A Promising Future: Comparable Imaging Capability of MRI-Compatible Silicon Photomultiplier and Conventional Photosensor Preclinical PET Systems.

UNLABELLED: We recently completed construction of a small-animal PET system-the MiniPET-3-that uses state-of-the-art silicon photomultiplier (SiPM) photosensors, making possible dual-modality imaging with MRI. In this article, we compare the MiniPET-3 with the MiniPET-2, a system with the same crystal geometry but conventional photomultiplier tubes (PMTs). METHODS: The standard measurements proposed by the National Electrical Manufacturers Association NU 4 protocols were performed on both systems. These measurements included spatial resolution, system sensitivity, energy resolution, counting rate performance, scatter fraction, spillover ratio for air and water, recovery coefficient, and image uniformity. The energy windows were set to 350-650 keV on the MiniPET-2 and 360-662 keV on the MiniPET-3. RESULTS: Spatial resolution was approximately 17% better on average for the MiniPET-3 than the MiniPET-2. The systems performed similarly in terms of peak absolute sensitivity (∼1.37%), spillover ratio for air (∼0.15), spillover ratio for water (∼0.25), and recovery coefficient (∼0.33, 0.59, 0.81, 0.89, and 0.94). Uniformity was 5.59% for the MiniPET-2 and 6.49% for the MiniPET-3. Minor differences were found in scatter fraction. With the ratlike phantom, the peak noise-equivalent counting rate was 14 kcps on the MiniPET-2 but 24 kcps on the MiniPET-3. However, with the mouselike phantom, these values were 55 and 91 kcps, respectively. The optimal coincidence time window was 6 ns for the MiniPET-2 and 8 ns for the MiniPET-3. CONCLUSION: Images obtained with the SiPM-based MiniPET-3 small-animal PET system are similar in quality to those obtained with the conventional PMT-based MiniPET-2.

Population based ranking of frameless CT-MRI registration methods.

BACKGROUND: Clinical practice often requires simultaneous information obtained by two different imaging modalities. Registration algorithms are commonly used for this purpose. Automated procedures are very helpful in cases when the same kind of registration has to be performed on images of a high number of subjects. Radiotherapists would prefer to use the best automated method to assist therapy planning, however there are not accepted procedures for ranking the different registration algorithms. PURPOSE: We were interested in developing a method to measure the population level performance of CT-MRI registration algorithms by a parameter of values in the [0,1] interval. MATERIALS AND METHODS: Pairs of CT and MRI images were collected from 1051 subjects. Results of an automated registration were corrected manually until a radiologist and a neurosurgeon expert both accepted the result as good. This way 1051 registered MRI images were produced by the same pair of experts to be used as gold standards for the evaluation of the performance of other registration algorithms. Pearson correlation coefficient, mutual information, normalized mutual information, Kullback-Leibler divergence, L1 norm and square L2 norm (dis)similarity measures were tested for sensitivity to indicate the extent of (dis)similarity of a pair of individual mismatched images. RESULTS: The square Hellinger distance proved suitable to grade the performance of registration algorithms at population level providing the developers with a valuable tool to rank algorithms. CONCLUSIONS: The developed procedure provides an objective method to find the registration algorithm performing the best on the population level out of newly constructed or available preselected ones.

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI.

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.