Corporate governance and related party transactions research:an assessment of theories and methodologies

This paper critically evaluates the paradigm, theory, and methodology that dominate research on related party transactions (RPTs). RPTs have been debated in the literature whether they are a facet of conflict of interest between major and minor shareholders or they are normal efficient transactions that help the firms to achieve asset utilization. Literature has been widely interested in studying the association between corporate governance and RPTs especially that according to the agency theory it is assumed that corporate governance as a monitoring tool should impede the negative consequences of RPTs and ensure they are conducted to achieve better asset utilization.

Evaluating techniques to improve visual performance with and assessment of premium intraocular lenses

Premium Intraocular Lenses (IOLs) such as toric IOLs, multifocal IOLs (MIOLs) and accommodating IOLs (AIOLs) can provide better refractive and visual outcomes compared to standard monofocal designs, leading to greater levels of post-operative spectacle independence. The principal theme of this thesis relates to the development of new assessment techniques that can help to improve future premium IOL design. IOLs designed to correct astigmatism form the focus of the first part of the thesis. A novel toric IOL design was devised to decrease the effect of toric rotation on patient visual acuity, but found to have neither a beneficial or detrimental impact on visual acuity retention. IOL tilt, like rotation, may curtail visual performance; however current IOL tilt measurement techniques require the use of specialist equipment not readily available in most ophthalmological clinics. Thus a new idea that applied Pythagoras’s theory to digital images of IOL optic symmetricality in order to calculate tilt was proposed, and shown to be both accurate and highly repeatable. A literature review revealed little information on the relationship between IOL tilt, decentration and rotation and so this was examined. A poor correlation between these factors was found, indicating they occur independently of each other. Next, presbyopia correcting IOLs were investigated. The light distribution of different MIOLs and an AIOL was assessed using perimetry, to establish whether this could be used to inform optimal IOL design. Anticipated differences in threshold sensitivity between IOLs were not however found, thus perimetry was concluded to be ineffective in mapping retinal projection of blur. The observed difference between subjective and objective measures of accommodation, arising from the influence of pseudoaccommodative factors, was explored next to establish how much additional objective power would be required to restore the eye’s focus with AIOLs. Blur tolerance was found to be the key contributor to the ocular depth of focus, with an approximate dioptric influence of 0.60D. Our understanding of MIOLs may be limited by the need for subjective defocus curves, which are lengthy and do not permit important additional measures to be undertaken. The use of aberrometry to provide faster objective defocus curves was examined. Although subjective and objective measures related well, the peaks of the MIOL defocus curve profile were not evident with objective prediction of acuity, indicating a need for further refinement of visual quality metrics based on ocular aberrations. The experiments detailed in the thesis evaluate methods to improve visual performance with toric IOLs. They also investigate new techniques to allow more rapid post-operative assessment of premium IOLs, which could allow greater insights to be obtained into several aspects of visual quality, in order to optimise future IOL design and ultimately enhance patient satisfaction.

The therapeutic effects of Millon Clinical Multiaxial Inventory -III assessment feedback

A study was conducted to test the therapeutic effects of assessment feedback on rapport-building and self-enhancement variables (self-verification, self-discovery, self-esteem), as well as symptomatology. Assessment feedback was provided in the form of interpretive information based on the results of the Millon Clinical Multiaxial Inventory-III (MCMI-III). Participants (N = 89) were randomly assigned to three groups: a Feedback group, a Reflective-Counseling group, and a No-Feedback group. The Feedback group was provided with assessment feedback, the Reflective-Counseling group was asked to comment on the meaning of the taking the MCMI-III, the No-Feedback group received general information about the MCMI-III. Results revealed that assessment feedback, when provided in the form of interpretive interpretation positively affects rapport-building and self-enhancement variables (self-verification and self-discovery). No significant results were found in terms of self-esteem or symptom decrease as a function of feedback. However, a significant decrease in symptoms across groups was found. Results indicate that assessment feedback in the form of interpretive information can be used as a starting point in therapy. Implications of the findings are discussed with respect to theory and clinical practice. ^

Integrated Performance Assessment of Engineering Projects at the Interface of Emergent Properties and Uncertainty

Investigation of the performance of engineering project organizations is critical for understanding and eliminating inefficiencies in today’s dynamic global markets. The existing theoretical frameworks consider project organizations as monolithic systems and attribute the performance of project organizations to the characteristics of the constituents. However, project organizations consist of complex interdependent networks of agents, information, and resources whose interactions give rise to emergent properties that affect the overall performance of project organizations. Yet, our understanding of the emergent properties in project organizations and their impact on project performance is rather limited. This limitation is one of the major barriers towards creation of integrated theories of performance assessment in project organizations. The objective of this paper is to investigate the emergent properties that affect the ability of project organization to cope with uncertainty. Based on the theories of complex systems, we propose and test a novel framework in which the likelihood of performance variations in project organizations could be investigated based on the environment of uncertainty (i.e., static complexity, dynamic complexity, and external source of disruption) as well as the emergent properties (i.e., absorptive capacity, adaptive capacity, and restorative capacity) of project organizations. The existence and significance of different dimensions of the environment of uncertainty and emergent properties in the proposed framework are tested based on the analysis of the information collected from interviews with senior project managers in the construction industry. The outcomes of this study provide a novel theoretical lens for proactive bottom-up investigation of performance in project organizations at the interface of emergent properties and uncertainty

The therapeutic effects of Millon Clinical Multiaxial Inventory-III assessment feedback

A study was conducted to test the therapeutic effects of assessment feedback on rapport-building and self-enhancement variables (self-verification, self-discovery, self-esteem), as well as symptomatology. Assessment feedback was provided in the form of interpretive information based on the results of the Millon Clinical Multiaxial Inventory- III (MCMI-III). Participants (N = 89) were randomly assigned to three groups: a Feedback group, a Reflective-Counseling group, and a No-Feedback group. The Feedback group was provided with assessment feedback, the Reflective-Counseling group was asked to comment on the meaning of the taking the MCMI-III, the No- Feedback group received general information about the MCMI-III. Results revealed that assessment feedback, when provided in the form of interpretive interpretation positively affects rapport-building and self-enhancement variables (self-verification and self-discovery). No significant results were found in terms of self-esteem or symptom decrease as a function of feedback. However, a significant decrease in symptoms across groups was found. Results indicate that assessment feedback in the form of interpretive information can be used as a starting point in therapy. Implications of the findings are discussed with respect to theory and clinical practice.

Increasing nurse's knowledge of neonatal pain assessment through inservice education program

Recognizing neonatal pain is a challenge for nurses working with newborns due to the complexity of the pain phenomenon. Pain is subjective, and infants lack the ability to communicate, and their pain is difficult to recognize. The purpose of this study is to determine the effectiveness of education on the NICU nurses' ability to assess neonatal pain. With a better understanding of pain theory and the effects of pain on the newborn the nurse will be better able to assess newborns with pain. Designed as a quasi-experimental one-group pretest and posttest study, the data was collected on a convenience sample of 49 registered nurses employed in the neonatal and special care nursery units at a Childrens Hospital in the Miami area. The nurses were surveyed on the assessment of neonatal pain using the General Information and Pain Sensitivity Questionnaire. After the initial survey, the nurses were inserviced on neonatal pain assessment using a one hour inservice education program. One week after the intervention the nurse was asked to complete the questionnaire again. Data analysis involved comparision of pre and post intervention findings using descriptive methods, t test, correlation coefficients, and ANOVA , where applicable. Findings revealed a significant ( p=.006) increase in nurse's knowledge of neonatal pain assessment after completing the educational inservice when comparing the pre-test and post-test results.

A Naturalistic Assessment of the Organization of Children’s Memories Predicts Cognitive Functioning and Reading Ability

To explore the relationship between memory and early school performance, we used graph theory to investigate memory reports from 76 children aged 6–8 years. The reports comprised autobiographical memories of events days to years past, and memories of novel images reported immediately after encoding. We also measured intelligence quotient (IQ) and theory of mind (ToM). Reading and Mathematics were assessed before classes began (December 2013), around the time of report collection (June 2014), and at the end of the academic year (December 2014). IQ and ToM correlated positively with word diversity and word-to-word connectivity, and negatively with word recurrence. Connectivity correlated positively with Reading in June 2014 as well as December 2014, even after adjusting for IQ and ToM. To our knowledge, this is the first study demonstrating a link between the structure of children’s memories and their cognitive or academic performance.

A Naturalistic Assessment of the Organization of Children’s Memories Predicts Cognitive Functioning and Reading Ability

To explore the relationship between memory and early school performance, we used graph theory to investigate memory reports from 76 children aged 6–8 years. The reports comprised autobiographical memories of events days to years past, and memories of novel images reported immediately after encoding. We also measured intelligence quotient (IQ) and theory of mind (ToM). Reading and Mathematics were assessed before classes began (December 2013), around the time of report collection (June 2014), and at the end of the academic year (December 2014). IQ and ToM correlated positively with word diversity and word-to-word connectivity, and negatively with word recurrence. Connectivity correlated positively with Reading in June 2014 as well as December 2014, even after adjusting for IQ and ToM. To our knowledge, this is the first study demonstrating a link between the structure of children’s memories and their cognitive or academic performance.

Quantitative assessment of intrinsic noise for visually guided behaviour in zebrafish

Supported by Royal Society of London (University Research Fellowship), Medical Research Council (New Investigator Research Grant) and CNRS.

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

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.

Assessment of the EEE-4 oral test: a discourse analysis based on complex networks.

With the development of information technology, the theory and methodology of complex network has been introduced to the language research, which transforms the system of language in a complex networks composed of nodes and edges for the quantitative analysis about the language structure. The development of dependency grammar provides theoretical support for the construction of a treebank corpus, making possible a statistic analysis of complex networks. This paper introduces the theory and methodology of the complex network and builds dependency syntactic networks based on the treebank of speeches from the EEE-4 oral test. According to the analysis of the overall characteristics of the networks, including the number of edges, the number of the nodes, the average degree, the average path length, the network centrality and the degree distribution, it aims to find in the networks potential difference and similarity between various grades of speaking performance. Through clustering analysis, this research intends to prove the network parameters’ discriminating feature and provide potential reference for scoring speaking performance.

Bootcamp EMMA MOOC Assessment for learning in practice

At the EDEN Conference an introduction to the EMMA platform will be combined with learning activities relevant to the topic of Formative Assessment. EDEN conference participants will have an opportunity to join the MOOC prior to the conference or at the conference. A range of learning activities will be offered during the training to ensure that all interested have sufficient opportunities to learn.

Exploring the mobile structural assessment tool: Concept maps for learning website: Concept Maps for Learning Website

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

A literature review to Examine the Validity and Reliability of group poster presentations as an assessment method of a module in an undergraduate nursing degree programme

The introduction of a poster presentation as a formative assessment method over a multiple choice examination after the first phase of a three phase “health and well-being” module in an undergraduate nursing degree programme was greeted with a storm of criticism from fellow lecturers stating that poster presentations are not valid or reliable and totally irrelevant to the assessment of learning in the module. This paper seeks to investigate these criticisms by investigating the literature regarding producing nurses fit for practice, nurse curriculum development and wider nurse education, the purpose of assessment, validity and reliability to critically evaluate the poster presentation as a legitimate assessment method for these aims.