Bayesian SPECT lung imaging for visualization and quantification of pulmonary perfusion
- Duke University Medical Center, Durham, NC (United States)
In this paper, the authors quantitatively and qualitatively examine the use of a Gibbs prior in maximum a posteriori (MAP) reconstruction of SPECT images of pulmonary perfusion using the expectation-maximization (EM) algorithm. This Bayesian approach is applied to SPECT projection data acquired from a realistic torso phantom with spherical defects in the lungs simulating perfusion deficits. Both the scatter subtraction constant (k) and the smoothing parameter beta ({beta}) characterizing the prior are varied to study their effect on image quality and quantification. Region of interest (ROI) analysis is used to compare MAP-EM radionuclide concentration estimates with those derived from a ``clinical`` implementation of filtered backprojection (CFBP), and a quantitative implementation of FBP (QFBP) utilizing nonuniform attenuation and scatter compensation. Qualitatively, the MAP-EM images contain reduced artifacts near the lung boundaries relative to the FBP implementations. Generally, the MAP-EM image`s visual quality and the ability to discern the areas of reduced radionuclide concentration in the lungs depend on the value of {beta} and the total number of iterations. For certain choices of {beta} and total iterations, MAP-EM lung images are visually comparable to FBP. Based on profile and ROI analysis, SPECT QFBP and MAP-EM images have the potential to provide quantitatively accurate reconstructions when compared to CFBP. The computational burden, however, is greater for the MAP-EM approach. To demonstrate the clinical efficacy of the methods, the authors present pulmonary images of a patient with lung cancer.
- Sponsoring Organization:
- National Cancer Inst., Bethesda, MD (United States); USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG02-96ER62150
- OSTI ID:
- 323711
- Report Number(s):
- CONF-971147--
- Journal Information:
- IEEE Transactions on Nuclear Science, Journal Name: IEEE Transactions on Nuclear Science Journal Issue: 6Pt2 Vol. 45; ISSN 0018-9499; ISSN IETNAE
- Country of Publication:
- United States
- Language:
- English
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