Value of a lower limb immobilization device for SPECT/CT image fusion optimization

The foot and the ankle are small structures commonly affected by disorders, and their complex anatomy represent significant diagnostic challenges. SPECT/CT Image fusion can provide missing anatomical and bone structure information to functional imaging, which is particularly useful to increase diagnosis certainty of bone pathology. However, due to SPECT acquisition duration, patient’s involuntary movements may lead to misalignment between SPECT and CT images. Patient motion can be reduced using a dedicated patient support. We aimed at designing an ankle and foot immobilizing device and measuring its efficacy at improving image fusion. Methods: We enrolled 20 patients undergoing distal lower-limb SPECT/CT of the ankle and the foot with and without a foot holder. The misalignment between SPECT and CT images was computed by manually measuring 14 fiducial markers chosen among anatomical landmarks also visible on bone scintigraphy. Analysis of variance was performed for statistical analysis. Results: The obtained absolute average difference without and with support was 5.1±5.2 mm (mean±SD) and 3.1±2.7 mm, respectively, which is significant (p<0.001). Conclusion: The introduction of the foot holder significantly decreases misalignment between SPECT and CT images, which may have clinical influence in the precise localization of foot and ankle pathology.

Modifications of MCM-22 zeolite through sequential post-synthesis treatments. Implications on the acidic and catalytic behaviour

Desilication and a combination of alkaline followed by acid treatment were applied to MCM-22 zeolite using two different base concentrations. The samples were characterised by powder X-ray diffraction, Al-27 and Si-29 MAS-NMR spectroscopy, SEM, TEM and low temperature N-2 adsorption. The acidity of the samples was study through pyridine adsorption followed by FTIR spectroscopy and by the analyses of the hydroxyl region. The catalytic behaviour, anticipated by the effect of post-synthesis treatments on the acidity and space available inside the two internal pore systems was evaluated by using the model reaction of m-xylene transformation. The generation of mesoporosity was achieved upon alkaline treatment with 0.05 M NaOH solution and practically no additional gain was obtained when the more concentrate solution, 0.1 M, was used. Instead, Al extraction takes place along with Si, as shown by Si-29 and Al-27 MAS-NMR data, followed by Al deposition as extraframework species. Samples submitted to alkaline plus acid treatments present distinct behaviour. When the lowest NaOH solution was used no relevant effect was observed on the textural characteristics. Additionally, when the acid treatment was performed on an already fragilized MCM-22 structure, due to previous desilication with 0.1 M NaOH solution, the extraction of Al from both internal pore systems promotes their interconnection, evolving from a 2-D to a 3-D porous structure. This transformation has a marked effect in the catalytic behaviour, allowing an increase of m-xylene conversion as a consequence of an easier and faster molecular traffic in the 3-D structure. On the other hand, the continuous deposition of extraframework Al species inside the pores leads to a shape selective effect that privileges the formation of the more valuable isomer p-xylene.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.