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

Prestige Medical Imaging partners with Esaote and Glassbeam Expands portfolio to include MR, ultrasound and analytical software

First 7T whole-body MR scanner in Canada installed in Montreal Produces high-resolution images at pixel dimensions measured in tenths of a millimeter

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Philips and MIM Software collab to streamline radiotherapy treatment planning Integrate portfolios of CT, MR and software solutions

Dennis Durmis MITA names chair of board of directors

Ohio State to treat epilepsy patients with focused ultrasound in world's first clinical trial For when seizures can't be controlled with medication

FDA gives nod to Siemens' MAGNETOM Lumina 3T MR Cost efficient alternative to MAGNETOM Vida MR system

IRADIMED halts Europe-bound deliveries of MR vital sign monitor CE Mark expiring this month

NYU releases biggest ever MR data set in AI Facebook collaboration

by Thomas Dworetzky , Contributing Reporter
MR scans could soon be done 10 times faster, thanks to a a large-scale MR dataset just released to the public from fastMRI, a collaboration between Facebook AI Research and NYU Langone's Department of Radiology.

“We hope that the release of this landmark data set, the largest-ever collection of fully-sampled MR raw data, will provide researchers with the tools necessary to overcome the challenges inherent in accelerating MR imaging,” Dr. Michael P. Recht, chair and the Louis Marx Professor of Radiology at NYU Langone said in a statement following his announcement of the news in a plenary address at the 2018 annual meeting of the Radiological Society of North America (RSNA).

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Recht also shared baseline results from the collaboration, made up of 1.5 million MR images of the knee from 10,000 scans, plus raw measurement data from almost 1,600 scans. The collaboration demonstrates that acceleration of MR imaging by a factor of four “is already possible".

The data set is fully anonymized, HIPAA-compliant information from NYU's medical school – and no Facebook data. Future releases will add data from liver and brain scans.

In addition, the open source tool is expected to boost the development of AI systems that are capable of deciphering MR scans, boost research reproducibility, and open the door for more consistent evaluation methods. Plans call for the collaboration to develop a suite of tools and baseline metrics to compare results in an organized challenge that will be announced “in the near future,” according to the NYU report.

“This collaboration focuses on applying the strengths of machine learning to reconstruct high-value images in new ways. Rather than using existing images to train AI algorithms, we will radically change the way medical images are acquired in the first place,” Dr. Daniel K. Sodickson, director of NYU's Center for Advanced Imaging Innovation and Research, added in a statement. “Our aim is not merely to enhance data mining with AI, but rather create new capabilities for medical visualization, to benefit human health.”

MR scans can generate a huge amount of valuable information but are slow in nature. By applying AI to exams, researchers believe they can cut down on the amount of data captured, while maintaining and even boosting the richness of the information in images.

Such features are expected to benefit patients who cannot tolerate the typical length of the process, including very young children, elderly adults and those who are claustrophobic. It may also reduce the need for drug administration to calm jittery patients.
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