Nuovi metodi diretti per la stima dei parametri cinetici da immagini PET dinamiche

Riassunto Riuscire ad ottenere una stima diretta dei parametri cinetici dai dati prodotti da un esame dinamico di tomografia ad emissione di positroni (PET) rappresenta ad oggi un obiettivo ambizioso quanto ancora lontano dall'effettiva applicazione alla pratica clinica. Attualmente, il protocollo standard per l'analisi cinetica quantitativa del processo di metabolizzazione del può essere descritto come un metodo indiretto, strutturato in due passaggi distinti: partendo dai dati di emissione si ricostruisce la sequenza di immagini dinamiche che, successivamente, viene analizzata mediante modelli compartimentali. L'algoritmo che sarà oggetto di questo lavoro andrà invece a combinare in un'unica formula queste due operazioni: lavorare direttamente sui dati di proiezione consente di migliorare sia la qualità che la velocità della stima ottenuta. Dopo aver fornito una descrizione teorica del modello utilizzato, ne è stata realizzata un'implementazione software per mezzo della quale si è andati quindi a valutare, tramite simulazioni Monte Carlo su un fantoccio di test omogeneo, le prestazioni dello stesso nella risoluzione di problemi di stima parametrica e ricostruzione di immagini dinamiche in diverse condizioni di rapporto segnale-rumore, al fine di verificare la robustezza del metodo in condizioni operative non ideali. Successivamente sono state svolte simulazioni su fantocci realistici che riproducessero delle sezioni anatomiche di interesse (zona toracica e cerebrale) che hanno portato, infine, ad un primo test esplorativo su dati reali sperimentali. Abstract Being able to obtain a direct kinetic parameters estimate from dynamic positron emission tomography (PET) data today represents an ambitious goal as yet far from the actual application to clinical practice. Currently, the standard protocol for quantitative kinetic analysis of tracer’s metabolization process can be described as an indirect method, structured in two separate steps: starting from the emission data, a sequence of dynamic images is first reconstructed and then analyzed by compartmental models. The algorithm that will be the subject of this work aims to combine into a single formula these two operations: working directly on the projection data can improve both the quality and the speed of the resulting estimate. After providing a theoretical description of the model used, we present a software implementation by which we could evaluate, via Monte Carlo simulations on an homogeneous test phantom, its performance in the resolution of task related to parameter estimation and reconstruction of dynamic images in different conditions of signal to noise ratio, in order to verify the robustness of the method in non-ideal operating conditions. Subsequently simulations of realistic phantoms were carried out to reproduce the behaviour of some anatomical sections of interest (thoracic and cerebral). This led, ultimately, to a first exploratory test on real experimental data

The influence of noise in dynamic PET direct reconstruction

In 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

A Conway–Maxwell–Poisson (CMP) model to address data dispersion on positron emission tomography

Positron 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

Images in clinical medicine: gouty arthritis with osteomyelitis

Gout is one of the most common inflammatory arthropathies, characterized by the deposition of monosodium urate crystals in the synovial membrane, articular cartilage and periarticular tissues and leading to inflammation. The natural history of articular gout is typically composed of four periods: asymptomatic hyperuricemia, episodes of acute attacks of gout (acute gouty arthritis) with asymptomatic intervals (intercritical gout), and chronic tophaceous gout. Tophi develop in 12-35% of gouty patients without adequate control of uricemia. Initially, they do not cause significant complaints or function limitation of the nearby joints. However, if they become larger, joint instability and movement range limitation, joint function impairment and bone erosions and infection at the sites of their penetration can develop.We report a case of a poorly controlled polyarticular tophaceous gout complicated by osteomyelitis

4D Tomographic Image Reconstruction and Parametric Maps Estimation: a model-based strategy for algorithm design using Bayesian inference in Probabilistic Graphical Models

This work is inspired by the search for an answer to two grand challenges affecting 4D emission tomography, namely the solution of the inverse problem of computing the rate of emission in the imaging volume in case of an extreme photon-limited regime, and the estimation of maps of pharmacokinetic parameters. The strategy to tackle these issues proposed in this thesis is based on the idea that a unified and synergistic approach to the estimation of both dynamic activity time series and parametric maps could provide mutual benefits, by integrating the lack of measured information with predictions made by the chosen model. Framing emission tomography imaging in the Bayesian framework via probabilistic graphical models, we are able to define a model-based approach to the design of integrated inference algorithms. From one side, this modeling approach has shown itself able to encompass traditional literature about emission tomography image reconstruction. From another, it provides a flexible tool to describe causal relationships between variables, and a straightforward strategy to derive inference algorithms from such a combination of graphical and probabilistic representations. A number of different models are proposed, justified and discussed, in the light of the model-based inference framework proposed in this thesis. A comprehensive description of the phenomenon of image formation allows us to devise unified inference approaches to tackle at once and in a synergistic way the solution of multiple problems that traditionally are dealt with in a sequential way. At the deeper level, pharmacokinetic models can be used to concisely describe in a mathematical way the physiological interactions between tissues and tracer; these interactions are responsible for determining how the injected radiotracer distributes within tissues, in space, and thus of what we eventually see in the form of images; lastly, the spatial location of radioactive molecules is the source of the measured coincidence photons on which we base our inference. The formulations presented in this thesis are unifying in several ways, combining in a single model information from multiple domains, and attempting to unify reconstruction and kinetic modeling, tasks usually addressed with a sequential approach. Moreover, this modeling approach is able to abstract over details that are specific of a certain imaging modality in such a way that the inference strategies developed for PET can be (quite) easily adapted to other imaging modalities that may face similar challenges (like the case of DCE-MRI discussed in this work), requiring just minor changes of the assumptions made during model-design

Gli stressor ambientali condizionano il benessere

Con riferimento agli \u201cstressor ambientali\u201d, ossia a quelle componenti palesemente irritanti di cui sarebbe preferibile, bench\ue9 comunque impossibile, la totale assenza (polveri, odori, rumori), l\u2019elaborazione delle osservazioni dirette e della documentazione specifica, sia pure non sempre copiosa, consente di trarre conferme e nuove indicazioni circa la conseguente riduzione del benessere nelle diverse fasi di allevamento del suino: esse vanno dalla riduzione delle performance (elemento comune e sempre primo segnale di ridotto benessere) all\u2019alterazione dello stato sanitario (con particolare evidenza per le polveri e per la componente \u201cammoniaca\u201d degli odori) ai disturbi comportamentali e fisiologici (con particolare evidenza per i rumori). In tutti i casi, esiste spazio e si possono individuare prospettive per operare, attraverso il continuo miglioramento degli aspetti strutturali e di management, una riduzione dell\u2019impatto dei citati fattori sull\u2019animale e sull\u2019ambiente: tecniche di distribuzione degli alimenti a limitata produzione di polveri insieme a impianti di ventilazione adeguati e sempre meno rumorosi, drenaggio delle urine, trattamenti mirati (della lettiera, dell\u2019aria, delle deiezioni), strategie alimentari ad hoc, preparazione e scelta oculata del personale (per favorire la conoscenza degli effetti dannosi dei rumori forti, costanti o improvvisi che siano), diffusione di musica dolce e a basso volume, in sonorizzazione degli ambienti di sosta negli stabilimenti di macellazione. Da sottolineare, infine, l\u2019opportunit\ue0 di mantenere l\u2019illuminazione, in termini sia di intensit\ue0 che di durata, a livelli elevati