Stereo Algorithm Grimson’s From Images to Surfaces stereo algorithm Multi-resolution Proceed from coarse to fine level Assume 0 initial disparity — depth-dependent displacement of image features Detect edges Marr-Hildreth edge detector size  Zero-crossings of Laplacian of Gaussian  2 G  Match edges along epipolar lines Matches most likely within 1  Edge matching yields sparse disparity map Interpolation yields dense disparity map Surface height computation from geometry 2. Procedure Web cameras were mounted outside a 3-headed IRIX SPECT system to acquire optical images simultaneously with the gamma emission images (Figure 1). Before patient data can be acquired, the optical cameras must be calibrated to the gamma camera. The calibration and operation phases are sketched in Figure 2. Figures 3–5 show the processing steps during the operation phase. Abstract Objectives: Patient motion, which causes artifacts in reconstructed images, can be a serious problem in SPECT imaging. If patient motion can be detected and quantified, the reconstruction algorithm can compensate for the motion. The goal of this work is to design a system architecture for detecting, modeling, and correcting patient motion in SPECT imaging using information in addition to the emission counts themselves. Methods: Web cameras were mounted outside a SPECT system to acquire optical images simultaneously with the emission images. The web cameras view the patient surface, from which a surface map may be computed using stereo techniques. When the patient moves, the surface map is recomputed. Changes in the surface map over time enable tracking of the patient surface. Motion within the patient body is computed by interpolating from the surface, taking into account a model of the patient body. When patient motion is detected, the estimated slices are reoriented during iterative reconstruction and notification is made that compensation has been performed. Results: Implementation of the architecture is currently underway. Conclusions: Further work remains, especially in the area of using non-isotropic body models for interpolation. Detecting Patient Motion in SPECT Imaging Using Stereo Optical Cameras Michael A. Gennert 1,2, Philippe P. Bruyant 1, Manoj V. Narayanan 1, Michael A. King 1 1 University of Massachusetts Medical School, Worcester, MA 2 Worcester Polytechnic Institute, Worcester, MA Figure 1. System view. Head 3 Head 2 Head 1 Patient Cam To PC Figure 4. Low cost Logitech web cameras are used to acquire stereo image pairs during gamma camera data acquisition. Patient Frame 1 Frame 2 Figure 5. Stereo and Motion Geometry. Edges are matched in left and right images. Changes in match positions over time indicate patient motion. Interpolation Need motion throughout body Interpolate from surface motion Possible models Rigid motion Elastically deformable Compressible Use tissue elasticity from MCAT phantom Bone: rigid Tissue: elastically deformable Lung: compressible surface map body motion surface motion Motion Alert Detect Motion image pair Optical Cameras Camera Parameters Compute Stereo Interpolate Body Tomographic Reconstruction Body Model Figure 3. Operation phase. 1. Introduction Patient motion causes many problems in SPECT imaging, such as blur and other motion artifacts. If patient motion is excessive, it may be necessary to repeat an acquisition, with consequent cost and inconvenience. Previous approaches to motion detection relied on inconsistency checks or motion tracking, or were limited to rigid body parts, such as the head. The goal of this work is to design a more general system architecture for detecting, modeling, and correcting patient motion in SPECT imaging using information in addition to the emission counts themselves. Figure 2. System phases. Calibration phase is performed as needed. Operation phase is performed for each patient acquisition. Setup Compute Parameters Acquire Images Reconstruct Images Compute Motion Acquire Images Operation phase Calibration phase PhantomImages Body Motion Camera Parameters 3. Project Status Calibration and Image Acquisition modules are complete. The Stereo Computation module is currently under development. Motion Detection, Interpolation, and Tomographic Reconstruction modules have not yet been implemented. Reconstruction Acquire projections in list mode Compensate for body motion by: Rebin patient data to reconstruct motion-free ideal patient, or Recompute System Matrix whenever motion detected Left ImageRight Image