SAFE: A Novel Microwave Imaging System Design for Breast Cancer Screening and Early Detection—Clinical Evaluation

Janjic, Aleksandar; Çayören, Mehmet; Akduman, İbrahim; Yilmaz, Tuba; Bugdayci, Onur; Arıbal, Erkin

doi:10.3390/diagnostics11030533

Cited by 38 publications

(50 citation statements)

References 32 publications

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“…When ideal skin suppression is used, as displayed in Figure 3a, no beamformer has a practical sensitivity. If adipose and fibroglandular reference subtraction is performed, the beamformers have improved performance that is within the range of reported sensitivity estimates in the literature (74-100% in [8,9,[32][33][34][35][36][37]). The specificity of the DAS and DMAS beamformers was observed to be worse than a random classifier acting on images of healthy breasts.…”

Section: Resultssupporting

confidence: 78%

“…The ORR beamformer outperformed the DAS and DMAS reconstruction methods in all cases. While the specificity of ORR is relatively poor compared to existing clinical modalities, it is similar to the 62% specificity reported in [37], and the relative increase in specificity compared to the literature standard DAS and DMAS beamformers is promising. No existing technique has been demonstrated to have specificity that would be clinically acceptable, but the relative improvement of 40% (compared to DMAS) demonstrates the potential of the ORR algorithm for BMS.…”

Section: The Diagnostic Performance Of Das Dmas and Orrsupporting

confidence: 51%

“…The other study estimated the specificity to be 20% and estimated the specificity using five phantoms [32]. Neither estimates were promising, and while the sensitivity of BMS has been estimated in several publications and has been reported to be between 74% and 100% [8,9,[32][33][34][35][36][37], insufficient attention has been awarded to the specificity of the modality, which must be considered in tandem with the sensitivity.…”

Section: The Diagnostic Performance Of Das Dmas and Orrmentioning

confidence: 98%

“…Both studies used radar-based image reconstruction methods. One study [37] estimated the specificity to be 62% in a trial with 103 breast images from a patient trial. The other study estimated the specificity to be 20% and estimated the specificity using five phantoms [32].…”

Section: The Diagnostic Performance Of Das Dmas and Orrmentioning

confidence: 99%

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An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Reimer

Pistorius

2021

Sensors

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Breast microwave sensing (BMS) has been studied as a potential technique for cancer detection due to the observed microwave properties of malignant and healthy breast tissues. This work presents a novel radar-based image reconstruction algorithm for use in BMS that reframes the radar image reconstruction process as an optimization problem. A gradient descent optimizer was used to create an optimization-based radar reconstruction (ORR) algorithm. Two hundred scans of MRI-derived breast phantoms were performed with a preclinical BMS system. These scans were reconstructed using the ORR, delay-and-sum (DAS), and delay-multiply-and-sum (DMAS) beamformers. The ORR was observed to improve both sensitivity and specificity compared to DAS and DMAS. The estimated sensitivity and specificity of the DAS beamformer were 19% and 44%, respectively, while for ORR, they were 27% and 56%, representing a relative increase of 42% and 27%. The DAS reconstructions also exhibited a hot-spot image artifact, where a localized region of high intensity that did not correspond to any physical phantom feature would be present in an image. This artifact appeared like a tumour response within the image and contributed to the lower specificity of the DAS beamformer. This artifact was not observed in the ORR reconstructions. This work demonstrates the potential of an optimization-based conceptualization of the radar image reconstruction problem in BMS. The ORR algorithm implemented in this work showed improved diagnostic performance and fewer image artifacts compared to the widely employed DAS algorithm.

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Section: Resultssupporting

confidence: 78%

Section: The Diagnostic Performance Of Das Dmas and Orrsupporting

confidence: 51%

Section: The Diagnostic Performance Of Das Dmas and Orrmentioning

confidence: 98%

Section: The Diagnostic Performance Of Das Dmas and Orrmentioning

confidence: 99%

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An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Reimer

Pistorius

2021

Sensors

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“…MBI has been investigated as a novel modality for the detection of breast disease, offering a non-ionizing, non-compressive approach [4][5][6] and as a potential diagnostic management strategy in the monitoring of neoadjuvant chemotherapy [7]. To date, a total of at least 10 MBI system prototypes have been employed in human subject tests, to investigate the clinical utility of MBI [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Automated Breast Lesion Detection and Characterization with the Wavelia Microwave Breast Imaging System: Methodological Proof-of-Concept on First-in-Human Patient Data

Fasoula¹,

Duchesne²,

Cano³

et al. 2021

Applied Sciences

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Microwave Breast Imaging (MBI) is an emerging non-ionizing imaging modality, with the potential to support breast diagnosis and management. Wavelia is an MBI system prototype, of 1st generation, which has recently completed a First-In-Human (FiH) clinical investigation on a 25-symptomatic patient cohort, to explore the capacity of the technology to detect and characterize malignant (invasive carcinoma) and benign (fibroadenoma, cyst) breast disease. Two recent publications presented promising results demonstrated by the device in this FiH study in detecting and localizing, as well as delineating size and malignancy risk, of malignant and benign palpable breast lesions. In this paper, the methodology that has been employed in the Wavelia semi-automated Quantitative Imaging Function (QIF), to support breast lesion detection and characterization in the FiH clinical investigation of the device, is presented and the critical design parameters are highlighted.

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On the utilization of the adjoint method in microwave tomography

Soydan,

Top,

Gençer

2024

Numer Methods Biomed Eng

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In microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm. Because of the problem‐specific nature of the adjoint formulations, the dissimilarities between the adjoint calculations may be overlooked. Here, we have classified the adjoint method formulations into two groups: the direct and indirect methods. The direct method involves calculating the derivative of the cost function, whereas, in the indirect method, the derivative of the predicted data is calculated. In this review, the direct and indirect adjoint methods are presented, compared, and discussed. The formulations are explicitly derived using the two‐dimensional wave equation in frequency and time domains. Finite‐difference time‐domain simulations are conducted to show the different uses of the adjoint methods for both single source‐multiple receiver, and multiple transceiver scenarios. This study demonstrated that an appropriate adjoint method selection is significant to achieve improved computational efficiency for the applied optimization algorithm.

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SAFE: A Novel Microwave Imaging System Design for Breast Cancer Screening and Early Detection—Clinical Evaluation

Cited by 38 publications

References 32 publications

An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Automated Breast Lesion Detection and Characterization with the Wavelia Microwave Breast Imaging System: Methodological Proof-of-Concept on First-in-Human Patient Data

On the utilization of the adjoint method in microwave tomography

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