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Abstract #4850

Automated Identification of Noise Signal in Spinal DCE-MRI using Independent Component Analysis and Unsupervised Machine Learning

Lucy Wang1, Yi Wang2,3, Murat Alp Oztek4, Nina Mayr4, Simon Lo4, William Yuh4, and Mahmud Mossa-Basha3

1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States, 2Philips Healthcare, Gainsville, FL, United States, 3Radiology, University of Washington, Seattle, WA, United States, 4Radiation Oncology, University of Washington, Seattle, WA, United States

Dynamic Contrast-Enhanced (DCE) MR perfusion has shown early promise in evaluation of spinal metastatic disease and can improve prediction of treatment responses and post-treatment complications. However, spinal DCE-MRI exams frequently suffer from suboptimal image quality due to factors including cerebral spinal fluid (CSF) and vascular pulsation, respiration, bowel motion and patient bulk motion. Independent component analysis has been successfully used as a method to identify and remove motion artifacts from functional MR images. In this work, we combine ICA with an unsupervised machine learning method to automatically identify image components arising from contrast-enhancing tissues and those due to artifacts.

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