PET image reconstruction method based on non-local features and total variation joint constraint

Abstract

The invention discloses a PET image reconstruction method based on non-local features and total variation joint constraint. The method comprises the following steps: establishing a mathematical modelof a reconstruction problem, adding the low-rank constraint based on non-local features and adding the total variation constraint to perform PET image reconstruction. The low-rank constraint filters noises in the structural information of an image according to the non-local similarity feature of the image. Meanwhile, the defect that a step effect exists in the total variation method is overcome. The total variation serves as a constraint for edge protection and noise reduction based on pixel points. At the same time, the defect that the edge of an image exists on the basis of an image block isovercome. The convergence speed is accelerated. Therefore, the constraint joint solving means is effectively utilized, and the defects of two methods are overcome. The problems that the result is lowin resolution and the noise interference is generated in the PET image reconstruction process can be further solved. Compared with an experiment of an existing reconstruction method, the reconstruction method disclosed by the invention has the advantage that a good reconstruction effect can be obtained.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

This patented technology helps solve problems related to reconstructing high quality images from computed tomography (CT) data due to poor imagery caused by imperfections or variations during medical procedures such as CT scans. By adding lower rank terms along certain dimensions, we aim to ensure good accuracy when performing image restoration tasks while addressing issues like blurriness and streaks associated therewith. Additionally, this technique uses fully varying techniques instead of just one type of algorithm, which speeds up the overall process compared to traditional algorithms. Overall, it provides an improved way to accurately recover high-quality 3D images without generating unwanted artifacts.

Problems solved by technology

This patents describes two technical means: 1) Computational Fluid Dynamics (CFD)-based techniques such as X-ray computed tomography or magnetic resonance spectroscopy (MRS). These techniques allow us to study complex systems like living cells at atomic resolutions through their ability to measure various chemical species involved in cellular functions. 2) Quantitative detection of specific molecules within these structures provides valuable insights into disease progresses and treatment options.

Images