1,721,063 research outputs found
Measured PET Data Characterization with the Negative Binomial Distribution Model
No full textAccurate statistical model of PET measurements is a prerequisite for a correct image reconstruction when using statistical image reconstruction algorithms, or when pre-filtering operations must be performed. Although radioactive decay follows a Poisson distribution, deviation from Poisson statistics occurs on projection data prior to reconstruction due to physical effects, measurement errors, correction of scatter and random coincidences. Modelling projection data can aid in understanding the statistical nature of the data in order to develop efficient processing methods and to reduce noise. This paper outlines the statistical behaviour of measured emission data evaluating the goodness of fit of the negative binomial (NB) distribution model to PET data for a wide range of emission activity values. An NB distribution model is characterized by the mean of the data and the dispersion parameter α that describes the deviation from Poisson statistics. Monte Carlo simulations were performed to evaluate: (a) the performances of the dispersion parameter α estimator, (b) the goodness of fit of the NB model for a wide range of activity values. We focused on the effect produced by correction for random and scatter events in the projection (sinogram) domain, due to their importance in quantitative analysis of PET data. The analysis developed herein allowed us to assess the accuracy of the NB distribution model to fit corrected sinogram data, and to evaluate the sensitivity of the dispersion parameter α to quantify deviation from Poisson statistics. By the sinogram ROI-based analysis, it was demonstrated that deviation on the measured data from Poisson statistics can be quantitatively characterized by the dispersion parameter α, in any noise conditions and corrections
Variable density randomized stack of spirals (VDR-SoS) for compressive sensing MRI
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The influence of noise in dynamic PET direct reconstruction
Full text linkIn the present work a study is carried out in order to assess the efficiency of the direct reconstruction algorithms on noisy dynamic PET data. The study is performed via Monte Carlo simulations of a uniform cylindrical phantom whose emission values change in time according to a kinetic law. After generating the relevant projection data and properly adding the effects of different noise sources on them, the direct reconstruction and parametric estimation algorithm is applied. The resulting kinetic parameters and reconstructed images are then quantitatively evaluated with appropriate indexes. The simulation is repeated considering different sources of noise and different values of them. The results obtained allow us to affirm that the direct reconstruction algorithm tested maintains a good efficiency also in presence of noise
Automatic Assessment of Myocardial Fibrosis by Delayed Enhanced Magnetic Resonance Imaging
No full textA Matlab toolbox for morphological analysis and 3D reconstruction of arterial histological sections
No full textThe present paper describes a Matlab toolbox for automatic analysis of histological arterial sections, for both morphometric investigations and 3D reconstructions. The aim of this program is to simplify and improve the processing of histological images, which is usually carried out by means of specific or standard image editing software, where measuring is mainly mouse-based, therefore manual and affected by subjective errors. A user-friendly interface has been also developed, to help a non Matlab-expert through the steps of the elaboration.
The main phases in the image processing are: unwarping, registration, identification of the three layers in the arterial thickness, stacking and smoothing of each cross section, definition of surfaces and volumes for 3D reconstruction. The most delicate step is the edge detection for the identification of the layers, that requires the validation of three algorithms, one for each edge.
A medical application is reported that investigates the effects of balloon angioplasty on the biological tissues. Several porcine arteries, healthy or treated with balloon, have been observed for evaluating lumen area and the layers’ thicknesses.
It is worth noting also that Finite Element models of arterial segments can be conveniently developed using 3D reconstruction, carried out with this toolbox, exploiting the high level of morphometric information of histological images
Sviluppo di un tool per stima dell'esposizione professionale a campi elettromagnetici in risonanza magnetica
No full textLa Risonanza Magnetica (RM) è una tecnica diagnostica largamente utilizzata in diversi campi della medicina e per il suo corretto funzionamento ricorre a diverse tipologie di campi elettromagnetici (EM). Lo staff RM durante il lavoro giornaliero è esposto in maniera prolungata al campo magnetico statico e spazialmente eterogeneo presente in ogni momento nella sala RM. Muovendosi all’interno della stanza RM per espletare le loro funzioni, i tecnici sono esposti a campo magnetico variabile lentamente nel tempo che induce correnti elettriche nel corpo. Nonostante non ci siano ancora conferme sui possibili effetti nocivi sulla salute umana di questa corrente indotta [1], la Comunità Europea (CE) ha redatto una Direttiva (Direttiva 2004/40/EC) [2] nella quale si pone un limite di sicurezza a questa corrente. Tale Direttiva dovrà essere recepita da tutti gli stati membri entro aprile 2012. L’utilizzo di modelli matematici e di simulazioni al calcolatore permette di stimare la corrente indotta e di avere quindi informazioni importanti sull’esposizione degli operatori durante il turno giornaliero e in diverse condizioni operative.
In questo lavoro viene presentato un nuovo strumento per la stima della densità di corrente indotta a causa dei movimenti dei lavoratori nel campo magnetico statico di uno scanner MR. Il tool rappresenta un valido strumento per rivelare situazioni in cui i limiti di esposizione della direttiva possono essere superati, consentendo di addestrare gli operatori ad evitare, per quanto possibile, comportamenti a rischio di alta esposizione
A Conway–Maxwell–Poisson (CMP) model to address data dispersion on positron emission tomography
Full text linkPositron emission tomography (PET) in medicine exploits the properties of positron-emitting unstable nuclei. The pairs of γ- rays emitted after annihilation are revealed by coincidence detectors and stored as projections in a sinogram. It is well known that radioactive decay follows a Poisson distribution; however, deviation from Poisson statistics occurs on PET projection data prior to reconstruction due to physical effects, measurement errors, correction of deadtime, scatter, and random coincidences. A model that describes the statistical behavior of measured and corrected PET data can aid in understanding the statistical nature of the data: it is a prerequisite to develop efficient reconstruction and processing methods and to reduce noise. The deviation from Poisson statistics in PET data could be described by the Conway-Maxwell-Poisson (CMP) distribution model, which is characterized by the centring parameter λ and the dispersion parameter ν, the latter quantifying the deviation from a Poisson distribution model. In particular, the parameter ν allows quantifying over-dispersion (ν<1) or under-dispersion (ν>1) of data. A simple and efficient method for λ and ν parameters estimation is introduced and assessed using Monte Carlo simulation for a wide range of activity values. The application of the method to simulated and experimental PET phantom data demonstrated that the CMP distribution parameters could detect deviation from the Poisson distribution both in raw and corrected PET data. It may be usefully implemented in image reconstruction algorithms and quantitative PET data analysis, especially in low counting emission data, as in dynamic PET data, where the method demonstrated the best accuracy
Direct Parametric Maps Estimation from Dynamic PET Data: An Iterated Conditional Modes Approach
Full text linkWe propose and test a novel approach for direct parametric image reconstruction of dynamic PET data. We present a theoretical description of the problem of PET direct parametric maps estimation as an inference problem, from a probabilistic point of view, and we derive a simple iterative algorithm, based on the Iterated Conditional Mode (ICM) framework, which exploits the simplicity of a two-step optimization and the efficiency of an analytic method for estimating kinetic parameters from a nonlinear compartmental model. The resulting method is general enough to be flexible to an arbitrary choice of the kinetic model, and unlike many other solutions, it is capable to deal with nonlinear compartmental models without the need for linearization. We tested its performance on a two-tissue compartment model, including an analytical solution to the kinetic parameters evaluation, based on an auxiliary parameter set, with the aim of reducing computation errors and approximations. The new method is tested on simulated and clinical data. Simulation analysis led to the conclusion that the proposed algorithm gives a good estimation of the kinetic parameters in any noise condition. Furthermore, the application of the proposed method to clinical data gave promising results for further studies
Technological innovations in magnetic resonance for early detection of cardiovascular diseases
Full text linkMost recent technical innovations in cardiovascular MR imaging (CMRI) are presented in this review. They include hardware and software developments, and novelties in parametric mapping. All these recent improvements lead to high spatial and temporal resolution and quantitative information on the heart structure and function. They make it achievable ambitious goals in the field of mapletic resonance, such as the early detection of cardiovascular pathologies.
In this review article, we present recent innovations in CMRI, emphasizing the progresses performed and the solutions proposed to some yet opened technical problems
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