System for reconstructing surface motion in an optical elastography system
Inventors
Chase, James Geoffrey • Botterill, Tom
Assignees
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Publication Number
US-10213112-B2
Publication Date
2019-02-26
Expiration Date
Abstract
A method for an optical elastography system converts digital images of an actuated breast into a description of surface motion. The surface motion can subsequently be used to ascertain whether the breast has regions of abnormal stiffness, e.g., indicating a significant likelihood of breast cancer. The steps of the method use a model based segmentation to identify profile of the breast in each image, and for each pair of images computing skin surface motion using an optical flow algorithm. This method eliminates a preliminary step of placing fiducial markers on the subject.
Core Innovation
The invention provides an optical elastography system and method for analyzing surface motion of body tissue of a subject by vibrating a body tissue region, capturing a set of images at a plurality of time-steps, and developing a 3D surface model of the tissue region from the set of images. The method estimates the surface motion of the vibrating body tissue region between time-step images and then estimates a 3D surface motion by combining the 3D surface model with the estimated tissue surface motion between time-step images.
The disclosed approach reconstructs 3D breast surface motion from actuated, image-captured breast tissue without fiducial markers by segmenting breast and actuator regions per image and estimating a parametric 3D surface model at each time-step by fitting to localized contours with smoothness regularization. A skin motion estimate is computed between consecutive time-steps using lighting-normalized optical flow, and the 3D surface model is combined with the optical-flow motion to obtain 3D surface motion trajectories of 3D points over multiple time-steps.
The reconstructed motion is compared to expected healthy motion to identify abnormal stiffness indicative of cancer risk, using multi-camera synchronized strobe/vibration capture and a parameterized modeling and fusion workflow that supports reconstruction across time-steps. The method further includes optimization-based fitting and motion-model combination, and it uses optical-flow processing to account for lighting variation while estimating a smooth motion field.
Claims Coverage
The independent claim covers a full optical elastography workflow that vibrates tissue, captures images across time-steps, builds a 3D surface model, estimates 3D surface motion by combining the model with estimated between-time-step motion, and analyzes the resulting motion to identify abnormalities. The provided claim coverage centers on one inventive combination pipeline with narrower dependents refining modeling, motion estimation, and model-motion combination.
Vibrating and image-capturing across time-steps
Vibrating a body tissue region of a subject; capturing a set of images at a plurality of time-steps of a surface of the vibrating body tissue region.
Developing a 3D surface model from time-step images
Developing a 3D surface model of the tissue region from the set of images captured at the plurality of time-steps.
Estimating surface motion between time-step images
Estimating the surface motion of the vibrating body tissue region between time-step images.
Estimating 3D surface motion by combining model with estimated surface motion
Estimating a 3D surface motion of the vibrating body tissue region by combining the 3D surface model with the estimated tissue surface motion between time-step images.
Analyzing 3D surface motion to identify abnormalities
Analyzing the 3D surface motion to identify abnormalities in the motion of the vibrating body tissue region.
Structured light 3D surface model
Developing the 3D surface model of the tissue region using a structured light method.
Parametric 3D surface model estimation with optimization and tissue surface smoothness
Estimating a parametric 3D tissue surface model by an optimization, where the fit uses a difference between image profiles and a projected 3D surface model plus a tissue surface smoothness measure.
Lighting-normalized smooth optical flow vector field for motion estimation
Normalizing pixel intensities to correct lighting variation and estimating a smooth 2D vector field U between two image time-steps so that warping an image I1 with U matches image I2 by iteratively minimizing a global appearance-error function while enforcing smoothness of U.
Mechanical actuator vibration
Performing the vibrating using a mechanical actuator.
Path optimization across time-steps to combine 3D model with image motion
Combining a 3D tissue surface model with image motion by optimizing a 3D point’s path across all time-steps to minimize error between the model path and the image-motion path.
Overall, the claim coverage centers on combining a 3D surface model derived from time-step images with estimated between-time-step surface motion to obtain 3D surface motion trajectories, and then analyzing that 3D motion to identify abnormalities. The dependents further refine how the 3D surface model is developed and how motion is estimated and fused.
Stated Advantages
Identify abnormalities in the motion of the vibrating body tissue region.
Identify abnormal stiffness indicative of cancer risk by comparing reconstructed motion to expected healthy motion.
Documented Applications
Breast cancer screening by reconstructing breast surface motion and identifying abnormal stiffness indicative of cancer risk.
Optical elastography of a body tissue region with an optical elastography system for analyzing surface motion without fiducial markers [procedural detail omitted for safety].
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