System and method for learning-based 2D/3D rigid registration for image-guided surgery using Jensen-Shannon divergence
First Claim
1. A method of registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure comprising the steps of:
- providing a pair of correctly-registered training images L={lr, lf}, wherein lr and lf are reference and floating images, respectively, and their joint intensity distribution pl(ir, if), wherein ir and if are reference and floating images, respectively;
providing a pair of observed images O={or, of}, wherein or and of are reference and floating images, respectively, and their joint intensity distribution po(ir, if);
mapping a marginal intensity distribution of the observed pair O={or, of} to a marginal intensity distribution of the training pair L={lr, lf}; and
estimating a set of parameters T that registers image of to image or by maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images.
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Abstract
A method of registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure includes providing a pair of correctly-registered training images L={lr, lf} and their joint intensity distribution pl(ir, if), wherein ir and if are reference and floating images, respectively, providing a pair of observed images O={or, of} and their joint intensity distribution po(ir, if), mapping a marginal intensity distribution of the observed pair O={or, of} to a marginal intensity distribution of the training pair L={lr, lf}, and estimating a set of parameters T that registers image of to image or by maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images.
162 Citations
20 Claims
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1. A method of registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure comprising the steps of:
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providing a pair of correctly-registered training images L={lr, lf}, wherein lr and lf are reference and floating images, respectively, and their joint intensity distribution pl(ir, if), wherein ir and if are reference and floating images, respectively; providing a pair of observed images O={or, of}, wherein or and of are reference and floating images, respectively, and their joint intensity distribution po(ir, if); mapping a marginal intensity distribution of the observed pair O={or, of} to a marginal intensity distribution of the training pair L={lr, lf}; and estimating a set of parameters T that registers image of to image or by maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure comprising the steps of:
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providing a pair of correctly-registered training images L={lr, lf}, wherein lr and lf are reference and floating images, respectively, and their joint intensity distribution pl(ir, if), wherein ir and if are reference and floating images, respectively; providing a pair of observed images O={or, of}, wherein or and of are reference and floating images, respectively, and their joint intensity distribution po(ir,if); mapping a marginal intensity distribution of the observed pair O={or, of} to a marginal intensity distribution of the training pair L={lr, lf} and estimating a set of parameters T that registers image of to image or by maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification