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Abstract #3410

Assessing the Relation between Image Quality Metrics and Brain Volume in a Scan-Rescan Dataset

Ricardo A. Corredor-Jerez1,2,3, Jonas Richiardi1,2,3, Mário João Fartaria1,2,3, Bénédicte Maréchal1,2,3, Adrian Tsang4, Robert Bermel5, Stephen E. Jones5, Izlem Izbudak6, Ellen M Mowry6, Yvonne W. Lui7, Lauren Krupp7, Elizabeth Fisher4, and Tobias Kober1,2,3

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Biogen, Cambridge, MA, United States, 5Cleveland Clinic, Cleveland, OH, United States, 6Johns Hopkins University, Baltimore, MD, United States, 7New York University, New York, NY, United States

Satisfactory image quality is essential to accurately assess brain volume using automated methods for evaluating neurodegenerative diseases. Variations in image quality may cause volume estimation errors hard to distinguish from disease-induced changes. We studied the relationship between brain volume estimations and image quality metrics in a scan-rescan study. Two segmentation methods were used to quantify brain volume in FLAIR and MPRAGE images. Volume estimations on MPRAGE varied less with hardware, compared to the estimations on FLAIR. We found a significant correlation between hardware and several image quality metrics, suggesting that these can be used to render volume estimations more hardware-independent.

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