joins a growing a list of life sciences research organizations that have turned to Star-P to boost research productivity and overcome computational roadblocks. For example, researchers in MIT's Computational and Systems Biology Initiative (CSBi) are using Star-P to create new biological models that may someday yield new drug discoveries. It's also being used by scientists at the National Cancer Institute's (NCI's) Pediatric Oncology Branch to mine vast public databases of genomic information for potential new medical discoveries. And, researchers at Australia's Howard Florey Institute are using Star-P to rapidly analyze large MRI datasets that may someday reveal correlations between brain structure and conditions such as ADHD, Alzheimer's, auditory hallucinations and primal urges (e.g. thirst).
The TONE research project was funded by a grant from the U.S. Army Congressionally Directed Medical Research Program in Breast Cancer. The technology is patent pending and the project results will be published in an upcoming issue of IEEE Transactions on Medical Imaging.
About the University of Virginia School of Engineering and Applied Science
Founded in 1836, the University of Virginia School of Engineering and Applied Science combines research and educational opportunities at the undergraduate and graduate levels. Within the undergraduate programs, courses in engineering, ethics, mathematics, the sciences and the humanities are available to build a strong foundation for careers in engineering and other professions. Its abundant research opportunities complement the curriculum and educate young men and women to become thoughtful leaders in technology and society. At the graduate level, the Engineering School collaborates with the University's highly ranked medical and business schools on interdisciplinary research projects and entrepreneurial initiatives. With a distinguished faculty and a student body of 2,200 undergraduates and 700 graduate students, the Engineering School offers an array of engineering disciplines, including cutting-edge research programs in computer and information science and engineering, bioengineering and nanotechnology. For more information, visit http://www.seas.virginia.edu .
About Interactive Supercomputing
Interactive Supercomputing (ISC) launched in 2004 to commercialize Star-P, an interactive parallel computing platform. With automatic parallelization and interactive execution of existing desktop technical applications, Star-P merges two previously distinct environments — desktop computers and high performance servers — into one. Based in Waltham, Mass., the privately held company markets Star-P for a range of biomedical, financial, and government laboratory research applications. Additional information is available at http://www.interactivesupercomputing.com .
Star-P is a trademark of Interactive Supercomputing Inc. All other trademarks mentioned herein are the property of their respective owners.
Contacts: Ilya Mirman Michelle Dillon Interactive Supercomputing Beaupre & Co. Public Relations 781-419-5088 603-559-5835 email@example.com
SOURCE Interactive Supercomputing