Information Based Machine Learning Approach to Elasticity Imaging
AssigneeThe Board of Trustees of the University of Illinois
InventorsJamshid Ghaboussi, Cameron Lee Hoerig, Michael F Insana
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Abstract
Systems and methods are provided for employing informational models trained using the Autoprogressive Algorithm to learn the mechanical behavior of biological materials using a sparse sampling of force and displacement measurements. The constitutive matrix normally used to solve the inverse problem is replaced with ANNs.
Claims21
A computer-implemented method for estimating stress and strain distributions in biological tissue without requiring estimations of underlying constitutive models, the method comprising:
- applying with a probe, a plurality of forces to the tissue;
- recording a series of displacement measurements respectively over a period of time resulting from said plurality of forces;
- wherein said measurements are recorded from a plurality of locations on said tissue;
- providing said force and displacement measurements to a processor based device;
- and,
- using said processor based device to generate, based at least in part on the force and displacement measurements, said estimates of stress and strain distributions in said tissue.
A system for characterization of mechanical properties of biological tissue, the system comprising:
- a force producing device;
- a measuring device;
- wherein said measuring device is configured to measure external force applied to said biological tissue;
- and,
- a processor in electrical communication with said measuring device;
- said processor programmed to measure a set of internal displacements from said tissue;
- said processor being further programmed to develop an informational model of mechanical properties of the tissue using the Autoprogressive (“AutoP”) Algorithm.
A method for generating a nonparametric, informational model of mechanical properties of biological tissue, the method comprising:
- compressing, with an ultrasound probe having a linear array transducer, the tissue at a first position and transmitting a radio frequency (“RF”) signal into the tissue;
- compressing with the ultrasound probe, the tissue at another position and transmitting the RF signal into the tissue;
- acquiring and recording an RF echo frame each time said RF signal is transmitted into the tissue;
- recording with a force-torque transducer, a surface force generated by the probe after each compression;
- measuring an internal displacement using a speckle-tracking algorithm;
- and,
- entering said force and said displacement into the Autoprogressive (“AutoP”) Algorithm.
Dependencies are inferred from the claim text.