R.D. Beach

Feasibility of stereo-infrared tracking to monitor patient motion during cardiac SPECT imaging

Patient motion during cardiac SPECT imaging can cause diagnostic imaging artifacts. We investigated the feasibility of monitoring patient motion using the Polaris motion-tracking system. This system uses passive infrared reflection from small spheres to provide real-time position data with vendor stated 0.35 mm accuracy and 0.2 mm repeatability. In our configuration, the Polaris system views through the SPECT gantry toward the patients head. List-mode event data were temporally synchronized with motion-tracking data utilizing a modified LabVIEW virtual instrument that we have employed in previous optical motion-tracking investigations. Calibration of SPECT to Polaris coordinates was achieved by determining the transformation matrix necessary to align the position of four reflecting spheres as seen by Polaris, with the location of Tc-99m activity placed inside the sphere mounts as determined in SPECT reconstructions. We have successfully tracked targets placed on volunteers in simulated imaging positions on the table of our SPECT system. We obtained excellent correlation (R/sup 2/>0.998) between the change in location of the targets as measured by our SPECT system and the Polaris. We have also obtained excellent agreement between the recordings of the respiratory motion of four targets attached to an elastic band wrapped around the abdomen of volunteers and from a pneumatic bellows. We used the axial motion of point sources as determined by the Polaris to correct the motion in SPECT image acquisitions yielding virtually identical point source full-width at half-maximum and full-width at tenth-maximum values, and profiled maximum heart wall counts of cardiac phantom images, compared to the reconstructions with no motion.

A robust visual tracking system for patient motion detection in SPECT: hardware solutions

Our overall research goal is to devise a robust method of tracking and compensating patient motion by combining an emission data based approach with a visual tracking system (VTS) that provides an independent estimate of motion. Herein, we present the latest hardware configuration of the VTS, a test of the accuracy of motion tracking by it, and our solution for synchronization between the SPECT and the optical acquisitions. The current version of the VTS includes stereo imaging with sets of optical network cameras with attached light sources, a SPECT/VTS calibration phantom, a black stretchable garment with reflective spheres to track chest motion, and a computer to control the cameras. The computer also stores the JPEG files generated by the optical cameras with synchronization to the list-mode acquisition of events on our SPECT system. Five Axis PTZ 2130 network cameras (Axis Communications AB, Lund, Sweden) were used to track motion of spheres with a highly retroreflective coating using stereo methods. The calibration phantom is comprised of seven reflective spheres designed such that radioactivity can be added to the tip of the mounts holding the spheres. This phantom is used to determine the transformation to be applied to convert the motion detected by the VTS into the SPECT coordinates system. The ability of the VTS to track motion was assessed by comparing its results to those of the Polaris infrared tracking system (Northern Digital Inc., Waterloo, ON, Canada). The difference in the motions assessed by the two systems was generally less than 1 mm. Synchronization was assessed in two ways. First, optical cameras were aimed at a digital clock and the elapsed time estimated by the cameras was compared to the actual time shown by the clock in the images. Second, synchronization was also assessed by moving a radioactive and reflective sphere three times during concurrent VTS and SPECT acquisitions and comparing the time at which motion occurred in the optical and SPECT images. The results show that optical and SPECT images stay synchronized within a 150-ms range. The 100-Mbit network load is less than 10%, and the computers CPU load is between 15% and 25%; thus, the VTS can be improved by adding more cameras or by increasing the image size and/or resolution while keeping an acquisition rate of 30 images per second per camera.

Use of three-dimensional Gaussian interpolation in the projector/backprojector pair of iterative reconstruction for compensation of known rigid-body motion in SPECT

Due to the extended imaging times employed in single photon emission computed tomography (SPECT) and positron emission tomography (PET), patient motion during imaging is a common clinical occurrence. The fast and accurate correction of the three-dimensional (3-D) translational and rotational patient motion in iterative reconstruction is thus necessary to address this important cause of artifacts. We propose a method of incorporating 3-D Gaussian interpolation in the projector/backprojector pair to facilitate compensation for rigid-body motion in addition to attenuation and distance-dependent blurring. The method works as the interpolation step for moving the current emission voxel estimates and attenuation maps in the global coordinate system to the new patient location in the rotating coordinate system when calculating the expected projection. It also is employed for moving back the backprojection of the ratio of the measured projection to the expected projection and backprojection of the unit value (sensitivity factor) to the original location. MCAT simulations with known six-degree-of-freedom (6DOF) motion were employed to evaluate the accuracy of our method of motion compensation. We also tested the method with acquisitions of the data spectrum anthropomorphic phantom where motion during SPECT acquisition was measured using the Polaris IR motion tracking system. No motion artifacts were seen on the reconstructions with the motion compensation

Estimation of the Rigid-Body Motion From Three-Dimensional Images Using a Generalized Center-of-Mass Points Approach

We present an analytical method for the estimation of rigid-body motion in sets of three-dimensional (3-D) SPECT and PET slices. This method utilizes mathematically defined generalized center-of-mass points in images, requiring no segmentation. It can be applied to compensation of the rigid-body motion in both SPECT and PET, once a series of 3-D tomographic images are available. We generalized the formula for the center-of-mass to obtain a family of points comoving with the objects rigid-body motion. From the family of possible points we chose the best three points which resulted in the minimum root-mean-square difference between images as the generalized center-of-mass points for use in estimating motion. The estimated motion was used to sum the sets of tomographic images, or incorporated in the iterative reconstruction to correct for motion during reconstruction of the combined projection data. For comparison, the principle-axes method was also applied to estimate the rigid-body motion from the same tomographic images. To evaluate our method for different noise levels, we performed simulations with the MCAT phantom. We observed that though noise degraded the motion-detection accuracy, our method helped in reducing the motion artifact both visually and quantitatively. We also acquired four sets of the emission and transmission data of the Data Spectrum Anthropomorphic Phantom positioned at four different locations and/or orientations. From these we generated a composite acquisition simulating periodic phantom movements during acquisition. The simulated motion was calculated from the generalized center-of-mass points calculated from the tomographic images reconstructed from individual acquisitions. We determined that motion-compensation greatly reduced the motion artifact. Finally, in a simulation with the gated MCAT phantom, an exaggerated rigid-body motion was applied to the end-systolic frame. The motion was estimated from the end-diastolic and end-systolic images, and used to sum them into a summed image without obvious artifact. Compared to the principle-axes method, in two of the three comparisons with anthropomorphic phantom data our method estimated the motion in closer agreement to the Polaris system than the principal-axes method, while the principle-axes method gave a more accurate estimation of motion in most cases for the MCAT simulations. As an image-driven approach, our method assumes angularly complete data sets for each state of motion. We expect this method to be applied in correction of respiratory motion in respiratory gated SPECT, and respiratory or other rigid-body motion in PET

Motion correction for cardiac SPECT using a RBI-ML partial-reconstruction approach

In single photon computed tomography, patient motion can significantly affect image quality. Methods to correct for patient motion rely on available information about the motion or stable algorithms have to be developed to detect and describe object motion. In this work we introduce a reconstruction method that corrects for rigid body motion and we investigate a method to exploit the motion information that might be hidden in the projection data and possibly modify motion descriptions that were retrieved by external motion tracking devices. The search method was based on reconstructing only parts of the projection angles using an RBI-ML partial-reconstruction approach (PRA). It tests a motion description with only one forward projection per detector head instead of a full reconstruction using all projections. A figure of merit based on the per angle likelihood function in projection space was introduced. With simulated MCAT data we could show that the PRA was able to identify the optimum 3D rigid body motion correction with an error of 1 pixel in each cartesian direction. Future work will address 3D translations, multiple motions, detector resolution compensation and will also concentrate on the improvement of the figure of merit.

Estimation of the rigid-body motion from images using a generalized center-of-mass points approach

We present an analytical method for the estimation of rigid-body motion in three-dimensional SPECT and PET slices. This method utilizes mathematically defined generalized center-of-mass points in images, requiring neither segmentation nor an iterative process. It can be applied to compensation of the rigid-body motion in both SPECT and PET. We generalized the formula for the center-of-mass and obtained a family of points co-moving with the objects rigid-body motion. In calculation of the generalized center-of-mass points and estimation of the rigid-body motion, we optimized a Gaussian smoothing function and chose the best three points, which resulted in the minimum root-mean-square difference between images. The estimated motion was used to generate a summed image, or incorporated in the iterative reconstruction of the motion-present data. To evaluate this method for different noise levels, we performed simulations with the MCAT phantom. We observed that though noise degraded the motion-detection accuracy, this method helped in reducing the motion artifact both visually and quantitatively. We also acquired four sets of the emission and transmission data of the Data Spectrum Anthropomorphic Phantom positioned at four different locations and/or orientations. From these we generated a composite acquisition simulating phantom movements during acquisition. The simulated motion was calculated from the generalized center-of-mass points on the images reconstructed from individual acquisitions. We determined that motion-compensation greatly reduced the motion artifact. Finally, in a simulation with the gated MCAT phantom, an exaggerated rigid-body motion was applied to the end-systolic frame. The motion was estimated from the end-diastolic and end-systolic images, and used to sum them into a summed image without obvious artifact. As an image-driven approach this method assumes angularly complete data sets for each state of motion. We expect this method to be applied in correction of the respiratory motion in respiratory gated SPECT and respiratory or other rigid-body motion in PET.