Study on the Image Registeration Integrated with the LQ Model | Best Medical Research Papers For Sale
Title Study on the Image Registeration Integrated with the LQ Model
Abstract

With the applications of 3D conformal radiotherapy and intensity modulated radiation therapy, the more conformal, higher dose of target volumes and lower complicate of the normal tissue become possible. At the same time, higher dose gradients between the target volumes and the normal tissues appear. The precision of patient positions and organ motion become important and it needs to monitor them when patient on the treatment table. The registration of images acquired before treatment fractions and at planning realizes the image guide radiation therapy. The accumulated dose for multiple fractions based on the image registration can be used to modify the original plan and makes the adaptive radiation therapy possible. So image registration plays a core role in modern radiotherapy.Image registration is a process of creation correspondence between a pair of images. However, physical one-to-one correspondence may not exist due to the tissues response over the course of radiotherapy. The purpose of the study was to develop an image registration algorithm to deal with the situations where no correspondence exists between the two images to be registered. It allowed to accurately accumulate the dose fraction by fraction and made the adaptive radiotherapy feasible.By far the most common and most accepted of radiobiological models is the linear quadratic (LQ) model. In the LQ, the fraction S, of cells that survive absorbed dose D is given by S=exp(-αD-βD~2), andαandβare two constants. Based on it we calculated the cells survival probability within voxels of the planning image and the image intensity was modified. Then the modified planning image was registered with the treatment image. The image-intensity modification procedure achieved better registration accuracy than a regular registration.

Category Special Medical
Keywords "demons"algorithm, Image registration, LQ model,
FileType PDF
Pages 131
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