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

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Title: Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.
Authors: Di Sciacca, Giuseppe1,2 (AUTHOR) giuseppe.sciacca.17@ucl.ac.uk, Maffeis, Giulia2 (AUTHOR) giulia.maffeis@polimi.it, Farina, Andrea3 (AUTHOR) andrea.farina@cnr.it, Dalla Mora, Alberto2 (AUTHOR) alberto.dallamora@polimi.it, Pifferi, Antonio2,3 (AUTHOR) antonio.pifferi@polimi.it, Taroni, Paola2,3 (AUTHOR) paola.taroni@polimi.it, Arridge, Simon1 (AUTHOR) s.arridge@cs.ucl.ac.uk
Source: Journal of Biomedical Optics. Jan-Mar2022, Vol. 27 Issue 3, p36003-36003. 1p.
Subjects: Optical tomography, Breast, Breast tumors, Optical properties, Ultrasonic imaging, Optical images, Tumor classification
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:10833668
DOI:10.1117/1.JBO.27.3.036003