Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Magnetic resonance tissue tagging allows noninvasive in vivo measurement of soft tissue deformation. Planes of magnetic saturation are created, orthogonal to the imaging plane, which form dark lines (stripes) in the image. The authors describe a method for tracking stripe motion in the image plane, and show how this information can be incorporated into a finite element model of the underlying deformation. Human heart data re acquired from several imaging planes in different orientations and re combined using a deformable model of the left ventricle wall. Each tracked stripe point provided information on displacement orthogonal to the original tagging plane, i.e., a one-dimensional (1-D) constraint on the motion. Three-dimensional (3-D) motion and deformation was then reconstructed by fitting the model to the data constraints by linear least squares. The average root mean squared (rms) error beten tracked stripe points and predicted model locations was 0.47 mm (n=3,100 points). In order to validate this method and quantify the errors involved, the authors applied it to images of a silicone gel phantom subjected to a known, ll-controlled, 3-D deformation. The finite element strains obtained re compared to an analytic model of the deformation known to be accurate in the central axial plane of the phantom. The average rms errors re 6% in both the reconstructed shear strains and 16% in the reconstructed radial normal strain. >
['Alistair A. Young', 'Dara L. Kraitchman', 'Lawrence Dougherty', 'Leon Axel']
I am writing a research paper on a idea Tracking and finite element analysis of stripe deformation in magnetic resonance tagging
[{"from": "human", "value": "I am writing a research paper on a idea Tracking and finite element analysis of stripe deformation in magnetic resonance tagging"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Magnetic resonance tissue tagging allows noninvasive in vivo measurement of soft tissue deformation. Planes of magnetic saturation are created, orthogonal to the imaging plane, which form dark lines (stripes) in the image. The authors describe a method for tracking stripe motion in the image plane, and show how this information can be incorporated into a finite element model of the underlying deformation. Human heart data re acquired from several imaging planes in different orientations and re combined using a deformable model of the left ventricle wall. Each tracked stripe point provided information on displacement orthogonal to the original tagging plane, i.e., a one-dimensional (1-D) constraint on the motion. Three-dimensional (3-D) motion and deformation was then reconstructed by fitting the model to the data constraints by linear least squares. The average root mean squared (rms) error beten tracked stripe points and predicted model locations was 0.47 mm (n=3,100 points). In order to validate this method and quantify the errors involved, the authors applied it to images of a silicone gel phantom subjected to a known, ll-controlled, 3-D deformation. The finite element strains obtained re compared to an analytic model of the deformation known to be accurate in the central axial plane of the phantom. The average rms errors re 6% in both the reconstructed shear strains and 16% in the reconstructed radial normal strain. >"}]