Search for Dark Matter in events with a highly energetic jet and missing transverse momentum in proton-proton collisions at root out s = 8 TeV with the ATLAS detector

The experiments at the Large Hadron Collider at the European Centre for Particle Physics, CERN, rely on efficient and reliable trigger systems for singling out interesting events. This thesis documents two online timing monitoring tools for the central trigger of the ATLAS experiment as well as the adaption of the central trigger simulation as part of the upgrade for the second LHC run. Moreover, a search for candidates for so-called Dark Matter, for which there is ample cosmological evidence, is presented. This search for generic weakly interacting massive particles (WIMPs) is based on the roughly 20/fb of proton-proton collisions at a centre-of-mass-energy of sqrt{s}=8 TeV recorded with the ATLAS detector in 2012. The considered signature are events with a highly energetic jet and large missing transverse energy. No significant deviation from the theory prediction is observed. Exclusion limits are derived on parameters of different signal models and compared to the results of other experiments. Finally, the results of a simulation study on the potential of the analysis at sqrt{s}=14 TeV are presented.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Performance evaluation of detector for digital radiography

Lo scopo di questo lavoro è la caratterizzazione fisica del flat panel PaxScan4030CB Varian, rivelatore di raggi X impiegato in un ampio spettro di applicazioni cliniche, dalla radiografia generale alla radiologia interventistica. Nell’ambito clinico, al fine di una diagnosi accurata, è necessario avere una buona qualità dell’immagine radiologica mantenendo il più basso livello di dose rilasciata al paziente. Elemento fondamentale per ottenere questo risultato è la scelta del rivelatore di radiazione X, che deve garantire prestazioni fisiche (contrasto, risoluzione spaziale e rumore) adeguati alla specifica procedura. Le metriche oggettive che misurano queste caratteristiche sono SNR (Signal-to-Noise Ratio), MTF (Modulation Transfer Function) ed NPS (Noise Power Spectrum), che insieme contribuiscono alla misura della DQE (Detective Quantum Efficiency), il parametro più completo e adatto a stabilire le performance di un sistema di imaging. L’oggettività di queste misure consente anche di mettere a confronto tra loro diversi sistemi di rivelazione. La misura di questi parametri deve essere effettuata seguendo precisi protocolli di fisica medica, che sono stati applicati al rivelatore PaxScan4030CB presente nel laboratorio del Centro di Coordinamento di Fisica Medica, Policlinico S.Orsola. I risultati ottenuti, conformi a quelli dichiarati dal costruttore, sono stati confrontati con successo con alcuni lavori presenti in letteratura e costituiscono la base necessaria per la verifica di procedure di ottimizzazione dell’immagine radiologica attraverso interventi sul processo di emissione dei raggi X e sul trattamento informatico dell’immagine (Digital Subtraction Angiography).

Novel materials for direct X-ray detectors based on semiconducting organic polymers

Conventional inorganic materials for x-ray radiation sensors suffer from several drawbacks, including their inability to cover large curved areas, me- chanical sti ffness, lack of tissue-equivalence and toxicity. Semiconducting organic polymers represent an alternative and have been employed as di- rect photoconversion material in organic diodes. In contrast to inorganic detector materials, polymers allow low-cost and large area fabrication by sol- vent based methods. In addition their processing is compliant with fexible low-temperature substrates. Flexible and large-area detectors are needed for dosimetry in medical radiotherapy and security applications. The objective of my thesis is to achieve optimized organic polymer diodes for fexible, di- rect x-ray detectors. To this end polymer diodes based on two different semi- conducting polymers, polyvinylcarbazole (PVK) and poly(9,9-dioctyluorene) (PFO) have been fabricated. The diodes show state-of-the-art rectifying be- haviour and hole transport mobilities comparable to reference materials. In order to improve the X-ray stopping power, high-Z nanoparticle Bi2O3 or WO3 where added to realize a polymer-nanoparticle composite with opti- mized properities. X-ray detector characterization resulted in sensitivties of up to 14 uC/Gy/cm2 for PVK when diodes were operated in reverse. Addition of nanoparticles could further improve the performance and a maximum sensitivy of 19 uC/Gy/cm2 was obtained for the PFO diodes. Compared to the pure PFO diode this corresponds to a five-fold increase and thus highlights the potentiality of nanoparticles for polymer detector design. In- terestingly the pure polymer diodes showed an order of magnitude increase in sensitivity when operated in forward regime. The increase was attributed to a different detection mechanism based on the modulation of the diodes conductivity.

Proposal of a Continuous Read-out Implementation in the ALICE-TOF detector

The main purpose of ultrarelativistic heavy-ion collisions is the investigation of the QGP. The ALICE experiment situated at the CERN has been specifically designed to study heavy-ion collisions for centre-of-mass energies up to 5.5 per nucleon pair. Extended particle identification capability is one of the main characteristics of the ALICE experiment. In the intermediate momentum region (up to 2.5 GeV/c for pi/K and 4 GeV/c for K/p), charged particles are identified in the ALICE experiment by the Time of Flight (TOF) detector. The ALICE-TOF system is a large-area detector based on the use of Multi-gap Resistive Plate Chamber (MRPC) built with high efficiency, fast response and intrinsic time resolution better than 40 ps. This thesis work, developed with the ALICE-TOF Bologna group, is part of the efforts carried out to adapt the read-out of the detector to the new requirements after the LHC Long Shutdown 2. Tests on the feasibility of a new read-out scheme for the TOF detector have been performed. In fact, the achievement of a continuous read-out also for the TOF detector would not be affordable if one considers the replacement of the TRM cards both for hardware and budget reasons. Actually, the read-out of the TOF is limited at 250 kHz i.e. it would be able to collect up to just a fourth of the maximum collision rate potentially achievable for pp interactions. In this Master’s degree thesis work, I discuss a different read-out system for the ALICE-TOF detector that allows to register all the hits at the interaction rate of 1 MHz foreseen for pp interactions after the 2020, by using the electronics currently available. Such solution would allow the ALICE-TOF detector to collect all the hits generated by pp collisions at 1 MHz interaction rate, which corresponds to an amount four times larger than that initially expected at such frequencies with the triggered read-out system operated at 250 kHz for LHC Run 3. The obtained results confirm that the proposed read-out scheme is a viable option for the ALICE TOF detector. The results also highlighted that it will be advantageous if the ALICE-TOF group also implement an online monitoring system of noisy channels to allow their deactivation in real time.

A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Multi-modal diffeomorphic demons registration based on point-wise mutual information

In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.

Automated detection of fiducial screws from CT/DVT volume data for image-guided ENT surgery

This paper presents an automated solution for precise detection of fiducial screws from three-dimensional (3D) Computerized Tomography (CT)/Digital Volume Tomography (DVT) data for image-guided ENT surgery. Unlike previously published solutions, we regard the detection of the fiducial screws from the CT/DVT volume data as a pose estimation problem. We thus developed a model-based solution. Starting from a user-supplied initialization, our solution detects the fiducial screws by iteratively matching a computer aided design (CAD) model of the fiducial screw to features extracted from the CT/DVT data. We validated our solution on one conventional CT dataset and on five DVT volume datasets, resulting in a total detection of 24 fiducial screws. Our experimental results indicate that the proposed solution achieves much higher reproducibility and precision than the manual detection. Further comparison shows that the proposed solution produces better results on the DVT dataset than on the conventional CT dataset.

Copper filtration in pediatric digital X-ray imaging: its impact on image quality and dose

The effect of copper (Cu) filtration on image quality and dose in different digital X-ray systems was investigated. Two computed radiography systems and one digital radiography detector were used. Three different polymethylmethacrylate blocks simulated the pediatric body. The effect of Cu filters of 0.1, 0.2, and 0.3 mm thickness on the entrance surface dose (ESD) and the corresponding effective doses (EDs) were measured at tube voltages of 60, 66, and 73 kV. Image quality was evaluated in a contrast-detail phantom with an automated analyzer software. Cu filters of 0.1, 0.2, and 0.3 mm thickness decreased the ESD by 25-32%, 32-39%, and 40-44%, respectively, the ranges depending on the respective tube voltages. There was no consistent decline in image quality due to increasing Cu filtration. The estimated ED of anterior-posterior (AP) chest projections was reduced by up to 23%. No relevant reduction in the ED was noted in AP radiographs of the abdomen and pelvis or in posterior-anterior radiographs of the chest. Cu filtration reduces the ESD, but generally does not reduce the effective dose. Cu filters can help protect radiosensitive superficial organs, such as the mammary glands in AP chest projections.

Real-Time Multimodal Retinal Image Registration for a Computer Assisted Laser Photocoagulation System

An algorithm for the real-time registration of a retinal video sequence captured with a scanning digital ophthalmoscope (SDO) to a retinal composite image is presented. This method is designed for a computer-assisted retinal laser photocoagulation system to compensate for retinal motion and hence enhance the accuracy, speed, and patient safety of retinal laser treatments. The procedure combines intensity and feature-based registration techniques. For the registration of an individual frame, the translational frame-to-frame motion between preceding and current frame is detected by normalized cross correlation. Next, vessel points on the current video frame are identified and an initial transformation estimate is constructed from the calculated translation vector and the quadratic registration matrix of the previous frame. The vessel points are then iteratively matched to the segmented vessel centerline of the composite image to refine the initial transformation and register the video frame to the composite image. Criteria for image quality and algorithm convergence are introduced, which assess the exclusion of single frames from the registration process and enable a loss of tracking signal if necessary. The algorithm was successfully applied to ten different video sequences recorded from patients. It revealed an average accuracy of 2.47 ± 2.0 pixels (∼23.2 ± 18.8 μm) for 2764 evaluated video frames and demonstrated that it meets the clinical requirements.

Minimization of Intra-Operative Shaping of Orthopaedic Fixation Plates: A Population-Based Design

In this paper we present a new population-based method for the design of bone fixation plates. Standard pre-contoured plates are designed based on the mean shape of a certain population. We propose a computational process to design implants while reducing the amount of required intra-operative shaping, thus reducing the mechanical stresses applied to the plate. A bending and torsion model was used to measure and minimize the necessary intra-operative deformation. The method was applied and validated on a population of 200 femurs that was further augmented with a statistical shape model. The obtained results showed substantial reduction in the bending and torsion needed to shape the new design into any bone in the population when compared to the standard mean-based plates.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Feature-invariant image registration method for quantification of surgical outcomes in patients with craniosynostosis: a preliminary study

Craniosynostosis consists of a premature fusion of the sutures in an infant skull that restricts skull and brain growth. During the last decades, there has been a rapid increase of fundamentally diverse surgical treatment methods. At present, the surgical outcome has been assessed using global variables such as cephalic index, head circumference, and intracranial volume. However, these variables have failed in describing the local deformations and morphological changes that may have a role in the neurologic disorders observed in the patients. This report describes a rigid image registration-based method to evaluate outcomes of craniosynostosis surgical treatments, local quantification of head growth, and indirect intracranial volume change measurements. The developed semiautomatic analysis method was applied to computed tomography data sets of a 5-month-old boy with sagittal craniosynostosis who underwent expansion of the posterior skull with cranioplasty. Quantification of the local changes between pre- and postoperative images was quantified by mapping the minimum distance of individual points from the preoperative to the postoperative surface meshes, and indirect intracranial volume changes were estimated. The proposed methodology can provide the surgeon a tool for the quantitative evaluation of surgical procedures and detection of abnormalities of the infant skull and its development.