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Abstract #3514

Machine Learning-based Analysis of Heterogeneous, Multi-center MR Datasets: Impact of Scan Variability

Mariana Bento1,2, Justin Park2,3, and Richard Frayne1
1Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 2Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada, 3Mechanical Engineering, University of Calgary, Calgary, AB, Canada

Multi-centre heterogeneous imaging datasets are frequently required to develop image-based computer-aided diagnosis and treatment monitoring tools. However, these datasets may present large underlying variability, potentially impacting the performance of the developed tools. Here, as a proof-of-concept, we propose a machine learning method to study scan variability related to the scanner vendor and magnetic field strength in brain MR images from two cohorts of healthy subjects. Our model has high accuracy rates (>92%), confirming the presence of scan variability in heterogeneous, multi-centre datasets. This model may be further incorporated into automated diagnostic tools, potentially allowing more reliable and robust results.

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