21 resultados para microwave medical imaging


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The aim of this study is to compare the positioning accuracy at different gantry angles of two electronic portal imaging devices (EPIDs) support arm systems by using EPID difference images as a measure for displacement. This work presents a comparison of the mechanical performance of eight Varian aS500 (Varian Medical Systems, Palo Alto, CA) EPIDs, mounted using either the Varian Exact-arm or R-arm.

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It is demonstrated that the electromagnetic (EM) transmission through a subwavelength or non-resonant aperture in a conductive screen can be dramatically enhanced by loading it with folded metallic strips exhibiting resonant properties. When illuminated by an EM plane wave these loaded apertures enable very tight, subwavelength, collimation of the EM power in the near field zone. We propose planar and quasi-planar resonant insertion geometries that should allow, for the first time, two-dimensional dual-polarization subwavelength field confinement along with ability to focus both electric and magnetic fields. The proposed technique for resonance transmission enhancement and near field confinement forms a basis for a new class of microwave near field imaging probe with subwavelength resolution capable of operating over a wide range of imaging distances (0.05–$0.25lambda$). Measurement results demonstrate the possibility of high contrast (more than 3 dB in amplitude and 40 degrees in phase) near field subwavelength imaging of 2D and 3D resonant and non-resonant metallic and dielectric targets in free space and in moderately lossy layered media.

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The overall aim of this study was to assess the accuracy, reproducibility and stability of a high resolution passive stereophotogrammetry system to image a female mannequin torso, to validate measurements made on the textured virtual surface compared with those obtained using manual techniques and to develop an approach to make objective measurements of the female breast. 3D surface imaging was carried out on a textured female torso and measurements made in accordance with the system of mammometrics. Linear errors in measurements were less than 0.5 mm, system calibration produced errors of less than 1.0 mm over 94% over the surface and intra-rater reliability measured by ICC = 0.999. The mean difference between manual and digital curved surface distances was 1.36 mm with maximum and minimum differences of 3.15 mm and 0.02 mm, respectively. The stereophotogrammetry system has been demonstrated to perform accurately and reliably with specific reference to breast assessment. (C) 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

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A novel type of microwave probes based on the loaded aperture geometry has been proposed and experimentally evaluated for dielectrics characterisation and high-resolution near-field imaging. Experimental results demonstrate the possibility of very accurate microwave spectroscopic characterisation of thin lossy dielectric samples and biological materials containing water. High-resolution images of the subwavelength lossy dielectric strips and wet and dry leaves have been obtained with amplitude contrast around 10-20 dB and spatial resolution better than one-tenth of a wavelength in the near-field zone. A microwave imaging scenario for the early-stage skin cancer identification based on the artificial dielectric model has also been explored. This model study shows that the typical resolution of an artificial malignant tumour with a characteristic size of one-tenth of a wavelength can be discriminated with at least 6 dB amplitude and 50° phase contrast from the artificial healthy skin and with more than 3 dB contrast from a benign lesion of the same size. It has also been demonstrated that the proposed device can efficiently deliver microwave energy to very small, subwavelength, focal areas which is highly sought in the microwave hyperthermia applications.

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The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.