In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems.

A recently published systematic review on 3D multi-segment foot models has illustrated the lack of repeatability studies providing evidence for appropriate clinical decision making. The aim of the current study was to assess the repeatability of the recently published model developed by Leardini et al. [10]. Foot kinematics of six healthy adults were analyzed through a repeated-measures design including two therapists with different levels of experience and four test sessions. For the majority of the parameters moderate or good repeatability was observed for the within-day and between-day sessions. A trend towards consistently higher within- and between-day variability was observed for the junior compared to the senior clinician. The mean inter-session variability of the relative 3D rotations ranged between 0.9-4.2° and 1.6-5.0° for respectively the senior and junior clinician whereas for the absolute angles this variability increased to respectively 2.0-6.2° and 2.6-7.8°. Mean inter-therapist standard deviations ranged between 2.2° and 6.5° for the relative 3D rotations and between 2.8° and 7.6° for the absolute 3D rotations. The ratio of inter-therapist to inter-trial errors ranged between 1.8 and 5.5 for the relative 3D rotations and between 2.4 and 9.7 for the absolute 3D rotations. Absolute angle representation of the planar angles was found to be more difficult. Observations from the current study indicate that an adequate normative database can be installed in gait laboratories, however, it should be stressed that experience of therapists is important and gait laboratories should therefore be encouraged to put effort in training their clinicians.

Abstract-Image reconstruction in emission tomography may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject. One way to implement this, is to use the anatomical image for defining the a-priori distribution in a maximum-a-posteriori reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate three different anatomical priors for PET brain imaging, using MRI for the anatomical image. The priors are: 1) a prior based on a segmentation of the MRI image; 2) the joint entropy prior; 3) a prior (proposed by Bowsher et al.[1]) that encourages smoothness within a position dependent neighborhood, computed from the MRI image. The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. The three priors produced a compromise between noise and bias that was significantly better than that obtained with post-smoothed MLEM. The performance of the joint entropy prior was slightly worse than that of the other two priors. In contrast to the joint entropy prior, the Bowsher prior is easily tuned and does not pose convergence problems due to local maxima.