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In this paper medical applications on a Grid infrastructure, the MAGIC-5 Project, are presented and discussed. MAGIC-5 aims at developing Computer Aided Detection (CADe) software for the analysis of medical images on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images improves radiologists’ performance; in addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of Grid Services for the management of distributed computing resources and data. Grid technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated with the diagnosis in the screening programs. The MAGIC-5 project develops algorithms for the analysis of mammographies for breast cancer detection, Computed- Tomography (CT) images for lung cancer detection and Positron Emission Tomography (PET) images for the early diagnosis of Alzheimer Disease (AD). A Virtual Organization (VO) has been deployed, so that authorized users can share data and resources and implement the following use cases: screening, tele-training and tele-diagnosis for mammograms and lung CT scans, statistical diagnosis by comparison of candidates to a distributed data-set of negative PET scans for the diagnosis of the AD. A small-scale prototype of the required Grid functionality was already implemented for the analysis of digitized mammograms.

Total Compton, individual shell and Compton energy-absorption scattering cross-sections are evaluated in the energy region 0.005 to 10 MeV for H, C, N, O, P and Ca. Compton energy absorption cross-sections deviate numerically with available values. The cause of the numerical discrepancies are not fully understood but can be attributed to Doppler broadening of the Compton scattered photons through a given angle.

We describe a portable system for mammographic x-ray spectroscopy, based on a 2 X 2 X 1 mm3 cadmium telluride (CdTe) solid state detector, that is greatly improved over a similar system based on a 3 X 3 X 2 mm3 cadmium zinc telluride (CZT) solid state detector evaluated in an earlier work. The CdTe system utilized new pinhole collimators and an alignment device that facilitated measurement of mammographic x-ray spectra. Mammographic x-ray spectra acquired by each system were comparable. Half value layer measurements obtained using an ion chamber agreed closely with those derived from the x-ray spectra measured by either detector. The faster electronics and other features of the CdTe detector allowed its use with a larger pinhole collimator than could be used with the CZT detector. Additionally, the improved pinhole collimator and alignment features of the apparatus permitted much more rapid setup for acquisition of x-ray spectra than was possible on the system described in the earlier work. These improvements in detector technology, collimation and ease of alignment, as well as low cost, make this apparatus attractive as a tool for both laboratory research and advanced mammography quality control.

A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. Our collaboration, among italian physicists and radiologists, has built a large distributed database of digitized mammographic images and has developed a Computer Aided Detection (CADe) system for the automatic analysis of mammographic images and installed it in some Italian hospitals by a GRID connection. Regarding microcalcifications, in our CADe digital mammogram is divided into wide windows which are processed by a convolution filter; after a self-organizing map analyzes each window and produces 8 principal components which are used as input of a neural network (FFNN) able to classify the windows matched to a threshold. Regarding massive lesions we select all important maximum intensity position and define the ROI radius. From each ROI found we extract the parameters which are used as input in a FFNN to distinguish between pathological and non-pathological ROI. We present here a test of our CADe system, used as a second reader and a comparison with another (commercial) CADe system., Comment: 4 pages, Proceedings of the 4th International Symposium on Nuclear and Related Techniques 2003, Vol. unico, pp. d10/1-d10/4 Havana, Cuba

Traditionally X-ray sources used in mammography are X-ray tubes. Synchrotron radiation sources have shown better imaging performances, but they cannot replace conventional X-ray tube systems in routine mammographic examinations. A new generation of quasi-mono chromatic, high-flux X-ray sources is currently under development, based on Thomson backscattering of photons produced by a laser on a highly focused electron beam. They offer important potential applications in the medical field. In this work, we will discuss an application in the field of mammography, by using a Monte Carlo code, in which the effect of different spectral distributions and different mean energies on the image quality is studied. A test object, consisting of a block of Polymethyl Methacrylate (PMMA) containing air-filled holes (Contrast Detail Phantom) is used for the simulations. Results show 1-2 keV of energy spread for a quasi-monochromatic source produce images whose quality is comparable within 3-4% with those obtained by monochromatic sources and whose visibility is dramatically enhanced with respect to images obtained with X-ray tubes.

FIRST (Fragmentation of Ions Relevant for Space and Therapy) is an experiment aimed at the measurement of double-differential cross sections, with respect to kinetic energy and scattering polar angle, of nuclear fragmentation processes in the energy range between 100 and 1000 MeV/u. The experiment was performed with the SIS accelerator at GSI (Darmstadt, Germany). During August 2011 a first set of data was collected using a 400 MeV/u 12C ion beam on carbon and gold targets. We present a description of the experimental apparatus and some preliminary results from the data acquisition and from the data analysis.

In this work a comparison of different classification methods for the identification of mass lesions in digitized mammograms is performed. These methods, used in order to develop Computer Aided Detection (CAD) systems, have been implemented in the framework of the MAGIC-5 Collaboration. The system for identification of mass lesions is based on a three-step procedure: a) preprocessing and segmentation, b) region of interest (ROI) searching, c) feature extraction and classification. It was tested on a very large mammographic database (3369 mammographic images from 967 patients). Each ROI is characterized by eight features extracted from a co-occurrence matrix containing spatial statistics information on the ROI pixel grey tones. The reduction of false-positive cases is performed using a classification system. The classification systems we compared are: Multi Layer Perceptron (MLP), Probabilistic Neural Network (PNN), Radial Basis Function Network (RBF) and K-Nearest Neighbours classifiers (KNN). The results in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of nonpathological ROIs correctly classified) are presented. MLP and RBF outperform other classification algorithms by about 8% of the area under the ROC curve.

The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis., Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 2003

Item does not contain fulltext Breast computed tomography (BCT) is an emerging application of X-ray tomography in radiological practice. A few clinical prototypes are under evaluation in hospitals and new systems are under development aiming at improving spatial and contrast resolution and reducing delivered dose. At the same time, synchrotron-radiation phase-contrast mammography has been demonstrated to offer substantial advantages when compared with conventional mammography. At Elettra, the Italian synchrotron radiation facility, a clinical program of phase-contrast BCT based on the free-space propagation approach is under development. In this paper, full-volume breast samples imaged with a beam energy of 32 keV delivering a mean glandular dose of 5 mGy are presented. The whole acquisition setup mimics a clinical study in order to evaluate its feasibility in terms of acquisition time and image quality. Acquisitions are performed using a high-resolution CdTe photon-counting detector and the projection data are processed via a phase-retrieval algorithm. Tomographic reconstructions are compared with conventional mammographic images acquired prior to surgery and with histologic examinations. Results indicate that BCT with monochromatic beam and free-space propagation phase-contrast imaging provide relevant three-dimensional insights of breast morphology at clinically acceptable doses and scan times.

Absorption X-ray imaging is a well-established technique. However it is still a challenging task in its search for a compromise between the need for high spatial resolution and high contrast and the request to keep the dose delivered to the patient within acceptable values. New imaging techniques are under investigation, like the use of new X-ray sources, phase contrast imaging or K-edge imaging. Monte Carlo or analytic simulations are often the best way to test and predict the effectiveness of these techniques. A new simulation tool for X-ray imaging will be presented together with some applications to the characterization of new X-ray sources, in-line phase contrast effect and angiographic K-edge imaging. Simulation results will be compared also with experimental data

A first generation CT-scanner was designed and constructed to carry out Compton images. This CT-scanner is composed of a 80 kV, 5 mA X-ray tube and a NaI(Tl) X-ray detector; the tube is strongly collimated, generating a X-ray beam of 2 mm diameter, whilst the detetor is not collimated to collect Compton photons from the whole irradiated cylinder. The performances of the equipment were tested contemporaneous transmission and Compton images.

Mammography is a challenging field of medical imaging. Early detection of breast cancer requires identifying small contrast details. The choice of the appropriate monochromatic energy enhances the visibility of such details. Thomson scattering source can provide tunable quasi-monochromatic X-ray beams. In this work, we investigate by Monte Carlo simulations the optimal monochromatic energy to image mammographic phantoms. In order to mimic a Thomson scattering source, we consider the effect on image quality of the presence of an energy spread and of the presence of higher-order harmonics.

The CALMA (Computer Assisted Library for MAmmography) project is a five years plan developed in a physics research frame in collaboration between INFN (Istituto Nazionale di Fisica Nucleare) and many Italian hospitals. At present a large database of digitized mammographic images (more than 6000) was collected and a software based on neural network algorithms for the search of suspicious breast lesions was developed. Two tools are available: a microcalcification clusters hunter, based on supervised and unsupervised feedforward neural network, and a massive lesions searcher, based on a hibrid approach. Both the algorithms analyzed preprocessed digitized images by high frequency filters. Clinical tests were performed to evaluate sensitivity and specificity of the system, considering the system as alone and as secon reader. Results show that the system is ready to be implemented by medical industry. The CALMA project, just ended, has its natural development in the GPCALMA (Grid Platform for CALMA) project, where distributed users join common resources (images, tools, statistical analysis)., Comment: 3 pages, 1 figure; to appear in the Proceedings of Frontier Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31 May 2003, La Biodola, Isola d'Elba, Italy

Open Access.-- et al., The FIRST (Fragmentation of Ions Relevant for Space and Therapy) experiment at GSI has been designed to study carbon fragmentation, measuring 12C double differential cross sections (∂2σ/∂θ∂E) for different beam energies between 100 and 1000 MeV/u. The experimental setup integrates newly designed detectors in the, so called, Interaction Region around the graphite target. The Interaction Region upstream detectors are a 250 μm thick scintillator and a drift chamber optimized for a precise measurement of the ions interaction time and position on the target. In this article we review the design of the upstream detectors along with the preliminary results of the data taking performed on August 2011 with 400 MeV/u fully stripped carbon ion beam at GSI. Detectors performances will be reviewed and compared to those obtained during preliminary tests, performed with 500 MeV electrons (at the BTF facility in the INFN Frascati Laboratories) and 80 MeV/u protons and carbon ions (at the INFN LNS Laboratories in Catania)., This work has been supported by the European Community FP7 - Capacities, contract ENSAR nº 262010. This work was also supported by Junta de Andalucia and the Spanish Ministerio de Ciencia e Innovacion Contracts P07-FQM-02894, FIS2008-04189 and FPA2008-04972-C03.

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron., {"references": ["", "Smith R.A., \"Epidemiology of breast cancer\", in \"A categorical course in physics. Imaging considerations and medical physics responsibilities\", Madison, Wisconsin, Medical Physics Publishing, 1991.", "Peto R., Boreham J., Clarke m., Davies c., Deral V, correspondence \"UK and USA Breast cancer deaths down 25% in year 2000 at ages 20-69 years\", LANCET 2000, 355, (9217) pp. 1822-1823, 2000.", "Bird R., Wallace T., Yankaskas B., \"Analysis of cancer missed at screening mammography\", Radiology 1992: 184; pp.613-617, 1992.", "Haus A.G. and Yaffe M. (editors), \"A categorical course in physics. 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Giger, \"Computer vision and artificial intelligence in mammography\", AJR 162, 699-708, 1994.\n[11] Bottigli U, Delogu P, Fantacci ME, Fauci F, Golosio B, Lauria A, Palmiero R, Raso G, Stumbo S, Tangaro S Search of Microcalcification clusters with the CALMA CAD station. The International Society for Optical Engineering (SPIE) 4684: 1301-1310, 2002\n[12] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S. C. Cheran, F. De Carlo, G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E.Lopez Torres, R. Magro, G. L. Masala, P.Oliva, M. Quarta, G. Raso, A. Retico, S.Tangaro, Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Network, Proc. IEEE Medical Imaging Conference, October 16-22 2004, Rome, Italy; M2- 373/1-5, 2004.\n[13] S. 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On Multimedia Data Mining, USA, 2001.\n[28] Vyborny CJ., Giger ML., Computer vision and artificial intelligence in mammography, AJR: 162; 699-708, 1994.\n[29] Lai S., Li X., Bischof W., On techniques for detecting circumscribed masses in mammograms\", IEEE Transaction on Medical Imaging: 8(4); 377-386, 1989.\n[30] Aapo Hyv\u252c\u00bfarinen, Erkki Oja, \"Indipendent Component Analysis: Algorithms and Applications\", Neural Networks Research Centre, Helsinki University of Technology, Finland, \"Neural Networks\", 13 (4- 5):411-430, 2000.\n[31] Hanley JA, McNeil B, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.\n[32] Hanley JA, McNeil B, A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology: 148; 839-843, 1983.\n[33] U. Bottigli, B. Golosio, G. L. Masala, P. Oliva, S. Stumbo, D. Cascio, F. Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S.Tangaro, I. De Mitri, G. De Nunzio, M. Quarta, A. Preite Martinez, P. Cerello, S. C. Cheran, E.Lopez Torres \"Dissimilarity Application for Medical Imaging Classification\" on proceedings of The 9th World Multi-Conference on Systemics, Cybernetics and Informatics WMSCI 2005, Orlando 10-13 July 2005, vol III pag 258-262, 2005.\n[34] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. Cheran, E. L. Torres, “Classifiers trained on dissimilarity representation of medical pattern: a comparative study" on Nuovo Cimento C, Vol. 028, Issue 06, pp 905-912 , 2005."]}

The proper use of imaging equipment in radiological units is based on an appropriate knowledge of the physical characteristics of the X-ray beam used. The FLUXEN PROJECT is working on a portable apparatus which, together with dedicated software, is able to perform an exact spectral reconstruction of the radiation produced in diagnostic X-ray tubes. The apparatus characterizes the energy spectrum of radiological tubes and also provides a measurement of the emitted flux. The acquisition system is based on a commercial CZT detector (3×3×2 mm 3 ), produced by AMPTEK, cooled by a Peltier cell, with a high efficiency in the diagnostic X-ray energy range and modified in the shaping electronics so as to obtain a faster response. The acquiring section lies on a NuDAQ I/O card with a sampling frequency of up to 20 MHz. The signal produced by the X-ray tube is wholly acquired and an off-line analysis is made so as to make possible an accurate recognition of pile-up events and a reconstruction of the emitted spectra. The reconstructed spectra of a General Electric Senographe DMR mammographic X-ray tube are shown.

The MAGIC-5 Project aims at developing Computer Aided Detection (CAD) software for Medical Applications on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images has been demonstrated to improve the radiologist performance. In addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of GRID Services for the management of distributed computing resources and data. GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated to the diagnosis in screening programs. The MAGIC-5 project develops algorithms for the analysis of mammographies for breast cancer detection, Computed-Tomography (CT) images for lung cancer detection and Positron Emission Tomography (PET) imaging for the early diagnosis of the Alzheimer Disease (AD). We plan to configure a Virtual Organization (VO), so that authorized users can share data and resources and implement the following use cases: screening, tele-training and tele-diagnosis for mammograms and lung CT scans; statistical diagnosis by comparison of candidates to a distributed data-set of negative PET scans for the diagnosis of the Alzheimer Disease. A smallscale prototype of the required GRID functionality was already implemented for the analysis of digitized mammograms.