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Diabetes Mellitus is one of the most common diseases worldwide, considered a major health issue. The International Diabetes Federation has reported that approximately four million people decease each year and ten million more experience terminal complications, such as the diabetic foot. Early detection remains a challenge, since several factors may contribute to prone injuries on the foot. Thus, this paper presents a method to retrieve temperature differences between the sole and regions with abnormal temperature patterns, based on IR thermography and image processing. The database was integrated of 12 patients diagnosed with diabetes mellitus without diabetic foot background. The samples were capture by a cooled IR sensor, characterized to measure human skin temperature. The average temperature was estimated for each segmented region and compared to the sole temperature. Although the temperature differences for the positive cases were less than 1°C, the algorithm prove to retrieve automatically the temperature differences once abnormal patterns were detected and segmented. The contribution lies in the analysis of early detection of the diabetic foot with a quantitative radiometric data processing, that could support the anticipated diagnosis and treatment of the diabetic foot.

Background and objective Initially, analysis of Electroencephalogram (EEG) signals was purely visual, tedious, time-consuming, and required a physician. Changing this old approach to classification proves to be an extraordinary task that gained much attention and a great deal of effort. With this intention, this comparison study focused on the development of polynomial-based feature extraction methods for epileptic and eye states EEG signals detection using kernel machines. Method Polynomial transforms are applied to decompose EEG signals in the frequency domain before their analysis using linear and non-linear measures. Thereafter, the standard and kernel extension methods are applied to determine principal components and discriminants which help to extract informative and discriminative low-dimensional features. For direct detection of EEG signals, extracted features are fed into kernel machines namely simple multilayer perceptron neural network (sMLPNN) and least-square support vector machine (LS-SVM). Results Using the publicly available Bonn-University database, experimental results demonstrated that features extracted using kernel methods are more discriminative than the ones using standard methods. In addition, compared to the LS-SVM, polynomial-based features with sMLPNN gained higher performances. Moreover, obtained predictivity, accuracy, and area under receiver operating curve also demonstrate that kernel machines can detect epileptic and eye states EEG signals with highest performances of 100%, 100% and 1, respectively. Conclusion Thus, the proposed framework can be efficient for EEG diagnosis. Overall, given the complexity and heterogeneity of the brain, it is likely frameworks of this type that will be required to configure intelligent devices for treating epilepsy and to configure eye-brain-computer interface.

Background and objectives Epilepsy is the most prevalent neurological disorder in humans which is characterized by recurrent seizures resulting in neurologic, cognitive, psychological and social consequences. In addition, the automatic identification of brain conditions can be helpful in eye-brain-computer interface. That is why during these last few decades, many researchers focused on the development of classification systems for the automatic analysis and detection of epileptic seizures and eye states, which then can be integrated into implantable devices intended to detect the onset of seizures and trigger a focal treatment to block or suppress the seizures progression and to improve the living conditions of patients. In this impetus, this work aims to develop a novel automated classification system which can be performed to detect and identify whether electroencephalogram (EEG) signals belong to epileptic patients in seizure or seizure-free conditions, or to normal individuals with opened or closed eyes. Methods The proposed classification system consisted of models based on Jacobi polynomial transforms (JPTs). Discrete Legendre transform (DLT) and discrete Chebychev transform (DChT) firstly extract the beta (β) and gamma (γ) rhythms of EEG signals. Thereafter, different measures of complexity are computed from the EEG signals and their extracted rhythms, and applied as inputs of the least-square support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel. Results The Kruskal-Wallis statistical test is performed and demonstrated that computed JPTs-based measures of complexity sufficiently discriminate EEG signals since their values are significantly higher for seizure EEG signals as compared to those of seizure-free and normal EEG signals with opened or closed eyes. In addition, these measures are used to construct eleven relevant classification problems using the LS-SVM which gained out area under curve (AUC) between 0.983 and 1 which is still proportional to maximum classification accuracies of 88.75% and 100% for normal with opened eyes versus normal with closed eyes, and normal versus seizure or seizure-free versus seizure classification problems, respectively. Conclusion Overall, it is found that the proposed classification system extends to be less complex for practical applications and can be suitable for automatic epileptic seizures detection and eye states identification.

International audience; According to the International Diabetes Federation, approximately four million people die from diabetes each year, and ten million more suffer disabling or potentially terminal complications, such as diabetic foot disease. Therefore, the proposal is the early detection by detecting non-homogeneous temperature regions with radiometric data and image processing by IR thermography. The samples were taken from 12 patients diagnosed with diabetes mellitus without a history of diabetic foot or visible anatomical alterations, by a cooled IR sensor programmed exclusively to measure human skin temperature. The radiometric data was processed to eliminate interferences and segment the lower limb since the acquisition was carried out in a non-controlled environment; where lies the relevance of this paper. The results were presented as binary images with a defined segmented region against a contrasting background, reviling areas corresponded to higher temperatures regard the rest of the sole fo the foot as a consequence of non-homogeneous temperature pattern. A total of 6 cases showed a normal homogeneous temperature, 2 cases had fault origins and 4 cases presented spotted regions that are prospective to prone lesions. This procedure set a precedent for the analysis of early detection of the diabetic foot with radiometric data processing instead of using IR images as input; which could be used to retrieve the coordinates and carry out an exhaustive numerical analysis or pattern recognition (i.e. thermal fingerprint) to support the anticipated diagnosis based on support systems such as medical thermography as perspective.

International audience; This study presents a new Parkinson’s disease diagnosis technique based on wavelets extracted features and machine learning paradigms. The present-day diagnosis techniques suffer from low diagnosis accuracy and also require the patient to go to a medical facility, where the diagnosis is done by a specialist. In this work, we propose an automatic diagnosis method where by, all the patient has to do is to type some keys on their keyboard, and the algorithm will calculate the latency time, flight time and hold time of each key pressed, to make a diagnosis of Parkinson’s disease. We use several wavelets to extract some features that are classified into Parkinson’s disease or non-Parkinson’s disease. The results are very encouraging and we obtain a classification accuracy of up to 100% in some of the cases, using a ten-fold cross-validation technique. Wavelets are a tool that can be used to complement and improve the detection of Parkinson’s disease. These results will permit the amelioration of some state-of-the-art methods which use a similar technique to detect Parkinson’s disease.

International audience; This article reports the identification of two groups of patterns in time and frequency, also a muscular intensity characteristic associated to hand movement per user, all this results were obtained by superficial myoelectric signal analysis using Hilbert-Huang transform. Muscular signals were acquired from five muscles of the right forearm from five healthy subjects. Using a four channels acquisition system with differential configuration myoelectric signals were recorded for six movements of the fingers in flexion and extension. Electrodes were placed over five forearm anterior, posterior and finger linked muscles. Time pattern corresponds to sinusoidal oscillations with average length of 24.5 ms, corresponding to voluntary contraction and relaxation of the muscles. These oscillations are present into all acquisition channels and there is evidence about groups of two consecutive oscillations with a range of 30 ms between them. Also, there found a pattern of three frequencies: 83.3 Hz, 96.7 Hz and 113.3 Hz, present into contraction and relaxation timeslot. Using Hilbert transform were identified the instantaneous frequencies that indicate the state of the muscles from doss to active and vice versa. There is an intensity characteristic identified in the recordings about behavior of muscles per movement for each subject being repetitive and unique. These processes could decrease time computing to obtain intrinsic characteristics for myoelectric signals.

Background and objective: The diagnosis of intestinal parasitosis disease relies on physiological symptoms and stool examination. Often, few specialists are available, and manual stool exam is slow, prone to error, and can cause eye fatigue. Our aim was to design and implement a medical expert system that would be automated and helpful for diagnosis of human intestinal parasitosis. Methods: The system was developed based on a decision algorithm. A knowledge base was constructed through information gleaned from books and physicians with information pertaining to the disease. The user interacts with the system by answering questions. The symptoms information collected led to a microscopic examination of stools, which was run on the system to detect parasites. The paradigm for automated microscopic examination of stools consisted of a combined distance regularized level set evolution, automatically initialized by a circular Hough transform, and a trained neuro-fuzzy classifier. The neuro-fuzzy classifier was trained for analysis of twenty human intestinal parasites. Results: We combined the reasoning scheme of diagnosis and the automated clinical exam of stools in the same system. The parasites found in microscopic imagery confirmed the suspicious disease. The final recommendation of diagnosis was then completed, with appropriate proposed therapy. The system was evaluated with sixty cases of infection, and compared to the diagnosis of two expert doctors; we obtained fifty eight correct diagnoses, corresponding to a 96.6% accuracy. Conclusions: The proposed system is automated, since the parameters of segmentation, feature extraction and classification are set to be computationally guided by the type of suspicious parasite. The system is potentially an important contribution for medical healthcare assistance. Keywords: Parasitic diseases diagnosis, Distance regularized level set evolution, Neuro-fuzzy classifier, Expert system

Retinal blood vessels analysis is of interest for medical screening, especially in the diagnosis of diabetic retinopathy. In this paper, we propose a new method for the segmentation of blood vessels in retinal photographs. This method is based on classical edge detection filters and artificial neural networks. Firstly, edge detection filters are applied to extract the features vector. The resulting features are used to train an artificial neural network in order to recognize each pixel as belonging to blood vessels or not. The obtained algorithm is evaluated with the publicly available DRIVE, CHASE and STARE datasets, containing retinal images frequently used for this goal. The performance of the proposed system is calculated in terms of detection accuracy, sensitivity, specificity, and the area under the ROC curve. Our model is compared to other vessel segmentation models with encouraging results obtained. The proposed algorithm is a suitable tool for automated retinal image analysis.

Electroencephalogram (EEG) remains the primary technique in the diagnosis and localization of partial epilepsy seizures. Despite the advent of modern neuroimaging techniques, the use of EEG signals for locating epilepsy-affected brain areas is still convenient. That is why during these last decades, several computer-aided detection (CAD) methodologies have been proposed to detect and discriminate focal (F) EEG signals, and hence locate epileptogenic foci. In this impetus, this paper applied Jacobi polynomial transforms (JPTs)-based entropy measures to analyze the complexity and discriminate the bivariate focal (F) and non-focal (NF) EEG signals. Jacobi polynomial transforms namely discrete Legendre transform (DLT) and discrete Chebyshev transform (DChT) are applied to separate F and NF EEG signals into their different rhythms. Furthermore, entropy measures like approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PermEn), fuzzy entropy (FuzzyEn) and increment entropy (IncrEn) are extracted. For direct discrimination between F and NF EEG signals, extracted entropies are combined to define different features vectors that are fed as inputs of two kernel machines namely the least-squares support vector machine (LS-SVM) and simple multi-layer perceptron neural network (sMLPNN). Experimental results demonstrated that our methodology achieved the highest performance of 98.33% sensitivity, 98.00% specificity, and 98.17% accuracy in discriminating F and NF EEG signals with sMLPNN classifier. In addition, our methodology will be useful to clinicians in providing an accurate and objective paradigm for locating epilepsy-affected brain areas.

International audience; Electroencephalogram (EEG) signals are useful in understanding the human brain diseases like epilepsy which is characterized by an enduring predisposition to generate epileptic seizures and by neurologic, cognitive, psychological and social consequences of these conditions. That is why this paper proposed a novel full processing chain of EEG signals analysis for epileptic seizures detection that applied a new alternative approach for EEG rhythms decomposition. EEG signals are decomposed into their different background rhythms using discrete Jacobi polynomial transforms (JPTs) and a 28-dimension feature vector is extracted in frequency and time domains. Due to the high dimension and redundancy of these features, the linear discriminant analysis (LDA) is applied for dimensionality reduction and informative and discriminative low dimension feature vectors are computed and feed as inputs of the support vector machines (SVM) classifiers. The validation of this processing chain is done experimentally using an online available database which consists of five hundred EEG signals. Analysis demonstrated that the EEG rhythms decomposition using JPTs can be implemented as a single-step process and provides more abundant information for class discrimination. In addition, twelve experiments closely related to clinical applications are examined and promising results are achieved with maximum accuracies between 96.25–100 %. Overall, it is found that the proposed processing chain will be useful in providing an accurate and objective scheme for automatic EEG rhythms decomposition and epileptic seizures detection that can be integrated into implantable devices intended to predict the onset of seizures and trigger a focal treatment to block the seizures progression.

Les endoscopes ont un role essentiel pour l’inspection des organes creux et des cavites du corps. Ils fournissent des images a haute resolution avec des couleurs et textures naturelles requises pour un diagnostic, le suivi des patients et les interventions chirurgicales. Cependant, leur champ de vue reduit est un facteur limitant majeur pour une interpretation aisee des scenes. Les techniques de mosaiquage permettent le calcul d’images panoramiques de toute la zone d’interet qui inclut, par exemple, des lesions et des reperes anatomiques. Cet article donne un apercu sur l’etat de l’art lie a tous les aspects du mosaiquage, a savoir la correction, le recalage et la cartographie des images.

In this work, a new scheme of image encryption based on chaos and Fast Walsh Transform (FWT) has been proposed. We used two chaotic logistic maps and combined chaotic encryption methods to the two-dimensional FWT of images. The encryption process involves two steps: firstly, chaotic sequences generated by the chaotic logistic maps are used to permute and mask the intermediate results or array of FWT, the next step consist in changing the chaotic sequences or the initial conditions of chaotic logistic maps among two intermediate results of the same row or column. Changing the encryption key several times on the same row or column makes the cipher more robust against any attack. We tested our algorithms on many biomedical images. We also used images from data bases to compare our algorithm to those in literature. It comes out from statistical analysis and key sensitivity tests that our proposed image encryption scheme provides an efficient and secure way for real-time encryption and transmission biomedical images.

International audience; In the last decade, several studies have emphasised the need to understand and optimise the computed tomography (CT) procedures in order to reduce the radiation dose applied to paediatric patients. To evaluate the influence of the technical parameters on the radiation dose and the image quality, a statistical model has been developed using the design of experiments (DOE) method that has been successfully used in various fields (industry, biology and finance) applied to CT procedures for the abdomen of paediatric patients. A Box–Behnken DOE was used in this study. Three mathematical models (contrast-to-noise ratio, noise and CTDIvol) depending on three factors (tube current, tube voltage and level of iterative reconstruction) were developed and validated. They will serve as a basis for the development of a CT protocol optimisation model.

Resume Objectifs de l’etude La dosimetrie in vivo de transit utilisant l’imageur portal est une approche prometteuse pour l’assurance qualite en radiotherapie. Une evaluation comparative a ete menee entre une solution commerciale, EPIgray ® et un controle de dosimetrie in vivo de reference utilisant des diodes a semi-conducteurs. Materiel et methodes Les performances des deux methodes de dosimetrie in vivo ont ete evaluees. Le critere principal etait l’ecart de dose au point de prescription par rapport aux mesures de la chambre d’ionisation sur fantome ou par rapport a la dose previsionnelle calculee par le systeme de planification de traitement avec les patients. Le seuil de tolerance de l’ecart de dose etait defini a ±5 %. Au total, 107 patients traites par irradiation conformationnelle tridimensionnelle avec ou sans modulation d’intensite pour des tumeurs de l’encephale, du thorax, de la tete et du cou ont ete inclus prospectivement. Resultats La precision dosimetrique d’EPIgray ® sur fantome etait comparable a celle des mesures par diodes en termes de repetabilite (0,11 %) et de reproductibilite (0,29–0,51 %), avec un ecart de dose moyen de 0,17 % (ecart-type : 1,11). Les taux de seances hors tolerances pour l’encephale (irradiation conformationnelle tridimensionnelle avec ou sans modulation d’intensite), le thorax (irradiation conformationnelle tridimensionnelle) et la tete et le cou (irradiation conformationnelle avec modulation d’intensite) etaient respectivement de 0 % ; 9,6 % et 5,3 % avec un ecart de dose moyen entre 0,49 % et 1,53 %. La moyenne de l’ecart de dose sur trois seances consecutives avec EPIgray ® permettait de valider 99,1 % des traitements. Conclusion Les performances d’EPIgray ® en dosimetrie in vivo sont compatibles avec les recommandations de l’European Society for Radiotherapy and Oncology (ESTRO) et equivalentes a celles des diodes a semi-conducteur pour l’irradiation conformationnelle tridimensionnelle. Elle permet un controle satisfaisant des irradiations conformationnelles avec modulation d’intensite, techniquement difficiles a realiser avec les diodes.

Background and purpose To compare the accuracy of the Block Matching deformable registration (DIR) against rigid image registration (RIR) for head-and-neck multi-modal images CT to cone-beam CT (CBCT) registration. Material and methods Planning-CT and weekly CBCT of 10 patients were used for this study. Several volumes, including medullary canal (MC), thyroid cartilage (TC), hyoid bone (HB) and submandibular gland (SMG) were transposed from CT to CBCT images using either DIR or RIR. Transposed volumes were compared with the manual delineation of these volumes on every CBCT. The parameters of similarity used for analysis were: Dice Similarity Index (DSI), 95%-Hausdorff Distance (95%-HD) and difference of volumes (cc). Results With DIR, the major mean difference of volumes was −1.4 cc for MC, revealing limited under-segmentation. DIR limited variability of DSI and 95%-HD. It significantly improved DSI for TC and HB and 95%-HD for all structures but SMG. With DIR, mean 95%-HD (mm) was 3.01 ± 0.80, 5.33 ± 2.51, 4.99 ± 1.69, 3.07 ± 1.31 for MC, TC, HB and SMG, respectively. With RIR, it was 3.92 ± 1.86, 6.94 ± 3.98, 6.44 ± 3.37 and 3.41 ± 2.25, respectively. Conclusion Block Matching is a valid algorithm for deformable multi-modal CT to CBCT registration. Values of 95%-HD are useful for ongoing development of its application to the cumulative dose calculation.

International audience; Surface EMG signals have many important characteristics that could be useful to anticipate user's movements for orthotic and prosthetic devices. New methods for signal processing have appeared, but not all of them apply to non-linear and non-stationary processes such like myolectric signals. To perform a real-time analysis over these signals is important to simplify the processing and reduce time computing. Using the Empirical Mode Decomposition (EMD) method were obtained the Intrinsic Mode Functions (IMFs) for a multichannel signal to filter, discard and identify characteristics. sEMG signals were acquired using NI-DAQ system with four differential inputs for four forearm muscles. Seven hand movements were considered, that signals were recorded into a 4 × 20480 matrices in 1000 records. These records were divided in three segments: doss, transitory and contraction; through EMD method we obtained their IMFs to analyze with Hilbert transform. Results will be considered to simplify the sEMG signal for real-time analysis to control a prosthetic device.

International audience; Telemedicine is becoming increasingly, with applications in many areas of healthcare, such as home telecare of the elderly, diagnosis at a distance and robotic surgery. The simultaneous transmission of several leads of biomedical signals should be considered in telemedicine, given the many benefits it brings. Code division multiple access (CDMA) is a multiple access technique that enables users to transmit independent information simultaneously within the same bandwidth. The direct sequence CDMA (DS-CDMA) is a variant of the CDMA technique in which a pseudorandom sequence having a higher bandwidth than the information signal is used to modulate the information signal directly. Biomedical signals are confidential; thus, their transmission must be secured. In this paper we propose a protocol similar to DS-CDMA for the simultaneous transmission of all of the leads of some multichannel biomedical signals. We assigned orthogonal codes to different leads of a signal. The convolution of each lead with the code gives a signal spread over a broad frequency band. All of the spread signals are then mixed to produce a single composite signal. This composite signal is frequency modulated, amplified and transmitted. At the reception, inverse functions to the previous are developed to perform demodulation, demultiplexing and extraction of the physiological signals transmitted. We used the discrete Walsh functions as codes. The results obtained are satisfactory, even in situations where the noise disturbances are significant.

Purpose of the Report. Statistical Parametric Mapping (SPM) is widely used for the quantitative analysis of brain images from F-18 fluorodeoxyglucose Positron Emission Tomography (FDG-PET). SPM requires an initial step of spatial normalization to align all images to a standard anatomical model (the template), but this may lead to image distortion and artefacts, especially in cases of marked brain abnormalities. This study aimed at assessing a Block-Matching (BM) normalization algorithm, where most transformations are not directly computed on the overall brain volume but through small blocks, a principle that is likely to minimize artefacts

International audience; Cystoscopy is used as a reference clinical examination in the detection and visualization of pathological bladder lesions. Evolution observation and analysis of these lesions is easier when panoramic images from internal bladder walls are used instead of video sequences. This work describes a fast and automatic mosaicing algorithm applied to cystoscopic video sequences, where perspective geometric transformations link successive image pairs. This mosaicing algorithm begins with a fast initialization of translation parameters computed by a cross-correlation of images, followed by an iterative optimization of transformation parameters. Finally, registered images are projected onto a global common coordinate system. A quantifying test protocol applied over a phantom yielded a mosaicing mean error lower than 4 pixels for a 1947×1187 pixels panoramic image. Qualitative evaluation of 10 panoramic images resulting from videos of clinical cystoscopies was performed. An analysis performed over translation values from these clinical sequences (in vivo) is used to modify the mosaicing algorithm to be able to do a dynamic selection of image pairs. Construction time of panoramic images takes some minutes. At last, algorithm limits are discussed.

International audience; This paper introduces a complete methodology for abdominal sounds analysis, from signal acquisition to statistical data analysis. The goal is to evaluate if and how phonoenterograms can be used to detect different functioning modes of the normal gastrointestinal tract, both in terms of localization and of time evolution during the digestion. After the description of the acquisition protocol and the employed instrumentation, several signal processing steps are presented: wavelet denoising and segmentation, artifact suppression and source localization. Next, several physiological features are extracted from the processed signals issued from a data-base of 14 healthy volunteers, recorded during 3 hours after a standardized meal. Data analysis is performed using a multi-factorial statistical method. Based on the introduced approach, we show that the abdominal regions of healthy volunteers present statistically significant phonoenterographic characteristics, which evolve differently during the normal digestion. The most significant feature allowing to distinguish regions and time differences is the number of recorded sounds, but important information is also carried by sound amplitudes, frequencies and durations. Depending on the considered feature, the sounds produced by different abdominal regions (especially stomach, ileo-caecal and lower abdomen regions) present a specific distribution over space and time. This information, statistically validated, is usable in further studies as a comparison term with other normal or pathological conditions.

Resume L’objectif de ce travail est d’indexer les doses d’irradiation sur l’activite tumorale et non plus seulement sur la geometrie de la tumeur. L’elaboration d’un nouveau protocole d’irradiation hautement precis, fondee sur l’activite tumorale requiere une configuration specifique des volumes impliquees afin d’utiliser ce type d’irradiation. En utilisant un systeme de planification inverse et deux fantomes tete et cou specialement crees, nous avons realise la caracterisation du systeme suivant divers plans de traitement. A cette fin, deux modeles ont ete crees et utilises : un modele geometrique et un modele anatomique. Nos resultats ont montre qu’une irradiation hautement precise dans des zones limitees est possible avec la radiotherapie conformationnelle avec modulation d’intensite quand plusieurs conditions sont reunis comme la localisation, le nombre d’organes a risque, la distance entre volume cible previsionnel et organes a risque, le volume et la localisation de l’activite tumorale et le nombre de faisceaux. Ainsi, afin d’utiliser cette methode d’irradiation adaptee a l’activite tumorale, une geometrie precise sera necessaire. Cependant, de telles hautes doses totales ou fractionnees doivent etre evaluees avec beaucoup de precaution avant etre prescrites en clinique.

The objective of this work is to identify similarities in the spatio-temporal dynamics of epileptic seizures, record with scalp EEG. A comprehensive method is proposed and applied in EEG of the patients who suffer from temporal lobe epilepsy. The method is based on the computation of the time-varying degree of non linear correlation between scalp electrodes at seizure onset and during seizure spread, determined by a nonlinear regression analysis. The quantification and coding of these similarity relations allow the comparison between two epileptic networks. Results show that reproducible patterns may be extracted from different seizures of the same patient and confirm the existence of different subtypes of temporal lobe epilepsy.

International audience; Parasites live in a host and get its food from or at the expensive of that host. Cysts represent a form of resistance and spread of parasites. The manual diagnosis of microscopic stools images is time-consuming and depends on the human expert. In this paper, we propose an automatic recognition system that can be used to identify various intestinal parasite cysts from their microscopic digital images. We employ image pixel feature to train the probabilistic neural networks (PNN). Probabilistic neural networks are suitable for classification problems. The main novelty is the use of features vectors extracted directly from the image pixel. For this goal, microscopic images are previously segmented to separate the parasite image from the background. The extracted parasite is then resized to 12x12 image features vector. For dimensionality reduction, the principal component analysis basis projection has been used. 12x12 extracted features were orthogonalized into two principal components variables that consist the input vector of the PNN. The PNN is trained using 540 microscopic images of the parasite. The proposed approach was tested successfully on 540 samples of protozoan cysts obtained from 9 kinds of intestinal parasites. - See more at: http://thesai.org/Publications/ViewPaper?Volume=4&Issue=9&Code=ijarai&SerialNo=6#sthash.S5fRMF9g.dpuf

International audience; Geant4 application for tomographic emission (GATE), a Monte-Carlo simulation platform, has previously been used for optimizing tomoscintigraphic images recorded with scintillation Anger cameras but not with the new-generation heart-centric cadmium-zinc-telluride (CZT) cameras. Using the GATE platform, this study aimed at simulating the SPECT recordings from one of these new CZT cameras and to assess this simulation by direct comparison between simulated and actual recorded data, ranging from point sources to human images. Geometry and movement of detectors, as well as their respective energy responses, were modeled for the CZT 'D.SPECT' camera in the GATE platform. Both simulated and actual recorded data were obtained from: (1) point and linear sources of (99m)Tc for compared assessments of detection sensitivity and spatial resolution, (2) a cardiac insert filled with a (99m)Tc solution for compared assessments of contrast-to-noise ratio and sharpness of myocardial borders and (3) in a patient with myocardial infarction using segmented cardiac magnetic resonance imaging images. Most of the data from the simulated images exhibited high concordance with the results of actual images with relative differences of only: (1) 0.5% for detection sensitivity, (2) 6.7% for spatial resolution, (3) 2.6% for contrast-to-noise ratio and 5.0% for sharpness index on the cardiac insert placed in a diffusing environment. There was also good concordance between actual and simulated gated-SPECT patient images for the delineation of the myocardial infarction area, although the quality of the simulated images was clearly superior with increases around 50% for both contrast-to-noise ratio and sharpness index. SPECT recordings from a new heart-centric CZT camera can be simulated with the GATE software with high concordance relative to the actual physical properties of this camera. These simulations may be conducted up to the stage of human SPECT-images even if further refinement is needed in this setting.