Search results

We report the findings from a study exploring the experiences of individuals undergoing MRI scanning for research. Semi‐structured interviews took place before and after scanning with 17 participants; 12 were healthy volunteers and five were patients with a diagnosis of remitted depression. Themes of apprehension and curiosity prior to scanning were common in both groups. Patients were often confused about the procedure. Negative feelings were an issue at the outset, characterised by shock related to the physical surroundings, after which positive feelings, for example relaxation, were often experienced, and in the case of patients, learning more about their brain. Written information about imaging was deemed satisfactory; however the ability to ‘experience’ aspects of scanning beforehand was suggested. Scanning may be viewed as a process beginning prior to the procedure itself and involving positive and negative emotions. Increased information, reassurance and a more interactive intervention to reduce anxiety may be beneficial and may improve individuals' experience of this widely used procedure.

This chapter presents a sociological analysis of the work involved in producing neuroimaging scans used in clinical practice. Drawing on fieldwork in magnetic resonance imaging (MRI) units in hospitals and free-standing imaging centers; in-depth interviews with technologists, radiologists, and neurologists; and reviews of relevant medical literatures, this analysis demonstrates how assembly line techniques structure neuroimaging work. Neuroimages (after being ordered by the referring clinician) are created in an image production line where scans of brains, breasts, livers, and other body parts are all produced: although some facilities may focus on one area of the body, most create an array of scans. Following MRI scans as they are produced demonstrates how medical work emphasizes repetition, specialization, and efficiency – key features of mass production. On the medical assembly line, the organization of work aims to transform patients into objects – ones that multiply as scans are created and circulated. Neurologists, radiologists, and technologists are positioned as skilled workers who manage the flow of bodies and the production of knowledge with the aim of producing health or, at the very least, knowledge of illness. Patients are also actors who actively impact the imaging production process. Previous scholarship has shown that diagnostic work involves a distributed form of expertise; one that involves patients, other medical professionals, machines, and neurologists. This chapter demonstrates that the deployment and synchronization of this expertise is a form of labor, involving distinct professions, professional hierarchies, and reimbursement systems. Working conditions are central to the production of MRI scans as knowledge and contribute to the social shaping of neuroimaging techniques.

The purpose of this paper is to investigate the relationship between hospital adoption and use of computed tomography (CT) scanners, and magnetic resonance imaging (MRI) machines and in-patient mortality and length of stay.

This study used panel data (2007–2010) from 124 hospital corporations operating in Ontario, Canada. Imaging use focused on medical patients accounting for 25 percent of hospital discharges. Main outcomes were in-hospital mortality rates and average length of stay. A model for each outcome-technology combination was built, and controlled for hospital structural characteristics, market factors and patient characteristics.

In 2010, 36 and 59 percent of hospitals had adopted MRI machines and CT scanners, respectively. Approximately 23.5 percent of patients received CT scans and 3.5 percent received MRI scans during the study period. Adoption of these technologies was associated with reductions of up to 1.1 percent in mortality rates and up to 4.5 percent in length of stay. The imaging use–mortality relationship was non-linear and varied by technology penetration within hospitals. For CT, imaging use reduced mortality until use reached 19 percent in hospitals with one scanner and 28 percent in hospitals with 2+ scanners. For MRI, imaging use was largely associated with decreased mortality. The use of CT scanners also increased length of stay linearly regardless of technology penetration (4.6 percent for every 10 percent increase in use). Adoption and use of MRI was not associated with length of stay.

These results suggest that there may be some unnecessary use of imaging, particularly in small hospitals where imaging is contracted out. In larger hospitals, the results highlight the need to further investigate the use of imaging beyond certain thresholds. Independent of the rate of imaging use, the results also indicate that the presence of CT and MRI devices within a hospital benefits quality and efficiency.

To the authors’ knowledge, this study is the first to investigate the combined effect of adoption and use of medical imaging on outcomes specific to CT scanners and MRI machines in the context of hospital in-patient care.

Trans-oral robotic surgery (TORS) is increasingly employed in obstructive sleep apnoea (OSA) management. Objective outcomes are generally assessed through polysomnography. Pre-operative magnetic resonance imaging (MRI) can be a useful adjunct in objective upper airway assessment, in particular the tongue base, providing useful information for surgical planning and outcome assessment, though care must be taken in patient positioning during surgery. The purpose of this paper is to identify pitfalls in this process and suggest a protocol for pre-operative MRI scanning in OSA.

This study is a four-patient prospective case-series and literature review. Outcome measures include pre- and post-operative volumetric changes in the pharynx as measured on MRI and apnoea–hypopnea indices (AHI), with cure being OSA resolution or a 50 per cent reduction in AHI.

All patients achieved AHI reduction and/or OSA cure following TORS, despite a decrease in pharyngeal volume measurements at the tongue base level. This study and others lacked standardisation in the MRI scanning protocol, which resulted in an inability to effectively compare pre- and post-operative scans. Pitfalls were related to variation in head/tongue position, soft-tissue marker usage and assessed area boundary limits.

TORS appears to be effective in OSA management. A new protocol for patient positioning and anatomical landmarks is suggested.

The findings could provide directly comparable data between scans and may allow correlation between tongue base volumetric changes and AHI through subsequent and historical study meta-analysis.

The purpose of this paper is to provide a detailed accounting of energy and materials consumed during magnetic resonance imaging (MRI).

The first and second stages of ISO standard (ISO 14040:2006 and ISO 14044:2006) were followed to develop life cycle inventory (LCI). The LCI data collection took the form of observations, time studies, real-time metered power consumption, review of imaging department scheduling records and review of technical manuals and literature.

The carbon footprint of the entire MRI service on a per-patient basis was measured at 22.4 kg CO₂eq. The in-hospital energy use (process energy) for performing MRI is 29 kWh per patient for the MRI machine, ancillary devices and light fixtures, while the out-of-hospital energy consumption is approximately 260 percent greater than the process energy, measured at 75 kWh per patient related to fuel for generation and transmission of electricity for the hospital, plus energy to manufacture disposable, consumable and reusable products. The actual MRI and standby energy that produces the MRI images is only about 38 percent of the total life cycle energy.

The focus on methods and proof-of-concept meant that only one facility and one type of imaging device technology were used to reach the conclusions. Based on the similar studies related to other imaging devices, the provided transparent data can be generalized to other healthcare facilities with few adjustments to utilization ratios, the share of the exam types, and the standby power of the facilities’ imaging devices.

The transparent detailed life cycle approach allows the data from this study to be used by healthcare administrators to explore the hidden public health impact of the radiology department and to set goals for carbon footprint reductions of healthcare organizations by focusing on alternative imaging modalities. Moreover, the presented approach in quantifying healthcare services’ environmental impact can be replicated to provide measurable data on departmental quality improvement initiatives and to be used in hospitals’ quality management systems.

No other research has been published on the life cycle assessment of MRI. The share of outside hospital indirect environmental impact of MRI services is a previously undocumented impact of the physician’s order for an internal image.

This paper seeks to provide evidence that the long‐term success of capital‐intensive technology products requires continuous integration of innovations in the form of new features and capabilities that meet broad user preferences.

Magnetic resonance imaging (MRI) research centers, which represent lead users in this industry, are used as a case study. An online survey was developed to identify and rank the main factors behind brand switching, then secondary sources are used to confirm the research results.

A multi‐faceted approach to data collection is used to show that product innovations in the form of specific features are the main motive for switching to a new technology, consistent with the expectation that lead users seek technologies that maintain leading‐edge positions.

There are limitations to generalizing from this case study to other industries. The findings can be generalized to industries with similar characteristics, such as aircraft and heavy machinery manufacturing. In practice, managers should find a reliable strategy to assess factors underpinning brand switching that is unique to their industry. Determining the main factors behind switching is a critical matter when defining the appropriate strategy to keep their market share from eroding.

The literature reports considerable research that investigates brand switching. However, most of it focuses on highly competitive markets for consumer goods. This paper addresses a paucity of knowledge about what influences lead users of capital‐intensive products to switch between brands.

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

The breast is composed of two main types of soft tissues: glandular tissue and adipose tissue. Wearing a brassiere makes them deform easily. In order to design comfortable brassieres by which the body shape is adjusted, it is important to clarify the relationship between the breast deformation and the internal structure of the breast. The purpose of this paper is to assess a method to determine the structure inside the breast. Breast shape comparison was performed to assess the relationship between the external deformation caused by wearing a brassiere and the internal structure of the breast.

The subjects were five adult females. The breast MRI imaging in the sitting position was carried out using the vertical MRI systems under bare breasts condition and under wearing a brassiere condition. By creating 3D images from the MRI images obtained, the internal structure of the breast was determined. The 3D images under the wearing brassiere conditions were superimposed on the images under the bare breasts condition, and the breast shape comparison was performed to assess the relationship between the external deformation caused by wearing a brassiere and the internal structure of the breast.

The internal 3D structure of the breast, which had been unmeasurable in the sitting position, could be obtained using the vertical MRI system. Additionally the effect of wearing a brassiere on the breast was assessed in terms of the relation between the external deformation and the internal structure of the breast.

This paper's results can be utilized for human body model in simulation, and to provide fruitful data for the design of comfortable brassieres.

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.

The purpose of this paper is to provide a brief overview of the methodology of several brain imaging techniques and in particular, functional magnetic resonance imaging (fMRI) and its potential implications for market research. The aim is to enable the reader both to understand this emerging methodology and to conduct independent research in the area.

A short introduction on current neuroimaging methods used in behavioral neuroscience is provided by means of a literature review. The ensuing discussion focuses on fMRI as the currently most popular neuroimaging technique. Having described the fMRI methodology, an outline of the analysis of functional neuroimaging data follows, after which there is a discussion of some key research issues.

Although in its infancy, fMRI seems to be a useful and promising tool for market researchers. Initial studies in the field reveal that fMRI is able to shed light on subconscious processes such as affective aspects of consumer behavior.

Because brand positioning, advertising strategies and even pricing strategies are often based on constructs such as emotions, neuropsychological findings and methods should have important implications for practitioners in the field of brand management and advertising. Nonetheless, far more basic research is needed before fMRI can be adopted for marketing practice.

This paper is one of the first in the marketing literature to provide a methodological overview of fMRI and discuss the potential implications for marketing research.