Frontiers in Biomedical Technologies
Volume:1 Issue: 2, Spring 2014

Positron Emission Tomography (PET) imaging offers the possibility of measuring brain metabolic activity in vivo. However, brain PET images remain difficult to interpret in clinical setting because of the limited spatial resolution of current generation clinical PET scanners. Therefore, the resulting partial volume effect (PVE) is a challenging issue for brain PET image interpretation and quantitative analysis. To overcome this limitation, several algorithms allowing the correction for PVE (PVC) have been developed and assessed mainly in research setting. In this work, we perform a comparative study of 5 different PVC methods using clinical studies. 17 clinical studies of patients suffering from neurodegenerative disease were included in our study protocol. 3D T1-weighted MRI and FDG-PET were acquired on dedicated MR and PET-CT systems, respectively. MR images were rigidly co-registered to corresponding PET images using the Hermes multimodality platform and segmented using statistical parametric mapping package (SPM8). The resulting images were corrected for PVE using four voxel-based techniques proposed by different groups including Alfano, Muller- Gartner, Meltzer, and Shidahara, and one volume of interest (VOI)-based technique proposed by Rousset. Our results demonstrate a significant increase of the activity concentration in the gray matter. Consequently, the activity in the white matter decreases considerably when using all PVC methods, except for Meltzer and Shidahara. The comparative analysis demonstrates that, among all considered techniques, Alfano’s method appears to substantially increase the GM signal. When applying the different PVC methods to specific regions of interest linked to a specific pathology, the results highlight the bias when using uncorrected PET images, but still respecting specific modification patterns of the disease. Our results confirm the necessity of applying PVC to brain PET images in order to obtain more reliable and accurate quantification. This applies particularly to elderly patients with neurodegenerative disease where atrophy induces underestimation of the true PET signal.

Metal-induced artifacts are known to degrade CT image quality and deteriorate the quantitative value of the images. Therefore, numerous metal artifact reduction techniques have been proposed and their performances have been evaluated using different qualitative or quantitative approaches. Various approaches and measures have been applied for the validation process visual assessment of the corrected images being one of the most commonly applied techniques. A high proportion of the presented techniques are not properly validated in the clinical environment, which hampers an unbiased comparison of the techniques and as such the clinical acceptability of the techniques remains questionable. Accurate quantitative evaluation of the processed images guarantees the reliability of the correction method. The main motivation of this work was to present the qualitative and quantitative validation approaches and metrics used in various metal artifact reduction studies in both phantom and clinical experiments. Considering the challenging task of validation of the clinical studies, where the gold standard is not present, having a proper knowledge about the potential solutions would assist the researchers to apply the right validation approaches.

Clinical myocardial perfusion SPECT is commonly performed using static imaging. Dynamic SPECT enables extraction of quantitative as well as relative perfusion information. We aimed to evaluate the ability of dynamic SPECT for regular perfusion assessment in comparison to conventional SPECT in the context of thallium-201. Simulations were performed utilizing a 4D-NCAT phantom for a dual-head gamma camera via the SIMIND Monte-Carlo simulator. 64s acquisition time-frames were used to track these dynamic changes. Different summations of time-frames were performed to create each dataset, which were compared to a standard static dataset. In addition, the effect of different delay-times post-injection was assessed. Twenty-segment analysis of perfusion was performed via the QPS analyser. Dynamic data were subsequently acquired in clinical studies using simulation-optimized protocols. For different summations of time-frames, perfusion scores in the basal and mid regions revealed 14.4% and 7.3% increases in dynamic SPECT compared to conventional imaging, with maximum changes in the basal anterior, while the distal and apical segments did not show noticeable changes. Specifically, dynamic imaging including 4 to 6 time-frames yielded enhanced correlation (R=0.957) with conventional imaging, in comparison to the usage of less time frames. Greatest correlation with conventional imaging was obtained for post-injection delays of 320 to 448s (R=0.982 to R=0.988). While dynamic SPECT opens up an important opportunity for quantitative assessment (e.g. via generation of kinetic parameters), it was shown to generate highly consistent perfusion information compared to established conventional imaging. Future work focuses on merging these two important capabilities.

In this study we present a classification method based on aggregation operators, using Adaptive Network-based Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) neural network to differentiate between arthrometric data of normal and ACL-ruptured knees. The data involves 132 samples consisting of 59 patients with injured knee and 73 normal subjects. ANFIS hybrid training algorithm is implemented using Fuzzy C-Means (FCM) and subtractive data clustering. The Levenberg–Marquardt (LM) training algorithm is used for MLP neural network. The results of ANFIS and MLP are then combined using aggregation operators. The best accuracy (96%) is obtained by applying Choquet integral to the outputs of ANFIS classifier with the antecedent parameters selected using FCM algorithm. The experimental results show that aggregation operators enhance the outcomes of ANFIS and MLP classifiers in discriminating between ACL raptured knees and normal subjects.

this study, we have addressed the prediction aspect of human decision making from neurological, experimental and modeling points of view. We used a predictive reinforcement learning framework to simulate the human decision making behavior, concentrating on the role of frontal brain regions which are responsible for predictive control of human behavior. The model was tested in a maze task and the human subjects were asked to do the same task. A group of six volunteers including three men and three women at the age of 23-26 participated in this experiment. The similarity between responses of the model and the human behavior was observed after varying the prediction horizons. We found that subjects with less risky choices usually decide based on considering long term advantages of their action selections, which is equal to the longer prediction horizon. However, they are more susceptible to reach suboptimal solutions if their predictions become wrong due to some reasons like changing environment or inaccurate models. The concept of prediction result in faster learning and minimizing future losses in decision making problems. Since the problem solving in human beings is very faster than a trial and error system, considering this ability will help to describe the human behavior more desirably. This observation is compatible to the recent findings about the role of Dorsolateral Prefrontal Cortex in prediction and its relations to Anterior Cingulate Cortex with the ability of conflict monitoring and action selection.

Intra-operative ultrasound imaging as a non-ionized and being real time has been found very applicable as an intra-operative update of patient data in image guided neurosurgery system. The main point is the accurate registration of intra-operative with pre-operative images. Due to speckle noise in ultrasound images, scale differentiation between MR and ultrasound images and their different resolution, an accurate registration of ultrasound images with pre-operative MR images is a challenging problem. In this paper the effect of different steps of the Iterative Closest Point is considered and, then, the best modified version of ICP is introduced for this type of data. To perform this study, a Poly Vinyl Alcohol-Cryogel brain phantom is used which allows simulating brain deformation. The performance of the best version of ICP is compared to a well-known point based algorithm, Coherent Point Drift in terms of accuracy and speed. The results proved CPD algorithm was more robust than ICP algorithms in the presence of noise, although with a more computational cost. Changing different steps in conventional ICP has led to improve the performance of the ICP. As the results of our phantom study confirm the best version of ICP has not only achieved an accuracy close to CPD method, but also in a much faster approach. According to a trade off between the speed and accuracy of nine implemented versions of ICP algorithms, using some modified version of ICP is preferred to CPD method.

In order to decrease the risk of dental caries and improve exposure of the teeth to fluoride, glass ionomer cements were introduced in restorative dentistry. Since fluoride releases from some dental materials, the gradual reuptake ability of fluoride in these cements is important in the long-term. In this study we intended to compare the amount of fluoride release in three common glass ionomer cements (FUJI 1, SDS, and FUJI PLUS) at 1,3,7,14 and 28 days. First, 24 disc shaped samples were fabricated from FUJI 1, SDS and FUJI PLUS glass ionomer cements. The discs were, then, drained from fluoride ions in a period of 56 days. Discs were randomly selected and, then, divided into control and experimental groups. In the experimental groups, each sample was dried and exposed to Colgate Total 1000 ppm fluoride toothpaste; afterwards, they were washed and stored in distilled water at 37°C. Amounts of fluoride ion release were evaluated at 1, 3,7,14 and 28 days for all the experimental samples. In the control groups, the same procedure was done but with no exposure to fluoride. Differences in the release of fluoride ion from the tested products were evaluated using two-way analysis of variance (ANOVA) and the mixed model. A P- value of <0.05 was considered statistically significant. There were statistically significant differences between the experimental and control groups in all three materials during the 28-day experiment. The amount of fluoride release increased from day 1 to 7 and then decreased up to day 28. On days 1, 3 and 7, SDS had the largest and Fuji I had the lowest amount of fluoride release and on days 14 and 28 the largest amount of fluoride release was seen in FUJI PLUS, SDS and FUJI I, respectively. SDS as a newly released and less expensive glass ionomer can release fluoride ions as effectively as FUJI PLUS. All glass ionomer cements evaluated in this study may be effectively recharged with fluoride ions in order to effectively release them in time to aid tooth remineralization.

Optical imaging is established as one of the modalities applied to molecular imaging studies. Molecular imaging can be used to visualization of molecular events in the cellular or sub-cellular level. One of the main goals in optical imaging is giving source distribution. The Forward problem seeks to determine the photon density on the surface of the subject. Among the different methods, Green functions provide a fast method for modeling the diffusion. Green functions are different depending on the source shape, set up and geometry. In this study, a new optical set up was implemented in the cylindrical geometry. The software is developed and written in MATLAB programming to estimate the intensity on the object’s surface. The algorithm is based on diffusion approximation and evaluated by phantom experiment. The results showed significant correlation coefficients (R >0.9) which demonstrated the high accuracy of the algorithm. We have presented an approximate algorithm that solves the 3D diffusion equation in homogeneous turbid phantom like tissue. The algorithm can be used as part of the reconstruction program using FMT.