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21 March 2014 Motion estimation for nuclear medicine: a probabilistic approach
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Abstract
Accurate, Respiratory Motion Modelling of the abdominal-thoracic organs serves as a pre-requisite for motion correction of Nuclear Medicine (NM) Images. Many respiratory motion models to date build a static correspondence between a parametrized external surrogate signal and internal motion. Mean drifts in respiratory motion, changes in respiratory style and noise conditions of the external surrogate signal motivates a more adaptive approach to capture non-stationary behavior. To this effect we utilize the application of our novel Kalman model with an incorporated expectation maximization step to allow adaptive learning of model parameters with changing respiratory observations. A comparison is made with a popular total least squares (PCA) based approach. It is demonstrated that in the presence of noisy observations the Kalman framework outperforms the static PCA model, however, both methods correct for respiratory motion in the computational anthropomorphic phantom to < 2mm. Motion correction performed on 3 dynamic MRI patient datasets using the Kalman model results in correction of respiratory motion to ≈ 3mm.
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Rhodri Smith, Ashrani Aizzuddin Abd. Rahni, John Jones, Fatemeh Tahavori, and Kevin Wells "Motion estimation for nuclear medicine: a probabilistic approach", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342Z (21 March 2014); https://doi.org/10.1117/12.2044141
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