![\"\"](\"https:\/\/blogs.mathworks.com\/headlines\/files\/2018\/05\/MRI-Brain-Scan-1.jpg\")
<\/a><\/p>\nImagine holding completely still for 60 minutes<\/h2>\n
Now imagine keeping a wiggly toddler still for that long.<\/p>\n
To be fair, toddlers aren\u2019t the only ones to have trouble holding still for that long. It is difficult for almost anyone to remain completely still for an hour with their head inside an MRI scanner. For people with movement disorders, it can be downright impossible.<\/p>\n
The problem with MRIs is that any movement can blur the resulting images, compromising the quality of the data. And MRIs are expensive, costing thousands of dollars for each test.<\/p>\n
Knowing exactly how much the patient squirms can help shorten MRI duration<\/h2>\n
MRI scans are lengthy, largely because the operators need to ensure that they capture enough quality data. But it\u2019s difficult for the operator to determine when they have acquired the needed data. It’s preferable to err on the side of too much data rather than risk the expense and time needed to retest a patient.<\/p>\n
Researchers from Washington University School of Medicine and the Oregon Health and Science University developed a suite of software that provides real-time statistics on head movement of patients in the MRI scanner. This lets the MRI scanner operators stop the scan as soon as they have enough low-movement images collected.<\/p>\n
The software is called Framewise Integrated Real-time MRI Monitoring (FIRMM<\/a>), and provides scanner operators with head-motion analytics in real time. The software monitors the patients\u2019 movements, and determines if they are \u201clow movers\u201d, \u201chigh movers\u201d, or that even trickier area to differentiate, \u201cmedium movers\u201d.<\/p>\n FIRMM’s components are the compiled MATLAB<\/a> binary backend, shell scripts for\u00a0image processing, a Docker image containing\u00a0image processing software dependencies, and a Django web application front end. The compiled \u00a0MATLAB<\/a> binary backend monitors an incoming folder waiting for a new subfolder that has the current date and contains images created within the last few minutes. The backend does shell script image processing only on new functional images.<\/p>\n Image processing converts the echo planar imaging (EPI) to the standard DICOM format since it is standard for most scanner equipment and supports offline analysis. \u00a0To determine head movement, FIRMM uses a framewise displacement (FD) algorithm. A linear model is developed based on the FD concept which uses\u00a0machine learning algorithms\u00a0for predictions.<\/p>\n The researchers published the results of their study in ScienceDirect<\/a>, stating, \u201c\u00a0Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total\u00a0brain MRI scan\u00a0times and associated costs by 50% or more.\u201d<\/p>\n 50% less time to quality data. That means 50% less time in the MRI machine. This is great news for young patients, and, well, anyone that must endure an MRI. And a 50% reduction in cost is appreciated by all as well.<\/p>\n According to Analytics India<\/a>, <\/em>\u201c\u2026 real-time image data analysis using\u00a0machine learning\u00a0plays a crucial role in improving\u00a0data\u00a0quality \u2014\u00a0 in addition to reducing high costs incurred in MRIs.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":" A magnetic resonance imaging (MRI) brain scan uses the body\u2019s natural magnetic properties and radio waves to produce detailed images of the\u00a0brain\u00a0and the\u00a0brain\u00a0stem. Since no radiation is used, it is… read more >><\/a><\/p>\n","protected":false},"author":138,"featured_media":-1,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/posts\/1512"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/users\/138"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/comments?post=1512"}],"version-history":[{"count":12,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/posts\/1512\/revisions"}],"predecessor-version":[{"id":3699,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/posts\/1512\/revisions\/3699"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/media?parent=1512"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/categories?post=1512"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/headlines\/wp-json\/wp\/v2\/tags?post=1512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}How it works<\/h2>\n
Machine learning helps cut MRI duration, and therefore costs<\/h2>\n
![\"\"](\"http:\/\/firmm.io\/img\/high_mover.gif\")
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