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Abstract #2603

Deep Learning Reconstructed T2-weighted Dixon Imaging of the Spine: Impact on Acquisition Time and Diagnostic Performance

Thierno D. Diallo1, Zeynep Berkarda1, Simon Wiedemann1, Caroline Wilpert1, Ralph Strecker2, Gregor Koerzdorfer3, Dominik Nickel3, Fabian Bamberg1, Matthias Benndorf1, and Jakob Weiß1
1Radiology, University Medical Center Freiburg, Freiburg, Germany, 2EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany, 3MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

Synopsis

Keywords: MSK, Machine Learning/Artificial IntelligenceMagnetic resonance imaging of the spine is considered one of the most commonly performed examinations in clinical routine. The raising demand for high quality imaging of the spine creates the need for tailored examination protocols, especially with regard to increasingly limited scanner capacities. Deep Learning based imaging reconstruction has emerged as promising novel technique to accelerate MR imaging while maintaining image quality. This study analyzed a novel deep learning accelerated T2-weighted Dixon sequence of the spine in terms of diagnostic performance. The results suggest that the here presented sequence is feasible with a diagnostic performance comparable to standard imaging.

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