Explore more about: Computed Tomography (CT)

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The lungs are one of the most difficult organs for physicians to navigate. A collaborative team of NIH-funded researchers have built a compact robotic system that can autonomously steer around anatomical obstacles within the lungs of live animals.
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Researchers have found that AI models could accurately predict self-reported race in several types of medical images, suggesting that race information could be unknowingly incorporated into image analysis models.
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One team helped develop the first photon-counting detector (PCD)-CT system, which is superior to current CT technology. Another team has also been using artificial intelligence to lower the dose of radiation given to a patient when they are undergoing a conventional CT brain scan.
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After less than two years of data collection and processing, RSNA has successfully delivered over 30,000 de-identified imaging studies to the Medical Imaging and Data Resource Center (MIDRC) project, an open-access platform which publishes data to be used for research. MIDRC is funded by NIBIB. Source: RSNA News

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Researchers have demonstrated the first experimental cross-sectional medical image that doesn't require tomography, a mathematical process used to reconstruct images in CT and PET scans. The work could lead to cheaper, easier and more accurate medical imaging.
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The Medical Imaging and Data Resource Center (MIDRC), a collaboration of leading medical imaging organizations funded by NIBIB, has launched a repository of imaging data to facilitate medical imaging research on COVID-19.
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Every year thousands of Americans, mostly over age 75, require replacement of their aortic valve. Now 3D printed patient-specific models of the aorta can aid presurgical planning and improve outcomes of minimally invasive valve replacement.
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A team of engineers has demonstrated how a new algorithm they developed was able to successfully predict whether or not a COVID-19 patient would need ICU intervention. This artificial intelligence-based approach could be a valuable tool in determining a proper course of treatment for individual patients.
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Engineers have demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT.
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A new article looks at the use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the coronavirus disease (COVID-19) pandemic.