Weighted delay-and-sum beamformer for breast cancer detection using microwave imaging

Karam, Seyed Abbas Shah; O’Loughlin, Declan; Oliveira, Bárbara Luz; O’Halloran, Martin; Asl, Babak Mohammadzadeh

doi:10.1016/j.measurement.2021.109283

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…where the radius L is the tumor radius or the tumor radius plus a margin such as 5 mm; • P cub (L) where the length of the cube is 1.5 cm or 3 cm; • P sph (L) but centered at p tum instead of p max where the radius L is based on the FWHM or maximum dimension of the region identified by P t (t). Typically, 50% is chosen as the threshold t. A variant of the SCR metric is used in [14]- [18], [21], [26], [28]- [31]. under the name of SCR or SMXR.…”

Section: A Common Image-based Metricsmentioning

confidence: 99%

“…as used in [14]- [18], [21], [28]- [30]. In [23], a fractional equivalent is used relative to the width of the breast.…”

Section: A Common Image-based Metricsmentioning

confidence: 99%

“…Recent work from the literature would suggest that the use of individual, image-based metrics is not useful for comparing algorithms in a clinical context, but that database metrics such as those reviewed in the following section would be more beneficial [34]- [36]. Φ max max P sig : twice FWHM of image response [15], [26], [28] between images with and without target [14], [21] other or undefined [18] Localization Error (LE) [14], [15], [17], [18], [21], [23], [28]- [30] Furthermore, no standardized set of metrics or implementations for individual image assessment exist, however, a number of open-source toolboxes and analysis tools have been proposed with specific implementations [37]- [40]. While using different data formats, structures and addressing different use cases, these toolboxes help to publish and standardise implementations and work flows for tasks such as image segmentation and analysis.…”

Section: A Common Image-based Metricsmentioning

confidence: 99%

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Pathway to Demonstrating Clinical Efficacy of Microwave Breast Imaging: Qualitative and Quantitative Performance Assessment

Porter

O’Loughlin

2022

IEEE J. Electromagn. RF Microw. Med. Biol.

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In the last five years alone, there has been an increasing number of operational microwave breast imaging systems used in clinical trials, with increasingly large and diverse patient populations. However, despite this increased activity and volume of clinical evidence motivating research in the modality, large differences exist in how studies evaluate and report their findings. In this work, the qualitative and quantitative metrics used to measure both image quality and clinical effectiveness and efficacy are reviewed in detail. Image quality, effectiveness and efficacy do not have precise or agreed definitions and the differences between these definitions are discussed in detail. Finally, based on these understandings, the current evidence for clinical acceptance of microwave breast imaging is reviewed, with an emphasis on gaps in the trial populations to date.

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Section: A Common Image-based Metricsmentioning

confidence: 99%

“…as used in [14]- [18], [21], [28]- [30]. In [23], a fractional equivalent is used relative to the width of the breast.…”

Section: A Common Image-based Metricsmentioning

confidence: 99%

Section: A Common Image-based Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Pathway to Demonstrating Clinical Efficacy of Microwave Breast Imaging: Qualitative and Quantitative Performance Assessment

Porter

O’Loughlin

2022

IEEE J. Electromagn. RF Microw. Med. Biol.

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“…These methods are usually referred to as radar-based methods. It uses the reflected or transmitted signals from the imaged object to reconstruct the resulting image using different beamformers [36]. These beamformers calculate the energy from a focal point and integrate the information to reconstruct an energy map.…”

Section: Principle Of Radar-based Algorithmsmentioning

confidence: 99%

Parallel Delay Multiply and Sum Algorithm for Microwave Medical Imaging Using Spark Big Data Framework

Ullah

Arslan

2021

Algorithms

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Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy.

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“…Microwave testing has been applied in varied fields, like aerospace engineering [9], mechanical engineering, food engineering [10], [11], petroleum engineering, chemical engineering [12], civil engineering, forestry and medical science. Taking the last type as an example, research has been conducted on the detection of skin cancer [13], breast cancer [14], neck diseases [15], blood glucose [16] and colonoscopy [17].…”

Section: Introductionmentioning

confidence: 99%

Non-destructive testing of human teeth using microwaves: a state-of-the-art review

2023

Journal of Electrical Engineering

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Tooth diseases including dental caries, periodontitis and cracks have been public health problems globally. How to detect them at the early stage and perform thorough diagnosis are critical for the treatment. The diseases can be viewed as defects from the perspective of non-destructive testing. Such a defect can affect the material properties (e.g., optical, chemical, mechanical, acoustic, density and dielectric properties). A non-destructive testing method is commonly developed to sense the change of one particular property. Microwave testing is one that is focused on the dielectric properties. In recent years, this technique has received increased attention in dentistry. Here, the dielectric properties of human teeth are presented first, and the measurement methods are addressed. Then, the research progress on the detection of teeth over the last decade is reviewed, identifying achievements and challenges. Finally, the research trends are outlined, including electromagnetic simulation, radio frequency identification and heating-based techniques.

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Weighted delay-and-sum beamformer for breast cancer detection using microwave imaging

Cited by 17 publications

References 42 publications

Pathway to Demonstrating Clinical Efficacy of Microwave Breast Imaging: Qualitative and Quantitative Performance Assessment

Pathway to Demonstrating Clinical Efficacy of Microwave Breast Imaging: Qualitative and Quantitative Performance Assessment

Parallel Delay Multiply and Sum Algorithm for Microwave Medical Imaging Using Spark Big Data Framework

Non-destructive testing of human teeth using microwaves: a state-of-the-art review

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