In vivo cardiac DTI is capable of probing the microstructure of the myocardium and its dynamics throughout the cardiac cycle. The typical cardiac DTI scan data will contain corrupted frames due to cardiac and respiratory motion. Currently an experienced observer identifies corrupted frames by means of a visual assessment and manually removes them. In this work we show that machine learning can be used to accurately assess DTI corrupted frames, reducing the user input, accelerating analysis and removing human subjectivity.
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