Paper
22 March 2007 Automated planning of MRI scans of knee joints
Daniel Bystrov, Vladimir Pekar, Stewart Young, Sebastian P. M. Dries, Harald S. Heese, Arianne M. van Muiswinkel
Author Affiliations +
Abstract
A novel and robust method for automatic scan planning of MRI examinations of knee joints is presented. Clinical knee examinations require acquisition of a 'scout' image, in which the operator manually specifies the scan volume orientations (off-centres, angulations, field-of-view) for the subsequent diagnostic scans. This planning task is time-consuming and requires skilled operators. The proposed automated planning system determines orientations for the diagnostic scan by using a set of anatomical landmarks derived by adapting active shape models of the femur, patella and tibia to the acquired scout images. The expert knowledge required to position scan geometries is learned from previous manually planned scans, allowing individual preferences to be taken into account. The system is able to automatically discriminate between left and right knees. This allows to use and merge training data from both left and right knees, and to automatically transform all learned scan geometries to the side for which a plan is required, providing a convenient integration of the automated scan planning system in the clinical routine. Assessment of the method on the basis of 88 images from 31 different individuals, exhibiting strong anatomical and positional variability demonstrates success, robustness and efficiency of all parts of the proposed approach, which thus has the potential to significantly improve the clinical workflow.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Bystrov, Vladimir Pekar, Stewart Young, Sebastian P. M. Dries, Harald S. Heese, and Arianne M. van Muiswinkel "Automated planning of MRI scans of knee joints", Proc. SPIE 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, 65092Z (22 March 2007); https://doi.org/10.1117/12.709255
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CITATIONS
Cited by 13 scholarly publications.
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KEYWORDS
3D modeling

Magnetic resonance imaging

Image segmentation

Diagnostics

Bone

Image quality

Statistical modeling

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