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Describes how senior managers and central support office staff at Alliance Medical – a company that provides health scans – visited front-line employees to get a flavor of how they operate.

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Explores the reasons for the initiative and its outcomes. Also describes how the company is meeting the increasing demand for scanning services.

Explains that the day-in-the-life initiative provided all parties with a better understanding of how the different functions work together to ensure a high-quality imaging service for patients.

Describes how the company has extended the hours of operation at its busiest sites, reduced waiting times and improved the level of care.

Reveals that, with the number of magnetic-resonance-imaging scans increasing by around 10 per cent a year, there is a strong financial case for extending hours and improving productivity through the most efficient use of scanning services. The Alliance Medical experience highlights one way in which this can be achieved.

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Emphasizes the importance of key performance indicators including hours of uptime and the number of patients scanned per hour. These statistics are reported daily right up to board level.

While computerized tomography (CT) and magnetic resonance imaging (MRI) technologies are highly commendable for their applications and usage, sometimes cases involving facial anatomy restoration may not necessarily require these highly sophisticated technologies. A suitable replacement that is also non‐contact and allows fast image capture is the laser digitizer surface scanner. This scanner takes only seconds to capture an image of the patient’s sound or healthy facial anatomy. By using the captured image data, it is possible, with the help of a surface data modeller rapid prototyping (RP) machine and vacuum casting machine, to manufacture the prosthesis for implant. Presents a novel approach for facial prosthesis fabrication through a case study of a prosthetic ear model using an integrated manufacturing system comprising the laser surface digitizer, surface data modeller, rapid prototyping system and vacuum casting system.

This paper aims to explore brain-based differences in national and own-label brands perceptions. Because price is a differentiating characteristic, able to discriminate between national and own-label brands, its influence is also studied.

The study uses the Save Holdings Or Purchase (SHOP) task with functional magnetic resonance imaging to explore the differences in brain functioning for national versus own-label branded products.

For the same product, the higher priced national brands and the lower priced own-label brands lead to more buying decisions. It is also found that there are brain structures that are more active/deactive for national than for own-label brands, both marked with real market prices. Price is a powerful driver of buying decisions and has its neural correlates. Parietal regions activate when brand information is subtracted from brand-plus-price information. The most surprising finding is that visual and visual associative areas are involved in the contrasts between branded products marked with switched prices and marked with real market prices.

The activation/deactivation brain patterns suggest that accepted models of brain functioning are not suitable for explaining brand decisions. Also, to our knowledge, this is the first time that a study directly addresses the brain’s functioning when subjects are stimulated with national versus own-label brands. It paves the way for a new approach to understanding how such brand categories are perceived, revealing the neural origins of the associated psychological processes.

Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.

In this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.

The proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.

The model can be used by doctors to identify the exact location of the tumorous region.

The proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

To describe the utility of three of the main cognitive neuroscientific techniques currently in use within the neuroscience community, and how they can be applied to the emerging field of neuromarket research.

A brief development of functional magnetic resonance imaging, magnetoencephalography and transcranial magnetic stimulation are described, as the core principles are behind their respective use. Examples of actual data from each of the brain imaging techniques are provided to assist the neuromarketer with subsequent data for interpretation. Finally, to ensure the neuromarketer has an understanding of the experience of neuroimaging, qualitative data from a questionnaire exploring attitudes about neuroimaging techniques are included which summarize participants' experiences of having a brain scan.

Cognitive neuroscientific techniques have great utility in market research and can provide more “honest” indicators of consumer preference where traditional methods such as focus groups can be unreliable. These techniques come with complementary strengths which allow the market researcher to converge onto a specific research question. In general, participants considered brain imaging techniques to be relatively safe. However, care is urged to ensure that participants are positioned correctly in the scanner as incorrect positioning is a stressful factor during an imaging procedure that can impact data quality.

This paper is an important and comprehensive resource to the market researcher who wishes to use cognitive neuroscientific techniques.

Autism spectrum disorder (ASD) has been studied as a neurodevelopmental disorder since Leo Kanner's early observations of abnormal head circumference in autistic children. In the past few years, there has been much progress made in elucidating the anatomical and functional abnormalities in ASD. This paper aims to summarise the extant research.

This paper provides a summary of relevant research findings in the neuroimaging of autism for the past 12 month period. Papers were identified using the Medline search terms: autism; ASD (functional); magnetic resonance imaging (MRI); neuroimaging; diffusion tensor imaging (DTI); and endophenotype.

Relatively recent techniques such as functional MRI and DTI have furthered the initial work derived from early histological and structural imaging studies. Even newer techniques, such as DTI tractography and support vector machine analysis, and other computer‐based learning methods have allowed us to move beyond regional variations in grey and white matter volume and study ASD as a disorder of connectivity, and of regional cerebral function and neural circuitry. Brain regions and neural circuits that are implicated in the core symptoms of ASD (deficits in social reciprocity, language and communication, and restricted and stereotyped interests) have been repeatedly shown to be abnormal in those individuals.

This paper aims to provide a background for clinicians to the current research and focuses on developments in the field of neuroimaging of ASD from the past year, which have generated further insights into the neurobiology of ASD.

Gives a bibliographical review of the finite element methods (FEMs) applied in biomedicine from the theoretical as well as practical points of view. The bibliography at the end of the paper contains 748 references to papers, conference proceedings and theses/dissertations dealing with the finite element analyses and simulations in biomedicine that were published between 1985 and 1999.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

Neuroimaging research has substantially enhanced our understanding of the neurobiological mechanisms of typical and atypical learning in children. These developments can advance the design of novel approaches to diagnosis and intervention for learning disabilities. Despite the promise of educational neuroscience, there are still walls between neuroscience and special education researchers such that more collaboration and understanding are needed between these disciplines. This chapter attempts to break down the walls by discussing how neuroimaging techniques can be incorporated into special education research. We also present arguments as to why neuroscience is “the next big thing” in special education research and the obstacles that must be overcome in order for neuroscience to be incorporated into education research. To describe how neurobiology might impact special education, we focus primarily on reading disability. We believe that educational neuroscience can aid in the identification and intervention of other learning disorders as well.

This study aims to provide an overview of rapid prototyping (RP) and shows the potential of this technology in the field of medicine as reported in various journals and proceedings. This review article also reports three case studies from open literature where RP and associated technology have been successfully implemented in the medical field.

Key publications from the past two decades have been reviewed.

This study concludes that use of RP-built medical model facilitates the three-dimensional visualization of anatomical part, improves the quality of preoperative planning and assists in the selection of optimal surgical approach and prosthetic implants. Additionally, this technology makes the previously manual operations much faster, accurate and cheaper. The outcome based on literature review and three case studies strongly suggests that RP technology might become part of a standard protocol in the medical sector in the near future.

The article is beneficial to study the influence of RP and associated technology in the field of medicine.