Abstract #3410

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

Ricardo A. Corredor-Jerez^1,2,3, Jonas Richiardi^1,2,3, Mário João Fartaria^1,2,3, Bénédicte Maréchal^1,2,3, Adrian Tsang⁴, Robert Bermel⁵, Stephen E. Jones⁵, Izlem Izbudak⁶, Ellen M Mowry⁶, Yvonne W. Lui⁷, Lauren Krupp⁷, Elizabeth Fisher⁴, and Tobias Kober^1,2,3

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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