CASE STUDY
The Low-Field Imaging Laboratory at Harvard Medical School is on the forefront of reducing the cost of medical imaging using Machine Learning .
Magnetic Resonance Imaging ( MRI ) is the gold standard of biomedical imaging , enabling doctors to understand structures and processes within the body that were not possible decades ago , but MRI scanners
ABOUT RETROSPECT today still require large superconducting magnets for high-resolution imaging .
At the lab , new applications of supervised learning enable it to reduce the costs of MRI machines by several orders of magnitude , enabling more affordable high-resolution imaging with reduced size at new point-of-care locations . Machine Learning requires huge quantities of data to analyze and extract relevant models . The lab must manage 80 TB of data as well as the protection of 20 laptops , desktops and servers in different parts of the building .
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Heterogeneous environment
The lab uses Retrospect Backup Single Server Unlimited to protect its entire environment . With Retrospect , the lab can protect its large servers , various desktop machines , roaming laptops and NAS storage from a Mac Mini .
Matthew Rosen , Director of Low-Field Imaging Laboratory in MGH Martinos Center , Harvard Medical School , uses ProactiveAI to ensure laptops are backed up whenever they are available , Rosen uses
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