Methodology for PET system modeling using imagespace techniques in the expectation maximization (EM) algorithm is presented. The approach, applicable to both list-mode data and projection data, is of particular significance to EM algorithm implementations which otherwise only use basic system models (such as those which calculate the system matrix elements on the fly). A basic version of the proposed technique can be implemented using image-space convolution, in order to include resolution effects into the system matrix, so that the EM algorithm gradually recovers the modeled resolution with each update. The improved system mod- eling (achieved by inclusion of two convolutions per iteration) results in both enhanced resolution and lower noise, and there is often no need for regularization-other than to limit the number of iterations. Tests have been performed with simulated list-mode data and also with measured projection data from a GE Advance PET scanner, for both [ 18 F]-FDG and [ 124 I]-NaI. The method demonstrates improved image quality in all cases when compared to the conventional FBP and EM methods presently used for clinical data (which do not include resolution modeling). The benefits of this approach for 124 I (which has a low positron yield and a large positron range, usually resulting in noisier and poorer resolution images) are particularly noticeable.Index Terms-Iterative image reconstruction, positron emission tomography (PET).

Efficient local monocyte/macrophage recruitment is critical for tissue repair. Recruited macrophages are polarized toward classical (proinflammatory) or alternative (prohealing) activation in response to cytokines, with tight temporal regulation crucial for efficient wound repair. Estrogen acts as a potent anti-inflammatory regulator of cutaneous healing. However, an understanding of estrogen/estrogen receptor (ER) contribution to macrophage polarization and subsequent local effects on wound healing is lacking. Here we identify, to our knowledge previously unreported, a role whereby estrogen receptor α (ERα) signaling preferentially polarizes macrophages from a range of sources to an alternative phenotype. Cell-specific ER ablation studies confirm an in vivo role for inflammatory cell ERα, but not ERβ, in poor healing associated with an altered cytokine profile and fewer alternatively activated macrophages. Furthermore, we reveal intrinsic changes in ERα-deficient macrophages, which are unable to respond to alternative activation signals in vitro. Collectively, our data reveal that inflammatory cell-expressed ERα promotes alternative macrophage polarization, which is beneficial for timely healing. Given the diverse physiological roles of ERs, these findings will likely be of relevance to many pathologies involving excessive inflammation.