Abstract: Introduction: Partial volumes effects (PVEs) are caused by the limited spatial resolution of the imaging system. They hinder accurate quantification of images of organs with diameters less than two-three times the full width half maximum of the imaging system. PVEs can manifest either spill-out or spill-in effects in non-radioactive and radioactive backgrounds respectively. Spill-out effects leads to underestimation of activity counts due "loss" of organ activity, spill-in effects results in overestimation due to movement of background activity counts into the organ. For successful implementation of diagnostic and therapeutic outcomes based on quantitative values, PVEs quantification must be prioritized. The objective of this study was to quantity PVEs in single photon computed tomography. Methods: Images of spheres A, B and C of diameters 26 mm; 20 mm and 16 mm (filled with technitium-99m of activity concentration 74 kBq/ml) mounted inside a Jaszczak phantom were acquired with a gamma camera. Three background activities (0%; 0.5% and 1% activity of 74 kBq/ml) were used. Images were quantified using ImageJ software. Results: Underestimation of image counts increased with decrease in sphere size. Quantification errors were: 54%; 55% and 66% in the order of decreasing sphere size for 0% background activity. For background activities 0.5% and 1% overestimation resulted in quantification errors of 65%; 61% and 55% and 58%; 53% and 46% respectively. Spill-out and spill-in effects cancelled out as background activity increased. Conclusion: Quantification of PVEs should be prioritised when monitoring radionuclide therapy and where quantitative values are required to reach diagnostic conclusions.

Abstract: Medical sensors nodes are capable of sensing, processing, and communicating vital signs, can be seamlessly integrated into wireless body networks (WBNs) for health monitoring. In This paper, the proposed architecture of the embedding electronics for telemedicine system based on mobile applications is presented. We covered most system requirement needs as portability, simplicity, easy to use, and low cost. It composes of 3 levels: Level-1: The application Platform for Data Processing: is supported by Android based operating system is used for monitoring the readings from the physical world into electrically defined signals in order to use these signals to map the readings to our core board. The processing phase consists of three algorithms for measuring Heart rate and Body temperature independently from patient’s body, process those readings, and submit them to tailored developed database related to patient profile. The specialized doctor can remotely examine and diagnose the patient’s condition remotely and efficiently. Level-2: The data Communication and Sensor nodes which composed of Central Control ADC module and sensors (level-3) are connected via wired connection, it is communicating with smartphone (level-1) using USB or via Bluetooth connection. Level-3: The data acquisition (Biological Sensors): they produce voltage (digital signal) that is indicative of physical variable they measure. Those signals are often imported into computer programs, stored in files, plotted and analyzed on computers and mobile applications. Unit (CCU) interface and Communication with sensors. In this phase the processing unit samples analog values from sensors through Analog to Digital Converter module (ADC) and send through Bluetooth connection based on serial communication between processing unit and Bluetooth module. The developed integrated system is efficient with respect to portability since it is a one sensors node, cost effective regarding the electronics components used, low power consumption which cover more than 25 hours with small power bank, and finally with efficient UI through a simple android mobile application.

Abstract: Intense advancement of cloud computing during the last years, convinced the experts to consider it as a proper and favorable substitution for traditional computing methods. Nowadays, many companies have moved their IT physical architecture to cloud computing platform for ease in managing and provisioning of different resources. In this paper a Cloud Computing environment is created using a product suite of VMware vSphere, which is based on two main parts: VMware ESXi hypervisor for virtualization technology and both VMware vSphere Client and Virtual Center (vCenter) for environment management. The aim is to provide efficient solution for designing and implementing an architecture of cloud computing.

Abstract: INTRODUCTION: Intrinsic uniformity test provides the quickest sensitive quality control test for determining slight changes in the performance of the gamma camera. It is performed daily before patient imaging to detect defects. A defective system acquires patient images of poor quality characterised by ring artefacts giving rise to false positive and false negative patient results. An irregular power supply affects the operational functions of the system particularly the photomultiplier tubes resulting in performance changes. Introduction of load shedding in South Africa raised questions and concerns among nuclear medicine physicians about the performance of the gamma camera and its ability to acquire patient images good quality images that can be used to fulfil diagnostic outcomes. This study was conducted to determine the performance of the Siemens E-Cam dual head gamma camera during load shedding period. Methods: A point source soaked into technetium-99m of activity 0.55 MBq was placed into a vial. The vial was then placed in a source holder drawn such that it remained centrally between two detectors positioned at 00 and 1800 with their collimators removed. With all conditions set, a static acquisition of 30 million counts was performed on a matrix size of 512 × 512 pixels with zoom fixed at one (1) Results: The integral uniformity (IU) values for central field of view (CFOV) for detector head 1 and 2 ranged from 2.15-4.50% and 2.00-5.30% respectively. For uniform field of view (UFOV) the values ranged from 2.60-5.18% and 2.32-5.70% respectively. The Differential Uniformity (DU) values for CFOV for detector heads 1 and 2 ranged from 1.12-2.5% and 1.03-2.41% respectively. For UFOV, they ranged from 1.15-2.75% and 1.18-3.15 % respectively. These values were within fell within the limits of acceptance tests, thus they gave the assurance that the gamma camera despite load shedding successfully acquired quality patient images. Conclusion: Load shedding did not affect the performance of the Siemens E-Cam dual gamma camera during the study period. It continued to acquire reliable good quality images.