Plate-specific gain map correction for the improvement of detective quantum efficiency in computed radiography.

PURPOSE: The purpose of this work is to improve the noise power spectrum (NPS), and thus the detective quantum efficiency (DQE), of computed radiography (CR) images by correcting for spatial gain variations specific to individual imaging plates. CR devices have not traditionally employed gain-map corrections, unlike the case with flat-panel detectors, because of the multiplicity of plates used with each reader. The lack of gain-map correction has limited the DQE(f) at higher exposures with CR. This current work describes a feasible solution to generating plate-specific gain maps. METHODS: Ten high-exposure open field images were taken with an RQA5 spectrum, using a sixth generation CR plate suspended in air without a cassette. Image values were converted to exposure, the plates registered using fiducial dots on the plate, the ten images averaged, and then high-pass filtered to remove low frequency contributions from field inhomogeneity. A gain-map was then produced by converting all pixel values in the average into fractions with mean of one. The resultant gain-map of the plate was used to normalize subsequent single images to correct for spatial gain fluctuation. To validate performance, the normalized NPS (NNPS) for all images was calculated both with and without the gain-map correction. Variations in the quality of correction due to exposure levels, beam voltage/spectrum, CR reader used, and registration were investigated. RESULTS: The NNPS with plate-specific gain-map correction showed improvement over the noncorrected case over the range of frequencies from 0.15 to 2.5 mm(-1). At high exposure (40 mR), NNPS was 50%-90% better with gain-map correction than without. A small further improvement in NNPS was seen from carefully registering the gain-map with subsequent images using small fiducial dots, because of slight misregistration during scanning. Further improvement was seen in the NNPS from scaling the gain map about the mean to account for different beam spectra. CONCLUSIONS: This study demonstrates that a simple gain-map can be used to correct for the fixed-pattern noise in a given plate and thus improve the DQE of CR imaging. Such a method could easily be implemented by manufacturers because each plate has a unique bar code and the gain-map for all plates associated with a reader could be stored for future retrieval. These experiments indicated that an improvement in NPS (and hence, DQE) is possible, depending on exposure level, over a wide range of frequencies with this technique.

Leveraging nanoscale plasmonic modes to achieve reproducible enhancement of light.

The strongly enhanced and localized optical fields that occur within the gaps between metallic nanostructures can be leveraged for a wide range of functionality in nanophotonic and optical metamaterial applications. Here, we introduce a means of precise control over these nanoscale gaps through the application of a molecular spacer layer that is self-assembled onto a gold film, upon which gold nanoparticles (NPs) are deposited electrostatically. Simulations using a three-dimensional finite element model and measurements from single NPs confirm that the gaps formed by this process, between the NP and the gold film, are highly reproducible transducers of surface-enhanced resonant Raman scattering. With a spacer layer of roughly 1.6 nm, all NPs exhibit a strong Raman signal that decays rapidly as the spacer layer is increased.

Plasmon-induced electrical conduction in molecular devices.

Metal nanoparticles (NPs) respond to electromagnetic waves by creating surface plasmons (SPs), which are localized, collective oscillations of conduction electrons on the NP surface. When interparticle distances are small, SPs generated in neighboring NPs can couple to one another, creating intense fields. The coupled particles can then act as optical antennae capturing and refocusing light between them. Furthermore, a molecule linking such NPs can be affected by these interactions as well. Here, we show that by using an appropriate, highly conjugated multiporphyrin chromophoric wire to couple gold NP arrays, plasmons can be used to control electrical properties. In particular, we demonstrate that the magnitude of the observed photoconductivity of covalently interconnected plasmon-coupled NPs can be tuned independently of the optical characteristics of the molecule-a result that has significant implications for future nanoscale optoelectronic devices.

Stereocomplexed poly(lactic acid)-poly(ethylene glycol) nanoparticles with dual-emissive boron dyes for tumor accumulation.

Responsive biomaterials play important roles in imaging, diagnostics, and therapeutics. Polymeric nanoparticles (NPs) containing hydrophobic and hydrophilic segments are one class of biomaterial utilized for these purposes. The incorporation of luminescent molecules into NPs adds optical imaging and sensing capability to these vectors. Here we report on the synthesis of dual-emissive, pegylated NPs with "stealth"-like properties, delivered intravenously (IV), for the study of tumor accumulation. The NPs were created by means of stereocomplexation using a methoxy-terminated polyethylene glycol and poly(D-lactide) (mPEG-PDLA) block copolymer combined with iodide-substituted difluoroboron dibenzoylmethane-poly(L-lactide) (BF2dbm(I)PLLA). Boron nanoparticles (BNPs) were fabricated in two different solvent compositions to study the effects on BNP size distribution. The physical and photoluminescent properties of the BNPs were studied in vitro over time to determine stability. Finally, preliminary in vivo results show that stereocomplexed BNPs injected IV are taken up by tumors, an important prerequisite to their use as hypoxia imaging agents in preclinical studies.

Characteristics of primary care office visits to nurse practitioners, physician assistants and physicians in United States Veterans Health Administration facilities, 2005 to 2010: a retrospective cross-sectional analysis.

UNLABELLED: BACKGROUND: Primary care, an essential determinant of health system equity, efficiency, and effectiveness, is threatened by inadequate supply and distribution of the provider workforce. The Veterans Health Administration (VHA) has been a frontrunner in the use of nurse practitioners (NPs) and physician assistants (PAs). Evaluation of the roles and impact of NPs and PAs in the VHA is critical to ensuring optimal care for veterans and may inform best practices for use of PAs and NPs in other settings around the world. The purpose of this study was to characterize the use of NPs and PAs in VHA primary care and to examine whether their patients and patient care activities were, on average, less medically complex than those of physicians. METHODS: This is a retrospective cross-sectional analysis of administrative data from VHA primary care encounters between 2005 and 2010. Patient and patient encounter characteristics were compared across provider types (PA, NP, and physician). RESULTS: NPs and PAs attend about 30% of all VHA primary care encounters. NPs, PAs, and physicians fill similar roles in VHA primary care, but patients of PAs and NPs are slightly less complex than those of physicians, and PAs attend a higher proportion of visits for the purpose of determining eligibility for benefits. CONCLUSIONS: This study demonstrates that a highly successful nationwide primary care system relies on NPs and PAs to provide over one quarter of primary care visits, and that these visits are similar to those of physicians with regard to patient and encounter characteristics. These findings can inform health workforce solutions to physician shortages in the USA and around the world. Future research should compare the quality and costs associated with various combinations of providers and allocations of patient care work, and should elucidate the approaches that maximize quality and efficiency.

Effects of Natural Organic Matter on the Dissolution Kinetics and Bioavailability of Metal Oxide Nanoparticles

The rapid development of nanotechnology and wider applications of engineered nanomaterials (ENMs) in the last few decades have generated concerns regarding their environmental and health risks. After release into the environment, ENMs undergo aggregation, transformation, and, for metal-based nanomaterials, dissolution processes, which together determine their fate, bioavailability and toxicity to living organisms in the ecosystems. The rates of these processes are dependent on nanomaterial characteristics as well as complex environmental factors, including natural organic matter (NOM). As a ubiquitous component of aquatic systems, NOM plays a key role in the aggregation, dissolution and transformation of metal-based nanomaterials and colloids in aquatic environments.

The goal of this dissertation work is to investigate how NOM fractions with different chemical and molecular properties affect the dissolution kinetics of metal oxide ENMs, such as zinc oxide (ZnO) and copper oxide (CuO) nanoparticles (NPs), and consequently their bioavailability to aquatic vertebrate, with Gulf killifish (Fundulus grandis) embryos as model organisms.

ZnO NPs are known to dissolve at relatively fast rates, and the rate of dissolution is influenced by water chemistry, including the presence of Zn-chelating ligands. A challenge, however, remains in quantifying the dissolution of ZnO NPs, particularly for time scales that are short enough to determine rates. This dissertation assessed the application of anodic stripping voltammetry (ASV) with a hanging mercury drop electrode to directly measure the concentration of dissolved Zn in ZnO NP suspensions, without separation of the ZnO NPs from the aqueous phase. Dissolved zinc concentration measured by ASV ([Zn]ASV) was compared with that measured by inductively coupled plasma mass spectrometry (ICP-MS) after ultracentrifugation ([Zn]ICP-MS), for four types of ZnO NPs with different coatings and primary particle diameters. For small ZnO NPs (4-5 nm), [Zn]ASV was 20% higher than [Zn]ICP-MS, suggesting that these small NPs contributed to the voltammetric measurement. For larger ZnO NPs (approximately 20 nm), [Zn]ASV was (79±19)% of [Zn]ICP-MS, despite the high concentrations of ZnO NPs in suspension, suggesting that ASV can be used to accurately measure the dissolution kinetics of ZnO NPs of this primary particle size.

Using the ASV technique to directly measure dissolved zinc concentration, we examined the effects of 16 different NOM isolates on the dissolution kinetics of ZnO NPs in buffered potassium chloride solution. The observed dissolution rate constants (kobs) and dissolved zinc concentrations at equilibrium increased linearly with NOM concentration (from 0 to 40 mg-C L-1) for Suwannee River humic acid (SRHA), Suwannee River fulvic acid and Pony Lake fulvic acid. When dissolution rates were compared for the 16 NOM isolates, kobs was positively correlated with certain properties of NOM, including specific ultraviolet absorbance (SUVA), aromatic and carbonyl carbon contents, and molecular weight. Dissolution rate constants were negatively correlated to hydrogen/carbon ratio and aliphatic carbon content. The observed correlations indicate that aromatic carbon content is a key factor in determining the rate of NOM-promoted dissolution of ZnO NPs. NOM isolates with higher SUVA were also more effective at enhancing the colloidal stability of the NPs; however, the NOM-promoted dissolution was likely due to enhanced interactions between surface metal ions and NOM rather than smaller aggregate size.

Based on the above results, we designed experiments to quantitatively link the dissolution kinetics and bioavailability of CuO NPs to Gulf killifish embryos under the influence of NOM. The CuO NPs dissolved to varying degrees and at different rates in diluted 5‰ artificial seawater buffered to different pH (6.3-7.5), with or without selected NOM isolates at various concentrations (0.1-10 mg-C L-1). NOM isolates with higher SUVA and aromatic carbon content (such as SRHA) were more effective at promoting the dissolution of CuO NPs, as with ZnO NPs, especially at higher NOM concentrations. On the other hand, the presence of NOM decreased the bioavailability of dissolved Cu ions, with the uptake rate constant negatively correlated to dissolved organic carbon concentration ([DOC]) multiplied by SUVA, a combined parameter indicative of aromatic carbon concentration in the media. When the embryos were exposed to CuO NP suspension, changes in their Cu content were due to the uptake of both dissolved Cu ions and nanoparticulate CuO. The uptake rate constant of nanoparticulate CuO was also negatively correlated to [DOC]×SUVA, in a fashion roughly proportional to changes in dissolved Cu uptake rate constant. Thus, the ratio of uptake rate constants from dissolved Cu and nanoparticulate CuO (ranging from 12 to 22, on average 17±4) were insensitive to NOM type or concentration. Instead, the relative contributions of these two Cu forms were largely determined by the percentage of CuO NP that was dissolved.

Overall, this dissertation elucidated the important role that dissolved NOM plays in affecting the environmental fate and bioavailability of soluble metal-based nanomaterials. This dissertation work identified aromatic carbon content and its indicator SUVA as key NOM properties that influence the dissolution, aggregation and biouptake kinetics of metal oxide NPs and highlighted dissolution rate as a useful functional assay for assessing the relative contributions of dissolved and nanoparticulate forms to metal bioavailability. Findings of this dissertation work will be helpful for predicting the environmental risks of engineered nanomaterials.

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.