Image-guided adaptive algorithm based on edge-preserving multi-scale deformable registration

Abstract

The invention discloses an image-guided adaptive algorithm based on edge-preserving multi-scale deformable registration, which includes the following steps that: (1) on the basis of a nonlinear diffusion TV (total variation) model, an edge-preserving multi-scale space is constructed, and carries out multi-scale decomposition on an image according to the geometric dimension of the contour structure in the image; (2) the multi-scale space is combined with a coarse-to-fine free-form deformation grid model, and according to the complexity of multi-scale image details, deformation grid density is adaptively adjusted; (3) by automatically estimating the smoothing parameter in the nonlinear diffusion model Lambda, a full-automatic multi-scale registration scheme is constructed. The algorithm can effectively increase the precision, speed and robustness of deformable registration.

Description

technical field [0001] The invention relates to a method in the technical field of medical image analysis and processing, in particular to an image-guided adaptive algorithm based on edge-protected multi-scale deformation registration. Background technique [0002] At present, in the IGRT system (Image guided radiation therapy, IGRT), the rigid body registration algorithm is mostly used for the registration of planned CT (PCT) and CBCT images during daily radiotherapy. [0003] Document 1—J. Pouliot, A. Bani-Hashemi, J. Chen, M. Svatos, F. Ghelmansarai, and et al., “Low-dose megavoltage cone-beam CT for radiation therapy,” International Journal of Radiation Oncology Biology Physics, vol.61, no.2, pp.552-560, 2005 (J.Pouliot, A.Bani-Hashemi, J.Chen, M.Svatos, F.Ghelmansarai, et al. Low-dose megavolt conical Radiation therapy with beam CT. International Journal of Radiation Oncology Biological Physics. 2005, 61 (2): 552-560) using low-dose MV-CBCT to obtain three-dimensional ...

AI Technical Summary

Problems solved by technology

In the above-mentioned registration studies, mutual information is often used as the similarity measure of the registration scheme, and the calculation of mutual information needs to estimate the probability density function. For 3D medical image data, the calculation amount is large, and it is difficult to meet the clinical real-time requirements.

In addition, the calculation of mutual information itself lacks spatial location information

Moreover, when planning CT and CBCT registration, global deformation and local deformation coexist, and the deformations of different cases are quite different. Many problems, such as complex algorithms, huge time-consuming, manual marking, and inability to realize automatic or semi-automatic registration, restrict their application in clinical image-guided radiology systems

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