1. Research Funding
  2. Division of Applied Science & Technology (Biomedical Imaging)

Division of Applied Science & Technology (Biomedical Imaging)

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This division supports the development of innovative biomedical imaging technologies to transform our understanding of biological and disease processes for improving diagnostics, image-guided therapies, and human health.

Meet our Data and Technology Advancement (DATA) National Service Scholars

Program Areas

Bioelectromagnetic

Shumin Wang, Ph.D.

Sensors

Shawn Mulvaney, Ph.D.

Image Guided Interventions

Guoying Liu, Ph.D.

MRI

Robert L. Barry, Ph.D.

Shumin Wang, Ph.D.

Molecural Probes

Tatjana Atanasijevic, Ph.D.

Nuclear Medicine

Martin Tornai, Ph.D.

Optical Imaging

Afrouz Anderson, Ph.D.

Behrouz Shabestari, Ph.D.

imaging of a hand

Randy King, Ph.D.

Ultrasound

Randy King, Ph.D.

Xray

Martin Tornai, Ph.D.

Collaborations

  • Medical Imaging and Data Resource Center (MIDRC) - This multi-institutional effort will harness the powers of artificial intelligence, and medical imaging to fight COVID-19 by creating new tools that physicians can use for early detection and personalized therapies for COVID-19 patients.  MIDRC will gather a large repository of images and lead the development and implementation of new diagnostics, including machine learning algorithms, that will allow rapid and accurate assessment of disease status and help physicians optimize patient treatment. In additional to NIBIB subject matter experts, the team of collaborators includes the University of Chicago, American College of Radiology Radiological Society of North America, and the American Association of Physicists in Medicine. MIDRC website (Kris Kandarpa and Guoying Liu)
  • Human Connectome Project (HCP) – Involves 16 NIH institutes and centers and is part of the NIH Blueprint for Neuroscience Research. The HCP supports research that uses cutting-edge imaging technologies to map the circuitry involved in brain function in healthy humans. (Guoying Liu)
  • Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative – Presidential project aimed at revolutionizing our understanding of the human brain. The goal is to map circuits of the brain, measure fluctuating patterns of electrical and chemical activity flowing within those circuits, and understand how their interplay creates our unique cognitive and behavioral capabilities. By accelerating the development and application of innovative technologies, researchers will be able to produce a dynamic picture of the brain that, for the first time, shows how individual cells and complex neural circuits interact in both time and space. It is expected that the application of these new tools and technologies will lead to new ways to treat, cure, and even prevent brain disorders. NIH is one of several federal agencies involved in this initiative. For more information see the BRAIN website. (Guoying Liu and Shumin Wang)
  • Sound Health - NIH project in partnership with the John F. Kennedy Center for the Performing Arts to collaborate on current research studying the interactions between music and the brain as well as how music can be used as therapy. NIBIB is participating in a R21 funding opportunity (RFA-NS-19-009) that encourages exploratory technical development and studies to increase our understanding of how music affects the brain, body, and behavior. For more information see the Sound Health website. (Guoying Liu)
  • Medical Device Research Interest Group (MDRIG) - Trans-NIH initiative that has representatives from 16 NIH institutes and centers as well as from the FDA and CMS. The goal of MDRIG is to provide a forum to discuss and collaborate on issues related to research and development and other topics critical to innovation of medical device technologies. For more information contact Dr. Kris Kandarpa.
  • NIH-NASA Biomedical Research Activities - Collaboration between 23 NIH institutes and centers and the National Aeronautics Space Administration that focuses on exploring how biomedical research can address the challenges of deep space exploration and benefit human health in space and on Earth. These efforts include establishing a framework of cooperation to encourage interaction between NIH and NASA research communities and integrating results from that research into improved understanding of human physiology and health. For more information see the NIH-NASA webpage. (Randy King)

Division Staff

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    Source: Society of Nuclear Medicine and Molecular Imaging News

     

  • From flat to overflowing: adding another dimension to tissue analysis

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    A team of NIBIB-funded researchers recently developed an AI platform that can analyze 3D pathology images to predict disease outcomes. Their method had improved performance in predicting prostate cancer outcomes when compared with traditional pathology approaches, such as analysis by expert pathologists using 2D images.

  • Rice lab pioneers noninvasive measurement of gene expression in target brain locations

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    A study funded in part by NIBIB published in Science Advances describes new technology developed by the Rice University lab of bioengineer Jerzy Szablowski that could be a game changer for brain-based gene therapy. The new noninvasive tool can measure expression of gene therapy or endogenous genes in specific brain regions.

    Source: Rice University News

  • Blood flow makes waves across the surface of the mouse brain

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    Researchers have, for the first time, visualized the full network of blood vessels across the cortex of awake mice, finding that blood vessels rhythmically expand and contract leading to “waves” washing across the surface of the brain. These findings, funded by the National Institutes of Health (NIH), improve the understanding of how the brain receives blood, though the function of the waves remains a mystery. Source: The National Institute of Neurological Disorders and Stroke 

  • AI Advancements Make the Leap into 3D Pathology Possible

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    Researchers from Mass General Brigham and their collaborators present Tripath: new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions. In collaboration with the University of Washington, the research team imaged curated prostate cancer specimens, using two 3D high-resolution imaging techniques. Tripath performed better than pathologists and outperformed deep learning models that rely on 2D morphology and thin tissue slices. Source: Mass General Brigham

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