61 research outputs found
Simulated and experimental spectroscopic performance of GaAs X-ray pixel detectors
No full textIn pixel detectors, the electrode geometry affects the signal shape and therefore the spectroscopic performance of the device. This effect is enhanced in semiconductors where carrier trapping is relevant. In particular, semi insulating (SI) GaAs crystals present an incomplete charge collection due to a high concentration of deep traps in the bulk. In the last few years, SI GaAs pixel detectors have been developed as soft X-ray detectors for medical imaging applications. In this paper, we present a numerical method to evaluate the local charge collection properties of pixel detectors. A bi-dimensional description has been used to represent the detector geometry, According to recent models, the active region of a reverse biased SI GaAs detector is almost neutral. Therefore, the electrostatic potential inside a full active detector has been evaluated using the Laplace equation. A finite difference method with a fixed step orthogonal mesh has been adopted. The photon interaction point has been generated with a Monte Carlo method according to the attenuation length of a monochromatic X-ray beam in GaAs. The number of photogenerated carriers for each interaction has been extracted using a gaussian distribution. The induced signal on the collecting electrode has been calculated according to the Ramo's theorem and the trapping effect has been modeled introducing electron and hole lifetimes. The noise of the charge preamplifier have been also taken into account. A comparison between simulated and experimental X-ray spectra from a Am-241 source acquired with different GaAs pixel detectors has been carried out. (C) 2001 Elsevier Science B.V. All rights reserved
A Model for a Linear a-Se Detector in Simulated X-Ray Breast Imaging With Monte Carlo Software
No full textIn-silico clinical trials with digital patient models and simulated devices are an alternative to expensive and long clinical trials on patient population for testing X-ray breast imaging apparatuses. In this work, we simulated a linear-response a-Se detector as an X-ray absorber, neglecting some physical processes, such as electro-hole tracking and thermal noise. In order to tune characteristics of the simulated images toward those of the clinical scanners, the detector response curve, modulation transfer function (MTF), and normalized noise power spectrum (NNPS) were measured on a clinical mammographic unit. The same tests were replicated in-silico via a custom-made Monte Carlo code in order to define a suitable model to modify simulated images and to have realistic pixel values, noise, and spatial resolution. The proposed approach resulted to restore the slope and the magnitude of the NNPS in simulated images toward curves evaluated on a clinical scanner. Similarly, the proposed strategy for tuning noise and spatial resolution in simulated images led to a contrast-to-noise ratio (CNR) evaluated on a custom-made phantom which differed from those in measured images less than 16% in absolute value
Normalized glandular dose coefficients for digital breast tomosynthesis systems with a homogeneous breast model
No full textThis work aims at calculating and releasing tabulated values of dose conversion coefficients, DgNDBT, for mean glandular dose (MGD) estimates in digital breast tomosynthesis (DBT). The DgNDBT coefficients are proposed as unique conversion coefficients for MGD estimates, in place of dose conversion coefficients in mammography (DgNDM or c, g, s triad as proposed in worldwide quality assurance protocols) used together with the T correction factor. DgNDBT is the MGD per unit incident air kerma measured at the breast surface for a 0° projection and the entire tube load used for the scan. The dataset of polyenergetic DgNDBT coefficients was derived via a Monte Carlo software based on the Geant4 toolkit. Dose coefficients were calculated for a grid of values of breast characteristics (breast thickness in the range 20-90 mm and glandular fraction by mass of 1%, 25%, 50%, 75%, 100%) and the simulated geometries, scan protocols, irradiation geometries and typical spectral qualities replicated those of six commercial DBT systems (GE SenoClaire, Hologic Selenia Dimensions, GE Senographe Pristina, Fujifilm Amulet Innovality, Siemens Mammomat Inspiration and IMS Giotto Class). For given breast characteristics, target/filter combination, tube voltage and half value layer (HVL), two spectra with two HVL values have been simulated in order to permit MGD estimates from experimental HVL values via mathematical interpolation from tabulated values. The adopted breast model assumes homogenous composition of glandular and adipose tissues; it includes a 1.45 mm thick skin envelope in place of the 4-5 mm envelope commonly adopted in dosimetry protocols. The simulation code was validated versus AAPM Task group 195 Monte Carlo reference data sets (absolute differences not higher than 1.1%) and by comparison to relative dosimetry measurements with radiochromic film in a PMMA test object (differences within the maximum experimental uncertainty of 11%). The calculated coefficients show maximum relative deviations of -17.6% and +6.1% from those provided by the DBT dose coefficients adopted in the EUREF protocol and of 1.5%, on average, from data in the AAPM TG223 report. A spreadsheet is provided for interpolating the tabulated DgNDBT coefficients for arbitrary values of HVL, compressed breast thickness and glandular fraction, in the corresponding investigated ranges, for each DBT unit modeled in this work
A scalable computer-aided detection system for microcalcification cluster identification in a pan-European distributed database of mammograms
No full textA computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different kinds of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report the FROG analyses of the CADe system performances on three different dataset of mammograms, i.e. images of the CALMA INFN-founded database collected in the Italian National screening program, the M1AS database and the so-far collected MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im have been obtained on the CALMA, MIAS and MammoGrid database, respectively. (c) 2006 Elsevier B.V. All rights reserved. RI Retico, Alessandra /I-6321-201
Approaches to juxta-pleural nodule detection in CT images within theMAGIC-5 Collaboration
No full textThis work is a part of the MAGI5 (Medical Applications on a Grid Infrastructure Connection) experiment of the Italian INFN (Istituto Nazionale di Fisica Nucleare). A simple CAD (Computer Assisted Detection) system for juxta pleural lung nodules in CT images is presented, with the purpose of comparing different
2D concavity patching techniques and assessing the respective efficiency in locating nodules
90P: Clinical validation of the M5L lung computer-assisted detection system
No full textBackground: Early diagnosis of lung cancer could be crucial in
trying to reduce the mortality. Alongside screening programmes,
several Computer-Assisted Detection (CAD) systems for the
automatic detection of pulmonary nodules, were developed, in
order to support radiologists in the diagnosis.
Methods: The M5L CAD, developed by the INFN in collaboration
with CEADEN (Habana, Cuba) combines the results of two
independent algorithms and provides a framework for further
extension to others. The sensitivity is about 80% in the 4–6
false positive findings/scan range, which, considering the fact
M5L was applied in a clinical-like approach, with no optimization
and no data selection, is satisfactory. The development team
tackled the issue of making it available to the largest possible user
community. Therefore, a Web/Cloud prototype was designed and
implemented: CT scans can be uploaded asynchronously by ICT
staff in health facilities, while the M5L results are directly sent
to the radiologist e-mail accounts in DICOM-compatible format.
Clinical validation on oncological patients undergoing staging or
restaging has recently started at IRCCS Candiolo, Italy. A panel
formed by three radiologists with different level of expertise
independently annotates the cases through the M5L web interface
in first-reader mode. Each annotation includes not only spatial
information about the nodule, but also several information about
its features (e.g. malignancy, speculation). After each annotation
is completed, M5L results are prompted to the radiologist, who
reviews the first-reading results.
Results: Preliminary results are showing a CAD sensitivity greater
than 80% at about 4 FP/ scan which is found to be higher than the
one obtained within the LIDC data-set. Furthermore, the usage of
CAD is sensitively increasing the performance of the radiologists.
Conclusions: We are proposing a CAD framework in clinical
routine which frees the user from buying additional software
/ hardware to access CAD results. Furthermore, this approach
not only allows to share CAD results and annotations between
radiologists not necessarily belonging to the same institution,
but also allows to combine different CADs developed by different
groups with no particular effort.
Legal entity responsible for the study: INFN Turin
Funding: Ministero della Istruzione e Ricerca
Disclosure: All authors have declared no conflicts of Clinical validation on oncological patients undergoing staging or
restaging has recently started at IRCCS Candiolo, Italy. A panel
formed by three radiologists with different level of expertise
independently annotates the cases through the M5L web interface
in first-reader mode. Each annotation includes not only spatial
information about the nodule, but also several information about
its features (e.g. malignancy, speculation). After each annotation
is completed, M5L results are prompted to the radiologist, who
reviews the first-reading results.
Results: Preliminary results are showing a CAD sensitivity greater
than 80% at about 4 FP/ scan which is found to be higher than the
one obtained within the LIDC data-set. Furthermore, the usage of
CAD is sensitively increasing the performance of the radiologists.
Conclusions: We are proposing a CAD framework in clinical
routine which frees the user from buying additional software
/ hardware to access CAD results. Furthermore, this approach
not only allows to share CAD results and annotations between
radiologists not necessarily belonging to the same institution,
but also allows to combine different CADs developed by different
groups with no particular effort
Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples
No full textPredictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML
Non-invasive assessment of Neuromuscular Disorders by 7 tesla Magnetic Resonance Imaging and Spectroscopy: Dedicated radio-frequency coil development
No full textMRI OF HUMAN KNEE AT 7 T WITH DEDICATED RADIOFREQUENCY COILS
No full textMagnetic Resonance (MR) Imaging is a valuable tool in the diagnosis and monitoring of various musculoskeletal pathologies. New Ultra-High Field (UHF) 7 T MRI systems, with their enhanced Signal-to-Noise Ratio, may offer increased image quality in terms of spatial resolution and/or shorter scanning time compared to lower field systems. However, these benefits can be difficult to obtain because of increased radio-frequency (RF) inhomogeneity, increased Specific Absorption Rate (SAR) and the relative lack of specialized and commercially available RF coils compared to lower field systems. The IMAGO7 Foundation in Pisa (Italy) owns the first and only 7 Tesla whole-body MR scanner (950-MR scanner, GE Medical Systems) in Italy. In this framework, a research collaboration between the IMAGO7 Foundation and the Italian National Institute for Nuclear Physics (INFN) aims to develop RF coils for specific MR applications, and to exploit the UHF potential in several research areas, including MSK imaging. This study reports the feasibility of imaging in trabecular bones and cartilages at UHF by means of dedicated radio-frequency coils for proton imaging which have been designed, developed, optimized and validated in vivo, and are now ready for clinical research studies. UHF MRI of the knee can allow an accurate characterization of morphology and biochemical quality of the cartilages for clinical assessment of early pathological conditions of cartilage in osteoarthritis. Six healthy (age 24-61y) and one pathological (alteration of the patellar cartilage, 62y) volunteers were considered for preliminary acquisitions. Morphological images have been acquired by means of 3D FIESTA (0.156 mm in-plane resolution, FA = 20, TR = 6.3 ms, TE = 2.5 ms, thickness = 0.8 mm sequences). The patellar cartilages was then segmented and the volumes of the segmented cartilages have been quantified. In order to evaluate also the biochemical behaviour of the cartilage in the pathological subject, T2 (with multi echo SE with 4 echo times sequence) and T2* (by 3D MERGE with 6 echo times sequence) maps have been also computed. The obtained results demonstrate that the research in trabecular bone and cartilages characterization, comprising quantitative assessment of cartilage volume and evaluation of biochemical behaviour, can take advantage from UHF MRI with dedicated coils
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