Abstract: An intrinsic limitation of the accuracy that can be achieved with Compton cameras results from the inevitable fact that the detectors, which comprise the camera, cannot have infinitely-accurate spatial resolution. To mitigate this loss of accuracy, a new imaging model is proposed. The implementation of the new imaging model, however, requires new camera designs. The results of a computer simulation indicate that the new imaging model can produce reasonable images, at least when noiseless simulated data are used. In the future, more work is needed to determine if the use of the new imaging model will improve the imaging capabilities of Compton cameras despite the loss of sensitivity caused by the use of the new camera designs. Regardless of the outcome of this work, the results presented here illustrate that new models for imaging from Compton scatters are possible and motivate the development of further models that could be more advantageous than the ones already developed.
Abstract: The purpose of this study is to revisit the relationship among information technology (IT), Process, and Strategy. We focus on the impact of mobile Point of Sales (mPOS) on changing of operational processes in the restaurant industry. This study investigates the changing of IT strategy and service strategy. The research model was developed based on the literature (strategic alignment model and situated change perspective) and inputs from the restaurant industry and IT experts. The data of this study are collected from observation and face-to-face interviews with both business and IT personnel from 10 restaurants in Taiwan. The findings of this study provide a comprehensive view about the ways processes change once restaurants implements mPOS. We also figure out the impact of this change on IT strategy and service strategy. This study’s results shed new light on IT implementation. Researchers need to look at IT in different ways and suggest suitable solutions for practitioners.
Abstract: Microwaves at the ISM (Industrial, Scientific and Medical, reserved internationally) frequency of 2450 or 5800 MHz have been used to prepare FeCoNiCuAl, FeCrNiTiAl and FeCoCrNiAl2.5 high entropy alloys by direct heating of pressed mixtures of metal powders. The aim of this work is to explore a new microwave-assisted near-net-shape technology, using a powder metallurgy approach for the preparation of high entropy alloys, able to overcome the limits of current melting technologies (defects formation) or solid state ones (time demanding). High entropy alloy compositions have been selected so as to comprise at least one ferromagnetic element and one highly reactive couple, like Ni-Al, Ti-Al, Co-Al or Fe-Al. Results show that direct microwave heating of the powder precursors occurs, and further heating generation is favored by the ignition of exothermal reactions in the load. Microwaves have been applied both for the ignition and sustaining of such reactions, showing that by the proposed technique, it is possible to control the cooling rate of the newly-synthesized high entropy alloys. Results showed also that microwave heating in predominant magnetic field regions of the microwave applicator is more effective at controlling the cooling rate. The herein proposed microwave-assisted powder metallurgy approach is suitable to retain the shape of the load imparted during forming by uniaxial pressing. The homogeneity of the prepared high entropy alloys in all cases was good, without the dendritic segregation typical of arc melting, even if some partially-unreacted powders were detected in the samples.
Abstract: With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs), a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc.) while using Back Propagation Algorithm (BPA). In this paper, we have used the Genetic Algorithm (GA) for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.
Abstract: Magnetic Resonance Imaging (MRI) plays a significant role in the current characterization and diagnosis of multiple sclerosis (MS) in radiological imaging. However, early detection of MS lesions from MRI still remains a challenging problem. In the present work, an information theoretic approach to cluster the voxels in MS lesions for automatic segmentation of lesions of various sizes in multi-contrast (T1, T2, PD-weighted) MR images, is applied. For accurate detection of MS lesions of various sizes, the skull-stripped brain data are rescaled and histogram manipulated prior to mapping the multi-contrast data to pseudo-color images. For automated segmentation of multiple sclerosis (MS) lesions in multi-contrast MRI, the improved jump method (IJM) clustering method has been enhanced via edge suppression for improved segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions if present. From this preliminary clustering, a pseudo-color to grayscale conversion is designed to equalize the intensities of the normal brain tissues, leaving the MS lesions as outliers. Binary discrete and 8-bit fuzzy labels are then assigned to segment the MS lesions throughout the full brain. For validation of the proposed method, three brains, with mild, moderate and severe hyperintense MS lesions labeled as ground truth, were selected. The MS lesions of mild, moderate and severe categories were detected with a sensitivity of 80%, and 96%, and 94%, and with the corresponding Dice similarity coefficient (DSC) of 0.5175, 0.8739, and 0.8266 respectively. The MS lesions can also be clearly visualized in a transparent pseudo-color computer rendered 3D brain.
Abstract: Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees from medical images is time consuming and error prone due to the tortuous geometry and weak signal in small blood vessels. To overcome errors and accelerate the image processing time, we introduce an automatic image processing pipeline for constructing subject specific computational meshes for entire cerebral vasculature, including segmentation of ancillary structures; the grey and white matter, cerebrospinal fluid space, skull, and scalp. To demonstrate the validity of the new pipeline, we segmented the entire intracranial compartment with special attention of the angioarchitecture from magnetic resonance imaging acquired for two healthy volunteers. The raw images were processed through our pipeline for automatic segmentation and mesh generation. Due to partial volume effect and finite resolution, the computational meshes intersect with each other at respective interfaces. To eliminate anatomically inconsistent overlap, we utilized morphological operations to separate the structures with a physiologically sound gap spaces. The resulting meshes exhibit anatomically correct spatial extent and relative positions without intersections. For validation, we computed critical biometrics of the angioarchitecture, the cortical surfaces, ventricular system, and cerebrospinal fluid (CSF) spaces and compared against literature values. Volumina and surface areas of the computational mesh were found to be in physiological ranges. In conclusion, we present an automatic image processing pipeline to automate the segmentation of the main intracranial compartments including a subject-specific vascular trees. These computational meshes can be used in 3D immersive visualization for diagnosis, surgery planning with haptics control in virtual reality. Subject-specific computational meshes are also a prerequisite for computer simulations of cerebral hemodynamics and the effects of traumatic brain injury.