In Amyotrophic Lateral Sclerosis (ALS) spinal cord (SC) showed a moderate increase in FDG uptake with respect to healthy subjects. The main aim of our study is to integrate the information concerning the divergent behavior of SC with skeletal muscle metabolism improving the informative potential of 18F-fluoro-2-deoxy-glucose (FDG) PET/CT imaging regarding specific pathophysiological mechanisms underlying ALS progression. We analyzed 50 ALS patients with spinal onset consecutively submitted to FDG PET/CT imaging. Obtained data were compared to the corresponding findings in 36 age and sex-matched controls. A computational method was used to extract psoas volume and attenuation coefficient from CT images. Psoas volume was normalized for patient ideal body weight (IBW). In co-registered PET images, FDG accumulation was defined by average normalized standardized uptake value (N-SUV). Average Hounsfield values (AVH) in the psoas were similar in patients and controls (39±8 AHV vs 39±11 AHV, respectively, p=ns). By contrast, ALS was associated with a significant reduction in psoas volume normalized for IBW (8.8±2.9 mL/Kg IBW vs 10.3±2.7 mL/Kg IBW, respectively, p<0.05). More interestingly, N-SUV was significantly higher in patients than in controls (0.44±0.19 vs 0.29±0.09; p<0.001). These SUV values predicted overall survival rate at Kaplan-Meyer analysis (p<0.05) with a predictive power that was confirmed by univariate as well as by multivariate Cox analysis (p<0.02). ALS is therefore associated with a psoas reduction in volume and increase in FDG uptake. The intensity of FDG uptake within this muscular district is related to disease aggressiveness.
Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement, renal damages and osteolytic lesions. The skeleton involvement of MM is at the core of the present paper, exploiting radiomics and artificial intelligence to identify image-based biomarkers for MM. Preliminary results show that MM is associated to an extension of the intrabone volume for the whole body and that machine learning can identify CT image features mostly correlating with the disease evolution. This computational approach allows an automatic stratification of MM patients relying of these biomarkers and the formulation of a prognostic procedure for determining the disease follow-up.
It has been recently proved that the computational analysis of X-ray Computed Tomography (CT) images allows clinicians to assess the alteration of compact bone asset due to hematological diseases. HT-BONE implements a new method, based on an extension of the Hough transform (HT) to a wide class of algebraic curves, for accurately measuring global and regional geometric properties of trabecular and compact bone districts. In the case of CT/PET analysis, the segmentation of the CT images provides masks for Positron Emission Tomography (PET) data, extracting the metabolic activity in the region surrounded by compact bone tissue. HT-BONE offers an intuitive, user-friendly, Matlab-based Graphical User Interface (GUI) for all input/output procedures and the automatic managing of the segmentation process also from non-expert users: the CT/PET data can be loaded and browsed easily and the only pre-preprocessing required from the user is the drawing of Regions Of Interest (ROIs) around the bone districts under consideration. For each bone district, specific families of curves, whose reliability has been already tested in previous works, is automatically selected for the recognition task via HT. As output, the software returns masks of the segmented compact bone regions, images of the Standard Uptake Values (SUV) in the masked regions of PET slices, and the values of the parameters in the curve equations utilized in the HT procedure. This information can be used for all pathologies and clinical conditions for which the alteration of the compact bone asset or bone marrow distribution plays a crucial role.
The Spectrometer Telescope for Imaging X-rays (STIX) is one of 10 instruments on-board Solar Orbiter mission of the European Space Agency (ESA) scheduled to be launched in 2017. STIX is aimed to provide imaging spectroscopy of solar thermal and non-thermal hard X-ray emissions from 4 keV to 150 keV using a Fourier-imaging technique. The instrument employs a set of tungsten grids in front of 32 pixelized CdTe detectors. These detectors are source of data collected and analyzed in real time by Instrument Data Processing Unit (IDPU). In order to support development and implementation of on-board algorithms a dedicated detector hardware simulator is designed and manufactured as a part of Electrical Ground Support Equipment (EGSE) for STIX instrument. Complementary to the hardware simulator is data analysis software which is used to generate input data and to analyze output data. The simulator will allow sending strictly defined data from all detectors’ pixels at the input of the IDPU for further analysis of instrument response. Particular emphasis is given here to the simulator hardware design.
The Spectrometer Telescope for Imaging X-rays (STIX) is one of 10 instruments on board Solar Orbiter, a confirmed Mclass mission of the European Space Agency (ESA) within the Cosmic Vision program scheduled to be launched in 2017. STIX applies a Fourier-imaging technique using a set of tungsten grids (at pitches from 0.038 to 1 mm) in front of 32 pixelized CdTe detectors to provide imaging spectroscopy of solar thermal and non-thermal hard X-ray emissions from 4 to 150 keV. The status of the instrument reviewed in this paper is based on the design that passed the Preliminary Design Review (PDR) in early 2012. Particular emphasis is given to the first light of the detector system called Caliste-SO.
Deconvolution of images of the same object from multiple sensors with different point spread functions (PSF), as shown by Berenstein and Patrick, can be a well-posed problem in the sense of distributions if the PSF satisfy some suitable conditions. More precisely, if these operators are represented by compactly supported distributions, a corresponding set of deconvolvers, also given by compactly supported distributions, may exist. Nevertheless, it must be observed that this inverse operator is not particularly useful if the multiple images which must be deconvolved are affected by noise, because continuity in the sense of distributions is too weak. This is the reason why a more effective approach is provided by the inverse methods typical of regularization theory. We have considered the case described by Berenstein and Patrick, in which the input function consists of the sum of two Gaussian pulses and the PSF are the characteristic functions of the intervals (-1, 1) and (- (root)2, 2). The two images we have obtained have been affected by Gaussian noise and then simulated data have been inverted by using various regularization techniques; in particular, in the case of iterative methods, it has also been possible to introduce the positivity constraint. The comparison between the reconstructions we have obtained and the input function allows to estimate the greater efficiency of the regularized multiple operators deconvolution, compared with the inversion of a single image, when linear filtering is applied. On the contrary the performance of the nonlinear constrained iterative method seems not to be particularly sensitive to the use of two images instead of one. An explanation of this fact is given and an example, where the use of multiple images can be advantageous, is presented.