Comparison of radar-based microwave imaging algorithms applied to experimental breast phantoms

Elahi, Muhammad Adnan; Lavoie, Benjamin R.; Porter, Emily; Olavini, M.; Jones, Edward; Fear, Elise C.; O’Halloran, Martin

doi:10.23919/ursigass.2017.8105082

Cited by 19 publications

(20 citation statements)

References 13 publications

(17 reference statements)

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“…Tomographic methods generate a contrast profile which may be used to estimate the dielectric properties (permittivity/conductivity) of the object under test, resulting in quantitative reconstruction. On the other hand, radar methods produce qualitative reconstruction, which can only identify and locate strong scatterers inside the object under study as can be seen from Figure 1 [13,14]. Holographic imaging involves the recording of the interference pattern or the 'hologram' and the reconstruction of this hologram using a reference signal.…”

Section: Introductionmentioning

confidence: 99%

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An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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Microwave imaging (MWI) is a non-ionizing, non-invasive and an upcoming affordable medical imaging modality. Over the last few decades, MWI has invited active research towards biomedical imaging, with special focus on breast tumor detection. After long years of intense research and clinical trials, a breast tumour monitoring unit based on MWI is finally entering clinical imaging scenarios. In this manuscript, the vast literature in MWI to date has been consolidated, and an indetail study of the state-of-the-art for breast tumor detection has been presented. The hurdles faced during clinical trials are discussed, and their possible solutions and future directions for a fast transition into clinical imaging have been presented. It is hoped that this paper can serve as a guide for MWI researchers and practitioners, especially those new to the field to comprehend the potential of MWI as a viable imaging tool for breast imaging.

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Section: Introductionmentioning

confidence: 99%

“…(a) (b) Figure 1. Typical outputs produced by (a) quantitative imaging showing the complete dielectric profile obtained [13] and by (b) qualitative imaging showing the tumor detection and localization alone [14].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

View full text Add to dashboard Cite

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“…Several imaging reconstruction algorithms for radar-based imaging have been developed (known as beamformers) and many introductory books, comprehensive reviews and open-source implementations of these algorithms have been published [12][13][14][15]. These beamformers have also been compared using a variety of test cases with both numerical and experimental breast phantoms and patient images [16][17][18][19][20][21][22]. However, many comparative studies to date have used a limited set of beamformers [16], simplified numerical models [17,20], idealized artefact removal algorithms [18], or considered only test situations with abnormalities [21].…”

Section: Introductionmentioning

confidence: 99%

“…These beamformers have also been compared using a variety of test cases with both numerical and experimental breast phantoms and patient images [16][17][18][19][20][21][22]. However, many comparative studies to date have used a limited set of beamformers [16], simplified numerical models [17,20], idealized artefact removal algorithms [18], or considered only test situations with abnormalities [21]. A more exhaustive comparative study using five clinical case studies identified that the Delay-Multiply-and-Sum (DMAS) algorithm achieved the highest signal-to-clutter ratios (SCRs) for the five clinical case studies, but did not consider the potential impact of variations in breast dielectric properties between the five patients [22].…”

Section: Introductionmentioning

confidence: 99%

Comparing Radar-Based Breast Imaging Algorithm Performance with Realistic Patient-Specific Permittivity Estimation

et al. 2019

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Radar-based breast imaging has shown promise as an imaging modality for early-stage cancer detection, and clinical investigations of two commercial imaging systems are ongoing. Many imaging algorithms have been proposed, which seek to improve the quality of the reconstructed microwave image to enhance the potential clinical decision. However, in many cases, the radar-based imaging algorithms have only been tested in limited numerical or experimental test cases or with simplifying assumptions such as using one estimate of permittivity for all patient test cases. In this work, the potential impact of patient-specific permittivity estimation on algorithm comparison is highlighted using representative experimental breast phantoms. In particular, the case studies presented help show that the permittivity estimate can impact the conclusions of the algorithm comparison. Overall, this work suggests that it is important that imaging algorithm comparisons use realistic test cases with and without breast abnormalities and with reconstruction permittivity estimation.

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“…However, Data Adaptive (DA) algorithms estimate the propagation model from the received signals and apply compensation factors based on this estimated channel model. Several studies have been performed to evaluate the performance of both DI and DA beamforming algorithms using a variety of numerical and physical breast models [ 7 , 12 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, these studies have often evaluated the performance of both DI and DA algorithms using a limited set of beamforming algorithms [ 7 ], simplified numerical phantoms [ 12 , 19 , 20 ], and an idealised artifact removal algorithm, while ignoring the impact of realistic artifact removal [ 15 , 16 , 18 ].…”

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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Comparison of radar-based microwave imaging algorithms applied to experimental breast phantoms

Cited by 19 publications

References 13 publications

An Overview of Microwave Imaging for Breast Tumor Detection

An Overview of Microwave Imaging for Breast Tumor Detection

Comparing Radar-Based Breast Imaging Algorithm Performance with Realistic Patient-Specific Permittivity Estimation

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

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