Measurement of the Cosmic Ray Helium Energy Spectrum from 70 GeV to 80 TeV with the DAMPE Space Mission.

The measurement of the energy spectrum of cosmic ray helium nuclei from 70 GeV to 80 TeV using 4.5 years of data recorded by the Dark Matter Particle Explorer (DAMPE) is reported in this work. A hardening of the spectrum is observed at an energy of about 1.3 TeV, similar to previous observations. In addition, a spectral softening at about 34 TeV is revealed for the first time with large statistics and well controlled systematic uncertainties, with an overall significance of 4.3σ. The DAMPE spectral measurements of both cosmic protons and helium nuclei suggest a particle charge dependent softening energy, although with current uncertainties a dependence on the number of nucleons cannot be ruled out.

Measurement of the cosmic ray proton spectrum from 40 GeV to 100 TeV with the DAMPE satellite.

The precise measurement of the spectrum of protons, the most abundant component of the cosmic radiation, is necessary to understand the source and acceleration of cosmic rays in the Milky Way. This work reports the measurement of the cosmic ray proton fluxes with kinetic energies from 40 GeV to 100 TeV, with 2 1/2 years of data recorded by the DArk Matter Particle Explorer (DAMPE). This is the first time that an experiment directly measures the cosmic ray protons up to ~100 TeV with high statistics. The measured spectrum confirms the spectral hardening at ~300 GeV found by previous experiments and reveals a softening at ~13.6 TeV, with the spectral index changing from ~2.60 to ~2.85. Our result suggests the existence of a new spectral feature of cosmic rays at energies lower than the so-called knee and sheds new light on the origin of Galactic cosmic rays.

Generating a minimal set of templates for the hippocampal region in MR neuroimages.

OBJECTIVES: We detail a procedure for generating a set of templates for the hippocampal region in magnetic resonance (MR) images, representative of the clinical conditions of the population under investigation. METHODS: The first step is robust standardization of the intensity scale of brain MR images, belonging to patients with different degrees of neuropathology (Alzheimer's disease). So similar tissues have similar intensities, even across images coming from different sources. After the automatic extraction of the hippocampal region from a large dataset of images, we address template generation, choosing by clusterization methods a small number of the extracted regions. RESULTS: We assess that template generation is largely independent on the clusterization method and on the number and the clinical condition of the patients. The templates are chosen as the most representative images in a population. The estimation of the "minimum" number of templates for the hippocampal region can be proposed, using a metric based on the geometrical position of the extracted regions. CONCLUSIONS: This study describes a simple and easily reproducible procedure to generate templates for the hippocampal region. It can be generalized and applied to other brain regions, which may be relevant for neuroimaging studies.

Suprapubic percutaneous sclero-embolization of symptomatic female pelvic varicocele under local anesthesia.

PURPOSE: To evaluate the safety and feasibility of supra-pubic percutaneous sclero-embolization (SE) in the treatment of symptomatic female pelvic varicocele (FPV), performed under local anesthesia. MATERIALS AND METHODS: The authors selected 28 patients screened by transabdominal and transvaginal ultrasound, with venous Doppler signal. Clinicians performed SE by transfemoral catheterization, under local anesthesia, using of a mix of 2 ml of lauromacrogol 400 (Atossisclerol 3%, Chemische F. Kreussler, Wiesbaden, Germany) and 2 ml of air, in a mixed foam fashion. RESULTS: The total operative time for SE was 7.6 +/- 2.1 min. Intra-surgical blood loss was 40 +/- 14 ml. No migration of sclerosant material occurred and postoperative analgesic request during a 48 hr period occurred in 6 patients. Technical success was 100%. The Authors embolized 8 women bilaterally (28.5%), 18 on the left ovarian vein (OV) (64.2%) and 2 only in the right OV (7.1%): 7 women complained of transitory flank pain (25%), which disappeared in few minutes. The major complications in 10 days after SE were: fever (> 38 degrees C for two days) in 2 patients (7.1%) and pelvic pain for 3 days in eight patients (28.5%). After 30 days only 6 women suffered of FPV lower symptoms which disappeared in 180 days. A substantial reduction in size of pelvic varicosities was noted in all patients. CONCLUSIONS: SE is a safe and feasible procedure. It reduces significantly the mean time of scopies, the intensity of radiation emission, and it is performed under local anaesthesia. This minimally invasive procedure could be proposed to all women with supra-pubic FPV for its reproducibility and feasibility.

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model.

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

A completely automated CAD system for mass detection in a large mammographic database.

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