Bayesian SPECT lung imaging for visualization and quantification of pulmonary perfusion

In this paper, we quantitatively and qualitatively examine the use of a Gib bs prior in maximum a posteriori (MAP) reconstruction of SPECT images of pu lmonary perfusion using the expectation-maximization (EM) algorithm. This B ayesian approach is applied to SPECT projection data acquired from a realis tic torso phantom with spherical defects in the lungs simulating perfusion deficits. Both the scatter subtraction constant (k) and the smoothing param eter beta (beta) characterizing the prior are varied to study their effect on image quality and quantification. Region of interest (ROI) analysis is u sed to compare MAP-EM radionuclide concentration estimates with those deriv ed from a "clinical" implementation of filtered backprojection (CFBP), and a quantitative implementation of FBP (QFBP) utilizing nonuniform attenuatio n and scatter compensation. Qualitatively, the MAP-EM images contain reduce d artifacts near the lung boundaries relative to the FBP implementations. G enerally, the MAP-FM image's visual quality and the ability to discern the areas of reduced radionuclide concentration in the lungs depend on the valu e of beta and the total number of iterations. For certain choices of beta a nd total iterations, MAP-EM lung images are visually comparable to FBP. Bas ed on profile and ROI analysis, SPECT QFBP and MAP-EM images have the poten tial to provide quantitatively accurate reconstructions when compared to CF BP. The computational burden, however, is greater for the MAP-EM approach. To demonstrate the clinical efficacy of the methods, we present pulmonary i mages of a patient with lung cancer.