Gap Filling Strategies for 3-D-FBP Reconstructions of High-Resolution Research Tomograph Scans

The high-resolution research tomograph (HRRT) is a dedicated human brain positron emission tomography scanner. Currently available iterative reconstruction algorithms show bias due to nonnegativity constraints. Consequently, implementation of 3-D filtered backprojection (3-D-FBP) is of interest. To apply 3-D-FBP all missing data including those due to gaps between detector heads need to be estimated. The aim of this study was to evaluate various gap filling strategies for 3-D-FBP reconstructions of HRRT data, such as linear and bilinear interpolation or constraint Fourier space gap filling (confosp). Furthermore, missing planes were estimated using segment 0 image data only (noniterative) or by using reconstructed images based on all previous segments (iterative method). Use of bilinear interpolation showed worst correspondence between reconstructed and true activity concentration, especially for small structures. Moreover, phantom data indicated that use of linear interpolation resulted in artifacts in planes located near the edge of the field-of-view. Use of confosp did not show these artifacts. Iterative estimations of the missing planes for |segments| > 0 improved image quality at the cost of more computation time. Therefore, use of confosp for filling sinogram gaps with both iterative and noniterative estimation of missing planes are recommended for quantitative 3-D-FBP of HRRT studies.