2D-3D registration of CT vertebra volume to fluoroscopy projection:a calibration model assessment

This study extends a previous research concerning intervertebral motion registration by means of 2D dynamic fluoroscopy to obtain a more comprehensive 3D description of vertebral kinematics. The problem of estimating the 3D rigid pose of a CT volume of a vertebra from its 2D X-ray fluoroscopy projection is addressed. 2D-3D registration is obtained maximising a measure of similarity between Digitally Reconstructed Radiographs (obtained from the CT volume) and real fluoroscopic projection. X-ray energy correction was performed. To assess the method a calibration model was realised a sheep dry vertebra was rigidly fixed to a frame of reference including metallic markers. Accurate measurement of 3D orientation was obtained via single-camera calibration of the markers and held as true 3D vertebra position; then, vertebra 3D pose was estimated and results compared. Error analysis revealed accuracy of the order of 0.1 degree for the rotation angles of about 1mm for displacements parallel to the fluoroscopic plane, and of order of 10mm for the orthogonal displacement. © 2010 P. Bifulco et al.

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

Assessment of the occurrence and potential risks of antibiotics and their metabolites in South Florida waters using liquid chromatography tandem mass spectrometry

An automated on-line SPE-LC-MS/MS method was developed for the quantitation of multiple classes of antibiotics in environmental waters. High sensitivity in the low ng/L range was accomplished by using large volume injections with 10-mL of sample. Positive confirmation of analytes was achieved using two selected reaction monitoring (SRM) transitions per antibiotic and quantitation was performed using an internal standard approach. Samples were extracted using online solid phase extraction, then using column switching technique; extracted samples were immediately passed through liquid chromatography and analyzed by tandem mass spectrometry. The total run time per each sample was 20 min. The statistically calculated method detection limits for various environmental samples were between 1.2 and 63 ng/L. Furthermore, the method was validated in terms of precision, accuracy and linearity. ^ The developed analytical methodology was used to measure the occurrence of antibiotics in reclaimed waters (n=56), surface waters (n=53), ground waters (n=8) and drinking waters (n=54) collected from different parts of South Florida. In reclaimed waters, the most frequently detected antibiotics were nalidixic acid, erythromycin, clarithromycin, azithromycin trimethoprim, sulfamethoxazole and ofloxacin (19.3-604.9 ng/L). Detection of antibiotics in reclaimed waters indicates that they can't be completely removed by conventional wastewater treatment process. Furthermore, the average mass loads of antibiotics released into the local environment through reclaimed water were estimated as 0.248 Kg/day. Among the surface waters samples, Miami River (reaching up to 580 ng/L) and Black Creek canal (up to 124 ng/L) showed highest concentrations of antibiotics. No traces of antibiotics were found in ground waters. On the other hand, erythromycin (monitored as anhydro erythromycin) was detected in 82% of the drinking water samples (n.d-66 ng/L). The developed approach is suitable for both research and monitoring applications.^ Major metabolites of antibiotics in reclaimed wates were identified and quantified using high resolution benchtop Q-Exactive orbitrap mass spectrometer. A phase I metabolite of erythromycin was tentatively identified in full scan based on accurate mass measurement. Using extracted ion chromatogram (XIC), high resolution data-dependent MS/MS spectra and metabolic profiling software the metabolite was identified as desmethyl anhydro erythromycin with molecular formula C36H63NO12 and m/z 702.4423. The molar concentration of the metabolite to erythromycin was in the order of 13 %. To my knowledge, this is the first known report on this metabolite in reclaimed water. Another compound acetyl-sulfamethoxazole, a phase II metabolite of sulfamethoxazole was also identified in reclaimed water and mole fraction of the metabolite represent 36 %, of the cumulative sulfamethoxazole concentration. The results were illustrating the importance to include metabolites also in the routine analysis to obtain a mass balance for better understanding of the occurrence, fate and distribution of antibiotics in the environment. ^ Finally, all the antibiotics detected in reclaimed and surface waters were investigated to assess the potential risk to the aquatic organisms. The surface water antibiotic concentrations that represented the real time exposure conditions revealed that the macrolide antibiotics, erythromycin, clarithromycin and tylosin along with quinolone antibiotic, ciprofloxacin were suspected to induce high toxicity to aquatic biota. Preliminary results showing that, among the antibiotic groups tested, macrolides posed the highest ecological threat, and therefore, they may need to be further evaluated with, long-term exposure studies considering bioaccumulation factors and more number of species selected. Overall, the occurrence of antibiotics in aquatic environment is posing an ecological health concern.^

Initial placement methods for Haitian adult ESOL students : the NYS place test plus profile data compared to subjective assessment

Few valid and reliable placement procedures are available to assess the English language proficiency of adults who enroll in English for Speakers of Other Languages (ESOL) programs. Whereas placement material exists for children and university ESOL students, the needs of students in adult community education programs have not been adequately addressed. Furthermore, the research suggests that a number of variables, such as, native language, age, prior schooling, length of residence, and employment are related to second language acquisition. Numerous studies contribute to our understanding of the relationship of these factors to second language acquisition of Spanish-speaking students. Again, there is a void in the research investigating the factors affecting second language acquisition and consequently, appropriate placement of Haitian Creole-speaking students. This study compared a standardized instrument, the NYS Place Test, used alone and in combination with a writing sample in English, to subjective judgement of a department coordinator for initial placement of Haitian adult ESOL students in a community education program. The study also investigated whether or not consideration of student profile data improved the accuracy of the test. Finally, the study sought to determine if a relationship existed between student profile data and those who withdrew from the program or did not enter a class after registering. Analysis of the data by crosstabulation and chi-square revealed that the standardized NYS Place Test was at least as accurate as subjective department coordinator placement and that one procedure could be substituted for li other. Although the writing sample in English improved accuracy of placement by the NYS test, the results were not significant. Of the profile variables, only length of residence was found to be significantly related to accuracy of placement using the NYS Place Test. The number of incorrect placements was higher for those students who lived in the host country from twenty-five to one hundred ten months. A post hoc analysis of NYS test scores according to level showed that those learners who placed in level three also had a significantly higher incidence of incorrect placements. No significant relationship was observed between the profile variables and those who withdrew from the program or registered but did not enter a class.

The framework of the virtual laser tracker - A systematic approach to the assessment of error sources and uncertainty in laser tracker measurement

Laser trackers have been widely used in many industries to meet increasingly high accuracy requirements. In laser tracker measurement, it is complex and difficult to perform an accurate error analysis and uncertainty evaluation. This paper firstly reviews the working principle of single beam laser trackers and state-of- The- Art of key technologies from both industrial and academic efforts, followed by a comprehensive analysis of uncertainty sources. A generic laser tracker modelling method is formulated and the framework of the virtual tracker is proposed. The VLS can be used for measurement planning, measurement accuracy optimization and uncertainty evaluation. The completed virtual laser tracking system should take all the uncertainty sources affecting coordinate measurement into consideration and establish an uncertainty model which will behave in an identical way to the real system. © Springer-Verlag Berlin Heidelberg 2010.

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.

Constructing the Vulvar Pain Assessment Questionnaire Inventory

The goals of this program of research were to examine the link between self-reported vulvar pain and clinical diagnoses, and to create a user-friendly assessment tool to aid in that process. These goals were undertaken through a series of four empirical studies (Chapters 2-6): one archival study, two online studies, and one study conducted in a Women’s Health clinic. In Chapter 2, the link between self-report and clinical diagnosis was confirmed by extracting data from multiple studies conducted in the Sexual Health Research Laboratory over the course of several years. We demonstrated the accuracy of diagnosis based on multiple factors, and explored the varied gynecological presentation of different diagnostic groups. Chapter 3 was based on an online study designed to create the Vulvar Pain Assessment Questionnaire (VPAQ) inventory. Following the construct validation approach, a large pool of potential items was created to capture a broad selection of vulvar pain symptoms. Nearly 300 participants completed the entire item pool, and a series of factor analyses were utilized to narrow down the items and create scales/subscales. Relationships were computed among subscales and validated scales to establish convergent and discriminant validity. Chapters 4 and 5 were conducted in the Department of Obstetrics & Gynecology at Oregon Health & Science University. The brief screening version of the VPAQ was employed with patients of the Program in Vulvar Health at the Center for Women’s Health. The accuracy and usefulness of the VPAQscreen was determined from the perspective of patients as well as their health care providers, and the treatment-seeking experiences of patients was explored. Finally, a second online study was conducted to confirm the factor structure, internal consistency, and test-retest reliability of the VPAQ inventory. The results presented in these chapters confirm the link between targeted questions and accurate diagnoses, and provide a guideline that is useful and accessible for providers and patients.

Assessment of the cortisol awakening response: Real-time analysis and curvilinear effects of sample timing inaccuracy

The cortisol awakening response (CAR) is typically measured in the domestic setting. Moderate sample timing inaccuracy has been shown to result in erroneous CAR estimates and such inaccuracy has been shown partially to explain inconsistency in the CAR literature. The need for more reliable measurement of the CAR has recently been highlighted in expert consensus guidelines where it was pointed out that less than 6% of published studies provided electronic-monitoring of saliva sampling time in the post-awakening period. Analyses of a merged data-set of published studies from our laboratory are presented. To qualify for selection, both time of awakening and collection of the first sample must have been verified by electronic-monitoring and sampling commenced within 15 min of awakening. Participants (n = 128) were young (median age of 20 years) and healthy. Cortisol values were determined in the 45 min post-awakening period on 215 sampling days. On 127 days, delay between verified awakening and collection of the first sample was less than 3 min (‘no delay’ group); on 45 days there was a delay of 4–6 min (‘short delay’ group); on 43 days the delay was 7–15 min (‘moderate delay’ group). Cortisol values for verified sampling times accurately mapped on to the typical post-awakening cortisol growth curve, regardless of whether sampling deviated from desired protocol timings. This provides support for incorporating rather than excluding delayed data (up to 15 min) in CAR analyses. For this population the fitted cortisol growth curve equation predicted a mean cortisol awakening level of 6 nmols/l (±1 for 95% CI) and a mean CAR rise of 6 nmols/l (±2 for 95% CI). We also modelled the relationship between real delay and CAR magnitude, when the CAR is calculated erroneously by incorrectly assuming adherence to protocol time. Findings supported a curvilinear hypothesis in relation to effects of sample delay on the CAR. Short delays of 4–6 min between awakening and commencement of saliva sampling resulted an overestimated CAR. Moderate delays of 7–15 min were associated with an underestimated CAR. Findings emphasize the need to employ electronic-monitoring of sampling accuracy when measuring the CAR in the domestic setting.

How Good Am I? Crude Self-Assessment vs. Examination

With the new academic year structure encouraging more in-term assessment to replace end-of-year examinations one of the problems we face is assessing students and keeping track of individual student learning without overloading the students and staff with excessive assessment burdens.
In the School of Electronics, Electrical Engineering and Computer Science, we have constructed a system that allows students to self-assess their capability on a simple Yes/No/Don’t Know scale against fine grained learning outcomes for a module. As the term progresses students update their record as appropriately, including selecting a Learnt option to reflect improvements they have gained as part of their studies.
In the system each of the learning outcomes are linked to the relevant teaching session (lectures and labs) and to online resources that students can access at any time. Students can structure their own learning experience to their needs and preferences in order to attain the learning outcomes.
The system keeps a history of the student’s record, allowing the lecturer to observe how the students’ abilities progress over the term and to compare it to assessment results. The system also keeps of any of the resource links that student has clicked on and the related learning outcome.
The initial work is comparing the accuracy of the student self-assessments with their performance in the related questions in the traditional end-of-year examination.

External quality assessment of BRAF molecular analysis in melanoma

The availability of BRAF inhibitors has given metastatic melanoma patients an effective new treatment choice and molecular testing to determine the presence or absence of a BRAF codon 600 mutation is pivotal in the clinical management of these patients. This molecular test must be performed accurately and appropriately to ensure that the patient receives the most suitable treatment in a timely manner. Laboratories have introduced such testing; however, some experience low sample throughput making it critical that an external quality assurance programme is available to help promote a high standard of testing, reporting and provide an educational aspect for BRAF molecular testing. Laboratories took part in three rounds of external quality assessment (EQA) during a 12-month period giving participants a measure of the accuracy of genotyping, clinical interpretation of the result and experience in testing a range of different samples. Formalin fixed paraffin embedded tissue sections from malignant melanoma patients were distributed to participants for BRAF molecular testing. The standard of testing was generally high but distribution of a mutation other than the most common, p.(Val600Glu), highlighted concerns with detection or reporting of the presence of rarer mutations. The main issues raised in the interpretation of the results were the importance of clear unambiguous interpretation of the result tailored to the patient and the understanding that the treatment is different from that given to other stratified medicine programmes. The variability in reporting and wide range of methodologies used indicate a continuing need for EQA in this field.

Assessment of turbulence model predictions for a centrifugal compressor simulation

Steady-state computational fluid dynamics (CFD) simulations are an essential tool in the design process of centrifugal compressors. Whilst global parameters, such as pressure ratio and efficiency, can be predicted with reasonable accuracy, the accurate prediction of detailed compressor flow fields is a much more significant challenge. Much of the inaccuracy is associated with the incorrect selection of turbulence model. The need for a quick turnaround in simulations during the design optimisation process, also demands that the turbulence model selected be robust and numerically stable with short simulation times.
In order to assess the accuracy of a number of turbulence model predictions, the current study used an exemplar open CFD test case, the centrifugal compressor ‘Radiver’, to compare the results of three eddy viscosity models and two Reynolds stress type models. The turbulence models investigated in this study were (i) Spalart-Allmaras (SA) model, (ii) the Shear Stress Transport (SST) model, (iii) a modification to the SST model denoted the SST-curvature correction (SST-CC), (iv) Reynolds stress model of Speziale, Sarkar and Gatski (RSM-SSG), and (v) the turbulence frequency formulated Reynolds stress model (RSM-ω). Each was found to be in good agreement with the experiments (below 2% discrepancy), with respect to total-to-total parameters at three different operating conditions. However, for the off-design conditions, local flow field differences were observed between the models, with the SA model showing particularly poor prediction of local flow structures. The SST-CC showed better prediction of curved rotating flows in the impeller. The RSM-ω was better for the wake and separated flow in the diffuser. The SST model showed reasonably stable, robust and time efficient capability to predict global and local flow features.

Multiphase flow in porous media: meta-modeling techniques for sensitivity analysis and risk assessment

One of the biggest challenges that contaminant hydrogeology is facing, is how to adequately address the uncertainty associated with model predictions. Uncertainty arise from multiple sources, such as: interpretative error, calibration accuracy, parameter sensitivity and variability. This critical issue needs to be properly addressed in order to support environmental decision-making processes. In this study, we perform Global Sensitivity Analysis (GSA) on a contaminant transport model for the assessment of hydrocarbon concentration in groundwater. We provide a quantification of the environmental impact and, given the incomplete knowledge of hydrogeological parameters, we evaluate which are the most influential, requiring greater accuracy in the calibration process. Parameters are treated as random variables and a variance-based GSA is performed in a optimized numerical Monte Carlo framework. The Sobol indices are adopted as sensitivity measures and they are computed by employing meta-models to characterize the migration process, while reducing the computational cost of the analysis. The proposed methodology allows us to: extend the number of Monte Carlo iterations, identify the influence of uncertain parameters and lead to considerable saving computational time obtaining an acceptable accuracy.

Montreal Cognitive Assessment (MoCA): validation study for vascular dementia

The Montreal Cognitive Assessment (MoCA) is a brief instrument developed for the screening of milder forms of cognitive impairment, having surpassed the well-known limitations of the MMSE. The aim of the present study was to validate the MoCA as well as its short version, which was proposed by the NINDS-CSN VCI Harmonization Standards for screening Vascular Dementia (VaD) patients. The results, based on a homogeneous sample of 34 VaD patients, indicate that the MoCA is a psychometrically valid and reliable instrument for cognitive screening in VaD patients, showing excellent discriminant validity. Both the full and short versions of the MoCA had excellent diagnostic accuracy in discriminating VaD patients, exhibiting an area under curve (AUC) higher than the MMSE [AUC(MoCA full version) = .950; 95% IC = .868-.988; AUC(MoCA short version) = .936; 95% IC = .849-.981; AUC(MMSE) = .860; 95% IC = .754-.932]. With a cutoff below 17 on the MoCA full version and 8 on the short version, the results for sensitivity, specificity, positive and negative predictive values, and classification accuracy were superior compared to the MMSE. In conclusion, both versions of the MoCA are valid, reliable, sensitive and accurate screening instruments for VaD patients.

A comparison of in situ vs. ex situ filtration methods on the assessment of dissolved and particulate metals at hydrothermal vents

The objective of this study was to assess the impact of the filtration method (in situ vs. ex situ) on the dissolved/particulate partitioning of 12 elements in hydrothermal samples collected from the Lucky Strike vent field (Mid-Atlantic Ridge; MAR). To do so, dissolved ( <0.45 mu m) and particulate Mg, Li, Mn, U, V, As, Ba, Fe, Zn, Cd, Pb and Cu were measured using different techniques (HR-ICP-MS, ICP-AES and CCSA). Using in situ filtration as a baseline, we showed that ex situ filtration (on-board and on shore after freezing) resulted in an underestimation of the dissolved pool, which was counterbalanced by an overestimation of the particulate pool for almost all the elements studied. We also showed that on-board filtration was acceptable for the assessment of dissolved and particulate Mn, Mg, Li and U for which the measurement bias for the dissolved fraction did not exceed 3%. However, in situ filtration appeared necessary for the accurate assessment of the dissolved and particulate concentrations of V, As, Fe, Zn, Ba, Cd, Pb and Cu. In the case of Fe, on-board filtration underestimated the dissolved pool by up to 96%. Laboratory filtration (after freezing) resulted in a large bias in the dissolved and particulate concentrations, unambiguously discounting this filtration method for deep-sea chemical speciation studies. We discuss our results in light of the precipitation processes that can potentially affect the accuracy of ex situ filtration methods.