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A digital mammography system based on a GaAs pixel detector has been developed by the INFN (Istituto Nazionale di Fisica Nucleare) collaboration MED46. The high atomic number makes the GaAs a very efficient material for low energy X-ray detection (10-30 keV is the typical energy range used in mammography). Low contrast details can be detected with a significant dose reduction to the patient. The system presented in this paper consists of a 4096 pixel matrix built on a 200 /spl mu/m thick semi-insulating GaAs substrate. The pixel size is 170/spl times/170 /spl mu/m/sup 2/ for a total active area of 1.18 cm/sup 2/. The detector is bump-bonded to a VLSI front-end chip which implements a single-photon counting architecture. This feature allows to enhance the radiographic contrast detection with respect to charge integrating devices. The system has been tested by using a standard mammographic tube. Images of mammographic phantoms will be presented and compared with radiographs obtained with traditional film/screen systems. Monte Carlo simulations have been also performed to evaluate the imaging capability of the system. Comparison with simulations and experimental results will be shown.

We have investigated the detection performance of GaAs detectors made with different thickness and contact geometries. A comparison is made between these detection capabilities and the imaging requirements for the following medical applications: digital mammography, digital chest radiography and nuclear medicine. Experimental results and preliminary images are presented and discussed.

The work presented here was developed in the framework of the SENTINEL Project and is devoted to the analysis of dental radiology dosimetric data. The procedure of data processing allows the analysis of some important aspects related to the protection of the patient and the staff because of the position of the operators near the patient and their exposure to the radiation scattered by the patient. Dental radiology data was collected in an Italian hospital. Following the Italian quality assurance (QA) protocols and suggestions by the leaders of the SENTINEL Project, X-ray equipment performances have been analysed in terms of: kVp accuracy, exposure time accuracy and precision, tube output, dose reproducibility and linearity, beam collimation, artefacts and light tightness. Referring to these parameters the physical quality index (QI) was analysed. In a single numerical value between 0 and 1, QI summarises the results of quality tests for radiological devices. The actual impact of such a figure (as suggested by international QA protocols or as adopted by local QA routine) on the policy of machine maintenance and replacement is discussed.

We report the status of the art of a prototype based on a GaAs pixel detector bump-bonded to a dedicated VLSI chip to be possibly used for imaging in the nuclear medicine field. This device, with a 200 μm thick pixel matrix (64×64 square pixels, 170 μm side), has already been tested with very good results for digital mammography applications (mean energy 20 keV). For more energetic photons, as in nuclear medicine, a 600 μm thick detector has been chosen. Using radioactive sources ( 241 Am, 60 keV and 99 m Tc, 140 keV photons) we have measured the performance of our prototype in terms of charge collection and detection efficiency of the detector, discrimination capability of the electronics and imaging properties of the whole system. In particular, we have evaluated the spatial resolution properties measuring the Point Spread Function and the imaging capabilities using a home made thyroid phantom. We present also the comparison between these results and those obtained with a traditional gamma camera and the evaluation, made by both experimental measurements and software simulations, of the geometry related to the use of a collimator.

We are characterising a prototype GaAs pixel detector able to perform digital mammography which contains two features which give enhanced performance compared with standard mammographic systems: it can detect low contrast test objects, inaccessible for traditional systems and, using its higher intrinsic detection efficiency, can reduce the radiation dose to the patient. The prototype is a hybrid assembly of a GaAs detector divided into 64×64 pixels, each of which measures 170 μm on the side, which is bump-bonded to a photon counting chip, PCC, developed by the Medipix collaboration. The cells of the readout chip are identical in size to the detector pixels and work in single photon counting mode. We have studied the detection efficiency of some assemblies for different bias voltages, reaching in some cases the detector breakdown point. To test the assembly in the same clinical conditions used in a standard mammographic system we have tested the prototype with radiation produced by a standard mammographic X-ray tube operating at 28 kVp. In particular, working not too far from the detector breakdown point, we are checking its imaging capabilities using a mammographic test phantom with known composition of the materials and therefore known contrast.

In this paper we present the Computer-Aided Library for MAmmography (CALMA Project), i.e. an automated search for the mammograms’ texture, the massive lesions and microcalcifications clusters. CALMA's main purpose is to collect a database of mammographic images, developing CAD tools to be used as a second radiologist in the classification of breast cancer diseases. In this moment, 2200 images are already in our database, which is, to our knowledge, the largest in Europe. One-third of our digitized images are pathological, and they are fully characterized by a consistent description and diagnosis. We try to perform automatically the classification of mammographic images on the bases of tissues’ textures. Such a classification should be done in an unbiased way with respect to radiologists and should support their interpretation of the mammographic image. Results obtained with non supervised neural networks are shown, as well as results coming from a mixed approach (features extraction combined with FF-ANN). Massive lesions are rather large objects to be detected, but they show up with a faint contrast slowly increasing with time. The need for tools able to recognize such a lesion at an early stage is therefore apparent. Our tools are based on a ROI hunter procedure for spiculated lesions showing a number of false positives of the order of 1.4 per image and keeping a 85% sensitivity on our sample. A microcalcication is a rather small (0.1–1.0 mm in diameter) but very brilliant. Some of them, either grouped in cluster or isolated may indicate the presence of a tumor. Up to now only 40 images with microcalcications from our database have been analyzed, and a CAD tool has been designed to detect clusters, reaching a correct classification of 90%.

The left ventricular reduction called Batista's procedure (BP) is a surgical technique able to improve the ventricular function in the heart disease known as Congestive Heart Failure (CHF). From a geometrical point of view the BP is a restyling of the left ventricle from a spherical configuration to an ellipsoidal one. The authors have written a program ("SoftHeart") which simulates the response of the heart after the BP. The main products of the authors' program are the new ejection fraction and the new stroke volume as a function of the equatorial surgical reduction. Using these curves it is possible to classify the patients into three categories corresponding to a Gaussian-like curve, a flat curve and a monotonic descending curve, respectively. A clinical trial performed on 165 patients allows a good correlation between the three categories built by "SoftHeart" and three different clinical situations. An explanation of the three categories produced by "SoftHeart" using the Laplace and the Frank-Starling laws is reported.