Abstract
MRI imaging with long acquisition times is prone to motion artifacts that can compromise image quality and lead to misinterpretation.
Aiming to address this challenge at the sub-millimeter level, we developed and evaluated a maxilla immobilization approach, which is known to have better performance than other non-invasive techniques, using a personalized mouthpiece connected to an external MRI-compatible frame.
The effectiveness of the device was evaluated by analyzing MRI imagery obtained in different immobilization conditions on a human volunteer. The SURF and Block Matching algorithms were assessed, supplemented by custom software.
Compared with simple cushioning, the immobilizer reduced the amplitudes of involuntary slow-drift movements of the head by more than a factor two in the axial plane, with final values of 0.25 mm and 0.060°. Faster involuntary motions, including those caused by breathing (which were identifiable), were also suppressed, with final standard deviation values below 0.045 mm and 0.025°.
It was also observed a strong restriction of intentional movements, translationally and angularly, by factors from 7.8 to 4.6, with final values of 0.5 mm and 0.2° for moderate forcing.
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Zagalo, F. et al. (2024). Development and Testing of an MRI-Compatible Immobilization Device for Head and Neck Imaging. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_44
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DOI: https://doi.org/10.1007/978-3-031-49018-7_44
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