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Digital mammography represents the most sensitive technique to detect and diagnose breast cancer and is hence the preferred method for clinical and screening diagnostic tests. Nevertheless, the absorbed dose in a mammographic procedure can not be neglected even due to the highly radiosensitivity of the breast. In this work we developed a practical method to assess the average absorbed dose both in digital mammography and digital breast tomosynthesis. A new dose index is here proposed as an easily evaluable dosimetric quantity according to the requests of the European Directive 2013/59 EURATOM.

Il progetto ARIANNA ha sviluppato un ambiente di ricerca collaborativo e interdisciplinare per lo studio dei Disturbi dello Spettro Autistico (DSA), facilmente accessibile alla comunità dei ricercatori che lavorano nell'ambito dei DSA tramite una piattaforma web (https://arianna.pi.infn.it). Il progetto oltre a fornire ai ricercatori l'accesso a risorse computazionali equipaggiate con i più moderni strumenti in termini di efficienza di calcolo, è stato sviluppato in modo da garantire che la gestione e conservazione dei dati fosse svolta secondo le indicazioni e le prescrizioni previste dalla normativa, così come applicata tramite i pareri dell'Autorità garante per la protezione dei dati personali.

We performed a study to predict global and local subject-specific Specific Absorption Rate (SAR) exposure in 7T Musculoskeletal (MSK) acquisition sequences. Such prediction, that is not available in current Magnetic Resonance (MR) exams, is possible combining sequences SAR exposure simulated on the generic anatomical model with subject-specific measured B+ 1 maps. The procedure has been implemented with a degenerate birdcage coil specifically designed for knee exams. Results show a good agreement between simulated B+ 1 and B+ 1 magnitude measure in-vivo, while values of global and local SAR calculated are below the recommended limit, respectively 8 W/kg and 20 W/kg.

Objective:Neuroimaging studies show structural differences in both cortical and subcortical brain regions in children and adults with autism spectrum disorder (ASD) compared with healthy subjects. Findings are inconsistent, however, and it is unclear how differences develop across the lifespan. The authors investigated brain morphometry differences between individuals with ASD and healthy subjects, cross-sectionally across the lifespan, in a large multinational sample from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) ASD working group.Method:The sample comprised 1,571 patients with ASD and 1,651 healthy control subjects (age range, 2–64 years) from 49 participating sites. MRI scans were preprocessed at individual sites with a harmonized protocol based on a validated automated-segmentation software program. Mega-analyses were used to test for case-control differences in subcortical volumes, cortical thickness, and surface area. Development of brain morphometry over the lifespan was modeled using a fractional polynomial approach.Results:The case-control mega-analysis demonstrated that ASD was associated with smaller subcortical volumes of the pallidum, putamen, amygdala, and nucleus accumbens (effect sizes [Cohen’s d], 0.13 to –0.13), as well as increased cortical thickness in the frontal cortex and decreased thickness in the temporal cortex (effect sizes, −0.21 to 0.20). Analyses of age effects indicate that the development of cortical thickness is altered in ASD, with the largest differences occurring around adolescence. No age-by-ASD interactions were observed in the subcortical partitions.Conclusions:The ENIGMA ASD working group provides the largest study of brain morphometry differences in ASD to date, using a well-established, validated, publicly available analysis pipeline. ASD patients showed altered morphometry in the cognitive and affective parts of the striatum, frontal cortex, and temporal cortex. Complex developmental trajectories were observed for the different regions, with a developmental peak around adolescence. These findings suggest an interplay in the abnormal development of the striatal, frontal, and temporal regions in ASD across the lifespan.

Sodium Magnetic Resonance Imaging (MRI) is an attractive imaging technique for early detection of osteoarthritis, as sodium concentrations correlate with proteoglycan content. However sodium yields much less signal in human tissues compared to hydrogen: it has a very rapid transverse signal decay (T2) and rapid longitudinal relaxation (T1), low gyromagnetic ratio and much smaller concentration than hydrogen. Ultra High Field (UHF) 7T MRI systems play an essential role to enhance the SNR, to achieve sufficient spatial resolution and to obtain images with diagnostically relevant contrast. This work shows how to design, develop and test a dual-tuned proton/sodium surface radio-frequency coil for 7T UHF MRI systems.

The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. The ARIANNA project has developed a collaborative interdisciplinary research environment that is easily accessible to the community of researchers working on ASD (https://arianna.pi.infn.it). The main goals of the project are: to analyze neuroimaging data acquired in multiple sites with multivariate approaches based on machine learning; to detect structural and functional brain characteristics that allow the distinguishing of individuals with ASD from control subjects; to identify neuroimaging-based criteria to stratify the population with ASD to support the future development of personalized treatments. Secure data handling and storage are guaranteed within the project, as well as the access to fast grid/cloud-based computational resources. This paper outlines the web-based architecture, the computing infrastructure and the collaborative analysis workflows at the basis of the ARIANNA interdisciplinary working environment. It also demonstrates the full functionality of the research platform. The availability of this innovative working environment for analyzing clinical and neuroimaging information of individuals with ASD is expected to support researchers in disentangling complex data thus facilitating their interpretation.

Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machines (SVMs), using as initial sets of brain voxels (ie, features): (1) the segmented grey matter (GM); (2) regions of interest (ROIs) by voxel-wise t-test filtering; (3) parceled ROIs, according to prior knowledge. The recursive feature elimination (RFE) is applied in all cases to investigate whether feature reduction improves the classification accuracy. We analyzed more than 600 AD Neuroimaging Initiative (ADNI) subjects, training the SVMs on the AD/CTRL dataset, and evaluating them on a trial MCI dataset. The classification performance, evaluated as the area under the receiver operating characteristic curve (AUC), reaches AUC = (88.9 ± .5)% in 20-fold cross-validation on the AD/CTRL dataset, when the GM is classified as a whole. The highest discrimination accuracy between MCI converters and nonconverters is achieved when the SVM-RFE is applied to the whole GM: with AUC reaching (70.7 ± .9)%, it outperforms both ROI-based approaches in predicting the AD conversion.

High static magnetic field magnetic resonance imaging (MRI) is commonly used for preclinical studies in rodents. In this context, minimization of coil losses is mandatory to scan samples that are small compared to the radiofrequency wavelength in the medium. In this study we construct a radiofrequency (RF) birdcage probe with distributed capacitors, operating in quadrature, tailored for 7.0T 1H MRI of small animals. The design eliminates the need for extra electrical components on the probe structure and affords a high SNR, a uniform field (homogeneity of 93% in the axial plain of the phantom) and a coil sensitivity of 9.8 . Feasibility experiments of mouse imaging are conducted and the competitive capability of a 7.0 T human system equipped with the proposed coil is demonstrated in both body and brain preclinical imaging. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 44B: 83–88, 2015

Purpose we predict SAR during MRI exam using a 7 T 1H 298 MHz eight-channel degenerate birdcage coil1 combining SAR simulations with subject-specific measured (RF) B 1 + maps. Materials and Methods We simulated the coil1 in CST MW Suite, loaded by a model of human knee (Fig. 1, top). B 1 + was calculated in an axial slice crossing the patella. The maximum local SAR for an Axial “Zero” Time-of-Echo (ZTE) sequence “SILENT”2 was calculated. We acquired ∣ B 1 , map + ∣ maps of an adult (female) knee with a Bloch-Siegert sequence on 7 axial slices, centered on the same slice of the simulation, on a GE MR950 7T human system. For each slice a coefficient C, proportional to avg ( ∣ B 1 , map + ∣ ) / B 1 , nominal + , was used to scale the SAR simulated3. Results Fig. 1 shows: bottom left, simulated B 1 + magnitude; bottom center, local SAR for an input of 1 W per channel; bottom right, simulated B 1 , nominal + magnitude for a FA = 90° (length = 3.2 ms) sinc-pulse in the slice previously chosen. Fig. 2 shows the subject-specific measured ∣ B 1 , map + ∣ for a FA = 90° sinc-pulse. The predicted SAR obtained with scaled B 1 + maps are 0.50 W/kg (global) and 3.68 W/kg (maximum). Conclusions we obtained a good agreement between simulated and measured in vivo B 1 + maps, and we were able to calculate the distribution of SAR exposure, a safety MRI parameter not available in current exams, where only global SAR is provided, combining simulations and subject-specific measurements. Limits on global and local SAR (20 W/kg) were met for this sequence [1] , [2] , [3] .

The efficient detection of lung nodules is an extremely important and challenging task, which has required in the recent years a joint effort by a wide community of scientists including chest doctors, radiologists, nuclear medicine physicians, and experts in medical instrumentation, image processing and artificial intelligence.

Computer-aided detection (CAD) systems for pulmonary nodule identification in CT images can represent valuable tools in assisting radiologists with a second opinion in the detection of early-stage lung cancers. This task becomes more and more challenging when low-dose CT protocols are implemented, which are the optimal choice for screening the asymptomatic population. The absolute performance of the CAD systems, as well as the reliability and reproducibility of the results across different data samples, is a fundamental requirement for these tools to be integrated in the diagnostic algorithm for lung cancer. An overview of the methods recently implemented to build CAD systems and a comparison of the performance achieved are provided. A fair comparison can be carried out only on common data samples, thus the validation of CAD systems on publicly available data sets, such as the Lung Image Database Consortium database, is highly recommended. The necessary steps to evaluate whether these systems can be valua...

A growing body of literature has identified volume alterations of the corpus callosum (CC) in subjects with autism spectrum disorders (ASD). However, to date very few investigations have been conducted on preschool-age ASD children. This study aims to compare the volume of CC and its sub-regions between pre-schoolers with ASD and controls (CON) and to examine their relationship to demographic and clinical variables (sex, age, non-verbal IQ -NVIQ-, expressive non-echolalic language, emotional and behavioural problems, and autism severity). The volume of CC of 40 pre-schoolers with ASD (20 males and 20 females; mean age: 49±12 months; mean NVIQ: 73±22) and 40 sex-, age-, and NVIQ-matched CON subjects (20 M and 20 F; mean age: 49±14 months; mean NVIQ: 73±23) were quantified applying the FreeSurfer automated parcellation software on Magnetic Resonance images. No significant volumetric differences in CC total volume and in its sub-regions between ASD and CON were found using total brain volume as a covariate. Analogously, absence of CC volumetric differences was evident when boys and girls with ASD were compared to their matched controls. The CC total volume of younger ASD male subjects was found significantly larger with respect to matched CON, which is consistent with the atypical growth trajectory widely reported in these young children. The CC total volume was negatively correlated with autism severity, whereas no association between CC volume and other clinical variables was detected. If replicated, the indirect relationship between CC volume and autism severity suggests the involvement of CC in core ASD symptoms. This article is protected by copyright. All rights reserved.

Age-related cognitive impairment and dementia are an increasing societal burden. Epidemiological studies indicate that lifestyle factors, e.g. physical, cognitive and social activities, correlate with reduced dementia risk; moreover, positive effects on cognition of physical/cognitive training have been found in cognitively unimpaired elders. Less is known about effectiveness and action mechanisms of physical/cognitive training in elders already suffering from Mild Cognitive Impairment (MCI), a population at high risk for dementia. We assessed in 113 MCI subjects aged 65–89 years, the efficacy of combined physical-cognitive training on cognitive decline, Gray Matter (GM) volume loss and Cerebral Blood Flow (CBF) in hippocampus and parahippocampal areas, and on brain-blood-oxygenation-level-dependent (BOLD) activity elicited by a cognitive task, measured by ADAS-Cog scale, Magnetic Resonance Imaging (MRI), Arterial Spin Labeling (ASL) and fMRI, respectively, before and after 7 months of training vs. usual life. Cognitive status significantly decreased in MCI-no training and significantly increased in MCI-training subjects; training increased parahippocampal CBF, but no effect on GM volume loss was evident; BOLD activity increase, indicative of neural efficiency decline, was found only in MCI-no training subjects. These results show that a non pharmacological, multicomponent intervention improves cognitive status and indicators of brain health in MCI subjects.

The theoretically known degeneracy condition of the band-pass birdcage coil has rarely been exploited in transmit coil designs. We have created an eight-channel degenerate birdcage for the human limbs at 7 T, with dedicated Tx/Rx switches and a Butler matrix. The coil can be split into two half cylinders, as required for its application to patients with limited mobility. The design of the coil, the Butler matrix, and Tx/Rx switches relied on a combination of analytical, circuital, and numerical simulations. The birdcage theory was extended to the degenerate case. The theoretical and practical aspects of the design and construction of the coil are presented. The performance of the coil was demonstrated by simulations, workbench, and scanner measurements. The fully assembled prototype presents good performance in terms of efficiency, B1 homogeneity, and signal-to-noise ratio, despite the asymmetry introduced by the splittable design. The first in vivo images of the knee are also shown. A novel RF coil design consisting of an eight-channel splittable degenerate birdcage has been developed, and it is now available for 7 T MRI applications of the human lower limbs, including high-resolution imaging of the knee cartilages and of the patellar trabecular structure.

A high performance implementation of an algorithm based on the learning process of the human vision as an edge-enhancing filter for medical images is proposed. It is based on a cognitive image processing algorithm that emulates the contour identification process that is believed to be used by the human brain. The algorithm is implemented on an innovative and high performance embedded system for real-time pattern matching. The system design uses Field Programmable Gate Arrays (FPGAs) and the powerful Associative Memory chip (an ASIC) to achieve real-time performance. We present the first results of the algorithm as a 3-D edge detection filter using Magnetic Resonance Images (MRI).

The identification of reliable brain endophenotypes of autism spectrum disorders (ASD) has been hampered to date by the heterogeneity in the neuroanatomical abnormalities detected in this condition. To handle the complexity of neuroimaging data and to convert brain images in informative biomarkers of pathology, multivariate analysis techniques based on Support Vector Machines (SVM) have been widely used in several disease conditions. They are usually trained to distinguish patients from healthy control subjects by making a binary classification. Here, we propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast to two-class classification, is based on a description of one class of objects only. This approach, by defining a multivariate normative rule on one class of subjects, allows recognizing examples from a different category as outliers. We applied the OCC to 314 regional features extracted from brain structural Magnetic Resonance Imaging (MRI) scans of young children with ASD (21 males and 20 females) and control subjects (20 males and 20 females), matched on age [range: 22–72 months of age; mean = 49 months] and non-verbal intelligence quotient (NVIQ) [range: 31–123; mean = 73]. We demonstrated that a common pattern of features characterize the ASD population. The OCC SVM trained on the group of ASD subjects showed the following performances in the ASD vs. controls separation: the area under the receiver operating characteristic curve (AUC) was 0.74 for the male and 0.68 for the female population, respectively. Notably, the ASD vs. controls discrimination results were maximized when evaluated on the subsamples of subjects with NVIQ ≥ 70, leading to AUC = 0.81 for the male and AUC = 0.72 for the female populations, respectively. Language regions and regions from the default mode network—posterior cingulate cortex, pars opercularis and pars triangularis of the inferior frontal gyrus, and transverse temporal gyrus—contributed most to distinguishing individuals with ASD from controls, arguing for the crucial role of these areas in the ASD pathophysiology. The observed brain patterns associate preschoolers with ASD independently of their age, gender and NVIQ and therefore they are expected to constitute part of the ASD brain endophenotype.

Diagnostic imaging based on the Nuclear Magnetic Resonance phenomenon has increasingly spread in the recent few decades, mainly owing to its exquisite capability in depicting a contrast between soft tissues, to its generally non-invasive nature, and to the priceless advantage of using non-ionizing radiation. Magnetic Resonance (MR)-based acquisition techniques allow gathering information on the structure (through Magnetic Resonance Imaging— MRI), the metabolic composition (through Magnetic Resonance Spectroscopy—MRS), and the functioning (through functional MRI —fMRI) of the human body. MR investigations are the methods of choice for studying the brain in vivo, including anatomy, structural wiring and functional connectivity, in healthy and pathological conditions. Alongside the efforts of the clinical research community in extending the acquisition protocols to allow the exploration of a large variety of pathologies affecting diverse body regions, some relevant technological improvements are on the way to maximize the impact of MR in medical diagnostic. The development of MR scanners operating at ultra-high magnetic field (UHF) strength (≥ 7 tesla), is pushing forward the spatial resolution of MRI and the spectral resolution of MRS, and it is increasing the specificity of fMRI to grey matter signal. UHF MR systems are currently in use for research purposes only; nevertheless, UHF technological advances are positively affecting MR investigations at clinical field strengths. To overcome the current major limitation of MRI, which is mostly based on contrast between tissues rather than on absolute measurements of physical quantities, a new acquisition modality is under development, which is referred as Magnetic Resonance Fingerprinting technique. Finally, as neuroimaging data acquired worldwide are reaching the typical size of Big Data, dedicated technical solutions are required to mine large amount of information and to identify specific biomarkers of pathological conditions.

Contains fulltext : 89675.pdf (Publisher’s version ) (Open Access) Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements. 01 december 2010

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by twelve morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with 6 FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.

Background Genetic, hormonal, and environmental factors contribute since infancy to sexual dimorphism in regional brain structures of subjects with typical development. However, the neuroanatomical differences between male and female children with autism spectrum disorders (ASD) are an intriguing and still poorly investigated issue. This study aims to evaluate whether the brain of young children with ASD exhibits sex-related structural differences and if a correlation exists between clinical ASD features and neuroanatomical underpinnings. Methods A total of 152 structural MRI scans were analysed. Specifically, 76 young children with ASD (38 males and 38 females; 2–7 years of age; mean = 53 months, standard deviation = 17 months) were evaluated employing a support vector machine (SVM)-based analysis of the grey matter (GM). Group comparisons consisted of 76 age-, gender- and non-verbal-intelligence quotient-matched children with typical development or idiopathic developmental delay without autism. Results For both genders combined, SVM showed a significantly increased GM volume in young children with ASD with respect to control subjects, predominantly in the bilateral superior frontal gyrus (Brodmann area –BA– 10), bilateral precuneus (BA 31), bilateral superior temporal gyrus (BA 20/22), whereas less GM in patients with ASD was found in right inferior temporal gyrus (BA 37). For the within gender comparisons (i.e., females with ASD vs. controls and males with ASD vs. controls), two overlapping regions in bilateral precuneus (BA 31) and left superior frontal gyrus (BA 9/10) were detected. Sex-by-group analyses revealed in males with ASD compared to matched controls two male-specific regions of increased GM volume (left middle occipital gyrus—BA 19—and right superior temporal gyrus—BA 22). Comparisons in females with and without ASD demonstrated increased GM volumes predominantly in the bilateral frontal regions. Additional regions of significantly increased GM volume in the right anterior cingulate cortex (BA 32) and right cerebellum were typical only of females with ASD. Conclusions Despite the specific behavioural correlates of sex-dimorphism in ASD, brain morphology as yet remains unclear and requires future dedicated investigations. This study provides evidence of structural brain gender differences in young children with ASD that possibly contribute to the different phenotypic disease manifestations in males and females. Electronic supplementary material The online version of this article (doi:10.1186/s13229-015-0067-3) contains supplementary material, which is available to authorized users.

The current availability of Magnetic Resonance (MR) systems that operate at ultra high magnetic field (≥ 7 Tesla) allows the representation of anatomical structures at sub-millimeter resolution. Interestingly, small structures of the brain, such as the subfields of the hippocampus, the inner structures of the basal ganglia and of the brainstem become visible. Suitable software packages that allow analyzing and measuring such small structures are not currently readily available. We developed a semi-automated procedure to measure the thickness of the stratum radiatum and lacunosum-moleculare (SRLM) of the hippocampus. The change in the thickness of this subfield of the hippocampal formation is supposed to have a role in the pathological cognitive decline. Once we developed and validated the semiautomatic procedure on the 7T high-resolution T2*-weighted images of a healthy volunteer, we carried out a preliminary study on a population affected by Mild Cognitive Impairment to investigate the correlations of the SRLM thickness with the clinical scores of the patients, e.g. the Mini-Mental State Examination score and the Free and Cued Selective Reminding Test.

Ultra-High Field (UHF) (4–9.4T) Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) are valuable tools in the diagnosis and monitoring of many diseases thanks to the enhanced Signal-to-Noise Ratio (SNR) and spectral/spatial resolution. However, such UHF MR applications require the development and optimization of specially designed Radio Frequency (RF) coils. In this study we report the design, construction, workbench and MR testing of a novel 7T double-tuned (1H/31P) RF surface coil made with microstrip technology. The RF coil is suitable for MRI and MRS human studies of the lower limbs and also of the human head. The double-tuned RF coil was tested at the IMAGO7 Foundation in Pisa (Italy). Ex vivo MR 1H images and 31P spectra obtained with a large size fresh calf veal sample showed an excellent SNR within about 50 mm from the RF surface coil. It is believed that the combined 7T MR 1H/31P will provide benefits in performing clinical follow-up of muscular disorders in young and aging patients. The proposed design can be easily extended to the simultaneous detection of the 1H and other low-gamma nuclei, such as 7Li, 11B, 13C, 17O, 23Na.

The hippocampus segmentation in Magnetic Resonance (MRI) scans is a relevant issue for the diagnosis of many pathologies.

Interictal electroencephalogram (EEG) abnormalities are frequently associated with autism spectrum disorders (ASD), although their relationship with the clinical features of ASD, particularly the regressive onset, remains controversial. The aim of this study was to investigate whether the characteristics of interictal EEG abnormalities might help to distinguish and predict definite phenotypes within the heterogeneity of ASD. We reviewed the awake and sleep interictal EEGs of 220 individuals with idiopathic ASD, either with or without a history of seizures. EEG findings were analyzed with respect to a set of clinical variables to explore significant associations. A brain morphometry study was also carried out on a subgroup of patients. EEG abnormalities were seen in 154/220 individuals (70 %) and were mostly focal (p

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive),more » containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be A{sub z}=0.783{+-}0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.« less

The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images.

Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible.

Magnetic Resonance (MR) Imaging and Spectroscopy of the muscle is a valuable tool in the diagnosis and monitoring of Neuromuscular Disorders (NMD). 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. In the study of NMD the new features provided by UHF MR may allow the use of functional techniques to improve biochemical and physiological information of skeletal muscle correlated to the pathogenesis and progression of the muscle involvement. This study reports the recent achievements in muscle imaging and spectroscopy obtained at the first Italian 7 T MR scanner available at the IMAGO7 Foundation (Pisa, Italy). Dedicated radio-frequency coils for proton imaging and phosphorous spectroscopy have been designed, developed and validated in vivo, and are now ready for clinical research studies.

A procedure for evaluating radio-frequency electromagnetic fields in anatomical human models for any matching and coupling conditions is introduced. The procedure resorts to the extraction of basis functions: such basis functions, which represent the fields produced by each individual port without any residual coupling, are derived through an algebraic procedure which uses the S parameter matrix and the fields calculated in one (only) full-wave simulation. The basis functions are then used as building-blocks for calculating the fields for any other S parameter matrix. The proposed approach can be used both for volume coil driven in quadrature and for parallel transmission configuration.

Local specific absorption rate (SAR) evaluation in ultra high field (UHF) magnetic resonance (MR) systems is a major concern. In fact, at UHF, radiofrequency (RF) field inhomogeneity generates hot-spots that could cause localized tissue heating. Unfortunately, local SAR measurements are not available in present MR systems; thus, electromagnetic simulations must be performed for RF fields and SAR analysis. In this study, we used three-dimensional full-wave numerical electromagnetic simulations to investigate the dependence of local SAR at 7.0 T with respect to subject size in two different scenarios: surface coil loaded by adult and child calves and quadrature volume coil loaded by adult and child heads. In the surface coil scenario, maximum local SAR decreased with decreasing load size, provided that the RF magnetic fields for the different load sizes were scaled to achieve the same slice average value. On the contrary, in the volume coil scenario, maximum local SAR was up to 15% higher in children than in adults.

PET imaging is a non-invasive technique for particle range verification in proton therapy. It is based on measuring the beta+ annihilations caused by nuclear interactions of the protons in the patient. In this work we present measurements for proton range verification in phantoms, performed at the CNAO particle therapy treatment center in Pavia, Italy, with our 10 x 10 cm^2 planar PET prototype DoPET. PMMA phantoms were irradiated with mono-energetic proton beams and clinical treatment plans, and PET data were acquired during and shortly after proton irradiation. We created 1-D profiles of the beta+ activity along the proton beam-axis, and evaluated the difference between the proximal rise and the distal fall-off position of the activity distribution. A good agreement with FLUKA Monte Carlo predictions was obtained. We also assessed the system response when the PMMA phantom contained an air cavity. The system was able to detect these cavities quickly after irradiation., Comment: 11 pages, 6 figures, Proceedings for International Workshop on Radiation Imaging Detectors, 2014

Several quantities relevant to phenomenological studies of the mixing of neutral B mesons are computed on the lattice. Our main results are: f_{Bd} sqrt(B_{Bd})=206(28)(7) MeV, f_{Bs} sqrt(B_{Bs})/f_{Bd}sqrt(B_{Bd})=1.16(7). We also obtain the related quantities f_{Bs}sqrt(B{Bs})=237(18)(8) MeV, f_{Bd}= 174(22)(+7-0)(-4-0) MeV, f_{Bs}= 204(15)(+7-0)(+3-0) MeV, f_{Bs}/f_{Bd}=1.17(4)(+0-1), f_{Bd}/f_{Ds}=0.74(5). After combining our results with the experimental world average (Delta m_d), we predict (Delta m_s)=15.8(2.1)(3.3) ps^{-1}. We have also computed the relevant parameters for mixing of neutral D mesons which may be useful in some extensions of the Standard Model. All the quantities were obtained from a quenched simulation with a non-perturbatively improved Clover action at beta=6.2, corresponding to a lattice spacing 1/a=2.7(1) GeV, on a sample of 200 gauge-field configurations. A discussion of the main systematic errors is also presented.

The evaluation of the local Specific Absorption Rate (local SAR) is a major concern in high field Magnetic Resonance (MR) systems. The investigation here presented is focused on the estimation of the Specific Absorption Rate in human head excited by a 7T surface coil. The aim of our work is to evaluate the maximum local SAR of the sample inside the scanner under realistic condition of use. The proposed methodology can be adopted to avoid the over-exposure of the body during the MR exam.

The aim of this work is the efficient implementation of the Hessian based filters. These filters are commonly used in medical image analysis and are employed in the Voxel Based Neural Approach (VBNA) lung CAD (Computer Aided Detection) system for lung nodule detection. This work mainly focuses on the optimization of the filter devoted to the detection of internal nodule candidates, called Multi Scale Dot Enhancement (MSDE) algorithm. Two fast variants of the MSDE algorithm are here proposed and compared: the first relies on an analytical optimization of the algorithm and it is implemented on a standard CPU, whereas the second consists in implementing the filter in the CUDA Graphical Processing Unit (GPU) framework. The algorithms were tested with computed tomography images belonging to the Lung Image Database Consortium (LIDC) public research database using an Intel Core i7 950 @ 3.07GHz and a NVIDIA GeForce GTX 580. Both the approaches lead to an improvement in the algorithm performance with respect to the original implementation, without any loss of precision. The initial implementation, realized in the Insight ToolKit open source image analysis framework (ITK), had an average execution time of 69 sec per CT using five scales of enhancement. The analyticallyoptimized CPU algorithm leads to a computational speed gain of 2.5× (28 sec per CT), whereas the parallel CUDA implementation leads to a speed-up of 38x (1.8 sec per CT) with respect to the original implementation, and 15x with respect to the analytical approach. This work has been developed in the framework of the INFN-funded MAGIC-5 project.

Several studies on structural MRI in children with autism spectrum disorders (ASD) have mainly focused on samples prevailingly consisting of males. Sex differences in brain structure are observable since infancy and therefore caution is required in transferring to females the results obtained for males. The neuroanatomical phenotype of female children with ASD (ASDf) represents indeed a neglected area of research. In this study, we investigated for the first time the anatomic brain structures of a sample entirely composed of ASDf (n=38; 2-7 years of age; mean=53 months; SD=18) with respect to 38 female age and non verbal IQ matched controls, using both mass-univariate and pattern classification approaches. The whole brain volumes of each group were compared using voxel-based morphometry (VBM) with diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) procedure, allowing us to build a study-specific template. Significantly more gray matter (GM) was found in the left superior frontal gyrus (SFG) in ASDf subjects compared to controls. The GM segments obtained in the VBM-DARTEL preprocessing are also classified with a support vector machine (SVM), using the leave-pair-out cross-validation protocol. Then, the recursive feature elimination (SVM-RFE) approach allows for the identification of the most discriminating voxels in the GM segments and these prove extremely consistent with the SFG region identified by the VBM analysis. Furthermore, the SVM-RFE map obtained with the most discriminating set of voxels corresponding to the maximum Area Under the Receiver Operating Characteristic Curve (AUC(max)=0.80) highlighted a more complex circuitry of increased cortical volume in ASDf, involving bilaterally the SFG and the right temporo-parietal junction (TPJ). The SFG and TPJ abnormalities may be relevant to the pathophysiology of ASDf, since these structures participate in some core atypical features of autism.

The purpose of the work here presented consists in the evaluation of the performance of CAD (Computer Aided Detection) systems for automated lung nodule identification on multislice CT examinations based on different analysis approaches and on their combination. Three different CADe systems, the CAMCAD (Channeler Ant Model), the RGVPCAD (Region Growing Volume Plateau) and the VBNACAD (Voxel Based Neural Approach) were tested on public research datasets and evaluated in terms of FROC (Free-response Receiver Operating Characteristics) curves both individually and combined.

Several clinical magnetic resonance applications at 1.5 T and higher field strengths require a careful selection of the RadioFrequency (RF) coil design to optimize the RF spatial distribution and sensitivity. Specifically, two basic requirements must be fulfilled for obtaining high quality images: in the transmission mode, RF coils must be able to produce a uniform magnetic field in the volume of interest so that the nuclei can be properly excited; in the receiving mode, a high signal to noise ratio is needed, and the coil must be able to collect the signal emitted by the nuclei with better sensitivity throughout the volume of interest. A number of analytical and numerical methods are reported in the literature to simulate the RF field distributions of surface and volume coils. In this work, we compared the performances of two computational methods (Method of Moments and Finite Elements Method), considering the modelling of both surface (single loop, figure of eight coil - FO8, dual loop) and volume (TEM) coils. Low (1.5 T) and high (7 T) field operating regimes have been analysed. Measurements obtained on the workbench and with a 1.5 T scanner are used for validation.

The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings.The combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches.Has been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed by means of the dedicated OsiriX plugin.

We propose a method for automatic lung juxtapleural nodule detection in thorax CT images, to be used as a Computer Assisted Detection (CAD) tool by radiologists. It is based on the calculation and automatic analysis of local curvature on the lung surface as extracted from high-resolution CT scans, and exploits uniformization to a sphere (e.g. through conformal mapping) to allow a global view of the lung surface, with marking of high curvature regions which can be suspected of being pleural nodules. Schematically, the tool works as follows. First, lung binary masks are extracted from the image by 3D segmentation of the CT scan. On these masks, pleural nodules appear as small surface concavities of the mask surface. After patching the entrance of vessels into the parenchyma in the hilus pulmonis, the lung frontier 'Sigma' is a smooth genus-0 surface. This surface is triangulated and is then uniformized to a sphere 'Sigma-prime' . In this parameterization a suitable function Psi of the mean and the Gaussian curvatures can be calculated over Sigma. Function Psi is displayed as a colour variation onto both Sigma and Sigma-prime, so marking regions that represent high-curvature concavities. A threshold on Psi is then applied and regions of interest (ROIs), containing little concavities with a low radius of curvature (such as pleural nodules), are detected. ROIs are then examined and classified; techniques such as spherical wavelets are available on the sphere, which will be used to distinguish between false and true positives, helping in diagnosing pleural nodules.