  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.
