RESUMO
Patient motion during cardiac SPECT imaging can cause diagnostic imaging artifacts. We have implemented a Neural Network (NN) approach to decompose monitored patient motion data, gathered during cardiac SPECT imaging, using the Polaris stereo-IR real-time motion-tracking system. Herein, we show the successful decomposition of Polaris motion data into rigid body motion (RBM) and respiratory motion (RM). The motivation for separating RM from RBM is that each is corrected using different methods. The NN requires the input of a RBM threshold sensitivity limit, as well as the median filter window width. A two step approach can be used in setting the median filter width. In the 1(st) NN run the median filter window width is initially set to a "fixed" width typical of the respiration period. This 1(st) NN run does an initial decomposition of the data into RM and RBM. The RM is then fed into an FFT algorithm to produce a respiratory period output file for use during a 2(nd) NN run, where the median filter width can "adapt" to the patient respiratory rate at each time point. Implementation of the NN was in the UNIX environment with Interactive Data Language (IDL). Decomposition of simulated "signals known exactly" RBM and RM resulted in average value errors less than 0.11 mm for RBM steps, and an overall root mean square error of only 0.3 mm for RM or RBM. Volunteer RBM and RM Polaris data were successfully decomposed by the NN with RBM steps resolved with an average difference of only 0.8 mm as compared to values displayed on the SPECT gantry console which are only to the nearest mm. A plot of the NN RM trace and the synchronized trace from a pneumatic bellows shows virtually identical characteristics. Anthropomorphic phantom RBM and RM were decomposed and used to correct motion in SPECT images during reconstruction. The motion corrected slices looked visually identical to slices acquired without motion, and comparison of slice count profiles further confirmed the correction.
RESUMO
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 retro-reflective 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 infra-red tracking system (Northern Digital Inc. Waterloo, ON, Canada). The difference in the motions assessed by the two systems was generally less than 1mm. 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 100Mbit network load is less than 10%, and the computer's 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.
RESUMO
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 patient's head. List-mode event data was 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(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 FWHM and FWTM values, and profiled maximum heart wall counts of cardiac phantom images, compared to the reconstructions with no motion.
