[{"innerBlocks":[],"name":"core\/freeform","postId":537,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Magnetic Resonance Imaging is a non-invasive and versatile technology, but MRI scans are expensive. Now, machine learning offers a potential solution to the costly procedure. \u201cIt\u2019s quite expensive\u2014it\u2019s about $2,500 per scan in the U.S. in a hospital setting,\u201d says Mert Sabuncu, an assistant professor in the School of Electrical and Computer Engineering at Cornell University, whose focus is on implementing innovative tools for analyzing biomedical images.According to Sabuncu, an important driver of MRI cost is scan time. He points out that the reason MRIs are so expensive are that they take a long time to acquire the scan\u2014anywhere from 15 minutes to 90 minutes per scan.However, Sabuncu contends that MRI can be accelerated through a compressed sensing approach that uses a novel unsupervised end-to-end learning framework.\u201cThe whole motivation of this work is to reduce the scan time to make this technology more accessible and more affordable,\u201d Sabuncu told last month\u2019s Machine Learning for Health Care conference, held in Ann Arbor, Mich.

The machine learning method trains a convolutional neural network on a set of full-resolution MRI scans, which are retrospectively under-sampled and forwarded to an anti-aliasing model that computes a reconstruction\u2014which, in turn, is compared with the input.In experiments with brain and knee MRI scans, Sabuncu contends that he and his colleagues demonstrate that the optimized under-sampling pattern can yield significantly more accurate reconstructions compared with standard under-sampling schemes.The machine learning method\u2014called Learning-based Optimization of the Under-sampling PattErn (LOUPE)\u2014was implemented by modifying a U-Net, a widely used convolutional neural network architecture.\u201cEven with an aggressive eight-fold acceleration rate, LOUPE\u2019s reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods,\u201d states a journal paper<\/a>, which was submitted in late July and is under review but is available in pre-print. \u201cOur experiments also show how LOUPE yielded optimal under-sampling patterns that were significantly different for brain vs. knee MRI scans.\u201d\u201cThis is relatively new work\u2014it\u2019s not like a fully baked, mature project,\u201d observes Sabuncu, who notes that the results of the experiments were presented in June at the Information Processing in Medical Imaging conference.He adds that the code is freely available online here<\/a> at GitHub. Going forward, Sabuncu says a pilot phase is beginning to prospectively validate the machine learning method in a \u201creal MRI scanner and showing that we can get good reconstructions out of this.\u201d","saveContent":"Magnetic Resonance Imaging is a non-invasive and versatile technology, but MRI scans are expensive. Now, machine learning offers a potential solution to the costly procedure. \u201cIt\u2019s quite expensive\u2014it\u2019s about $2,500 per scan in the U.S. in a hospital setting,\u201d says Mert Sabuncu, an assistant professor in the School of Electrical and Computer Engineering at Cornell University, whose focus is on implementing innovative tools for analyzing biomedical images.According to Sabuncu, an important driver of MRI cost is scan time. He points out that the reason MRIs are so expensive are that they take a long time to acquire the scan\u2014anywhere from 15 minutes to 90 minutes per scan.However, Sabuncu contends that MRI can be accelerated through a compressed sensing approach that uses a novel unsupervised end-to-end learning framework.\u201cThe whole motivation of this work is to reduce the scan time to make this technology more accessible and more affordable,\u201d Sabuncu told last month\u2019s Machine Learning for Health Care conference, held in Ann Arbor, Mich.

Machine learning approach looks to reduce MRI scan times, costs

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