Bndicte Mortamet1, Matt A. Bernstein2,
Clifford R. Jack2, Jeffrey L. Gunter2, Maria Shiung2,
Reto Meuli3, Jean-Philippe Thiran4, Gunnar Krueger1
1Advanced Clinical Imaging Technology,
Siemens Healthcare Sector IM&WS S - CIBM, Lausanne, Switzerland; 2Mayo
Clinic, Rochester, MI, United States; 3CHUV, Radiology, Lausanne,
Switzerland; 4Signal Processing Laboratory (LTS5) Ecole
Polytechnique Fdrale de Lausanne
The
FLAIR contrast is increasingly used as part of routine protocol for brain
MRI. It provides high sensitivity to a wide range of disease but is
susceptible to patient motion. Resulting artifacts may obscure the pathology
or mislead automated image analysis algorithms. We propose a method that
automates quality classification of T2w 2D-FLAIR data. The validation based on
99 head scans confirms the robustness and reliability of the method. It could
greatly improve clinical workflow as, in particular if integrated in online
image reconstruction, it could provide immediate feedback to the MR
technologist to repeat low-quality scans within the same session.
Keywords