Document Type dissertation Author Name Gu, Songxiang URN etd-070909-124855 Title Body Deformation Correction for SPECT Imaging Degree PhD Department Computer Science Advisors Michael A. Gennert, Advisor Michael A. King, Co-Advisor Matthew O. Ward, Committee Member Emmanuel Agu, Committee Member Stephen C. Moore, Committee Member Keywords SPECT Motion Correction Deformation Twist Bend Date of Presentation/Defense 2009-06-26 Availability unrestricted
Single Photon Emission Computed Tomography (SPECT) is a medical imaging modality that allows us to visualize functional information about a patient's specific organ or body systems. During 20 minute scan, patients may move. Such motion will cause misalignment in the reconstruction, degrade the quality of 3D images and potentially lead to errors in diagnosis. Body bend and twist are types of patient motion that may occur during SPECT imaging and which has been generally ignored in SPECT motion correction strategies. To correct for these types of motion we propose a deformation model and its inclusion within an iterative reconstruction algorithm. One simulation and three experiments were conducted to investigate the applicability of our model. The simulation employed simulated projections of the MCAT phantom formed using an analytical projector which includes attenuation and distance-dependent resolution to investigate applications of our model in reconstruction. We demonstrate in the simulation studies that twist and bend can significantly degrade SPECT image quality visually. Our correction strategy is shown to be able to greatly diminish the degradation seen in the slices, provided the parameters are estimated accurately. To verify the correctness of our deformation model, we design the first experiment. In this experiment, the return of the post-motion-compensation locations of markers on the body-surface of a volunteer to approximate their original coordinates is used to examine our method of estimating the parameters of our model and the parameters' use in undoing deformation. Then, we design an MRI based experiment to validate our deformation model without any reconstruction. We use the surface marker motion to alter an MRI body volume to compensate the deformation the volunteer undergoes during data acquisition, and compare the motion-compensated volume with the motionless volume. Finally, an experiment with SPECT acquisitions and modified MLEM algorithm is designed to show the contribution of our deformation correction for clinical SPECT imaging. We view this work as a first step towards being able to estimate and correct patient deformation based on information obtained from marker tracking data.
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