A prospective intervention study, using clinical practice improvement (CPI) methodology, was undertaken to reduce unnecessary x-ray examinations in the early management of patients presenting to the Emergency Department (ED). This was achieved through raising the awareness of medical and allied health staff to medical radiation by means of clinical education and implementing evidence based diagnostic imaging requisition. The main study was conducted in the ED of a public hospital located in the western Sydney, Australia. A second hospital within the area health service, with similar bed size, activity levels and demographics, was used as the control site. The first phase intervention raised the awareness of the health professionals to medical radiation. The second phase intervention used CPI methodology to attain efficient clinical practices so as to eliminate unnecessary examinations and requests. A multi-disciplinary CPI Project Team involved in the process of imaging examination requisition was empowered to improve the appropriateness of the requested examination utilisation. This it achieved mainly through the implementation of evidence based clinical decision rules and imaging guidelines. An additional method of validating the outcomes was provided through the simultaneous rollout of the interventions at another hospital within the same area health service. At the completion of the study, unnecessary examinations such as Skull, Ribs, Nasal Bone and Kidney Ureter Bladder (KUB) requests at the intervention Hospital site were significantly reduced by 92.6% (p (less than) 0.0001), whereas at the control site Hospital there was minimal reduction which was found to be not statistically significant (p=0.2110). Other frequently requested examinations such as Ankle, Knee and Spine requests were marginally though significantly reduced at the intervention Hospital by 22.7% (p (less than) 0.001), whereas at the control site Hospital the reduction was similarly found to be not significant (p=0.1055). Most importantly, the overall x-ray requisition for every 100 ED presentations at Hospital ‘B’ was reduced by 27%. The results of this study, demonstrated that Radiation Awareness educational programs, targeting medical and allied health staff, will reduce the unnecessary requisition of examinations found not to contribute to the process of the patient’s clinical management. The use of a CPI project approach was found to be important in the process of establishing, implementing and sustaining the achieved improvements, and in particular, the rules and guidelines of evidence based imaging requisition. Importantly, the study also confirmed that the CPI methodology that had been used for the main intervention was adaptable to other organisations when it was found to have been successfully rolled out at another hospital. This confirmed that the main outcomes of the investigation could be generalised to other health facilities. There was an immediate reduction in the requisition of unnecessary examination similar to the results at the main intervention hospital. The implementation and adoption of the CPI intervention across the health care system in general could significantly reduce unnecessary x-ray examinations, saving significant health care resources, and sparing patients from potential cancer risks associated with avoidable exposure to ionising medical radiation. / Doctor of Philosophy (PhD (Health))

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 115-121).

The high prevalence of back and neck pain results in enormous social, psychological, and economic burden. Most seeking help for back or neck pain consult general practitioners or chiropractors. Chiropractic is a regulated health profession (serving approximately 10 – 15% of the population) that has contributed to the health and well-being of North Americans for over a century. Despite available evidence for optimal management of back and neck pain, poor adherences to guidelines and wide variations in services have been noted. For instance, overuse and misuse of imaging services have been reported in the chiropractic literature. Inappropriate use of spine imaging has a number of potential adverse outcomes, including inefficient and potentially inappropriate invasive diagnosis and subsequent treatment, and unnecessary patient exposure to ionizing radiation. Although evidence-based diagnostic imaging guidelines for spinal disorders are available, chiropractors are divided on whether these guidelines apply to them. While guidelines can encourage practitioners to conform to best practices and lead to improvements in care, reviews have demonstrated that dissemination of guidelines alone is rarely sufficient to optimise care. Evidence regarding effective methods to promote the uptake of guidelines is still lacking. There is growing acceptance that problem analysis and development of interventions to change practice should be guided by relevant theories and tailored to the target audience. To date, very little knowledge translation research has addressed research-practice gaps in chiropractic. This thesis reports rigorous methods to: (1) assess practice and providers’ characteristics, (2) determine baseline rates and variations in spine x-ray ordering, (3) evaluate the impact of disseminating guidelines to optimise spine x-ray ordering, and (4) assess determinants of spine x-ray ordering and potential targets for change prior to the design of a tailored intervention. A mixed method using two disciplinary perspectives (epidemiology and psychology) was undertaken. A cross-sectional analysis of administrative claims data was carried out on a sample of chiropractors enlisted in a large American provider network. Despite available clinical practice guidelines, wide geographical variations in x-ray use persist. Higher x-ray ordering rates were associated with practice location (Midwest and South US census regions), setting (urban, suburban), chiropractic school attended, male provider, employment, and years in practice. The impact of web-based guideline dissemination was evaluated over a five year period using interrupted time series and demonstrated a stepwise relative reduction of 5.3% in the use of x-rays. Passive guidelines dissemination appeared to be a simple, cost effective strategy in this setting to improve but not optimise x-ray ordering rates. Focus groups using the theoretical domains framework were conducted among Canadian and US chiropractors to explore their beliefs about managing back pain without x-rays. Findings were used to develop a theory-based survey to identify theoretical constructs predicting spine x-ray ordering practice. Psychological theories and theoretical constructs explained a significant portion of the variance in both behavioural simulation and intention. Results from this thesis provide an empirically-supported, theoretical basis to design quality improvement strategies to increase guidelines adherence and promote behaviour change in chiropractic. Other researchers interested in improving uptake of evidenced-based information could use this method in their own setting to investigate determinants of behaviour among other professional groups. Future research may use knowledge gained to inform the development and evaluation of a theory-based tailored intervention to improve guideline adherence and reduce the use of spine x-rays among targeted providers.

One important topic in medical image analysis is segmenting wall structures from different cardiac medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This task is typically done by radiologists either manually or semi-automatically, which is a very time-consuming process. To reduce the laborious human efforts, automatic methods have become popular in this research. In this thesis, features insensitive to data variations are explored to segment the ventricles from CT images and extract the left atrium from MR images. As applications, the segmentation results are used to facilitate cardiac disease analysis. Specifically, 1. An automatic method is proposed to extract the ventricles from CT images by integrating surface decomposition with contour evolution techniques. In particular, the ventricles are first identified on a surface extracted from patient-specific image data. Then, the contour evolution is employed to refine the identified ventricles. The proposed method is robust to variations of ventricle shapes, volume coverages, and image quality. 2. A variational region-growing method is proposed to segment the left atrium from MR images. Because of the localized property of this formulation, the proposed method is insensitive to data variabilities that are hard to handle by globalized methods. 3. In applications, a geometrical computational framework is proposed to estimate the myocardial mass at risk caused by stenoses. In addition, the segmentation of the left atrium is used to identify scars for MR images of post-ablation.

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.

Gold nanoparticles strongly absorb and scatter visible and near infrared light because of the strongly enhanced electric fields at the surface. This provides the potential of designing novel optically active reagents for simultaneous molecular imaging and photothermal cancer therapy. In this thesis, gold nanospheres and nanorods conjugated with anti-epidermal growth factor receptor (anti-EGFR) antibodies that specifically target EGFR on the cell surface are shown to be used for dual diagnostics and therapy. Using micro-absorption spectroscopy and light scattering imaging, cancerous (HOC 313 and HSC 3) and noncancerous cells (HaCat) can be differentiated due to the overexpression of EGFR on the surface of cancer cells. By irradiating the cells with a CW laser, selective photothermal cancer therapy is realized in visible region by using gold nanospheres and in near infrared region by using gold nanorods. The use of nanorods allow for in vivo therapy due to the fact that their absorption is in the near infrared region at which the laser light meets less interference from the tissue absorption. In addition, the catalytic effect of gold nanoparticles on the oxidization of NADH to NAD+ is investigated. The addition of gold nanoparticles is found to quench the NADH fluorescence intensities but has no effect on the fluorescence lifetime. This suggests that the fluorescence quenching is not due to coupling with the excited state, but due to changing the ground state of NADH. The intensity of the 340 nm absorption band of NADH is found to decrease while that of the 260 nm band of NAD+ is found to increase as the concentration of gold nanoparticles increase. This conversion reaction is further supported by nuclear magnetic resonance and mass spectroscopy. The linear relationship between the initial reaction rate of NADH and the concentration of gold nanoparticles strongly supports that NADH is surface catalyzed by the gold nanoparticles. The catalytic property of this important reaction might have important future applications in biological and medical fields.

Computer vision approximates human vision using computers. Two subsets are explored in this work: image segmentation and visual tracking. Segmentation involves partitioning an image into logical parts, and tracking analyzes objects as they change over time. The presented research explores a key hypothesis: localizing analysis of visual information can improve the accuracy of segmentation and tracking results. Accordingly, a new class of segmentation techniques based on localized analysis is developed and explored. Next, these techniques are applied to two challenging problems: neuron bundle segmentation in diffusion tensor imagery (DTI) and plaque detection in computed tomography angiography (CTA) imagery. Experiments demonstrate that local analysis is well suited for these medical imaging tasks. Finally, a visual tracking algorithm is shown that uses temporal localization to track objects that change drastically over time.

Computer vision encompasses a host of computational techniques to process visual information. Medical imagery is one particular area of application where data comes in various forms: X-rays, ultrasound probes, MRI volumes, EEG recordings, NMR spectroscopy, etc. This dissertation is concerned with techniques for accurate reconstruction of neural pathways from diffusion magnetic resonance imagery (dMRI). This dissertation describes a filtered approach to neural tractography. Existing methods independently estimate the diffusion model at each voxel so there is no running knowledge of confidence in the estimation process. We propose using tractography to drive estimation of the local diffusion model. Toward this end, we formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by those previous. We argue that this approach is more accurate than conventional techniques. Experiments demonstrate that this filtered approach significantly improves the angular resolution at crossings and branchings. Further, we confirm its ability to trace through regions known to contain such crossing and branching while providing inherent path regularization. We also argue that this approach is flexible. Experiments demonstrate using various models in the estimation process, specifically combinations of Watson directional functions and rank-2 tensors. Further, this dissertation includes an extension of the technique to weighted mixtures using a constrained filter.

When geographical distance poses as a barrier, telepathology is designed to offer pathologists the opportunity to replicate their normal activities by using an alternative means of practice. The rapid progression in technology has greatly influenced the appeal of telepathology and its use in multiple domains. To that point, telepathology systems help to afford teleconsultation services for remote locations, improve the workload distribution in clinical environments, measure quality assurance, and also enhance educational programs. While telepathology is an attractive method to many potential users, the resource requirements for digitizing microscopic specimens have hindered widespread adoption. The use of image compression is extremely critical to help advance the pervasiveness of digital images in pathology. For this research, we characterize two different methods that we use to assess compression of pathology images. Our first method is characterized by the fact that image quality is human-based and completely subjective in terms of interpretation. Our second method is characterized by the fact that image analysis is introduced by using machine-based interpretation to provide objective results. Additionally, the objective outcomes from the image analysis may also be used to help confirm tumor classification. With these two methods in mind, the purpose of this dissertation is to quantify the effects of image compression on data interpretation as seen by human experts and a computerized algorithm for use in telepathology.

Ultrasound contrast agents (UCA) have tremendous potential for in vivo molecular imaging because of their high sensitivity and great spatial resolution of ultrasound imaging. However, the diagnostic potential of UCAs has been difficult to exploit because current contrast agents are based on pre-formed microbubbles, which can only detect cell surface receptors. In this work, we demonstrated that chemical reactions that generate gas forming molecules can be used to perform molecular imaging by ultrasound in vivo. This new approach for generating ultrasound contrast agents was demonstrated by imaging reactive oxygen species (ROS) in vivo with allylhydrazine, a compound that is converted into nitrogen and propylene gas after reacting with radical oxidants. We demonstrated that allylhydrazine encapsulated within liposomes (termed APLs) can detect a 10 uM concentration of radical oxidants by ultrasound, and can image oxidative stress in mice, induced by lipopolysaccharide (LPS), using a clinical ultrasound machine. We showed that a 1-2% increase in gas concentration above saturation can be detected acoustically and suggest that numerous biological targets can be imaged via appropriately designed gas forming reactions. This work was the first demonstration of in vivo imaging of ROS using ultrasound, and this work presented a new strategy to generate gas bubbles from reactions involving radical oxidants. We anticipate numerous applications of chemically generated microbubbles, given the excellent spatial resolution of ultrasound imaging, its widespread clinical use and its high sensitivity to detect gas bubbles.