Microwave Tomography

Noghanian, Sima; Sabouni, Abas; Desell, Travis; Ashtari, Ali

doi:10.1007/978-1-4939-0752-6

Cited by 32 publications

(13 citation statements)

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“…Several image reconstruction algorithms for CMI have been developed over the last two decades [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. These algorithms have been categorised as Data Independent (DI) beamforming and Data Adaptive (DA) beamforming algorithms in the literature [ 14 ]. Both DI and DA beamforming algorithms are based on the principle of coherent addition of backscattered radar signals, which are collected after illuminating the breast with Ultra-Wideband Radar (UWB) pulses.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Elahi

O’Loughlin

Lavoie

et al. 2018

Sensors

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Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Elahi

O’Loughlin

Lavoie

et al. 2018

Sensors

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“…The aim of a localization system is to find the position of the desired object in a defined coordinate system . Depending upon type of application, radio frequency and microwave‐based various solutions have been tested for the applications mentioned . In previous reported articles, the complete target localization process has been achieved using different stages like detection, decision followed by localization .…”

Section: Introductionmentioning

confidence: 99%

“…Localization scheme finds various applications in diverse area like medical scanning, tumor detection, disaster rescue, military operation, law enforcement system, underground object scanning and so on. [1][2][3][4] The aim of a localization system is to find the position of the desired object in a defined coordinate system. 5 Depending upon type of application, radio frequency and microwave-based various solutions have been tested for the applications mentioned.…”

Section: Introductionmentioning

confidence: 99%

“…5 Depending upon type of application, radio frequency and microwave-based various solutions have been tested for the applications mentioned. [1][2][3][4] In previous reported articles, the complete target localization process has been achieved using different stages like detection, decision followed by localization. 6 The final stage of localization is also processed with certain algorithms to track the moving target.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a simple stepped frequency continuous wave target localization system comprising two antennas based on common region of sensing

Singh

Bhattacharyya

Jain

2019

Int J RF Microw Comput Aided Eng

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This article focuses on the two antenna‐based target localization which operates on the principle of stepped frequency continuous wave radars. The detailed study of the system generated limitations for a two antenna‐based target localization system toward the practical implementation is considered and consequently, a common region of sensing (CROS) has been identified. The system capability in different sense like target tracking, spatial coverage, and through the wall localization has been investigated properly and it was found that the proposed CROS‐based localization scheme is an improved as well as simple method by utilizing small number of antenna, flexibility of antenna placing, less processing with higher accuracy, and the position fixing in a simple Cartesian coordinate with reference to the two antennas.

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“…Some drawbacks of the method are the not so efficient representation of curved surfaces due to grid representation and inefficient representation of highly resonant devices which require much longer simulations [4]. The areas of application of the FDTD include antenna design [5]- [7], optoelectronics [8] mine detection [9], microwave tomography [10], electromagnetic compatibility [11] and more.…”

Section: Introductionmentioning

confidence: 99%

Optimizing FDTD Memory Bandwidth by Using Block Float-Point Arithmetic

Pijetlovic¹,

Subotic²,

Pjevalica³

2018

ElAEE

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Finite-difference time-domain is a numerical method used for modelling of computational electrodynamics. The method is resource intensive, especially regarding memory usage. Multiple memory accesses are required per single computation so memory bandwidth acts as a bottleneck limiting the overall performance. Existing solutions use either fixedpoint or floating-point arithmetic, depending on the complexity of the target platform, to model the data. Floating-point requires less memory access but the computation is more intensive due to the normalisation. Fixed-point is the oppositesimple computation but with more memory access for the same precision. The novelty of this paper is in the block floatingpoint realization which is the middle ground between the two. The approach is less compute intensive than the floating-point solutions while using less memory than the fixed-point realization. This makes the solution an alternative for bit-exact platforms, such as field-programmable gate arrays. The results are compared to both floating-point and fixed-point implementations and the memory bandwidth and other resources needed for targeted platform are calculated.

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Microwave Tomography

Cited by 32 publications

References 0 publications

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Implementation of a simple stepped frequency continuous wave target localization system comprising two antennas based on common region of sensing

Optimizing FDTD Memory Bandwidth by Using Block Float-Point Arithmetic

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