A Kalman based approach with EM optimization for respiratory motion modelling in medical imaging
2018
Respiratory motion degrades quantitative and qualitative
analysis of medical images. Estimation and hence correction
of motion commonly uses static correspondence models
between an external surrogate signal and internal motion. This work presents a patient specific respiratory motion model with
the ability to adapt in the presence of irregular motion via
a Kalman filter with Expectation Maximisation for parameter
estimation. The adaptive approach introduces generalizability
allowing the model to account for a broader variety of motion.
This may be required in the presence of irregular breathing and
with different sensors monitoring the external surrogate signal.
The motion model framework utilizing an adaptive Kalman filter
approach is tested on dynamic MRI data of nine volunteers and
compared to a state-of-the-art static total least squares approach.
Results demonstrate the framework is capable of reducing motion
to the order of < 3mm and is significantly (p < 0:001) more
effective in the presence of irregular motion, assessed using the
F test for model comparison. Utilizing the total sum of squares
of estimated vector field error from the calculated ground truth,
we observe approximately a fifty percent reduction in root mean
square error and thirty percent reduction in standard deviation
utilizing the Kalman model (EKF) in comparison to a static
counterpart.
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