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The aim of this work is to describe the state of progress of a study developed in the framework of AIM (Artificial Intelligence in Medicine). It is a project funded by INFN, Italy, and it involves researchers from INFN, Hospital Meyer and Radiotherapy Unit of University of Florence. The aim of the proposed study is to apply a retrospective exploratory MR-CT-based radiomics and dosiomic analysis based on emerging machine-learning technologies, to investigate imaging biomarkers of clinical outcomes in paediatric patients affected by medulloblastoma, from images. Features from MR-CT scans will be associated with overall survival, recurrence-free survival, and loco-regional recurrence-free survival after intensity modulated radiotherapy. Dosimetric analysis data will be integrated with the objective of increase predictive value. This approach could have a large impact for precision medicine, as radiomic biomarkers are non-invasive and can be applied to imaging data that are already acquired in clinical settings.

Purpose The lack of inter-method agreement can produce inconsistent results in neuroimaging studies. We evaluated the intra-method repeatability and the inter-method reproducibility of two widely-used automatic segmentation methods for brain MRI: the FreeSurfer (FS) and the Statistical Parametric Mapping (SPM) software packages. Methods We segmented the gray matter (GM), the white matter (WM) and subcortical structures in test-retest MRI data of healthy volunteers from Kirby-21 and OASIS datasets. We used Pearson’s correlation (r), Bland-Altman plot and Dice index to study intra-method repeatability and inter-method reproducibility. In order to test whether different processing methods affect the results of a neuroimaging-based group study, we carried out a statistical comparison between male and female volume measures. Results A high correlation was found between test-retest volume measures for both SPM (r in the 0.98–0.99 range) and FS (r in the 0.95–0.99 range). A non-null bias between test-retest FS volumes was detected for GM and WM in the OASIS dataset. The inter-method reproducibility analysis measured volume correlation values in the 0.72–0.98 range and the overlap between the segmented structures assessed by the Dice index was in the 0.76–0.83 range. SPM systematically provided significantly greater GM volumes and lower WM and subcortical volumes with respect to FS. In the male vs. female brain volume comparisons, inconsistencies arose for the OASIS dataset, where the gender-related differences appear subtler with respect to the Kirby dataset. Conclusions The inter-method reproducibility should be evaluated before interpreting the results of neuroimaging studies.

Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct diagnostic criteria and different epidemiology. However, a common genetic background as well as overlapping clinical features between ASD and CAS have been recently reported. To date, brain structural language-related abnormalities have been detected in both the conditions, but no study directly compared young children with ASD, CAS and typical development (TD). In the current work, we aim: (i) to test the hypothesis that ASD and CAS display neurostructural differences in comparison with TD through morphometric Magnetic Resonance Imaging (MRI)-based measures (ASD vs. TD and CAS vs. TD), (ii) to investigate early possible disease-specific brain structural patterns in the two clinical groups (ASD vs. CAS), (iii) to evaluate predictive power of machine-learning (ML) techniques in differentiating the three samples (ASD, CAS, TD). We retrospectively analyzed the T1-weighted brain MRI scans of 68 children (age range: 34&ndash, 74 months) grouped into three cohorts: (1) 26 children with ASD (mean age &plusmn, standard deviation: 56 &plusmn, 11 months), (2) 24 children with CAS (57 &plusmn, 10 months), (3) 18 children with TD (55 &plusmn, 13 months). Furthermore, a ML analysis based on a linear-kernel Support Vector Machine (SVM) was performed. All but one brain structures displayed significant higher volumes in both ASD and CAS children than TD peers. Specifically, ASD alterations involved fronto-temporal regions together with basal ganglia and cerebellum, while CAS alterations are more focused and shifted to frontal regions, suggesting a possible speech-related anomalies distribution. Caudate, superior temporal and hippocampus volumes directly distinguished the two conditions in terms of greater values in ASD compared to CAS. The ML analysis identified significant differences in brain features between ASD and TD children, whereas only some trends in the ML classification capability were detected in CAS as compared to TD peers. Similarly, the MRI structural underpinnings of two clinical groups were not significantly different when evaluated with linear-kernel SVM. Our results may represent the first step towards understanding shared and specific neural substrate in ASD and CAS conditions, which subsequently may contribute to early differential diagnosis and tailoring specific early intervention.

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 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.

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.

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 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.

BACKGROUND AND PURPOSE Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI). METHODS The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation. RESULTS The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70. CONCLUSIONS Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.

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. Bioelectromagnetics. 36:358–366, 2015. © 2015 Wiley Periodicals, Inc.

L’articolo considera l’evoluzione nel periodo 2005-2010 del sistema di gestione dei rifiuti industriali in Veneto. La tendenza conferma un aumento dei rifiuti recuperati, di quelli pretrattati ai fini dello smaltimento, mentre quelli inceneriti e quelli smaltiti in discarica diminuiscono. Per quanto riguarda le esportazioni, aumenta l’incidenza di quelle verso i Paesi UE e la diminuzione di quelle verso Paesi extra UE. Per quanto riguarda questi ultimi, le destinazioni prevalenti sono Cina, Hong Kong, India, Indonesia e Pakistan. La destinazione finale di questi flussi resta comunque molto difficile da monitorare. Essi, inoltre, nascondono spesso esportazioni illegali. E necessaria una maggiore cooperazione a scala internazionale; ma anche a livello nazionale si puo fare molto: in particolare, l’atteso avvio del Sistema informatizzato di monitoraggio e gestione (Sistri) consentira un controllo piu efficiente dei flussi, oggi troppo frammentato.

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.

Introduction: The brainstem has a potential role in the pathophysiology of Autism Spectrum Disorders (ASD) (Roger, 2013). In particular, alterations in brainstem volume and their relationship with sensory/motor abnormalities have been suggested (Trevarthen & Delafield-Butt, 2013). However, the findings in volume alterations of subjects with ASD with respect to matched controls are controversial both in adults and children cohorts (Hardan, 2001; Piven, 1992; Kleiman, 1992). Moreover, the contribution to variability of brainstem volume measurements performed with different automated methods is still unclear. Methods: T1-weighted MRI brain scans of a cohort of 80 preschoolers (20 male controls, 20 male subjects with ASD, 20 female controls, 20 female subjects with ASD, mean age controls 49 months, std 12 months, mean age ASD 49 months, std 14) were processed with three different automated methods to measure the brainstem volume: Freesurfer 5.3 (Fischl, 2002), FSL-FIRST (Patenaude, 2011) and ANTs (Avants, 2011). Analysis of variance was then carried out taking into account gender and total brain volume in order to investigate potential brainstem volume differences between controls/ASD subjects for each method. A direct comparison of brainstem volume assessments in native space was then performed to assess inter-method reliability (correlation has been calculated by Pearson coefficient) and Dice similarity indexes were calculated to evaluate the segmentation agreement across methods. Results:The brainstem volume measurements are reported in scatter plots in Fig. 1 to show the agreement in terms of volume (in mm3) between different methods. The color represents the Dice similarity index (range 0-1 were 1 means total agreement) of the brainstem segmentations obtained by the methods under investigation. In Fig. 2 four examples of brainstem segmentations with the different methods are shown in sagittal view (brainstem segmentations are reported in red, green, blue for Freesurfer, FSL-FIRST and ANTs respectively). Pearson correlation coefficient between FSL-FIRST and Freesurfer brainstem volume assessments was 0.27 (p-value=0.02). It was 0.51 (p-value0.05).Conclusions:The inter-method reliability of automated algorithms for brainstem volume assessment is limited (the mean Dice similarity index barely reaches 0.8 in just one out of 3 comparisons). Inconsistencies across previous studies on brainstem and more in general the lack of evidence for brain biomarkers in ASD may in part be a result of this poor agreements in the extraction of structural features with different methods. Inter-method brainstem volume differences can be attributed to varying definitions of brainstem structure, the use of different templates (e.g. in our study only ANTs processed the brain scans by using an age-specific brain template) and the varying effects of imaging artifacts and acquisition settings. This study suggests that research on brain structure alterations should cross-validate findings across multiple methods before providing biological interpretations.

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.

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Dedicated computing environments are becoming increasingly important in neuroimaging applications. Indeed we are experiencing a fast development of non-invasive technologies that progress the research on human brain. These advanced techniques, such...

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.

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 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).

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

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.