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Abstract #2742

Estimation of Fatty Acid Composition in Mammary Adipose Tissue Using Unsupervised Approach of Deep Learning

Suneeta Chaudhary1, Elizabeth Lane2, Eileen Chang3, Anika McGrath2, Eralda Mema2, Allison Levy4, Melissa Reichman2, Katerina Dodelzon4, Marcel Dominik Nickel5, Linda Moy6, Michele Drotman2, and Sungheon Gene Kim1
1Radiology, Weill Cornell Medical College, New York, NY, United States, 2Weill Cornell Medical College, New York, NY, United States, 3Weill Cornell Medical college, New York, NY, United States, 4Weill Cornell Medical College, New york, NY, United States, 5MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, 6New York University School of Medicine, New York, NY, United States

Synopsis

Keywords: Breast, CancerThe purpose of this study is to develop a non-invasive imaging method to measure the fatty acid composition (FAC) of mammary adipose tissue (MAT) and to investigate its role in breast cancer. A novel unsupervised deep learning approach has been developed using the MRI signal equation of fat peaks in the loss function to generate the FAC maps without using any training data. It takes less computational efforts than conventional voxel-wise analysis techniques. The repeatability and reproducibility of the proposed method have been examined on six subjects, which showed no statistically significant difference between repeated analyses and scans.

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