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The authors compared 12 pairs of cerebral [18F]-fluoro-deoxyglucose (FDG) 2D/3D image sets from a GE/Advance PET scanner, incorporating the actual corrections used on human subjects. Differences in resolution consistent with other published values were found. There is a significant difference in axial resolution between 2D and 3D, and the authors focused on this as it is a scanner feature that cannot be readily changed. Previously published values for spatial axial resolution in 2D and 3D modes were used to model the differential axial smoothing at each image voxel. This model was applied to the 2D FDG images, and the resulting smoothed data indicate the published differences in axial resolution between 2D and 3D modes can account for 30-40% of the differences between these image sets. The authors then investigated the effect this difference might have on analysis typically performed on human FDG data. A phantom containing spherical hot- and cool-spots in a warm background to mimic a typical human cerebral FDG PET scan was scanned for a variety of time durations (30, 15, 5, 1 min). Only for the 1-minute frame (total counts 2D:6M, 3D:30M) is there an advantage to using 3D mode; for the longer frames which are more typical of a human FDG protocol, the reliability for extracting regions-of-interest is the same for either mode while 2D mode shows better quantitative accuracy
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We present a new sparse shape modeling framework on the Laplace-Beltrami (LB) eigenfunctions. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes by forming a Fourier series expansion. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we propose to filter out only the significant eigenfunctions by imposing l1-penalty. The new sparse framework can further avoid additional surface-based smoothing often used in the field. The proposed approach is applied in investigating the influence of age (38-79 years) and gender on amygdala and hippocampus shapes in the normal population. In addition, we show how the emotional response is related to the anatomy of the subcortical structures.
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The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.
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