Rosa Palmiero

The MAGIC-5 Project: medical applications on a GRID infrastructure connection

The MAGIC-5 Project aims at developing computer aided detection (CAD) software for medical applications on distributed databases by means of a GRID infrastructure connection. The use of automatic systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, computed-tomography (CT) images for lung cancer analysis, and the positron emission tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 Collaboration can be seen as a group of distributed users sharing their resources for implementing different virtual organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.

Diagnostic performance of radiologists with and without different CAD systems for mammography

The purpose of this study is the evaluation of the variation of performance in terms of sensitivity and specificity of two radiologists with different experience in mammography, with and without the assistance of two different CAD systems. The CAD considered are SecondLookTM (CADx Medical Systems, Canada), and CALMA (Computer Assisted Library in MAmmography). The first is a commercial system, the other is the result of a research project, supported by INFN (Istituto Nazionale di Fisica Nucleare, Italy); their characteristics have already been reported in literature. To compare the results with and without these tools, a dataset composed by 70 images of patients with cancer (biopsy proven) and 120 images of healthy breasts (with a three years follow up) has been collected. All the images have been digitized and analysed by two CAD, then two radiologists with respectively 6 and 2 years of experience in mammography indipendently made their diagnosis without and with, the support of the two CAD systems. In this work sensitivity and specificity variation, the Az area under the ROC curve, are reported. The results show that the use of a CAD allows for a substantial increment in sensitivity and a less pronounced decrement in specificity. The extent of these effects depends on the experience of the readers and is comparable for the two CAD considered.

Spectroscopic and imaging capabilities of a pixellated photon counting system

We are studying the performance of various thickness GaAs pixel detectors bump-bonded to a dedicated photon counting chip (PCC) for medical imaging applications in different energy ranges. In this work we present the experimental results obtained with a 600 μm thick pixel matrix (64×64 square pixels, 170 μm side) in the 60–140 keV energy range to evaluate the possible use of such a system in the nuclear medicine field. In particular, we have measured the spectroscopic properties of the detector (charge collection efficiency, energy resolution and detection efficiency) and evaluated the discrimination capability of the electronics. Then we have measured the imaging properties of the whole system in terms of Point Spread Function and using a home made thyroid phantom. We present also a comparison with a traditional gamma camera and an evaluation, made by both experimental measurements and software simulations, of the imaging characteristics related to the use of a collimation system.

The Calma Project

The CALMA (Computer_Assisted Library for MAmmography) project was born as a collaboration between radiologists and physicists. Its goal is to collect a set of digital mammographic images and to work out a suitable Computed Assisted Diagnosis tool to be used in screening mammography. The project is an Italian collaboration among the Istituto Nazionale di Fisica Nucleare sections of Bologna, Pisa, Torino, Udine and Trento and the Hospitals of Bari, Bologna, Livorno, Udine and Trento. Some preliminary results obtained in the classification of breast disease are described here.

Evaluation of the imaging properties of a direct detection single photon counting based system

We have tested the imaging properties of a system based on a GaAs matrix that performs the direct detection of photons in the range of radiographic interest. The detector is bump-bonded to a VLSI single photon counting electronics. The imaging properties of the system have been evaluated in terms of the presampling MTF and the DQE(ν), that allow an absolute characterization of imaging properties of digital systems. We have tested other radiographic imaging systems in standard clinical conditions, using the same functions and we have compared them with the GaAs matrix system.

The CALMA system: an artificial neural network method for detecting masses and microcalcifications in digitized mammograms

Abstract The Computer Assisted Library for MAmmography (CALMA) project is a 5 years plan developed in a physics research frame in collaboration between 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 lesion 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 second 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 Grid Platform for CALMA project, where distributed users join common resources (images, tools, and statistical analysis).

Comparison between different monitors to be used in the reading of digital mammographic images

Digital acquisition of mammographic images is becoming more diffuse in hospitals, as well as off line digitalization of analogical images to allow use of CAD, filing and statistical tools. Radiologists performance in reading digital images are strictly related to the quality of the images displayed on the monitor. We are investigating how different display devices, influence digital images reading. To reach the goal we are using phantoms for quality controls in mammography. Three different monitors are considered. The first one is the high resolution CRT display used as diagnostic monitor for the GE digital mammograph. The others are a high quality monitor for personal computer and the monitor of a high quality notebook. The phantoms used are the CDMAM 3.2 (a contrast-detail phantom) and the RMI 156 (which contains test objects that represent malignancies and small breast structures). Their images were acquired by the digital mammograph and were then analyzed by two expert radiologists by observing them on the different display devices, adopting the same procedure. The results about the reading of the phantoms and the interpretation of the images with different monitors are presented here.

GPCALMA, a mammographic CAD in a GRID connection

Abstract The purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. 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. Grid Platform Computer-Assisted Library for MAmmography (GPCALMA), a collaboration among Italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a Computer Aided Detection (CAD) system which, installed in an integrated station, can also be used for digitization, as archive, and to perform statistical analysis. With a GRID configuration, it would be possible for clinicians tele- and co-working in new and innovative groupings (‘virtual organizations’), and using the whole database, by the GPCALMA tools, several analysis can be performed. Furthermore, GPCALMA allows to be abreast of the CAD technical progressing into several hospital locations always with remote working by GRID connection. We report in the results obtained by the GPCALMA CAD software implemented with a GRID connection.

GPCALMA: A Tool For Mammography With A GRID‐Connected Distributed Database

The GPCALMA (Grid Platform for Computer Assisted Library for MAmmography) collaboration involves several departments of physics, INFN (National Institute of Nuclear Physics) sections, and italian hospitals. The aim of this collaboration is developing a tool that can help radiologists in early detection of breast cancer. GPCALMA has built a large distributed database of digitised mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) software which is integrated in a station that can also be used to acquire new images, as archive and to perform statistical analysis. The images (18×24 cm2, digitised by a CCD linear scanner with a 85 μm pitch and 4096 gray levels) are completely described: pathological ones have a consistent characterization with radiologist’s diagnosis and histological data, non pathological ones correspond to patients with a follow up at least three years. The distributed database is realized throught the connection of all the hospitals and research centers in GRID tecnology. In each hospital local patients digital images are stored in the local database. Using GRID connection, GPCALMA will allow each node to work on distributed database data as well as local database data. Using its database the GPCALMA tools perform several analysis. A texture analysis, i.e. an automated classification on adipose, dense or glandular texture, can be provided by the system. GPCALMA software also allows classification of pathological features, in particular massive lesions (both opacities and spiculated lesions) analysis and microcalcification clusters analysis. The detection of pathological features is made using neural network software that provides a selection of areas showing a given “suspicion level” of lesion occurrence. The performance of the GPCALMA system will be presented in terms of the ROC (Receiver Operating Characteristic) curves. The results of GPCALMA system as “second reader” will also be presented.

The search for spiculated lesions in the CALMA project: status and perspectives

A status report is presented on the massive lesions search in the frame of the CALMA project. 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 tumoural disease. 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. The work performed on images collected from Italian hospitals in the last year, as well as on images available from public databases is here presented, indicating, at this stage, a number of false positives of the order of 3 per image keeping a large sensitivity on our sample. This achievement although improvable, is comparable with results obtained by other groups working on the same subject.