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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]
Copyright of Journal of Biomedical Optics is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 156075700
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.
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  Data: <searchLink fieldCode="AR" term="%22Di+Sciacca%2C+Giuseppe%22">Di Sciacca, Giuseppe</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> giuseppe.sciacca.17@ucl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Maffeis%2C+Giulia%22">Maffeis, Giulia</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> giulia.maffeis@polimi.it</i><br /><searchLink fieldCode="AR" term="%22Farina%2C+Andrea%22">Farina, Andrea</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> andrea.farina@cnr.it</i><br /><searchLink fieldCode="AR" term="%22Dalla+Mora%2C+Alberto%22">Dalla Mora, Alberto</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> alberto.dallamora@polimi.it</i><br /><searchLink fieldCode="AR" term="%22Pifferi%2C+Antonio%22">Pifferi, Antonio</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> antonio.pifferi@polimi.it</i><br /><searchLink fieldCode="AR" term="%22Taroni%2C+Paola%22">Taroni, Paola</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> paola.taroni@polimi.it</i><br /><searchLink fieldCode="AR" term="%22Arridge%2C+Simon%22">Arridge, Simon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.arridge@cs.ucl.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Biomedical+Optics%22">Journal of Biomedical Optics</searchLink>. Jan-Mar2022, Vol. 27 Issue 3, p36003-36003. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Optical+tomography%22">Optical tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Breast%22">Breast</searchLink><br /><searchLink fieldCode="DE" term="%22Breast+tumors%22">Breast tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+properties%22">Optical properties</searchLink><br /><searchLink fieldCode="DE" term="%22Ultrasonic+imaging%22">Ultrasonic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+images%22">Optical images</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor+classification%22">Tumor classification</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Biomedical Optics is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=156075700
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1117/1.JBO.27.3.036003
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: 36003
    Subjects:
      – SubjectFull: Optical tomography
        Type: general
      – SubjectFull: Breast
        Type: general
      – SubjectFull: Breast tumors
        Type: general
      – SubjectFull: Optical properties
        Type: general
      – SubjectFull: Ultrasonic imaging
        Type: general
      – SubjectFull: Optical images
        Type: general
      – SubjectFull: Tumor classification
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    Titles:
      – TitleFull: Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.
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            – D: 01
              M: 03
              Text: Jan-Mar2022
              Type: published
              Y: 2022
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              Value: 27
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