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Abstract #0689

Machine Learning for Intelligent Detection and Quantification of Transplanted Cells in MRI

Muhammad Jamal Afridi1, Arun Ross2, and Erik M Shapiro3

1Michigan State University, East Lansing, MI, United States, 2Michigan State University, MI, United States, 3Radiology, Michigan State University, MI, United States

Cell based therapy (CBT) is promising for treating a number of diseases. The ability to serially and non-invasively measure the number and determine the precise location of cells after delivery would aid both the research and development of CBT and also its clinical implementation. MRI-based cell tracking, employing magnetically labeled cells has been used for the past 20 years to enable detection of transplanted cells, achieving detection limits of individual cells, in vivo.These individual cells can be detected as dark spots in T2* weighted MRI. Manual enumeration of these spots, and hence, counting cells, in an in vivo MRI is a tedious and highly time consuming task that is prone to inconsistency. Therefore, it becomes practically infeasible for an expert to conduct such manual enumeration for a very large scale analysis, consequentially affecting our ability to monitor CBT. To solve this challenge, we have designed a machine learning methodology for automatically quantifying transplanted cells in MRI in an accurate and efficient manner.

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