ALBIRA: A small animal PET/SPECT/CT imaging system

Purpose: The authors have developed a trimodal PET/SPECT/CT scanner for small animal imaging. The gamma ray subsystems are based on monolithic crystals coupled to multianode photomultiplier tubes (MA-PMTs), while computed tomography (CT) comprises a commercially available microfocus x-ray tube and a CsI scintillator 2D pixelated flat panel x-ray detector. In this study the authors will report on the design and performance evaluation of the multimodal system. Methods: X-ray transmission measurements are performed based on cone-beam geometry. Individual projections were acquired by rotating the x-ray tube and the 2D flat panel detector, thus making possible a transaxial field of view (FOV) of roughly 80 mm in diameter and an axial FOV of 65 mm for the CT system. The single photon emission computed tomography (SPECT) component has a dual head detector geometry mounted on a rotating gantry. The distance between the SPECT module detectors can be varied in order to optimize specific user requirements, including variable FOV. The positron emission tomography (PET) system is made up of eight compact modules forming an octagon with an axial FOV of 40 mm and a transaxial FOV of 80 mm in diameter. The main CT image quality parameters (spatial resolution and uniformity) have been determined. In the case of the SPECT, the tomographic spatial resolution and system sensitivity have been evaluated with a99mTc solution using single-pinhole and multi-pinhole collimators. PET and SPECT images were reconstructed using three-dimensional (3D) maximum likelihood and ordered subset expectation maximization (MLEM and OSEM) algorithms developed by the authors, whereas the CT images were obtained using a 3D based FBP algorithm. Results: CT spatial resolution was 85μm while a uniformity of 2.7% was obtained for a water filled phantom at 45 kV. The SPECT spatial resolution was better than 0.8 mm measured with a Derenzo-like phantom for a FOV of 20 mm using a 1-mm pinhole aperture collimator. The full width at half-maximum PET radial spatial resolution at the center of the field of view was 1.55 mm. The SPECT system sensitivity for a FOV of 20 mm and 15% energy window was 700 cps/MBq (7.8 × 10−2%) using a multi-pinhole equipped with five apertures 1 mm in diameter, whereas the PET absolute sensitivity was 2% for a 350–650 keV energy window and a 5 ns timing window. Several animal images are also presented. Conclusions: The new small animal PET/SPECT/CT proposed here exhibits high performance, producing high-quality images suitable for studies with small animals. Monolithic design for PET and SPECT scintillator crystals reduces cost and complexity without significant performance degradation.This study was supported by the Spanish Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica (I+D+I) under Grant No. FIS2010-21216-CO2-01 and Valencian Local Government under Grant PROMETEO 2008/114. The authors also thank Brennan Holt for checking and correcting the text.Sánchez Martínez, F.; Orero Palomares, A.; Soriano Asensi, A.; Correcher Salvador, C.; Conde Castellanos, PE.; González Martínez, AJ.; Hernández Hernández, L.... (2013). ALBIRA: A small animal PET/SPECT/CT imaging system. Medical Physics. 40(5):5190601-5190611. https://doi.org/10.1118/1.4800798S5190601519061140

Verfahren des maschinellen Lernens zur Entscheidungsunterstützung

Erfolgreiche Unternehmen denken intensiv über den eigentlichen Nutzen ihres Unternehmens für Kunden nach. Diese versuchen, ihrer Konkurrenz voraus zu sein, und zwar durch gute Ideen, Innovationen und Kreativität. Dabei wird Erfolg anhand von Metriken gemessen, wie z.B. der Anzahl der loyalen Kunden oder der Anzahl der Käufer. Gegeben, dass der Wettbewerb durch die Globalisierung, Deregulierung und technologische Innovation in den letzten Jahren angewachsen ist, spielen die richtigen Entscheidungen für den Erfolg gerade im operativen Geschäft der sämtlichen Bereiche des Unternehmens eine zentrale Rolle. Vor diesem Hintergrund entstammen die in der vorliegenden Arbeit zur Evaluation der Methoden des maschinellen Lernens untersuchten Entscheidungsprobleme vornehmlich der Entscheidungsunterstützung. Hierzu gehören Klassifikationsprobleme wie die Kreditwürdigkeitsprüfung im Bereich Credit Scoring und die Effizienz der Marketing Campaigns im Bereich Direktmarketing. In diesem Kontext ergaben sich Fragestellungen für die korrelativen Modelle, nämlich die Untersuchung der Eignung der Verfahren des maschinellen Lernens für den Bereich des Credit Scoring, die Kalibrierung der Wahrscheinlichkeiten, welche mithilfe von Verfahren des maschinellen Lernens erzeugt werden sowie die Konzeption und Umsetzung einer Synergie-Heuristik zwischen den Methoden der klassischen Statistik und Verfahren des maschinellen Lernens. Desweiteren wurden kausale Modelle für den Bereich Direktmarketing (sog. Uplift-Effekte) angesprochen. Diese Themen wurden im Rahmen von breit angelegten empirischen Studien bearbeitet. Zusammenfassend ergibt sich, dass der Einsatz der untersuchten Verfahren beim derzeitigen Stand der Forschung zur Lösung praxisrelevanter Entscheidungsprobleme sowie spezifischer Fragestellungen, welche aus den besonderen Anforderungen der betrachteten Anwendungen abgeleitet wurden, einen wesentlichen Beitrag leistet.Nowadays right decisions, being it strategic or operative, are important for every company, since these contribute directly to an overall success. This success can be measured based on quantitative metrics, for example, by the number of loyal customers or the number of incremental purchases. These decisions are typically made based on the historical data that relates to all functions of the company in general and to customers in particular. Thus, companies seek to analyze this data and apply obtained knowlegde in decision making. Classification problems represent an example of such decisions. Classification problems are best solved, when techniques of classical statistics and these of machine learning are applied, since both of them are able to analyze huge amount of data, to detect dependencies of the data patterns, and to produce probability, which represents the basis for the decision making. I apply these techniques and examine their suitability based on correlative models for decision making in credit scoring and further extend the work by causal predictive models for direct marketing. In detail, I analyze the suitability of techniques of machine learning for credit scoring alongside multiple dimensions, I examine the ability to produce calibrated probabilities and apply techniques to improve the probability estimations. I further develop and propose a synergy heuristic between the methods of classical statistics and techniques of machine learning to improve the prediction quality of the former, and finally apply conversion models to turn machine learning techqiques to account for causal relationship between marketing campaigns and customer behavior in direct marketing. The work has shown that the techniques of machine learning represent a suitable alternative to the methods of classical statistics for decision making and should be considered not only in research but also should find their practical application in real-world practices

Approaches for credit scorecard calibration: An empirical analysis

International audienc

Zelfcompassie versus uiterlijke focus. De impact van fitfluencerboodschappen op de lichaamstevredenheid en sportintenties van jongvolwassenen

Jongvolwassenen vergelijken zichzelf vaak met ‘ideale’ lichamen op sociale media. Zeker content van fitfluencers, die sterk geïdealiseerd is, zorgt regelmatig voor lichaamsontevredenheid en demotivatie om te sporten bij hun publiek. Deze experimentele studie (18-29 jaar, N = 211) vond geen directe noch indirecte effecten (via uiterlijke vergelijking en gemoedstoestand) van onderschriften die zelfcompassie promoten als buffer tegen deze negatieve effecten

Characterization of pinhole SPECT acquisition geometry

A method is presented to estimate the acquisition geometry of a pinhole single photon emission computed tomography (SPECT) camera with a circular detector orbit. This information is needed for the reconstruction of tomographic images. The calibration uses the point source projection locations of a tomographic acquisition of three point sources located at known distances from each other. It is shown that this simple phantom provides the necessary and sufficient information for the proposed calibration method. The knowledge of two of the distances between the point sources proves to be essential. The geometry is estimated by fitting analytically calculated projections to the measured ones, using a simple least squares Powell algorithm. Some mild a priori knowledge is used to constrain the solutions of the fit. Several of the geometrical parameters are however highly correlated. The effect of these correlations on the reconstructed images is evaluated in simulation studies and related to the estimation accuracy. The highly correlated detector tilt and electrical shift are shown to be the critical parameters for accurate image reconstruction. The performance of the algorithm is finally demonstrated by phantom measurements. The method is based on a single SPECT scan of a simple calibration phantom, executed immediately after the actual SPECT acquisition. The method is also applicable to cone-beam SPECT and X-ray CT.Bequé D., Nuyts J., Bormans G., Suetens P., Dupont P., ''Characterization of pinhole SPECT acquisition geometry'', IEEE transactions on medical imaging, vol. 22, no. 5, pp. 599-612, May 2003.status: publishe

Non-invasive imaging of neuropathology in a rat model of alpha-synuclein overexpression.

Parkinson's disease is a neurodegenerative disorder affecting the dopaminergic neurons in the substantia nigra. Aggregation of alpha-synuclein appears to play a central role in the pathogenesis. Novel animal models for neurodegeneration have been generated by lentiviral vector-mediated locoregional overexpression of disease-associated genes in the adult brain. We have used lentiviral vectors to overexpress a clinical mutant of alpha-synuclein, A30P, in the rat substantia nigra. This overexpression induced time-dependent cytoplasmic and neuritic accumulation of alpha-synuclein and neurodegeneration. A subgroup of the rats developed asymmetric rotational behavior after administration of amphetamine. In addition, these animals displayed reduced dopamine transporter binding visualized by 123I-FP-CIT microSPECT imaging. The behavioral and microSPECT data were validated by histological analysis. There was a strong correlation between the reduction of dopaminergic neurons in the substantia nigra and the reduction of dopamine transporter binding in the striatum. MicroSPECT imaging enables non-invasive imaging of the neurodegeneration allowing longitudinal follow-up in this new animal model for Parkinson's disease and the evaluation of neuroprotective drugs.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe