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New study shows value of 7T MR for evaluating MS progression Can detect cortical lesions better than conventional scanners

Trice Imaging connects imaging devices of large chain healthcare provider Aleris Patients and physicians can view images on laptops, cell phones

The benefits of intraoperative MR Q&A with Dr. John Huston Mayo Clinic neuroradiologist discusses what the advanced capabilities mean for patients – as well as providers

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Gadolinium not required in 3T MR multiple sclerosis follow-up scans Longitude subtraction mapping just as sensitive: study

New MR methodology may distinguish benign and cancerous lymph nodes Could be used before surgery and help determine treatment

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BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

NIH awards $1.8 million to Magnetic Insight for neurovascular MPI Detects magnetic nanoparticle tracers, enables deep-tissue imaging

Siemens focuses on digitalization at HIMSS Its expanded digital service portfolio will be on display

NYU releases biggest ever MR data set in AI Facebook collaboration

by Thomas Dworetzky , Contributing Reporter

“fastMRI not only could have an important impact in the medical field, it’s also an interesting research challenge that will help to advance the field of AI,” said Larry Zitnick, research manager, Facebook AI Research. “To be medically useful, our AI-reconstructed images need to be more than just good looking, they must also be accurate representations of the ground truth, even though they're created from significantly less data. The data set NYU Langone is releasing and the baseline models we've open-sourced will enable other researchers to join us in working on this challenging problem, and we believe this open approach will bring about positive results more quickly.”

The next step for the team is to explore AI-based image reconstruction techniques using the data set, according to Dr. Yvonne W. Lui, associate professor of radiology and associate chair of artificial intelligence at NYU. “Additionally, any progress made at NYU School of Medicine and FAIR will be part of a larger effort that spans multiple research communities,” she advised, explaining that “results will be compiled and compared on a fastMRI leaderboard, as well as in research papers and workshops to come.”
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When the collaboration was announced in August, Lui stressed the approach, which uses AI to reconstruct views skipped in a scan from underlying image structure, similar to the way people interpolate sensory information.

“MR is the gold standard imaging technology for soft tissues of the human body. However, its main limitation is the amount of time an exam takes,” Lui told HCB News at the time. “Using AI, our aim is to acquire significantly less data than typically needed for a high-quality medical image, allowing the examination to be completed in a significantly shorter period of time while maintaining diagnostic imaging quality.”

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