Breast tissue characterization by sound speed: correlation with mammograms using a 2D/3D image registration

. Significance : Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions. Aim : To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information. Approach : A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction. Results : The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%. Conclusions : A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.

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“…The mean speed of sound

and macroscopic standard deviation

of adipose tissues, glandular tissues, and lesions have been assigned following Ref. 51 . In Figs.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors

et al. 2022

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“…Below are the details of each stage. In fact, the sound speed in tumorous tissue was significantly higher than in glandular and fatty tissues [8]. When the wave passes through the tumor, its characteristics are modified (velocity, amplitude, diffraction, …).…”

Section: Physical Approachmentioning

confidence: 96%

Early Breast Lump Detection Using the Intelligent Bra "IN-bra"

Elias¹,

Rabih²,

Nassar³

2022

Act Sci wom hea

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This paper proposes a new smart Bra concept to detect any potential strange lumps in women's breasts by ultrasounds waves.These suspicious lumps can be a cyst, Abscess, Fibroadenoma, or Tumor. This system consists of 18 transducers positioned in a specific way, distributed on a ring, with a 5 MHz central frequency while respecting the anatomy of the breasts. We aim to minimize the main lobe's beamwidth (BW) and maximize the radiation field along the side lobes (SLL) of the transducer, with this wide lateral diffraction caused by the piezoelectric, we can detect any strange corps in the breast.Our hypothesis was confirmed by the correlation coefficient formula, where two signals are compared according to their spatial resemblance potentially. According to our findings, there are no suspicious lumps in the breast, while the correlation coefficient is as high as 0.2.

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“…FWI performs optimization on a tissue model, to minimize the residual between the measured signal and the simulation and is not dependent on knowing travel distances. This research is currently mostly focused on breast [10], [11], [15], [16], [17], [18], [19], and musculoskeletal [20], [21] imaging, both showing promising prospects. Current in vivo implementations require a full circumferential field of view, limiting them to small body parts.…”

Section: B Elastography and Full Waveform Inversionmentioning

confidence: 99%

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Feigin

Freedman

Anthony³

2020

IEEE Trans. Biomed. Eng.

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Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to highend ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates.Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from channel data.Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data.Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data.Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies.Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are possible.Index Terms-deep learning, inverse problems, ultrasound, sound speed inversion

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Breast tissue characterization by sound speed: correlation with mammograms using a 2D/3D image registration

Cited by 11 publications

References 10 publications

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors

Early Breast Lump Detection Using the Intelligent Bra "IN-bra"

A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

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