Bayesian SPECT lung imaging for visualization and quantification of pulmonary perfusion

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 (/spl 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 /spl beta/ and the total number of iterations. For certain choices of /spl 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.