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

Overview of attention for article published in Sensors (14248220), May 2018
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Title
Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data
Published in
Sensors (14248220), May 2018
DOI 10.3390/s18061678
Pubmed ID
Authors

Muhammad Elahi, Declan O’Loughlin, Benjamin Lavoie, Martin Glavin, Edward Jones, Elise Fear, Martin O’Halloran

Abstract

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Student > Bachelor 4 21%
Lecturer > Senior Lecturer 3 16%
Researcher 3 16%
Student > Postgraduate 2 11%
Other 2 11%
Readers by discipline Count As %
Engineering 14 74%
Medicine and Dentistry 3 16%
Nursing and Health Professions 1 5%
Unspecified 1 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 June 2018.
All research outputs
#10,387,389
of 13,022,532 outputs
Outputs from Sensors (14248220)
#3,758
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Outputs of similar age
#203,166
of 271,266 outputs
Outputs of similar age from Sensors (14248220)
#145
of 302 outputs
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