Abstract #4811

Pipeline for Robust Functional Lung Imaging with Oxygen-Enhanced MRI (OE-MRI) and Independent Component Analysis (ICA)

Sarah Helen Needleman¹, Mina Kim¹, Jamie R. McClelland¹, and Geoff J. M. Parker^1,2

¹Centre for Medical Image Computing (CMIC), Department of Medical Physics & Biomedical Engineering, University College London, London, United Kingdom, ²Bioxydyn Limited, Manchester, United Kingdom

Synopsis

Keywords: Oxygenation, Lung

Analysis of dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of substantial artefacts and poor SNR. Understanding and minimising sources of error is critical for reliable use of the method. We have created a pipeline using independent component analysis (ICA) for the automatic extraction of functional lung information from dynamic lung OE-MRI, for which confounding factors are reduced. The pipeline demonstrated good repeatability when utilised for the analysis of a scan-rescan dynamic lung OE-MRI study at 3.0 T; the algorithmic uncertainty of ICA on the analysis pipeline was found to be minimal.

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