Splitting Frames Based on Hypothesis Testing for Patient Motion Compensation in SPECT

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Splitting Frames Based on Hypothesis Testing for Patient Motion Compensation in SPECT

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Patient motion is a significant cause of artifacts in SPECT imaging. It is important to be able to detect when a patient undergoing SPECT imaging is stationary, and when significant motion has occurred, in order to selectively apply motion compensation. In our system, optical cameras observe reflective markers on the patient. Subsequent image processing determines the marker positions relative to the SPECT system, calculating patient motion. We use this information to decide how to aggregate detected gamma rays (events) into projection images (frames) for tomographic reconstruction. For the most part, patients are stationary, and all events acquired at a single detector angle are treated as a single frame. When a patient moves, it becomes necessary to split a frame into subframes during each of which the patient is stationary. This thesis presents a method for splitting frames based on hypothesis testing. Two competing hypotheses and probability model are designed. Whether to split frames is based on a Bayesian recursive estimation of the likelihood function. The estimation procedure lends itself to an efficient iterative implementation. We show that the frame splitting algorithm performance is good for a sample SNR. Different motion simulation cases are presented to verify the algorithm performance. This work is expected to improve the accuracy of motion compensation in clinical diagnoses.

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