Automated quality control of emission-transmission misalignment for attenuation correction in myocardial perfusion imaging with SPECT-CT systems

  • Chen J
  • Caputlu-Wilson S
  • Shi H
 et al. 
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Background. Emission-transmission misalignment with single-photon emission computed tomography (SPECT)-computed tomography (CT) systems can impair attenuation correction (AC) in myocardial perfusion imaging. This study was performed to develop automated quality control (Auto-QC) to detect critical misalignment that can significantly impact AC. Methods and Results. Auto-QC was developed to segment myocardium and mediastinum from emission and transmission reconstructions, respectively. Myocardium-mediastinum mismatch was used as the quality-control index (QCI). The QCI threshold for acceptable AC was determined with NCAT (NURBS [nonuniform rational B-spline]-based cardiac torso phantom) simulation and verified with 2 patients with minimal misalignment. Compromised data sets, generated by shifting the attenuation maps by 0.5, 1.0, 1.5, and 2.0 pixels along left-right, up-down, and head-foot directions, respectively, were qualitatively and quantitatively compared with the unshifted data sets. Auto-QC was tested with the 2 verification patients and 41 additional patients. Shifts by more than 1 pixel along any direction compromised AC. Auto-QC with the QCI threshold (3%) had highly concordant results with manual quality control in the detection of critical misalignment (sensitivity of 88% and 90% and specificity of 93% and 95% for the tests by use of the 2 verification patients and 41 additional patients, respectively). Conclusion. QCI quantitatively represented the severity of misalignment. Auto-QC can help clinicians be aware of critical misalignment and can assist in realignment of SPECT and CT images. Copyright © 2006 by the American Society of Nuclear Cardiology.

Author-supplied keywords

  • Attenuation correction
  • Automated quality control
  • Emission-transmission misalignment
  • Myocardial perfusion imaging

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  • Ji Chen

  • Serpil F. Caputlu-Wilson

  • Hongcheng Shi

  • James R. Galt

  • Tracy L. Faber

  • Ernest V. Garcia

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