Microwave Breast Screening in the Time-Domain: Identification and Compensation of Measurement-Induced Uncertainties

Porter, Emily; Kirshin, Evgeny; Santorelli, Adam; Popović, Milica

doi:10.2528/pierb13082207

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…Prior to inputting the data into an imaging algorithm, each signal is filtered to remove content outside of the frequency range of the transmitted pulse using a low-pass filter with a cutoff at the upper limit of the frequency range of interest. Jitter is compensated for by time aligning the data using the correlation alignment method presented in [16]. The algorithm used is the Delay-Multiplyand-Sum (DMAS) method [17].…”

Section: Imaging Analysis and Resultsmentioning

confidence: 99%

A Wearable Microwave Antenna Array for Time-Domain Breast Tumor Screening

Porter

Bahrami

Santorelli

et al. 2016

IEEE Trans. Med. Imaging

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171

101

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Abstract-In this work, we present a clinical prototype with a wearable patient interface for microwave breast cancer detection. The long-term aim of the prototype is a breast health monitoring application. The system operates using multistatic time-domain pulsed radar, with 16 flexible antennas embedded into a bra. Unlike the previously reported, table-based prototype with a rigid cup-like holder, the wearable one requires no immersion medium and enables simple localization of breast surface. In comparison with the table-based prototype, the wearable one is also significantly more cost-effective and has a smaller footprint. To demonstrate the improved functionality of the wearable prototype, we here report the outcome of daily testing of the new, wearable prototype on a healthy volunteer over a 28-day period. The resulting data (both signals and reconstructed images) is compared to that obtained with our table-based prototype. We show that the use of the wearable prototype has improved the quality of collected volunteer data by every investigated measure. This work demonstrates the proof-of-concept for a wearable breast health monitoring array, which can be further optimized in the future for use with patients with various breast sizes and tissue densities.

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Section: Imaging Analysis and Resultsmentioning

confidence: 99%

A Wearable Microwave Antenna Array for Time-Domain Breast Tumor Screening

Porter

Bahrami

Santorelli

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

171

101

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“…The physical factors that could affect measurement repeatability are also numerous. In [13] we saw that antenna movement within the radome can cause up to 11 mV of additional vertical noise (on the same order of magnitude as the signal for some transmit-receive antenna pairs). Thus, for test scenarios presented here, we never move the antennas.…”

Section: Sources Of Measurement Uncertainty and Errorsmentioning

confidence: 99%

“…Related to our system, in [12], we performed a study on homogeneous breast phantoms in which we identified sources of horizontal and vertical uncertainty, and saw how these uncertainties affected reconstructed breast images. Similarly, in [13] we analyzed how antennas affect the data and corresponding images generated from phantoms. In [14], we presented a small-scale study on four patients to observe the variation in scans performed on the same day.…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Microwave Radar Applied to Breast Imaging: Measurement Reliability in a Clinical Setting

Porter¹,

Santorelli²,

Popović³

2014

PIER

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Abstract-This work presents an evaluation of the measurement challenges in clinical testing of our microwave breast cancer screening system. The time-domain radar system contains a multistatic 16-antenna hemi-spherical array operating in the 2-4 GHz frequency range. We investigate, for the first time with such a system in clinical trials, the repeatability of measurements and its effect on image reconstruction. We record vertical and horizontal measurement uncertainties under different scenarios and verify using previously introduced compensation methods that they can be successfully reduced to an acceptable level from the standpoint of image reconstruction. We also examine how placement of an immersion medium can affect collected breast scan data. Finally, we probe the repeatability and consistency of measurements with patients. With the goal of confirming the feasibility of frequent breast health monitoring, with our system, we obtain a total of 342 breast scans collected over 57 patient visits to determine how much scan data varies when there are no changes in between scans, and how much it varies when the patient is repositioned in the system. We confirm that, by taking care in patient positioning in the system and with respect to the immersion medium, the measurement repeatability is high.

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“…By performing the data pre-processing, we remove the variability associated with measurement uncertainty. Here, we present a summary of the steps followed to mitigate these effects (a complete analysis can be found in [27]) and to prepare the data for the classification algorithms.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…These sources of jitter cause the misalignment of subsequent time-domain recordings; the signals recorded from various breast scans, will be out of phase with respect to each other. As in [27], we mitigate the effects of jitter by implementing a correlation alignment procedure; signals are shifted by some ∆T that maximizes the cross-correlation between the signals.…”

Section: Data Pre-processingmentioning

confidence: 99%

Investigation of Classifiers for Tumor Detection With an Experimental Time-Domain Breast Screening System

Santorelli¹,

Porter²,

Kirshin³

et al. 2014

PIER

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Abstract-In this work we examine, for the first time, the use of classification algorithms for earlystage tumor detection with an experimental time-domain microwave breast screening system. The experimental system contains a 16-element antenna array, and testing is done on breast phantoms that mimic breast tissue dielectric properties. We obtain experimental data from multiple breast phantoms with two possible tumor locations. In this work, we investigate a method for detecting the tumors within the breast but without the usual complexity inherent to image-generation methods, and confirm its feasibility on experimental data. The proposed method uses machine learning techniques, namely Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA), to determine whether the current breast being scanned is tumor-free. Our results show that both SVM and LDA methods have promise as algorithms supporting early breast cancer microwave screening.

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Microwave Breast Screening in the Time-Domain: Identification and Compensation of Measurement-Induced Uncertainties

Cited by 14 publications

References 14 publications

A Wearable Microwave Antenna Array for Time-Domain Breast Tumor Screening

A Wearable Microwave Antenna Array for Time-Domain Breast Tumor Screening

Time-Domain Microwave Radar Applied to Breast Imaging: Measurement Reliability in a Clinical Setting

Investigation of Classifiers for Tumor Detection With an Experimental Time-Domain Breast Screening System

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