Clinically feasible reconstruction of 3D whole-body PET/CT data using blurred anatomical labels

We present the results of utilizing aligned anatomical information from CT images to locally adjust image smoothness during the reconstruction of three-dimensional (3D) whole-body positron emission tomography (PET) data. The ability of whole-body PET imaging to detect malignant neoplasms is becoming widely recognized. Potentially useful, however, is the role of whole-body PET in quantitative estimation of tracer uptake. The utility of PET in oncology is often limited by the high level of statistical noise in the images. Reduction in noise can be obtained by incorporating a priori image smoothness information from correlated anatomical information during the reconstruction of PET data. A combined PET/CT scanner allows the acquisition of accurately aligned PET and x-ray CT whole-body data. We use the Fourier rebinning algorithm (FORE) to accurately convert the 3D PET data to two-dimensional (2D) data to accelerate the image reconstruction process. The 2D datasets are reconstructed with successive over-relaxation of a penalized weighted least squares (PWLS) objective function to model the statistics of the acquisition, data corrections, and rebinning. A 3D voxel label model is presented that incorporates the anatomical information via the penalty weights of the PWLS objective function. This combination of FORE + PWLS + labels was developed as it allows for both reconstruction of 3D whole-body data sets in clinically feasible times and also the inclusion of anatomical information in such a way that convergence can be guaranteed. Since mismatches between anatomical (CT) and functional (PET) data are unavoidable in practice, the labels are ‘blurred’ to reflect the uncertainty associated with the anatomical information. Simulated and experimental results show the potential advantage of incorporating anatomical information by using blurred labels to calculate the penalty weights. We conclude that while the effect of this method on detection tasks is complicated and unclear, there is an improvement on the estimation task.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48971/2/m20101.pd

Long-term semantic memory versus contextual memory in unconscious number processing

Subjects classified visible 2-digit numbers as larger or smaller than 55. Target numbers were preceded by masked 2-digit primes that were either congruent (same relation to 55) or incongruent. Experiments 1 and 2 showed prime congruency effects for stimuli never included in the set of classified visible targets, indicating subliminal priming based on long-term semantic memory. Experiments 2 and 3 went further to demonstrate paradoxical unconscious priming effects resulting from task context. For example, after repeated practice classifying 73 as larger than 55, the novel masked prime 37 paradoxically facilitated the “larger ” response. In these experiments task context could induce subjects to unconsciously process only the leftmost masked prime digit, only the rightmost digit, or both independently. Across 3 experiments, subliminal priming was governed by both task context and long-term semantic memory. This research started by asking how much semantic analysis occurs unconsciously in response to visually masked numbers. Experiment 1 set out specifically to resolve a discrepancy between two recently reported findings. When it became apparent that Experiment 1’s methods could address additional interesting questions about subliminal priming, those additional questions becam