Biomechanical imaging refers to the remote measurement of the mechanical properties of tissues, in-situ and in-vivo. Images of the tissue can be thus created by visualizing the mechanical property distributions. This technique relies on imaging tissue while it is deformed by a set of externally applied forces. Through image processing, the displacement field everywhere in the region of interest is inferred. An inverse problem for the relevant mechanical properties is then solved, given the measured displacement fields, an assumed form of the tissue's constitutive equation, and the law of conservation of momentum. Images of reconstructed parameters find applications in the detection, diagnosis and treatment monitoring of disease, and in designing patient specific models for surgical training and planning. Over the last decade we have developed a software package (NLACE) to solve this inverse problem eciently in different application domains. Through this award we will make enhancements to NLACE that will make it easier to utilize and modify, and extend its user base to a wider community.
The specific tasks for enhancing NLACE can be divided into two categories: (a) Steps to transition from a working prototype of NLACE to a software resource for the community. These include establishing an Input/Output standard for NLACE, creation of a GUI for input data and for monitoring the progress of the solution, hosting NLACE distributions, and creating sets of test data and documentation for its release. (b) Tasks that would enhance the functional capability of NLACE. These include the creation of a user-defined hyperelastic material model module to address a large class of tissue and material types, parallelization of NLACE on distributed memory, shared memory and GPU platforms, and quantifying uncertainty in the spatial distribution of the reconstructed parameters. We will measure our progress through user-feedback obtained during annual validation tests performed by a committed focus user-group that will test all aspects of the proposed research.
The proposed improvements to NLACE will further its application in the detection, diagnosis and treatment monitoring of diseases, generation of patient-specic models for surgical planning and image-guidance applications, and studies in biomechanics and mechanobiology. The parallel and uncertainty quantification strategies developed for NLACE can be applied to a broad class of inverse problem with PDE constraints, including acoustic and electromagnetic scattering, seismic inversion, diffuse optical tomography, aquifer permeability and thermometry. Our outreach plans ensure the dissemination of NLACE to our focus group and to a broader community through hosting on the Simtk NIH center website. Finally, two graduate students will be trained in the elds of computational science and mathematics and biomechanics, and results from the proposed research will be presented at conferences on computational and biomedical science and engineering.