Microwave imaging: perception and reality

Meaney, Paul M.

doi:10.1586/17434440.2013.835553

Cited by 4 publications

(3 citation statements)

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“…Many of the challenges faced when imaging patients identified from the clinical studies to date can be categorised in four broad areas: Inefficient coupling of energy into the breast [ 31 ]; Imaging domain changes during acquisition [ 31 , 32 , 33 ]; Intrapatient variation due to the menstrual cycle, hormonal changes or weight differences [ 33 ]; Interpatient variation in breast size, shape and composition [ 10 , 18 , 21 ]. …”

Section: Introductionmentioning

confidence: 99%

“…The imaging domain is also subject to change during the scan (challenge 2): the breast can move due to patient breathing or discomfort, or blood flow or temperature changes may occur in the living breast tissue [ 31 , 32 ]. Shorter acquisition times can help minimise the effects of these changes during acquisition, and later generations of MARIA were designed to acquire the complete scan in under one minute [ 9 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Patient-Specific Microwave Breast Imaging in Clinical Case Studies

O’Loughlin

Elahi

Lavoie

et al. 2021

Sensors

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Microwave breast imaging has seen increasing use in clinical investigations in the past decade with over eight systems having being trialled with patients. The majority of systems use radar-based algorithms to reconstruct the image shown to the clinician which requires an estimate of the dielectric properties of the breast to synthetically focus signals to reconstruct the image. Both simulated and experimental studies have shown that, even in simplified scenarios, misestimation of the dielectric properties can impair both the image quality and tumour detection. Many methods have been proposed to address the issue of the estimation of dielectric properties, but few have been tested with patient images. In this work, a leading approach for dielectric properties estimation based on the computation of many candidate images for microwave breast imaging is analysed with patient images for the first time. Using five clinical case studies of both healthy breasts and breasts with abnormalities, the advantages and disadvantages of computational patient-specific microwave breast image reconstruction are highlighted.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Patient-Specific Microwave Breast Imaging in Clinical Case Studies

O’Loughlin

Elahi

Lavoie

et al. 2021

Sensors

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“…Many reviews, books and editorials have been published about microwave imaging and microwave breast imaging in recent years [1,[11][12][13][14][15][28][29][30][31][32][33][34][35][36][37] which describe the mathematical background and existing literature of the method. Broadly speaking, microwave breast imaging algorithms attempt to infer information about the dielectric contrast of the breast, and correspondingly, information about any tumors or other abnormalities present.…”

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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