Search

Human brain mapping 43(1), 37-55 (2022). doi:10.1002/hbm.25029 special issue: "Special Issue: The ENIGMA Consortium: the first 10 years / Issue Edited by: P.M. Thompson, N. Jahanshad, L. Schmaal, J.A. Turner, A. Winkler, S.I. Thomopoulos, G.F. Egan, P. Kochunov", Published by Wiley-Liss, New York, NY

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls. Although these alterations affect multiple and widespread cortical regional asymmetries, the extent to which specific structural networks might be affected remains unknown. Inter-regional morphological covariance analysis can capture network connectivity relations between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1,455 individuals with ASD and 1,560 controls, across 43 independent datasets of the ENIGMA consortium’s ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered asymmetry of hemispheric networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, driven by shifts toward higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve working memory, executive functions, language, reading, and sensorimotor processes. Taken together, these findings provide new insights into how altered brain left-right asymmetry in ASD affects specific structural and functional brain networks. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Predictive 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.

Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental conditions characterized by impairments in social interaction and communication and restricted patterns of behavior, interests, and activities. Although the etiopathogenesis of idiopathic ASD has not been fully elucidated, compelling evidence suggests an interaction between genetic liability and environmental factors in producing early alterations of structural and functional brain development that are detectable by magnetic resonance imaging (MRI) at the group level. This work shows the results of a network-based approach to characterize not only variations in the values of the extracted features but also in their mutual relationships that might reflect underlying brain structural differences between autistic subjects and healthy controls. We applied a network-based analysis on sMRI data from the Autism Brain Imaging Data Exchange I (ABIDE-I) database, containing 419 features extracted with FreeSurfer software. Two networks were generated: one from subjects with autistic disorder (AUT) (DSM-IV-TR), and one from typically developing controls (TD), adopting a subsampling strategy to overcome class imbalance (235 AUT, 418 TD). We compared the distribution of several node centrality measures and observed significant inter-class differences in averaged centralities. Moreover, a single-node analysis allowed us to identify the most relevant features that distinguished the groups.

In particle therapy, the uncertainty of the delivered particle range during the patient irradiation limits the optimization of the treatment planning. Therefore, an in vivo treatment verification device is required, not only to improve the plan robustness, but also to detect significant interfractional morphological changes during the treatment itself. In this article, an effective and robust analysis to detect regions with a significant range discrepancy is proposed. This study relies on an in vivo treatment verification by means of in-beam Positron Emission Tomography (PET) and was carried out with the INSIDE system installed at the National Center of Oncological Hadrontherapy (CNAO) in Pavia, which is under clinical testing since July 2019. Patients affected by head-and-neck tumors treated with protons have been considered. First, in order to tune the analysis parameters, a Monte Carlo (MC) simulation was carried out to reproduce a patient who required a replanning because of significant morphological changes found during the treatment. Then, the developed approach was validated on the experimental measurements of three patients recruited for the INSIDE clinical trial (ClinicalTrials.govID: NCT03662373), showing the capability to estimate the treatment compliance with the prescription both when no morphological changes occurred and when a morphological change did occur, thus proving to be a promising tool for clinicians to detect variations in the patients treatments.

X-ray breast imaging techniques are an essential part of breast cancer screening programs and their improvements lead to gain in performance and accuracy. Radiation dose estimate and control play an important role in digital mammography and digital breast tomosynthesis investigations, since the risk of radioinduced cancer to the gland must be contained and dose delivered to the gland must be declared in the medical report. The actual dosimetric protocols suggest the assessment of radiation dose by means of Monte Carlo calculation on digital breast phantoms, providing the assumption of the homogeneous mixture of glandular and adipose tissues within the breast organ, leading to a drastic approximation. In line with the trend of other research groups, with the aim of improving the Monte Carlo model, in the current work a new heterogeneous digital breast model is proposed, involving a voxelized approach and disengaging from the concept of homogeneous phantom. The proposed model is based on new findings in the literature and after a validation process, the model is adopted to evaluate mean glandular dose discrepancies with the traditional model which is adopted in clinic for decades.

Lung Computed Tomography (CT) is an imaging technique useful to assess the severity of COVID-19 infection in symptomatic patients and to monitor its evolution over time. Lung CT can be analysed with the support of deep learning methods for both aforementioned tasks. We have developed a U-net based algorithm to segment the COVID-19 lesions. Unfortunately, public datasets populated with a huge amount of labelled CT scans of patients affected by COVID-19 are not available. In this work, we first review all the currently available public datasets of COVID-19 CT scans, presenting an extensive description of their characteristics. Then, we describe the design of the U-net we developed for the automated identification of COVID-19 lung lesions. Finally, we discuss the results obtained by using the different publicly available datasets. In particular, we trained the U-net on the dataset made available within the COVID-19 Lung CT Lesion Segmentation Challenge 2020, and we tested it on data from the MosMed and the COVID-19-CT-Seg datasets to explore the transferability of the model and to assess whether the image annotation process affects the detection performances. We evaluated the performance of the system in lesion segmentation in terms of the Dice index, which measures the overlap between the ground truth and the predicted masks. The proposed U-net segmentation model reaches a Dice index equal to 0.67, 0.42 and 0.58 on the independent validation sets of the COVID-19 Lung CT Lesion Segmentation Challenge 2020, on the MosMed and on the COVID-19-CT-Seg datasets, respectively. This work focusing on lesion segmentation constitutes a preliminary work for a more accurate analysis of COVID-19 lesions, based for example on the extraction and analysis of radiomic features. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

PURPOSE In-beam Positron Emission Tomography (PET) is one of the modalities that can be used for in-vivo non-invasive treatment monitoring in proton therapy. Although PET monitoring has been frequently applied for this purpose, there is still no straightforward method to translate the information obtained from the PET images into easy to interpret information for clinical personnel. The purpose of this work is to propose a statistical method for analyzing in-beam PET monitoring images that can be used to locate, quantify and visualize regions with possible morphological changes occurring over the course of treatment. MATERIALS AND METHODS We selected a patient treated for Squamous Cell Carcinoma (SCC) with proton therapy, to perform multiple Monte Carlo (MC) simulations of the expected PET signal at the start of treatment, and to study how the PET signal may change along the treatment course due to morphological changes. We performed voxel-wise two-tailed statistical tests of the simulated PET images, resembling the Voxel-Based Morphometry (VBM) method commonly used in neuroimaging data analysis, to locate regions with significant morphological changes and to quantify the change. RESULTS The VBM resembling method has been successfully applied to the simulated in-beam PET images, despite the fact that such images suffer from image artifacts and limited statistics. Three dimensional probability maps were obtained, that allowed to identify inter-fractional morphological changes and to visualize them superimposed on the Computed Tomography (CT) scan. In particular, the characteristic color patterns resulting from the two-tailed statistical tests lend themselves to trigger alarms in case of morphological changes along the course of treatment. CONCLUSIONS The statistical method presented in this work is a promising method to apply to PET monitoring data to reveal inter-fractional morphological changes in patients, occurring over the course of treatment. Based on simulated in-beam PET treatment monitoring images, we showed that with our method it was possible to correctly identify the regions that changed. Moreover we could quantify the changes, and visualize them superimposed on the CT scan. The proposed method can possibly help clinical personnel in the replanning procedure in adaptive proton therapy treatments. This article is protected by copyright. All rights reserved.

Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T1 and T2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7–1.3% for T1, 2.0–7.8% for T2, 1.4–2.5% for normalized PD) and reproducibility (CVs of 2.0–5.8% for T1, 7.4–10.2% for T2, 5.2–9.2% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T1 and T2, supporting the translation of MRF-based fast quantitative imaging into clinical applications.

ObjectiveAttention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders.MethodsStructural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures).ResultsWe found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed.ConclusionOur findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders.

Patient safety during Magnetic Resonance Imaging (MRI) examinations is currently guaranteed through the compliance with the limitations on the Specific Absorption Rate (SAR) exposure provided by the current regulations. SAR limits are implemented by scanner vendors in a strictly conservative way to nullify the risk of patient hazard, even at the cost of limiting sometimes the diagnostic power of the exam. Nonetheless, both in the case of ultra-high field applications (i.e. with static magnetic field of 7 T and above) and at clinical field strengths for example in the presence of metallic prosthetic implants, the inhomogeneities in the RF excitation field distribution may cause a local and subject-specific SAR increase. In those cases, local tissue temperatures could reach damaging levels. To directly quantify the local temperature increase caused by the more commonly used RF acquisition sequences in MRI of the human extremities, we have implemented Magnetic Resonance Thermometry (MRT) techniques on a 7 T MRI scanner. We demonstrated that temperature increase maps are easily and fast obtainable with MRT techniques, which are sensitive enough to detect potential hazardous tissue heating.

The hippocampus is one of the most interesting and studied brain regions because of its involvement in memory functions and its vulnerability in pathological conditions, such as neurodegenerative processes. In the recent years, the increasing availability of Magnetic Resonance Imaging (MRI) scanners that operate at ultra-high field (UHF), that is, with static magnetic field strength ≥7T, has opened new research perspectives. Compared to conventional high-field scanners, these systems can provide new contrasts, increased signal-to-noise ratio and higher spatial resolution, thus they may improve the visualization of very small structures of the brain, such as the hippocampal subfields. Studying the morphometry of the hippocampus is crucial in neuroimaging research because changes in volume and thickness of hippocampal subregions may be relevant in the early assessment of pathological cognitive decline and Alzheimer's Disease (AD). The present review provides an overview of the manual, semi-automated and fully automated methods that allow the assessment of hippocampal subfield morphometry at UHF MRI, focusing on the different hippocampal segmentation produced. © 2017 Wiley Periodicals, Inc.

Purpose To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF). Methods Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps. Results A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T1 and T2 estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T1 and 18 ms for T2 values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations. Conclusion We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.

Background. Brain atrophy in anorexia nervosa (AN) is one of the most marked structural brain changes observed in mental disorders. In this study, we propose a whole brain analysis approach to characterize global and regional cerebral volumes in adolescents with restricting-type anorexia nervosa (AN-r). Methods. A total of 48 adolescent females (age range 13–18 years) were enrolled in the study (24 right-handed AN-r in the early stages of the illness and treated in the same clinical setting and 24 age-matched healthy controls [HC]). High-resolution T1-weighted magnetic resonance images were acquired. Cerebral volumes, including the total amounts of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were obtained with the Statistical Parametric Mapping software (SPM8); specific cortical regional volumes were computed by applying an atlas-based cortical parcellation to the SPM8 GM segments. Analysis of variance (ANOVA) was performed to identify any significant between-group differences in global and regional brain volumes. Results. The analyses revealed reduced total GM volumes (p = 0.02) and increased CSF (p = 0.05) in AN-r, compared with HC. No significant between-group difference was found in WM volumes. At the regional level, significantly lower GM volumes in both frontal lobes (p = 0.006) and in the left insula (p = 0.016) were detected. No significant relationships were found between cerebral volumes and duration of illness, psychiatric comorbidities, psychopharmacological treatment, prepubertal phase, or presence of amenorrhea. Conclusions. The topographic distribution of GM reduction in a homogenous group of AN-r involves regions responsible for the emotional and cognitive deficits associated with the illness. These findings are discussed in relation to the roles of the insular cortex and the frontal lobes.

No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the application of Machine Learning (ML) classification methods to neuroimaging data has the potential to contribute to a better distinction between subjects with ASD and typical development controls (TD). This study is focused on the analysis of resting-state fMRI data of individuals with ASD and matched TD, available within the ABIDE collection. To reduce the multiple sources of heterogeneity that impact on understanding the neural underpinnings of autistic condition, we selected a subgroup of 190 subjects (102 with ASD and 88 TD) according to the following criteria: male children (age range: 6.5–13 years); rs-fMRI data acquired with open eyes; data from the University sites that provided the largest number of scans (KKI, NYU, UCLA, UM). Connectivity values were evaluated as the linear correlation between pairs of time series of brain areas; then, a Linear kernel Support Vector Machine (L-SVM) classification, with an inter-site cross-validation scheme, was carried out. A permutation test was conducted to identify over-connectivity and under-connectivity alterations in the ASD group. The mean L-SVM classification performance, in terms of the area under the ROC curve (AUC), was 0.75 ± 0.05. The highest performance was obtained using data from KKI, NYU and UCLA sites in training and data from UM as testing set (AUC = 0.83). Specifically, stronger functional connectivity (FC) in ASD with respect to TD involve (p < 0.001) the angular gyrus with the precuneus in the right (R) hemisphere, and the R frontal operculum cortex with the pars opercularis of the left (L) inferior frontal gyrus. Weaker connections in ASD group with respect to TD are the intra-hemispheric R temporal fusiform cortex with the R hippocampus, and the L supramarginal gyrus with L planum polare. The results indicate that both under- and over-FC occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks.

BackgroundLeft-right asymmetry is an important organizing feature of the healthy brain. Various studies have reported altered structural brain asymmetry in autism spectrum disorder (ASD). However, findings have been inconsistent, likely due to limited sample sizes and low statistical power.MethodsWe investigated 1,774 subjects with ASD and 1,809 controls, from 54 datasets, for differences in the asymmetry of thickness and surface area of 34 cerebral cortical regions. We also examined global hemispheric measures of cortical thickness and area asymmetry, and volumetric asymmetries of subcortical structures. Data were obtained via the ASD Working Group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. T1-weighted MRI data were processed with a single protocol using FreeSurfer and the Desikan-Killiany atlas.ResultsASD was significantly associated with reduced leftward asymmetry of total hemispheric average cortical thickness, compared to controls. Eight regional thickness asymmetries, distributed over the cortex, also showed significant associations with diagnosis after correction for multiple comparisons, for which asymmetry was again generally lower in ASD versus controls. In addition, the medial orbitofrontal surface area was less rightward asymmetric in ASD than controls, and the putamen volume was more leftward asymmetric in ASD than controls. The largest effect size had Cohen’sd= 0.15. Most effects did not depend on age, sex, IQ, or disorder severity.ConclusionAltered lateralized neurodevelopment is suggested in ASD, affecting widespread cortical regions with diverse functions. Large-scale analysis was necessary to reliably detect, and accurately describe, subtle alterations of structural brain asymmetry in this disorder.

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.

The intermethod agreement between automated algorithms for brainstem segmentation is investigated, focusing on the potential involvement of this structure in Autism Spectrum Disorders (ASD). Inconsistencies highlighted in previous studies on brainstem in the population with ASD may in part be a result of poor agreement in the extraction of structural features between different methods. A sample of 76 children with ASD and 76 age-, gender-, and intelligence-matched controls was considered. Volumetric analyses were performed using common tools for brain structures segmentation, namely FSL-FIRST, FreeSurfer (FS), and Advanced Normalization Tools (ANTs). For shape analysis SPHARM-MAT was employed. Intermethod agreement was quantified in terms of Pearson correlations between pairs of volumes obtained by the different methods. The degree of overlap between segmented masks was quantified in terms of the Dice index. Both Pearson correlations and Dice indices, showed poor agreement between FSL-FIRST and the other methods (ANTs and FS), which by contrast, yielded Pearson correlations greater than 0.93 and average Dice indices greater than 0.76 when compared with each other. As with volume, shape analyses exhibited discrepancies between segmentation methods, with particular differences noted between FSL-FIRST and the others (ANT and FS), with under- and over-segmentation in specific brainstem regions. These data suggest that research on brain structure alterations should cross-validate findings across multiple methods. We consistently detected an enlargement of brainstem volume in the whole sample and in the male cohort across multiple segmentation methods, a feature particularly driven by the subgroup of children with idiopathic intellectual disability associated with ASD.

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.

In this paper, we present a work on breast density classification performed with deep residual neural network and we discuss the future analysis we could perform. Breast density is one of the most important breast cancer risk factor and it represents the amount of fibroglandular tissue with respect to fat tissue as seen on a mammographic exam. However, it is not easy to include it in risk models because of its variability among women and its qualitative definition. We trained a deep CNN to perform breast density classification in two ways. First, we classified mammograms using two "super-classes" that are dense and non-dense breast. Second, we trained the residual neural network to classify mammograms according to the four classes of the BI-RADS standard. We obtained very good results compared to our literature knowledge in terms of accuracy and recall. In the near future, we are going to improve the robustness of our algorithm with respect to the mammographic systems used and we want to include pathological exams too. Then we want to study and characterize the CNN-extracted features in order to identify the most significant for breast density. Finally, we want to study how to quantitatively measure the precision of the network in capturing the significative part of the images.

Fully-quantitative MR imaging methods are useful for longitudinal characterization of disease and assessment of treatment efficacy. However, current quantitative MRI protocols have not been widely adopted in the clinic, mostly due to lengthy scan times. Magnetic Resonance Fingerprinting (MRF) is a new technique that can reconstruct multiple parametric maps from a single fast acquisition in the transient state of the MR signal. Due to the relative novelty of this technique, the repeatability and reproducibility of quantitative measurements obtained using MRF has not been extensively studied. Our study acquired test/retest data from the brains of nine healthy volunteers, each scanned on five MRI systems (two at 3.0 T and three at 1.5 T, all from a single vendor) located at two different centers. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived M0, T1 and T2 maps to an anatomical atlas, coefficients-of-variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated an excellent repeatability (CVs of 2–3% for T1, 5–8% for T2, 3% for normalized-M0) and a good reproducibility (CVs of 3–8% for T1, 8–14% for T2, 5% for normalized-M0) in grey and white matter.

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.

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.

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.

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

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 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.

Background 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. Subjects A reference group composed of 144 AD patients and 189 age-matched controls was used to train and test the procedure. It was then applied on a study group composed of 302 MCI subjects, 136 having progressed to clinically probable AD (MCI-converters) and 166 having remained stable or recovered to normal condition after a 24 month follow-up (MCI-non converters). All subjects came from the ADNI database. Methods We sampled the brain with 7 relatively small volumes, mainly centered on the MTL, and 2 control regions. These volumes were filtered to give intensity and textural MRI-based features. Each filtered region was analyzed with a Random Forest (RF) classifier to extract relevant features, which were subsequently processed with a Support Vector Machine (SVM) classifier. Once a prediction model was trained and tested on the reference group, it was used to compute a classification index (CI) on the MCI cohort and to assess its accuracy in predicting AD conversion in MCI patients. The performance of the classification based on the features extracted by the whole 9 volumes is compared with that derived from each single volume. All experiments were performed using a bootstrap sampling estimation, and classifier performance was cross-validated with a 20-fold paradigm. Results We identified a restricted set of image features correlated with the conversion to AD. It is shown that most information originate from a small subset of the total available features, and that it is enough to give a reliable assessment. We found multiple, highly localized image-based features which alone are responsible for the overall clinical diagnosis and prognosis. The classification index is able to discriminate Controls from AD with an Area Under Curve (AUC) = 0.97 (sensitivity ≃ 89% at specificity ≃ 94%) and Controls from MCI-converters with an AUC = 0.92 (sensitivity ≃ 89% at specificity ≃ 80%). MCI-converters are separated from MCI-non converters with AUC = 0.74(sensitivity ≃ 72% at specificity ≃ 65%). Findings The present automated MRI-based technique revealed a strong relationship between highly localized baseline-MRI features and the baseline clinical assessment. In addition, the classification index was also used to predict the probability of AD conversion within a time frame of two years. The definition of a single index combining local analysis of several regions can be useful to detect AD neurodegeneration in a typical MCI population.

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.

Support Vector Machine (SVM) classifiers are widely used to analyse features extracted from brain MRI data to identify useful biomarkers of pathology in several disease conditions. They are trained to distinguish patients from healthy control subjects by making a binary classification of image features extracted by image processing algorithms. This task is particularly challenging when dealing with psychiatric disorders, as the reported neuroanatomical alterations are often very small and quite un-replicated within different studies. Subtle signs of pathology are difficult to catch especially in extremely heterogeneous conditions such as Autism Spectrum Disorders (ASD). We propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast with two-class classification, is based on a description of one class of objects only. Then, new examples are tested for their similarity to the examples of this target class, end eventually considered as outliers. The application of the OCC to features extracted from brain MRI of children affected by ASD and control subjects demonstrated that a common pattern of features characterize the ASD population.

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.

This work reports a study of Neuromuscular Diseases (NMD) by Magnetic Resonance Imaging as reliable and non invasive instrument for NMD diagnosis and follow up. The evaluation of the images is now only visual, while standardization procedures and quantitative methods could be very useful instruments to optimize the diagnostic performances. We propose a new method to evaluate the fat infiltration in tissues developed and retrospectively applied to images of the human leg. Through a muscle segmentation algorithm on structural T1-weighted magnetic resonance images (MRIs), the estimated non-muscle percentage (eNMP) in the segmented muscle area has been evaluated in healthy subjects as a reference value. A semi-automated procedure allows extending the algorithm to MRIs of NMD patients. A strong correlation has been demonstrated between this index and the disease severity. The final aim is to obtain a quantitative evaluation of fat infiltration percentage and to relate it to the grade of muscle impairment in subjects affected by Neuromuscular Diseases.

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