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.