Cone-beam computed tomography image and X-ray image registration method

A tomography, optical image technology, applied in the field of oral clinical medicine and computer vision, can solve problems such as non-rigid and complex structural changes in three-dimensional craniofacial images that are difficult to process

Active Publication Date: 2017-12-22
PEKING UNIV
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The method based on convolutional neural network is used for 2D and 3D image registration, but the existing registration work based on convolutional neural network regression model only deals with

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  • Cone-beam computed tomography image and X-ray image registration method
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  • Cone-beam computed tomography image and X-ray image registration method

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[0052] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.

[0053] The convolutional neural network regression-based method provided by the present invention performs non-rigid registration between two-dimensional X-ray images and three-dimensional cone-beam CT images. A convolutional neural network-based regression model was used to establish the correlation between non-rigid deformation parameters of 2D X-ray images and 3D cone-beam CT images. Combining a regression model based on a hybrid residual convolutional neural network and an iterative optimization mechanism for deformation parameters, reliable online 2D and 3D image registration is achieved.

[0054] figure 1 It is a block flow diagram of the method of the present invention. The present invention will be further described below with reference to the accompanying drawings.

[0055] Step 1: Extrac...

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Abstract

The invention discloses a cone-beam computed tomography image and X-ray image registration method; the method uses a regression model based on a mixing residual error convolution nerve network to build associations between two-dimension X-ray images and three dimensional cone-beam CT image non-rigid deformation parameters, and uses a mixing residual error convolution nerve network and deformation parameter iteration optimization method to realize reliable online two-dimension three dimensional image registration; the method comprises the following steps: extracting to obtain an image channel; training the regression model based on the mixing residual error convolution nerve network; carrying out two-dimension three dimensional non-rigid registration based on regression; iterating and optimizing deformation parameters; obtaining a final body image determined by the deformation parameters, and thus realizing two-dimension three dimensional image registration based on iteration regression. The method can realize reliable online two-dimension three dimensional image registration, can be applied to oral cavity clinics, and can evaluate treatments and analyze craniofacial growth according to two-dimension three dimensional image registrations.

Description

technical field [0001] The invention relates to the technical fields of oral clinical medicine and computer vision, in particular to a method for registering cone-beam computed tomography images and X-ray images. Background technique [0002] Orthodontic treatment usually lasts for several years, and multiple images collected during treatment will be used to evaluate the difference between rigid and non-rigid shapes of maxillofacial structures, especially for minor patients. Nonrigid morphological change to structural growth. Before the widespread use of cone-beam computed tomography (cone-beam CT) images in clinical orthodontics, two-dimensional radiographs were the only medium to record changes in maxillofacial morphology. Nonrigid 2D-3D image registration is a key step in obtaining 3D images from in-treatment X-ray images. Traditional 2D and 3D image registration methods rely on iterative optimization to minimize the difference between digitally-reconstructed-radiograph...

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Application Information

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IPC IPC(8): G06T7/33
CPCG06T7/344G06T2207/10081G06T2207/10124G06T2207/20081G06T2207/20084
Inventor 裴玉茹秦海芳张云庚郭玉珂许天民查红彬
Owner PEKING UNIV
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