We present the design of a fluidic gas multi-sensors chamber to be integrated in a portable device for multi-modal detection and identification of gas. The multi-sensors chamber has 14 sensors; 4 fluorescent sensors, 2 Quartz crystal microbalances (QCM) and 8 surface-acoustic-wave sensors (SAW). The weight of the chamber is about 500 g with a 9 ml fluidic volume. We used the fluidic simulation to optimize the flow inside the chamber. The chamber design has been experimentally validated with gas test bench. This work, initially developed for civilian safety applications, can be extended to others applications such as environmental monitoring to detect and identify gas nature.

A portable device is reported to detect and identify in real time explosive vapors usually used by the terrorists. This device is composed of the multi-sensors chamber with three technologies of explosive vapors sensors: Quartz Crystal Microbalance (QCM), Surface Acoustic Wave (SAW) and fluorescence. The multi-sensors chamber was designed and optimized to guaranty an efficient fluidic repartition on each sensor to assure the suitable responses of sensors. A laptop controls the device. An algorithm has been specifically developed to detect and identify gas nature. On 33 experimentations with various explosives or interferents, the preliminaries results have shown the detection and the identification in about 1min.

At CEA-LETI, a DEXA approach for systems using a digital 2D radiographic detector has been developed. It relies on an original X-rays scatter management method, based on a combined use of an analytical model and of scatter calibration data acquired through different thicknesses of Lucite slabs. Since Lucite X-rays interaction properties are equivalent to fat, the approach leads to a scatter flux map representative of a 100% fat region. However, patients’ soft tissues are composed of lean and fat. Therefore, the obtained scatter map has to be refined in order to take into account the various fat ratios that can present patients. This refinement consists in establishing a formula relating the fat ratio to the thicknesses of Low and High Energy Lucite slabs leading to same signal level. This proportion is then used to compute, on the basis of X-rays/matter interaction equations, correction factors to apply to Lucite equivalent X-rays scatter map. Influence of fat ratio correction has been evaluated, on a digital 2D bone densitometer, with phantoms composed of a PVC step (simulating bone) and different Lucite/water thicknesses as well as on patients. The results show that our X-rays scatter determination approach can take into account variations of body composition.

In a previous paper(1) (SPIE Medical Imaging 2001), a dual energy method for bone densitometry using a 2D digital radiographic detector has been presented. In this paper, calcium content quantification performance of the approach is precised. The main challenge is to achieve quantification using scatter-corrected dual energy acquisitions. Therefore a scatter estimation approach, based on an expression of scatter as a functional of the primary flux, has been developed. This expression is-derived from the Klein and Nishina equation and includes tabulated scatter level values. The calcium quantification performances are validated on two configurations. A first one is issued from criteria developed by the French "Groupe de Recherche et d'Information sur les Osteoporoses". It is based on the use of a phantom made of five 3mm thick PVC sheets in the form of five steps, representing five different bone mineral density values, included in a lucite container filled with water. Additional lucite plates can be put over the phantom. This phantom has been used for evaluation of quantification robustness versus patient thickness and composition variations, and for accuracy evaluation. The second configuration, composed of small calcified objects (representative of lung nodules), is used for evaluating capacities to differentiate calcified from non calcified nodules and to test calcium content quantification Performance.

Dual-energy imaging has been proposed as a method for producing material-specific images, thus permitting separate examination of bone and soft-tissue structures. Interesting clinical results, particularly for chest, have been presented usually for screen-film or phosphor plate detectors and with single exposure. The purpose of the paper is to investigate double exposure dual-energy with a digital X-Ray detector.The study is performed with a CCD-based large field digital X-Ray detector (Paladio detector, Apelem) installed on a remote table. Dual exposure is feasible on this detector with little registration problem because we have a very short delay (< 0.5 s) between two acquisitions.For each examination, two radiographs are acquired at two different high and low energies and with adapted X-ray tube filtrations. X-ray generator energy voltages and filtrations are optimized in order to obtain thin energy peak spectra with good spectral separation (50 keV), much better than with single exposure systems.Tissue decomposition images are estimated from both acquisitions. The decomposition process is helped by the nice spectral separation. Scatter correction, applied to the raw dual-exposure acquisitions, provides an improvement of tissue decomposition. Results are shown for a chest phantom.

Dual Energy X-Rays Absorptiometry (DXA) is commonly used to separate soft tissues and bone contributions in radiographs. This decomposition leads to bone mineral density (BMD) measurement. Most clinical systems use pencil or fan collimated X-Rays beam with mono detectors or linear arrays. On these systems BMD is computed from bi-dimensional (2D) images obtained by scanning. Our objective is to take advantage of the newly available flat panels detectors and to propose a DXA approach without scanning, based on the use of cone beam X-Rays associated with a 2D detector. This approach yields bone densitometry systems with an equal X and Y resolution, a fast acquisition and a reduced risk of patient motion. Scatter in this case becomes an important issue. While scattering is insignificant on collimated systems, its level and geometrical structure may severely alter BMD measurement on cone beam systems. In our presentation an original DXA method taking into account scattering is proposed. This new approach leads to accurate BMD values. In order to evaluate the accuracy of our new approach, a phantom representative of the spine regions tissue composition (bone, fat, muscle) has been designed. The comparison between the expected theoretical and the reconstructed BMD values validates the accuracy of our method. Results on anthropomorphic spine and hip regions are also presented.

Detection of opacities in mammograms, and especially of spicularities, is an important point for an early detection of breast cancer. Because of the superimposition of complex structures in a mammogram, it is avery tricky task. In this paper we propose a detection scheme combining, on one hand, information provided by an analysis of each single mammogram, and on the other hand, information provided by a comparison between the right and left mammograms. At first the two mammograms are filtered and registered, the potential pathological sites are obtained on the basis of a distance criterion adapted to opacities detection between the two mammograms. Then a robust segmentation method delimits a region of interest (ROI) surrounding each potential pathological site. To limit the number of false positives and to provide the physicians with quantitative parameters, each detected region is characterized by a set of four parameters. This global approach has been evaluated on mammograms of the MIAS database, representative of different opacities shapes and different backgrounds. The results have shown that all sites identified as malignant have been detected with a low rate of false detections.

Scatter might significantly alter the diagnosis in mammography. For example, the sharpness of a mass edge is an important indicator of malignancy. Unfortunately scatter can blur the edges and so make the diagnosis harder. Sharpness enhancement, based on image processing might wrongly sharpen a smooth edge and so alter the diagnosis. We present here a scatter correction method based on the physical equations. The main problem of this approach lies on the non-knowledge of the three-dimensional structure of the object. In order to be able to calculate the map of scattered intensity using the physical laws, we propose an approximation of this structure derived from the primary X-ray flux map representative of its projection. This approximation takes into account the physical parameters relative to X-ray interactions in breast tissues, as well as geometric parameters characterizing the acquisition procedure. This approximation allows us to build an equivalent object for X-ray photons scattering, and the physical laws can be applied to this equivalent object. The validity of this approximation has been evaluated on simulated objects. Finally to remove the scattering flux, we propose an inversion scheme to build the primary flux from the observed flux. The obtained primary flux map is then representative of the projection of the 3D structures of the breast. To validate our scatter correction model, we have developed a direct simulation model taking into account the true 3D structures, for some geometrical distributions of tissues inside the breast. The scatter obtained with this accurate model is then compared to the result from the approximated model. The performances of our scatter correction approach will be validated on phantoms representative of the structure and the components of the breast.

The early detection of breast cancer is essential for increasing the survival rate of the disease. Today, mammography is the only breast screening technique capable of detecting breast cancer at a very early stage. The presence of a breast tumor is indicated by some features on the mammogram. One sign of malignancy is the presence of clusters of fine, granular microcalcifications. We present here a three-step method for detecting and characterizing these microcalcifications. We begin with the detection of potential candidates. The aim of this first step is to detect all the pixels that could be a microcalcification. Then we focus on our specific region growing technique which provides an accurate extraction of the shape of the region corresponding to each detected growing technique which provides an accurate extraction of the shape of the region corresponding to each detected seed. This second step is essential because microcalcifications shape is a very important feature for the diagnosis. It is then possible to determine precise parameters to characterize these microcalcifications. This three-step method has been evaluated on a set of images form the mammographic image analysis society database.

Microcalcifications are an important sign for breast cancer diagnosis. Here the authors propose a three steps approach for microcalcifications detection and characterization. Firstly, a new and efficient non-linear filter, based on the global prior of the microcalcifications' shape, is presented. This filter limits the false detections due to noise while providing seeds representative of suspicious regions. In a second step a precise segmentation of the suspicious regions is provided by a region growing technique initialized from the previously detected seeds. In a last step, the individual potential microcalcifications are characterized by a set of features and grouped in clusters. This step separates the false detections from the true ones, on the basis of the high level prior of the microcalcifications, and provides quantitative information useful for the physicians' diagnosis. This global approach has been tested and evaluated on mammograms from the MIAS database, representative of different pathologies and breast tissue structures.