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An Adaptive Approach to Decomposing Patient-Motion Tracking Data Acquired During Cardiac SPECT Imaging.

Richard D Beach1, Hans Depold, Guido Boening

  • 1R.D. Beach, H. Depold (consultant), G. Boening, P.P. Bruyant, B. Feng, H.C. Gifford and, M.A. King are from the University of Massachusetts Medical School, Division of Nuclear Medicine, Worcester, MA 01655 USAM.A. Gennert and S. Nadella are from the Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.

IEEE Transactions on Nuclear Science
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

A novel Neural Network (NN) approach accurately separates patient motion into rigid body motion (RBM) and respiratory motion (RM) during cardiac SPECT imaging. This decomposition enables effective artifact correction, improving diagnostic image quality.

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Patient motion during cardiac SPECT imaging introduces artifacts, compromising diagnostic accuracy.
  • Distinguishing between rigid body motion (RBM) and respiratory motion (RM) is crucial as they require different correction strategies.

Purpose of the Study:

  • To implement and validate a Neural Network (NN) for decomposing patient motion data into RBM and RM during cardiac SPECT imaging.
  • To enable motion artifact correction in SPECT imaging by accurately separating RBM and RM.

Main Methods:

  • A Neural Network (NN) was developed to decompose Polaris stereo-IR motion-tracking data into RBM and RM.
  • A two-step median filter approach was employed, with an initial fixed width and a subsequent adaptive width based on FFT analysis of RM.
  • The NN was implemented using Interactive Data Language (IDL) in a UNIX environment.

Main Results:

  • Simulated data showed high accuracy, with average errors <0.11 mm for RBM and RMSE of 0.3 mm for combined motion.
  • Volunteer data demonstrated successful decomposition, with RBM steps differing by only 0.8 mm from SPECT gantry readings.
  • NN-derived RM traces closely matched synchronized pneumatic bellows data.
  • Motion-corrected SPECT images from an anthropomorphic phantom were visually identical to non-motion images.

Conclusions:

  • The NN effectively decomposes patient motion into RBM and RM during cardiac SPECT imaging.
  • This method provides accurate motion data for correcting artifacts in SPECT images.
  • The NN approach holds significant potential for enhancing the diagnostic quality of cardiac SPECT imaging.