ABSTRACT
We present a modular framework for mechanically regularized nonrigid image registration of 3D ultrasound and for identification of tissue mechanical parameters. Mechanically regularized deformation fields are computed from sparsely estimated local displacements. We enforce image-based local motion estimates by applying concentrated forces at mesh nodes of a mechanical finite-element model. The concentrated forces are generated by the elongation of regularization springs connected to the mesh nodes as their free ends are displaced according to local motion estimates. The regularization energy corresponding to the potential energy stored in the springs is minimized when the mechanical response of the model matches the observed response of the organ. We demonstrate that this technique is suitable for identification of material parameters of a nonlinear viscoelastic liver model and demonstrate its benefits over traditional indentation methods in terms of improved volumetric agreement between the model response and the experiment.
Subject(s)
Algorithms , Elasticity Imaging Techniques/methods , Image Interpretation, Computer-Assisted/methods , Liver/diagnostic imaging , Liver/physiology , Models, Biological , Subtraction Technique , Animals , Computer Simulation , Elasticity , Image Enhancement/methods , In Vitro Techniques , Reproducibility of Results , Sensitivity and Specificity , Stress, Mechanical , SwineABSTRACT
Real-time three-dimensional ultrasound enables new intracardiac surgical procedures, but the distorted appearance of instruments in ultrasound poses a challenge to surgeons. This paper presents a detection technique that identifies the position of the instrument within the ultrasound volume. The algorithm uses a form of the generalized Radon transform to search for long straight objects in the ultrasound image, a feature characteristic of instruments and not found in cardiac tissue. When combined with passive markers placed on the instrument shaft, the full position and orientation of the instrument is found in 3D space. This detection technique is amenable to rapid execution on the current generation of personal computer graphics processor units (GPU). Our GPU implementation detected a surgical instrument in 31 ms, sufficient for real-time tracking at the 25 volumes per second rate of the ultrasound machine. A water tank experiment found instrument orientation errors of 1.1 degrees and tip position errors of less than 1.8mm. Finally, an in vivo study demonstrated successful instrument tracking inside a beating porcine heart.
