ABSTRACT
Super-resolution image reconstruction techniques play an important role for improving image resolution of lung 4D-CT. We presents a super-resolution approach based on fast sub-pixel motion estimation to reconstruct lung 4D-CT images. A fast sub-pixel motion estimation method was used to estimate the deformation fields between "frames", and then iterative back projection (IBP) algorithm was employed to reconstruct high-resolution images. Experimental results showed that compared with traditional interpolation method and super-resolution reconstruction algorithm based on full search motion estimation, the proposed method produced clearer images with significantly enhanced image structure details and reduced time for computation.
Subject(s)
Humans , Algorithms , Four-Dimensional Computed Tomography , Image Enhancement , Lung , Motion , Tomography, X-Ray ComputedABSTRACT
Maximum a Posteriori (MAP) method has been widely applied to the ill-posed problem of image reconstruction. The choice of prior is the crucial point on MAP methods. However, the most conventional priors will lead to a blurring of the whole image or cause ladder-like artifacts. We therefore proposed a Tsallis entropy-based prior for positron emission tomography (PET) iterative reconstruction in MAP framework. The method uses a Tsallis entropy-based prior to eliminate the uncertainty between prior information and the estimated images. We tested this method in the phantom image, compared it with the traditional prior methods. the results showed that the proposed algorithm could suppress noise and obtain better reconstructed image quality.
Subject(s)
Algorithms , Artifacts , Entropy , Image Processing, Computer-Assisted , Methods , Phantoms, Imaging , Positron-Emission Tomography , MethodsABSTRACT
In the end of last century, there was a leap in the technological quality of radiotherapy, which is incarnated in three new technologies: Stereotactic radiation surgery (SRS), three-dimensional conformal radiation therapy (3D-CRT) and intensity modulated radiation therapy (IMRT). However, the achievement of these technologies has a close relationship with the precise orientation of tumour. Especially, in terms of body stereotactic precise radiation therapy, its body mechanical orientation system is the kernel to guarantee the accuracy of radiotherapy. This paper presents a novel mechanical orientation system for body precise radiotherapy. It is characterized by flexible adjustment, deft removal, easy disassembly and accurate orientation using apart structure to substitute old integer structure and adopting single segment Z shape orientation marker staff. The new mechanical orientation system guarantees the effect of tumour radiotherapy, which is worthy to be recommended for clinical use.
Subject(s)
Humans , Image Processing, Computer-Assisted , Methods , Magnetic Resonance Imaging , Methods , Phantoms, Imaging , Radiography, Interventional , Radiotherapy Planning, Computer-Assisted , Radiotherapy, Conformal , Radiotherapy, Intensity-Modulated , Stereotaxic Techniques , Tomography, X-Ray Computed , MethodsABSTRACT
As an advanced conformal therapy, Intensity-modulated radiotherapy (IMRT) can increase the gain ratio of radiotherapy and improve the local control of the tumor. This paper applies genetic algorithm to optimization of IMRT inverse planning. The algorithm model and simulation result are introduced.
ABSTRACT
Generally,pencil beam kernels for photon beam calculation are obtained through Monte Carlo calculations.In this paper,a pencil beam model is set up with a method of deconvolution from measured broad beam profiles.These profiles are usually available in a radiotherapy planning system.Furthermore,this method is applied to computing dose distributions at different sizes.Comparisons with measurements show that the accuracy of the calculated dose distributions fits well in a1%error interval in high dose gradient regions.