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Abstract #2494

A generalized method for automated quality assessment in brain MRI

Bndicte Marchal 1,2 , Stephan Kannengiesser 3 , Kaely Thostenson 4 , Peter Kollasch 5 , Pavel Falkovskyi 1,2 , Jean-Philippe Thiran 2 , Reto Meuli 6 , Matt A. Bernstein 4 , and Gunnar Krueger 1,2

1 Siemens ACIT CHUV Radiology, Siemens Healthcare IM BM PI & Department of Radiology CHUV, Lausanne, Switzerland, 2 LTS5, cole Polytechnique Fdrale de Lausanne, Lausanne, Switzerland, 3 Siemens Healthcare, Erlangen, Germany, 4 Department of Radiology, Mayo Clinic, Rochester, MN, United States, 5 Siemens Healthcare, MN, United States, 6 CHUV Radiology, Lausanne, Switzerland

Automated quality assessment of MRI is of great importance to derive reliable diagnostic information. In this work, a synthetic noise-based method is proposed which allows automated data quality classification. Only a prescan measurement of noise and a single image acquisition are required. The validation based on 764 head scans confirms the robustness and reliability of the method. As integrated as a prototype in online image reconstruction, it can greatly improve clinical workflow as MR technologist is provided with immediate feedback and can potentially repeat low-quality scans within the same session.

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