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Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.

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Title: Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.
Authors: Chaudhary, Suneeta1 (AUTHOR), Lane, Elizabeth G.1 (AUTHOR), Levy, Allison1 (AUTHOR), McGrath, Anika1 (AUTHOR), Mema, Eralda1 (AUTHOR), Reichmann, Melissa1 (AUTHOR), Dodelzon, Katerina1 (AUTHOR), Simon, Katherine1 (AUTHOR), Chang, Eileen1 (AUTHOR), Nickel, Marcel Dominik2 (AUTHOR), Moy, Linda3 (AUTHOR), Drotman, Michele1 (AUTHOR), Kim, Sungheon Gene1 (AUTHOR) sgk4001@med.cornell.edu
Source: Magnetic Resonance in Medicine. May2025, Vol. 93 Issue 5, p2163-2175. 13p.
Subjects: Artificial neural networks, Saturated fatty acids, Adipose tissues, Fatty acids, Statistical significance
Abstract: Purpose: To develop a deep learning–based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue. Methods: A physics‐based unsupervised deep learning network for estimation of fatty acid composition‐network (FAC‐Net) is proposed to estimate the number of double bonds and number of methylene‐interrupted double bonds from multi‐echo bipolar gradient‐echo data, which are subsequently converted to saturated, mono‐unsaturated, and poly‐unsaturated fatty acids. The loss function was based on a 10 fat peak signal model. The proposed network was tested with a phantom containing eight oils with different FAC and on post‐menopausal women scanned using a whole‐body 3T MRI system between February 2022 and January 2024. The post‐menopausal women included a control group (n = 8) with average risk for breast cancer and a cancer group (n = 7) with biopsy‐proven breast cancer. Results: The FAC values of eight oils in the phantom showed strong correlations between the measured and reference values (R2 > 0.9 except chain length). The FAC values measured from scan and rescan data of the control group showed no significant difference between the two scans. The FAC measurements of the cancer group conducted before contrast and after contrast showed a significant difference in saturated fatty acid and mono‐unsaturated fatty acid. The cancer group has higher saturated fatty acid than the control group, although not statistically significant. Conclusion: The results in this study suggest that the proposed FAC‐Net can be used to measure the FAC of mammary adipose tissue from gradient‐echo MRI data of the breast. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell 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.)
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  Label: Title
  Group: Ti
  Data: Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.
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  Data: <searchLink fieldCode="AR" term="%22Chaudhary%2C+Suneeta%22">Chaudhary, Suneeta</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lane%2C+Elizabeth+G%2E%22">Lane, Elizabeth G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Levy%2C+Allison%22">Levy, Allison</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McGrath%2C+Anika%22">McGrath, Anika</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mema%2C+Eralda%22">Mema, Eralda</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reichmann%2C+Melissa%22">Reichmann, Melissa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dodelzon%2C+Katerina%22">Dodelzon, Katerina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Simon%2C+Katherine%22">Simon, Katherine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chang%2C+Eileen%22">Chang, Eileen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nickel%2C+Marcel+Dominik%22">Nickel, Marcel Dominik</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moy%2C+Linda%22">Moy, Linda</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Drotman%2C+Michele%22">Drotman, Michele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Sungheon+Gene%22">Kim, Sungheon Gene</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sgk4001@med.cornell.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. May2025, Vol. 93 Issue 5, p2163-2175. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Saturated+fatty+acids%22">Saturated fatty acids</searchLink><br /><searchLink fieldCode="DE" term="%22Adipose+tissues%22">Adipose tissues</searchLink><br /><searchLink fieldCode="DE" term="%22Fatty+acids%22">Fatty acids</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+significance%22">Statistical significance</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: To develop a deep learning–based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue. Methods: A physics‐based unsupervised deep learning network for estimation of fatty acid composition‐network (FAC‐Net) is proposed to estimate the number of double bonds and number of methylene‐interrupted double bonds from multi‐echo bipolar gradient‐echo data, which are subsequently converted to saturated, mono‐unsaturated, and poly‐unsaturated fatty acids. The loss function was based on a 10 fat peak signal model. The proposed network was tested with a phantom containing eight oils with different FAC and on post‐menopausal women scanned using a whole‐body 3T MRI system between February 2022 and January 2024. The post‐menopausal women included a control group (n = 8) with average risk for breast cancer and a cancer group (n = 7) with biopsy‐proven breast cancer. Results: The FAC values of eight oils in the phantom showed strong correlations between the measured and reference values (R2 > 0.9 except chain length). The FAC values measured from scan and rescan data of the control group showed no significant difference between the two scans. The FAC measurements of the cancer group conducted before contrast and after contrast showed a significant difference in saturated fatty acid and mono‐unsaturated fatty acid. The cancer group has higher saturated fatty acid than the control group, although not statistically significant. Conclusion: The results in this study suggest that the proposed FAC‐Net can be used to measure the FAC of mammary adipose tissue from gradient‐echo MRI data of the breast. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell 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.)
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        Value: 10.1002/mrm.30401
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 2163
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Saturated fatty acids
        Type: general
      – SubjectFull: Adipose tissues
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      – SubjectFull: Fatty acids
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      – SubjectFull: Statistical significance
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      – TitleFull: Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.
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              Text: May2025
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              Y: 2025
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