Reducing Artifacts in Pelvic Bone SPECT: An Assessment of Lesion Detectability Using Numerical and Human Observers

In pelvic bone SPECT using Tc-99m labeled compounds, physical effects such as nonhomogeneous attenuation and the accumulation of activity into the bladder during the data acquisition process can often result in data inconsistencies. With filtered backprojection (FBP) reconstruction, this may result in streak artifacts. Various methods for attenuation compensation can be employed to account for some of these streaks, but if the rate of uptake of tracer in to the bladder is sufficient, the resultant streaks may be significant enough to impair lesion detection. We have investigated various reconstruction methods in an effort to reduce these artifacts. In order to assess the impact of inconsistent bladder activities on the detection of pelvic lesions, SPECT imaging was simulated using the Zubal voxelized phantom, for cases of both a static bladder activity and a changing activity distribution. Reconstructions were performed using FBP, ordered subset-expectation maximization (OSEM) and dynamic expectation maximization (dEM) and was assessed for lesion detectability using a channelized, nonprewhitening (CNPW) numerical observer model. This observer model was used to optimize reconstruction strategies for a human LROC observer study. The human LROC observer study was performed in order to assess the various reconstruction methods in terms of lesion detectability. Three human observers were used in this test. The results of this test indicate that FBP performs significantly worse than static OSEM iterative reconstruction with attenuation correction when assessed using the area under the LROC curve (maximal $\pmb{A}_{\mathbf{LROC}}=\mathbf{0.47}$ for FBP versus 0.71 for OSEM). Visually, the dEM algorithm produces images with slightly reduced streak artifacts compared to OSEM, but this improvement was not reflected in significantly improved $\pmb{A}_{\mathbf{LROC}}$ values. In fact, when assessed using human LROC methodology, detectability actually decreased slightly when using dEM (maximal $\pmb{A}_{\mathbf{LROC}}=\mathbf{0.71}$ for OSEM versus 0.66 for dEM), although this reduction was not determined to be statistically significant. It is possible that the slightly reduced performance of the dEM algorithm may be due, in part, to not performing an optimization in the number of reconstruction iterations as was performed for the OSEM method.