Basic measurements of radiation from the Baseline Surface Radiation Network (BSRN) in the years 1992 to 2012, reference list of 6378 datasets

In the framework of the global energy balance, the radiative energy exchanges between Sun, Earth and space are now accurately quantified from new satellite missions. Much less is known about the magnitude of the energy flows within the climate system and at the Earth surface, which cannot be directly measured by satellites. In addition to satellite observations, here we make extensive use of the growing number of surface observations to constrain the global energy balance not only from space, but also from the surface. We combine these observations with the latest modeling efforts performed for the 5th IPCC assessment report to infer best estimates for the global mean surface radiative components. Our analyses favor global mean downward surface solar and thermal radiation values near 185 and 342 Wm**-2, respectively, which are most compatible with surface observations. Combined with an estimated surface absorbed solar radiation and thermal emission of 161 Wm**-2 and 397 Wm**-2, respectively, this leaves 106 Wm**-2 of surface net radiation available for distribution amongst the non-radiative surface energy balance components. The climate models overestimate the downward solar and underestimate the downward thermal radiation, thereby simulating nevertheless an adequate global mean surface net radiation by error compensation. This also suggests that, globally, the simulated surface sensible and latent heat fluxes, around 20 and 85 Wm**-2 on average, state realistic values. The findings of this study are compiled into a new global energy balance diagram, which may be able to reconcile currently disputed inconsistencies between energy and water cycle estimates.

Radiation fluxes, soil heat flux, air temperature, soil temperature, soil moisture and vegetation at dwarf shrubs and wet sedges in Kytalyk, NE Siberia

Vegetation changes, such as shrub encroachment and wetland expansion, have been observed in many Arctic tundra regions. These changes feed back to permafrost and climate. Permafrost can be protected by soil shading through vegetation as it reduces the amount of solar energy available for thawing. Regional climate can be affected by a reduction in surface albedo as more energy is available for atmospheric and soil heating. Here, we compared the shortwave radiation budget of two common Arctic tundra vegetation types dominated by dwarf shrubs (Betula nana) and wet sedges (Eriophorum angustifolium) in North-East Siberia. We measured time series of the shortwave and longwave radiation budget above the canopy and transmitted radiation below the canopy. Additionally, we quantified soil temperature and heat flux as well as active layer thickness. The mean growing season albedo of dwarf shrubs was 0.15 ± 0.01, for sedges it was higher (0.17 ± 0.02). Dwarf shrub transmittance was 0.36 ± 0.07 on average, and sedge transmittance was 0.28 ± 0.08. The standing dead leaves contributed strongly to the soil shading of wet sedges. Despite a lower albedo and less soil shading, the soil below dwarf shrubs conducted less heat resulting in a 17 cm shallower active layer as compared to sedges. This result was supported by additional, spatially distributed measurements of both vegetation types. Clouds were a major influencing factor for albedo and transmittance, particularly in sedge vegetation. Cloud cover reduced the albedo by 0.01 in dwarf shrubs and by 0.03 in sedges, while transmittance was increased by 0.08 and 0.10 in dwarf shrubs and sedges, respectively. Our results suggest that the observed deeper active layer below wet sedges is not primarily a result of the summer canopy radiation budget. Soil properties, such as soil albedo, moisture, and thermal conductivity, may be more influential, at least in our comparison between dwarf shrub vegetation on relatively dry patches and sedge vegetation with higher soil moisture.

A cafeína como agente modulador do ciclo atividade-repouso e memória em saguis (Callithrix jacchus)

Caffeine is the most consumed psychostimulant, with effects on attention, memory, and arousal. But when this substance is ingested near to bedtime there is a decrease on sleep, interfering on mnemonic processes. So, our ain was to investigate how the caffeine ingested near to sleep onset acts on sleep and memory in marmosets. We used 16 adult marmosets, single housed, in a 12:12h light-dark cycle. For registering locomotor activity were used two kinds of sensors. The gyroscope sensor registers activity each 30 sec and detects motion with good accuracy. Because of this we used this sensor for detecting nocturnal activity. The second sensor was based on infrared and accumulates activity each 5 min and it’s not able to detect nocturnal activity, just diurnal activity. We also used camera for registering Rest phase of one marmoset. For the cognitive task, the animals needed to learn a rewarded context (CR) when compared to a non-rewarded context CNR). This experiment comprises 5 phases: 1) Two days of habituation to apparatus; 2)Training for 8 days; 3) oral administration of caffeine (10 mg/kg) or placebo administration ±1h before sleep onset, for 8 days, with marmosets receiving placebo or caffeine; 4) retraining to apparatus and after that, placebo administration (placebo group-GP), or caffeine administration (with continuous group-GC and acute groupGA); 5) Test, for evaluating learning to CR. The sessions were filmed and each one had 8 min of duration. At 7 am started the habituation, training and test sessions, and at 3:15 pm started retraining. The results for gyroscope sensor showed that there was coincidence of 68,57% with nocturnal register of the cameras. Then, the gyroscope sensors detected nocturnal activity for all experimental groups Moreover, when compared sensor gyroscope with sensor based on infrared, was observed that both sensor presented similarity on patterns of activity curve. When we observed the effects of caffeine on Activity-Rest Cycle in GP, GA and GC, is possible to see that that gyroscope sensors and based on infrared presented only intra group differences. As behavioral results, the marmosets learned to discriminate CR when compared to CNR. Moreover, GP presented deficits on memory recall during the test, and GA increased the memory recall, when both were compared to GP. We concluded that the marmosets were able to learning the cognitive task and that the caffeine ingested near to sleep onset acts modulating memory in these animals. Moreover the gyroscope sensor can be used as alternative tool for investigating nocturnal activity. Then, the utilization of this non-invasive device allows marmosets exhibit their behavior within the laboratory conditions as natural as possible.

Proprietà di conduzione elettrica in campioni di grafene poroso

Il grafene, allotropo del carbonio costituito da un reticolo bidimensionale, è uno dei nanomateriali più promettenti allo stato attuale della ricerca nei campi della Fisica e della Chimica, ma anche dell'Ingegneria e della Biologia. Isolato e caratterizzato per la prima volta nel 2004 dai ricercatori russi Andre Geim e Konstantin Novoselov presso l'Università di Manchester, ha aperto la via sia a studi teorici per comprendere con gli strumenti della Meccanica Quantistica gli effetti di confinamento in due dimensioni (2D), sia ad un vastissimo panorama di ricerca applicativa che ha l'obiettivo di sfruttare al meglio le straordinarie proprietà meccaniche, elettriche, termiche ed ottiche mostrate da questo materiale. Nella preparazione di questa tesi ho personalmente seguito presso l'Istituto per la Microelettronica e i Microsistemi (IMM) del CNR di Bologna la sintesi mediante Deposizione Chimica da Fase Vapore (CVD) di grafene "tridimensionale" (3D) o "poroso" (denominato anche "schiuma di grafene", in inglese "graphene foam"), ossia depositato su una schiuma metallica dalla struttura non planare. In particolare l'obiettivo del lavoro è stato quello di misurare le proprietà di conduttività elettrica dei campioni sintetizzati e di confrontarle con i risultati dei modelli che le descrivono teoricamente per il grafene planare. Dopo un primo capitolo in cui descriverò la struttura cristallina, i livelli energetici e la conduzione dei portatori di carica nel reticolo ideale di grafene 2D (utilizzando la teoria delle bande e l'approssimazione "tight-binding"), illustrerò le differenti tecniche di sintesi, in particolare la CVD per la produzione di grafene poroso che ho seguito in laboratorio (cap.2). Infine, nel capitolo 3, presenterò la teoria di van der Pauw su cui è basato il procedimento per eseguire misure elettriche su film sottili, riporterò i risultati di conduttività delle schiume e farò alcuni confronti con le previsioni della teoria.

Il ruolo del substrato di rame nella sintesi di grafene cresciuto per deposizione chimica da fase vapore

La Chemical Vapor Deposition (CVD) permette la crescita di sottili strati di grafene con aree di decine di centimetri quadrati in maniera continua ed uniforme. Questa tecnica utilizza un substrato metallico, solitamente rame, riscaldato oltre i 1000 °C, sulla cui superficie il carbonio cristallizza sotto forma di grafene in un’atmosfera attiva di metano ed idrogeno. Durante la crescita, sulla superficie del rame si decompone il metano utilizzato come sorgente di carbonio. La morfologia e la composizione della superficie del rame diventano quindi elementi critici del processo per garantire la sintesi di grafene di alta qualità e purezza. In questo manoscritto si documenta l’attività sperimentale svolta presso i laboratori dell’Istituto per la Microelettronica e i Microsistemi del CNR di Bologna sulla caratterizzazione della superficie del substrato di rame utilizzato per la sintesi del grafene per CVD. L’obiettivo di questa attività è stato la caratterizzazione della morfologia superficiale del foglio metallico con misure di rugosità e di dimensione dei grani cristallini, seguendo l’evoluzione di queste caratteristiche durante i passaggi del processo di sintesi. Le misure di rugosità sono state effettuate utilizzando tecniche di profilometria ottica interferometrica, che hanno permesso di misurare l’effetto di livellamento successivo all' introduzione di un etching chimico nel processo consolidato utilizzato presso i laboratori dell’IMM di Bologna. Nell'ultima parte di questo manoscritto si è invece studiato, con tecniche di microscopia ottica ed elettronica a scansione, l’effetto di diverse concentrazioni di argon e idrogeno durante il trattamento termico di annealing del rame sulla riorganizzazione dei suoi grani cristallini. L’analisi preliminare effettuata ha permesso di individuare un intervallo ottimale dei parametri di annealing e di crescita del grafene, suggerendo importanti direzioni per migliorare il processo di sintesi attualmente utilizzato.

Analisi modellistica di un intenso ciclone di tipo tropicale nel Mediterraneo (7-8 novembre 2014): predicibilità, caratteristiche termodinamiche e confronto con osservazioni da satellite

Il lavoro di tesi riguarda lo studio dettagliato di un ciclone di tipo tropicale (tropical like cyclone, TLC) verificatosi nel Canale di Sicilia nel novembre 2014, realizzato attraverso un'analisi modellistica effettuata con i modelli BOLAM e MOLOCH (sviluppati presso il CNR-ISAC di Bologna) e il confronto con osservazioni. Nel primo capitolo è fornita una descrizione generale dei cicloni tropicali e dei TLC, indicando come la formazione di questi ultimi sia spesso il risultato dell'evoluzione di cicloni extratropicali baroclini nel Mediterraneo; sono identificate le aree geografiche con i periodi dell'anno maggiormente soggetti all'influenza di questi fenomeni, riportando un elenco dei principali TLC verificatisi nel Mediterraneo negli utlimi tre decenni e lo stato dell'arte sullo studio di questi eventi. Nel secondo capitolo sono descritte le modalità di implementazione delle simulazioni effettuate per il caso di studio e presentati i principali prodotti dell'analisi modellistica e osservazioni da satellite. Il terzo capitolo si apre con la descrizione della situazione sinottica e l'analisi osservativa con immagini Meteosat e rilevazioni radar che hanno permesso di ricostruire la traiettoria osservata del TLC. In seguito, viene dapprima fornito l'elenco completo delle simulazioni numeriche, quindi sono presentati alcuni dei più importanti risultati ottenuti, dai quali emerge che la previsione della traiettoria e intensità del TLC differisce notevolmente dalle osservazioni. Tenendo conto della bassa predicibilità che ha caratterizzato l'evento, nel quarto capitolo è descritto il metodo usato per ricostruire in maniera ottimale la traiettoria, utilizzando spezzoni da varie simulazioni, che ha permesso un confronto più realistico con i dati osservati e un'analisi dei processi fisici. Nel quinto capitolo sono riportati i principali risultati di alcuni test mirati a valutare l'impatto di aspetti legati all'implementazione delle simulazioni e ad altre forzanti fisiche.

Deterministic and probabilistic verification of multi-model meteorological forecasts on the subseasonal timescale

In questo studio, un multi-model ensemble è stato implementato e verificato, seguendo una delle priorità di ricerca del Subseasonal to Seasonal Prediction Project (S2S). Una regressione lineare è stata applicata ad un insieme di previsioni di ensemble su date passate, prodotte dai centri di previsione mensile del CNR-ISAC e ECMWF-IFS. Ognuna di queste contiene un membro di controllo e quattro elementi perturbati. Le variabili scelte per l'analisi sono l'altezza geopotenziale a 500 hPa, la temperatura a 850 hPa e la temperatura a 2 metri, la griglia spaziale ha risoluzione 1 ◦ × 1 ◦ lat-lon e sono stati utilizzati gli inverni dal 1990 al 2010. Le rianalisi di ERA-Interim sono utilizzate sia per realizzare la regressione, sia nella validazione dei risultati, mediante stimatori nonprobabilistici come lo scarto quadratico medio (RMSE) e la correlazione delle anomalie. Successivamente, tecniche di Model Output Statistics (MOS) e Direct Model Output (DMO) sono applicate al multi-model ensemble per ottenere previsioni probabilistiche per la media settimanale delle anomalie di temperatura a 2 metri. I metodi MOS utilizzati sono la regressione logistica e la regressione Gaussiana non-omogenea, mentre quelli DMO sono il democratic voting e il Tukey plotting position. Queste tecniche sono applicate anche ai singoli modelli in modo da effettuare confronti basati su stimatori probabilistici, come il ranked probability skill score, il discrete ranked probability skill score e il reliability diagram. Entrambe le tipologie di stimatori mostrano come il multi-model abbia migliori performance rispetto ai singoli modelli. Inoltre, i valori più alti di stimatori probabilistici sono ottenuti usando una regressione logistica sulla sola media di ensemble. Applicando la regressione a dataset di dimensione ridotta, abbiamo realizzato una curva di apprendimento che mostra come un aumento del numero di date nella fase di addestramento non produrrebbe ulteriori miglioramenti.

Learning and accepting quantum physics. Re-analysis of a teaching proposal

Nella tesi è analizzata nel dettaglio una proposta didattica sulla Fisica Quantistica elaborata dal gruppo di ricerca in Didattica della Fisica dell’Università di Bologna, in collaborazione con il gruppo di ricerca in Fisica Teorica e con ricercatori del CNR di Bologna. La proposta è stata sperimentata in diverse classi V di Liceo scientifico e dalle sperimentazioni sono emersi casi significativi di studenti che non sono riusciti ad accettare la teoria quantistica come descrizione convincente ad affidabile della realtà fisica (casi di non accettazione), nonostante sembrassero aver capito la maggior parte degli argomenti e essersi ‘appropriati’ del percorso per come gli era stato proposto. Da questa evidenza sono state formulate due domande di ricerca: (1) qual è la natura di questa non accettazione? Rispecchia una presa di posizione epistemologica o è espressione di una mancanza di comprensione profonda? (2) Nel secondo caso, è possibile individuare precisi meccanismi cognitivi che possono ostacolare o facilitare l’accettazione della fisica quantistica? L’analisi di interviste individuali degli studenti ha permesso di mettere in luce tre principali esigenze cognitive (cognitive needs) che sembrano essere coinvolte nell’accettazione e nell’apprendimento della fisica quantistica: le esigenze di visualizzabilità, comparabilità e di ‘realtà’. I ‘cognitive needs’ sono stati quindi utilizzati come strumenti di analisi delle diverse proposte didattiche in letteratura e del percorso di Bologna, al fine di metterne in luce le criticità. Sono state infine avanzate alcune proposte per un suo miglioramento.

Particelle di cellulosa come nuclei di ghiacciamento in nube

La formazione del ghiaccio nelle nubi avviene prevalentemente per nucleazione eterogenea, grazie alla presenza di nuclei di ghiacciamento (Ice Nucleating Particles, INP), ovvero particelle di aerosol (prevalentemente polveri minerali) in grado di favorire la solidificazione di una gocciolina o il passaggio diretto dalla fase vapore alla fase ghiaccio. Recentemente, l'interesse si è esteso anche all'aerosol di tipo biologico (funghi, spore, etc.), in grado di agire come INP a temperature più elevate rispetto all'aerosol minerale. Il lavoro sperimentale di questa tesi, svolto presso il laboratorio del gruppo Nubi e Precipitazioni dell’ISAC-CNR di Bologna, ha avuto come obiettivo lo studio della capacità della cellulosa di agire come INP. Esso si inserisce in un’attività di ricerca internazionale dedicata allo studio degli INP (progetto Ice NUclei research unIT, INUIT, Germania). Il lavoro sperimentale ha riguardato la messa a punto di due diversi sistemi di generazione dell'aerosol di cellulosa, la caratterizzazione dimensionale delle particelle, la loro osservazione al microscopio elettronico e la preparazione dei filtri per le misure di INP. I risultati ottenuti hanno evidenziato che le proprietà nucleanti della cellulosa sono inferiori rispetto alle polveri minerali ma paragonabili ad altri materiali, come la cenere vulcania e l'aerosol marino. Quindi, in aree ricche di vegetazione o dove le polveri minerali non sono abbondanti, la cellulosa potrebbe costituire un importante materiale per la formazione del ghiaccio nelle nubi miste. Misure preliminari hanno evidenziato come le particelle di cellulosa con dimensioni inferiori a 0,5μm risultano meno attive nella nucleazione del ghiaccio rispetto a quelle di dimensioni maggiori. I risultati ottenuti sono stati presentati al Congresso PM2016 (Roma, 17-19 maggio 2016) e saranno presentati in un poster alla prossima European Aerosol Conference (Tours, Francia, 4-6 settembre 2016).

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Dosimetric Evaluation of Metal Artefact Reduction using Metal Artefact Reduction (MAR) Algorithm and Dual-energy Computed Tomography (CT) Method

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

Near real time surface ocean velocity, Tyrrhenian Sea and Ligurian Sea in September 2016

The data set consists of maps of total velocity of the surface current in the North-Western Tyrrhenian Sea and Ligurian Sea averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by HF Radar are representative of the upper 0.3-2.5 meters of the ocean. Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations.

Heat fluxes and surface radiation measurements from Samoylov Island, Lena Delta, Siberia, October 2007 to March 2008

In this study, we present the winter time surface energy balance at a polygonal tundra site in northern Siberia based on independent measurements of the net radiation, the sensible heat flux and the ground heat flux from two winter seasons. The latent heat flux is inferred from measurements of the atmospheric turbulence characteristics and a model approach. The long-wave radiation is found to be the dominant factor in the surface energy balance. The radiative losses are balanced to about 60 % by the ground heat flux and almost 40 % by the sensible heat fluxes, whereas the contribution of the latent heat flux is small. The main controlling factors of the surface energy budget are the snow cover, the cloudiness and the soil temperature gradient. Large spatial differences in the surface energy balance are observed between tundra soils and a small pond. The ground heat flux released at a freezing pond is by a factor of two higher compared to the freezing soil, whereas large differences in net radiation between the pond and soil are only observed at the end of the winter period. Differences in the surface energy balance between the two winter seasons are found to be related to differences in snow depth and cloud cover which strongly affect the temperature evolution and the freeze-up at the investigated pond.

Ingegnerizzazione di materiali nanostrutturati fotocataliticamente attivi

Il lavoro oggetto di questa tesi è rivolto alla sintesi e alla caratterizzazione di materiali nanostrutturati costituiti da biossido di titanio e ossido di silicio da utilizzare come fotocatalizzatori per la depurazione delle acque. L’obiettivo è stato quello di realizzare un sistema in fase solida costituito da una matrice inerte, la silice, e una fase fotocataliticamente attiva, la nano-TiO2. Tale sistema si inserisce perfettamente nel settore di ricerca che studia la sintesi colloidale di eterostrutture nano e micro cristalline che combinano materiali diversi in un’unica particella. Il progetto nasce all’interno dell’ISTEC-CNR di Faenza, dove è stato svolto il lavoro.