Ivan De Mitri

Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease☆

BACKGROUND Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimers 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 144AD 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 24month 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.

Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent ‘fusion’ between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.

Automatic analysis of medial temporal lobe atrophy from structural MRIs for the early assessment of Alzheimer disease.

The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean age±standard deviation (SD), 75.8years±7.8; Mini Mental State Examination (MMSE) score, 24.1±3.1), 42 patients with aMCI (11 men, 31 women; mean age±SD, 75.2years±4.9; MMSE score, 27.9±1.9), and 30 elderly healthy controls (10 men, 20 women; mean age±SD, 74.7years±5.2; MMSE score, 29.1±0.8). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean age±SD, 76.3years±5.8; MMSE score, 23.2±4.1), 247 patients with aMCI (143 men, 104 women; mean age±SD, 75.3years±6.7; MMSE score, 27.0±1.8), and 135 elderly healthy controls (61 men, 74 women; mean age±SD, 76.4years±6.1). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences wereassessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, 0.28±0.03 vs 0.34±0.03, P<0.001; aMCI vs controls, 0.31±0.03 vs 0.34±0.03, P<0.001). Moreover, the subgroup of aMCI converters was significantly different from controls (0.27±0.034 vs 0.34±0.03, P<0.001). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.

The test results of the Silicon Tungsten Tracker of DAMPE

V. Gallo∗1, G. Ambrosi2, R. Asfandiyarov1, P. Azzarello1, P. Bernardini3,4, B. Bertucci2,5, A. Bolognini2,5, F. Cadoux1, M. Caprai2, I. De Mitri3,4, M. Domenjoz1, Y. Dong6, M. Duranti2,5, R. Fan6, P. Fusco7,8, F. Gargano7, K. Gong6, D. Guo6, C. Husi1, M. Ionica2,5, D. La Marra1, F. Loparco7,8, G. Marsella3,4, M.N. Mazziotta7,, A. Nardinocchi2,5, L. Nicola1, G. Pelleriti1, W. Peng6, M. Pohl1, V. Postolache2, R. Qiao6, A. Surdo4, A. Tykhonov1, S. Vitillo1, H. Wang6, M. Weber1, D. Wu6, X. Wu1, F. Zhang6

Fully automated hippocampus segmentation with virtual ant colonies

The development of tools for a fully automatic segmentation of the relevant brain structures, such as the hippocampus, is potentially very useful for pathologies detection. In this paper, a method for the automated hippocampal segmentation, based on virtual ant colonies, is proposed. The algorithm used, the Channeler Ant Model (CAM), represents an effective way to segment 3D objects with a complex shape in a noisy background. The CAM was modified by inserting a shape knowledge that is crucial to face the hippocampus segmentation. The algorithm was trained and tested using a database of 56 T1 weighted MRI images with a known manual segmentation of the hippocampus volume. The results are comparable to other methods: an average Dice Index of 0.74 and 0.72 is obtained over the left and right hippocampi, respectively. The lack of a heavy training procedure, because all the model parameters are fixed, and the speed make this approach very effective.

An innovative lung segmentation algorithm in CT images with accurate delimitation of the hilus pulmonis

This paper proposes a new segmentation method for the delimitation of the lung parenchyma in thorax Computed-Tomography (CT) datasets, which will be used as pre-processing step in the CAD (Computer Assisted Detection) system for lung nodule detection that is being developed by the MAGIC-5 (Medical Applications in a Grid Infrastructure Connection) Collaboration. Once finished, the CAD software will run in an integrated “grid” environment, where the potentiality of distributed resources for both data and computation will be exploited. The algorithm is fully automated and three-dimensional (3D). Its most innovative part - to the best of our knowledge - is the segmentation of the external airways (trachea and bronchi), obtained by 3D region growing with wavefront simulation and suitable stop conditions. Another original element is the technique used to check and solve the problem of the apparent ‘fusion’ between the lungs, caused by partial volume effects. A general overview of the algorithm is given, with some details of the innovative parts. The results of its application to a database of about 130 high-resolution low-dose images are discussed.

Automatic localization of the hippocampal region in MR images to asses early diagnosis of Alzheimer’s disease in MCI patients

Atrophy and other brain changes, which are typical of aging, generate wide inter-individual variability of morphology in the medial temporal lobe (MTL), including the hippocampal formation. Starting from a sample population of 133 MR images we developed a procedure that extracts from each MR two sub images, containing the hippocampal formations plus a portion of the adjacent tissues and cavities. Then, a small number of templates is selected among the previously obtained sub images, able to describe the morphological variability present in the whole population. Finally an automatic procedure is prepared which, on the basis of the given set of templates, is able to find both hippocampal formations in any new MR image. MR images ranging from normalcy to extreme atrophy can be successfully processed. The proposed approach, besides being a preliminary step towards the unsupervised segmentation of the hippocampus, extracts from the MR image information useful for diagnostic purposes and, in particular, could give the possibility of performing morphometric studies on the medial temporal lobe in an automated way. The automated analysis of MTL atrophy in the segmented volume is readily applied to the early assessment of Alzheimer Disease (AD), leading to discriminating converters from Mild Cognitive Impairment (MCI) to AD with an average three years follow-up. This procedure can quickly and reliably provide additional information in early diagnosis of AD.

Experimental verification of the HERD prototype at CERN SPS

The High Energy cosmic-Radiation Detection (HERD) facility is one of several space astronomy payloads of the cosmic light house program onboard Chinas Space Station, which is planned for operation starting around 2020 for about 10 years. Beam test with a HERD prototype, to verify the HERD specifications and the reading out method of wavelength shifting fiber and image intensified CCD, was taken at CERN SPS in November, 2015. The prototype is composed of an array of 5*5*10 LYSO crystals, which is 1/40th of the scale of HERD calorimeter. Experimental results on the performances of the calorimeter are discussed.

Measurement of the cosmic ray all-particle and light-component energy spectra with the ARGO-YBJ experiment

The ARGO-YBJ detector, located at high altitude in the Cosmic Ray Observatory of Yangbajing in Tibet (4300 m asl, about 600 g/cm2 of atmospheric depth) provides the opportunity for the study, with unprecedented resolution, of cosmic ray physics in the primary energy region between 1012 and 1016 eV. Preliminary results of the measurements of the all-particle and light-component (i.e. protons and helium) energy spectra between approximately 5 TeV and 5 PeV are reported and discussed.

Selected Topics in Cosmic Ray Physics

The search for the origin of cosmic rays is as active as ever, mainly driven by new insights provided by recent pieces of observation. Much effort is being channelled in putting the so-called supernova paradigm for the origin of galactic cosmic rays on firmer grounds, while at the highest energies we are trying to understand the observed cosmic-ray spectra and mass composition and relating them to potential sources of extragalactic cosmic rays. Interestingly, a topic that has acquired a dignity of its own is the investigation of the transition region between the galactic and extragalactic components, once associated with the ankle and now increasingly thought to be taking place at somewhat lower energies. Here, we summarize recent developments in the observation and understanding of galactic and extragalactic cosmic rays and we discuss the implications of such findings for the modelling of the transition between the two.