Numerical investigation of the mechanical loading of supporting soft tissue for partial dentures

The aim of this research was to study the impact of loading on partial dentures within the supporting soft tissue with respect to different attachment techniques. A finite element model was developed to calculate the stress and strain distribution in this tissue. The model consisted of the left half of a mandible with three remaining teeth that had suffered an atrophy in the anterior region, and a partial denture over the toothless area that was connected at the left mandibular canine using an attachment system. Resulting stress/strain distributions are presented for different load cases using a commercially available prefabricated attachment system.

Three-dimensional imaging methods for quantitative analysis of facial soft tissues and skeletal morphology in patients with orofacial clefts: a systematic review.

BACKGROUND Current guidelines for evaluating cleft palate treatments are mostly based on two-dimensional (2D) evaluation, but three-dimensional (3D) imaging methods to assess treatment outcome are steadily rising. OBJECTIVE To identify 3D imaging methods for quantitative assessment of soft tissue and skeletal morphology in patients with cleft lip and palate. DATA SOURCES Literature was searched using PubMed (1948-2012), EMBASE (1980-2012), Scopus (2004-2012), Web of Science (1945-2012), and the Cochrane Library. The last search was performed September 30, 2012. Reference lists were hand searched for potentially eligible studies. There was no language restriction. STUDY SELECTION We included publications using 3D imaging techniques to assess facial soft tissue or skeletal morphology in patients older than 5 years with a cleft lip with/or without cleft palate. We reviewed studies involving the facial region when at least 10 subjects in the sample size had at least one cleft type. Only primary publications were included. DATA EXTRACTION Independent extraction of data and quality assessments were performed by two observers. RESULTS Five hundred full text publications were retrieved, 144 met the inclusion criteria, with 63 high quality studies. There were differences in study designs, topics studied, patient characteristics, and success measurements; therefore, only a systematic review could be conducted. Main 3D-techniques that are used in cleft lip and palate patients are CT, CBCT, MRI, stereophotogrammetry, and laser surface scanning. These techniques are mainly used for soft tissue analysis, evaluation of bone grafting, and changes in the craniofacial skeleton. Digital dental casts are used to evaluate treatment and changes over time. CONCLUSION Available evidence implies that 3D imaging methods can be used for documentation of CLP patients. No data are available yet showing that 3D methods are more informative than conventional 2D methods. Further research is warranted to elucidate it.

Introduction to Soft-Collinear Effective Theory

Among resummation techniques for perturbative QCD in the context of collider and flavor physics, soft-collinear effective theory (SCET) has emerged as both a powerful and versatile tool, having been applied to a large variety of processes, from B-meson decays to jet production at the LHC. This book provides a concise, pedagogical introduction to this technique. It discusses the expansion of Feynman diagrams around the high-energy limit, followed by the explicit construction of the effective Lagrangian - first for a scalar theory, then for QCD. The underlying concepts are illustrated with the quark vector form factor at large momentum transfer, and the formalism is applied to compute soft-gluon resummation and to perform transverse-momentum resummation for the Drell-Yan process utilizing renormalization group evolution in SCET. Finally, the infrared structure of n-point gauge-theory amplitudes is analyzed by relating them to effective-theory operators. This text is suitable for graduate students and non-specialist researchers alike as it requires only basic knowledge of perturbative QCD.

Systematic literature review of digital three-dimensional superimposition techniques to create virtual dental patients

PURPOSE Digital developments have led to the opportunity to compose simulated patient models based on three-dimensional (3D) skeletal, facial, and dental imaging. The aim of this systematic review is to provide an update on the current knowledge, to report on the technical progress in the field of 3D virtual patient science, and to identify further research needs to accomplish clinical translation. MATERIALS AND METHODS Searches were performed electronically (MEDLINE and OVID) and manually up to March 2014 for studies of 3D fusion imaging to create a virtual dental patient. Inclusion criteria were limited to human studies reporting on the technical protocol for superimposition of at least two different 3D data sets and medical field of interest. RESULTS Of the 403 titles originally retrieved, 51 abstracts and, subsequently, 21 full texts were selected for review. Of the 21 full texts, 18 studies were included in the systematic review. Most of the investigations were designed as feasibility studies. Three different types of 3D data were identified for simulation: facial skeleton, extraoral soft tissue, and dentition. A total of 112 patients were investigated in the development of 3D virtual models. CONCLUSION Superimposition of data on the facial skeleton, soft tissue, and/or dentition is a feasible technique to create a virtual patient under static conditions. Three-dimensional image fusion is of interest and importance in all fields of dental medicine. Future research should focus on the real-time replication of a human head, including dynamic movements, capturing data in a single step.

Traditional education vs modern education. What is the impact of teaching techniques' evolution on students learning process?

The main objective of this article is to focus on the analysis of teaching techniques, ranging from the use of the blackboard and chalk in old traditional classes, using slides and overhead projectors in the eighties and use of presentation software in the nineties, to the video, electronic board and network resources nowadays. Furthermore, all the aforementioned, is viewed under the different mentalities in which the teacher conditions the student using the new teaching technique, improving soft skills but maybe leading either to encouragement or disinterest, and including the lack of educational knowledge consolidation at scientific, technology and specific levels. In the same way, we study the process of adaptation required for teachers, the differences in the processes of information transfer and education towards the student, and even the existence of teachers who are not any longer appealed by their work due which has become much simpler due to new technologies and the greater ease in the development of classes due to the criteria described on the new Grade Programs adopted by the European Higher Education Area. Moreover, it is also intended to understand the evolution of students’ profiles, from the eighties to present time, in order to understand certain attitudes, behaviours, accomplishments and acknowledgements acquired over the semesters within the degree Programs. As an Educational Innovation Group, another key question also arises. What will be the learning techniques in the future?. How these evolving matters will affect both positively and negatively on the mentality, attitude, behaviour, learning, achievement of goals and satisfaction levels of all elements involved in universities’ education? Clearly, this evolution from chalk to the electronic board, the three-dimensional view of our works and their sequence, greatly facilitates the understanding and adaptation later on to the business world, but does not answer to the unknowns regarding the knowledge and the full development of achievement’s indicators in basic skills of a degree. This is the underlying question which steers the roots of the presented research.

Selective SWIFT-R. A Flexible Software-Based Technique for Soft Error Mitigation in Low-Cost Embedded Systems

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

A Hardware-Software Approach for On-Line Soft Error Mitigation in Interrupt-Driven Applications

Integrity assurance of configuration data has a significant impact on microcontroller-based systems reliability. This is especially true when running applications driven by events which behavior is tightly coupled to this kind of data. This work proposes a new hybrid technique that combines hardware and software resources for detecting and recovering soft-errors in system configuration data. Our approach is based on the utilization of a common built-in microcontroller resource (timer) that works jointly with a software-based technique, which is responsible to periodically refresh the configuration data. The experiments demonstrate that non-destructive single event effects can be effectively mitigated with reduced overheads. Results show an important increase in fault coverage for SEUs and SETs, about one order of magnitude.

Visual performance of the human eye with soft hydrophilic contact lenses

The recording of visual acuity using the Snellen letter chart is only a limited measure of the visual performance of an eye wearing a refractive aid. Qualitative in addition to quantitative information is required to establish such a parameter: spatial, temporal and photometric aspects must all be incorporated into the test procedure. The literature relating to the correction of ametropia by refractive aids was reviewed. Selected aspects of a comparison between the correction provided by spectacles and contact lenses were considered. Special attention was directed to soft hydrophilic contact lenses. Despite technological advances which have produced physiologically acceptable soft lenses, there still remain associated with this recent form of refractive aid unpredictable visual factors. Several techniques for vision assessment were described, and previous studies of visual performance were discussed. To facilitate the investigation of visual performance in a clinical environment, a new semi-automated system was described: this utilized the presentation of broken ring test stimuli on a television screen. The research project comprised two stages. Initial work was concerned with the validation of the television system, including the optimization of its several operational variables. The second phase involved the utilization of the system in an investigation of visual performance aspects of the first month of regular daily soft contact lens wear by experimentally-naive subjects. On the basis of the results of this work an ‘homoeostatic’ model has been proposed to represent the strategy which an observer adopts in order to optimize his visual performance with soft contact lenses.

Applicability of contact angle techniques used in the analysis of contact lenses, part 1:comparative methodologies

Objectives and Methods: Contact angle, as a representative measure of surface wettability, is often employed to interpret contact lens surface properties. The literature is often contradictory and can lead to confusion. This literature review is part of a series regarding the analysis of hydrogel contact lenses using contact angle techniques. Here we present an overview of contact angle terminology, methodology, and analysis. Having discussed this background material, subsequent parts of the series will discuss the analysis of contact lens contact angles and evaluate differences in published laboratory results. Results: The concepts of contact angle, wettability and wetting are presented as an introduction. Contact angle hysteresis is outlined and highlights the advantages in using dynamic analytical techniques over static methods. The surface free energy of a material illustrates how contact angle analysis is capable of providing supplementary surface characterization. Although single values are able to distinguish individual material differences, surface free energy and dynamic methods provide an improved understanding of material behavior. The frequently used sessile drop, captive bubble, and Wilhelmy plate techniques are discussed. Their use as both dynamic and static methods, along with the advantages and disadvantages of each technique, is explained. Conclusions: No single contact angle technique fully characterizes the wettability of a material surface, and the application of complimenting methods allows increased characterization. At present, there is not an ISO standard method designed for soft materials. It is important that each contact angle technique has a standard protocol, as small protocol differences between laboratories often contribute to a variety of published data that are not easily comparable. © 2013 Contact Lens Association of Ophthalmologists.

Physical and biological properties of a novel siloxane adhesive for soft tissue applications

The aim of this study was to investigate the adhesive properties of an in-house amino-propyltrimethoxysilane-methylenebisacrylamide (APTMS-MBA) siloxane system and compare them with a commercially available adhesive, n-butyl cyanoacrylate (nBCA). The ability of the material to perform as a soft tissue adhesive was established by measuring the physical (bond strength, curing time) and biological (cytotoxicity) properties of the adhesives on cartilage. Complementary physical techniques, X-ray photoelectron spectroscopy, Raman and infrared imaging, enabled the mode of action of the adhesive to the cartilage surface to be determined. Adhesion strength to cartilage was measured using a simple butt joint test after storage in phosphate-buffered saline solution at 37°C for periods up to 1 month. The adhesives were also characterised using two in vitro biological techniques. A live/dead stain assay enabled a measure of the viability of chondrocytes attached to the two adhesives to be made. A water-soluble tetrazolium assay was carried out using two different cell types, human dermal fibroblasts and ovine meniscal chondrocytes, in order to measure material cytotoxicity as a function of both supernatant concentration and time. IR imaging of the surface of cartilage treated with APTMS-MBA siloxane adhesive indicated that the adhesive penetrated the tissue surface marginally compared to nBCA which showed a greater depth of penetration. The curing time and adhesion strength values for APTMS-MBA siloxane and nBCA adhesives were measured to be 60 s/0.23 MPa and 38 min/0.62 MPa, respectively. These materials were found to be significantly stronger than either commercially available fibrin (0.02 MPa) or gelatin resorcinol formaldehyde (GRF) adhesives (0.1 MPa) (P <0.01). Cell culture experiments revealed that APTMS-MBA siloxane adhesive induced 2% cell death compared to 95% for the nBCA adhesive, which extended to a depth of approximately 100-150 μm into the cartilage surface. The WST-1 assay demonstrated that APTMS-MBA siloxane was significantly less cytotoxic than nBCA adhesive as an undiluted conditioned supernatant (P <0.001). These results suggest that the APTMS-MBA siloxane may be a useful adhesive for medical applications. © VSP 2005.

Use of imaging technology to better understand soft contact lens fit dynamics

The principal theme of this thesis is the identification of additional factors affecting, and consequently to better allow, the prediction of soft contact lens fit. Various models have been put forward in an attempt to predict the parameters that influence soft contact lens fit dynamics; however, the factors that influence variation in soft lens fit are still not fully understood. The investigations in this body of work involved the use of a variety of different imaging techniques to both quantify the anterior ocular topography and assess lens fit. The use of Anterior-Segment Optical Coherence Tomography (AS-OCT) allowed for a more complete characterisation of the cornea and corneoscleral profile (CSP) than either conventional keratometry or videokeratoscopy alone, and for the collection of normative data relating to the CSP for a substantial sample size. The scleral face was identified as being rotationally asymmetric, the mean corneoscleral junction (CSJ) angle being sharpest nasally and becoming progressively flatter at the temporal, inferior and superior limbal junctions. Additionally, 77% of all CSJ angles were within ±50 of 1800, demonstrating an almost tangential extension of the cornea to form the paralimbal sclera. Use of AS-OCT allowed for a more robust determination of corneal diameter than that of white-to-white (WTW) measurement, which is highly variable and dependent on changes in peripheral corneal transparency. Significant differences in ocular topography were found between different ethnicities and sexes, most notably for corneal diameter and corneal sagittal height variables. Lens tightness was found to be significantly correlated with the difference between horizontal CSJ angles (r =+0.40, P =0.0086). Modelling of the CSP data gained allowed for prediction of up to 24% of the variance in contact lens fit; however, it was likely that stronger associations and an increase in the modelled prediction of variance in fit may have occurred had an objective method of lens fit assessment have been made. A subsequent investigation to determine the validity and repeatability of objective contact lens fit assessment using digital video capture showed no significant benefit over subjective evaluation. The technique, however, was employed in the ensuing investigation to show significant changes in lens fit between 8 hours (the longest duration of wear previously examined) and 16 hours, demonstrating that wearing time is an additional factor driving lens fit dynamics. The modelling of data from enhanced videokeratoscopy composite maps alone allowed for up to 77% of the variance in soft contact lens fit, and up to almost 90% to be predicted when used in conjunction with OCT. The investigations provided further insight into the ocular topography and factors affecting soft contact lens fit.

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.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

SUR Hand: A Soft Underactuated Robotic Hand

Soft robots are robots made mostly or completely of soft, deformable, or compliant materials. As humanoid robotic technology takes on a wider range of applications, it has become apparent that they could replace humans in dangerous environments. Current attempts to create robotic hands for these environments are very difficult and costly to manufacture. Therefore, a robotic hand made with simplistic architecture and cheap fabrication techniques is needed. The goal of this thesis is to detail the design, fabrication, modeling, and testing of the SUR Hand. The SUR Hand is a soft, underactuated robotic hand designed to be cheaper and easier to manufacture than conventional hands. Yet, it maintains much of their dexterity and precision. This thesis will detail the design process for the soft pneumatic fingers, compliant palm, and flexible wrist. It will also discuss a semi-empirical model for finger design and the creation and validation of grasping models.