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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