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- W3200619783 abstract "We explore the application of a Convolutional Neural Network (CNN) to image the shear modulus field of an almost incompressible, isotropic, linear elastic medium in plane strain using displacement or strain field data. This problem is important in medicine because the shear modulus of suspicious and potentially cancerous growths in soft tissue is elevated by about an order of magnitude as compared to the background of normal tissue. Imaging the shear modulus field therefore can lead to high-contrast medical images. Our imaging problem is: Given a displacement or strain field (or its components), predict the corresponding shear modulus field. Our CNN is trained using 6000 training examples consisting of a displacement or strain field and a corresponding shear modulus field. We observe encouraging results which warrant further research and show the promise of this methodology." @default.
- W3200619783 created "2021-09-27" @default.
- W3200619783 creator A5067403414 @default.
- W3200619783 date "2021-09-16" @default.
- W3200619783 modified "2023-09-27" @default.
- W3200619783 title "Solving an elastic inverse problem using Convolutional Neural Networks" @default.
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- W3200619783 hasPublicationYear "2021" @default.
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