Robust CBCT reconstruction method based on low-rank tensor decomposition and total variation regularization

Abstract

The invention provides a robust CBCT reconstruction method based on low-rank tensor decomposition and total variation regularization, and the method comprises the steps: employing a Huber loss function as a data fidelity term in CBCT reconstruction, so that impulse noise re-constructed under low radiation dose has robustness; with the low-rank tensor characteristic as a priori term so that characteristic is beneficial to recovery of structural information loss caused by impulse noise; integrating a 3D TV priori item to reduce the influence of Gaussian noise, so that a CBCT reconstruction modelis proposed; and solving an optimization problem through an alternating minimization method, and obtaining and outputting a reconstructed image.Under the state that Gaussian noise and impulse noise are mixed, the influence of the noise can be effectively eliminated, the edge can be well reserved, and a high-quality three-dimensional image can be reconstructed.

Description

technical field [0001] The invention belongs to the technical field of image reconstruction, mainly relates to a CBCT reconstruction method under mixed Gaussian pulse noise, and is widely applicable to many clinical applications. Background technique [0002] Cone beam computed tomography (CBCT) technology is a new type of computed tomography (CT) technology, which is based on a cone-shaped X-ray beam centered on a two-dimensional detector, which can rotate in different directions. Angle captures a series of two-dimensional projection images, and reconstructs the internal three-dimensional structure image of the object through a specific algorithm. Due to the characteristics of low cost, high resolution and short data acquisition time, CBCT technology plays an important role in numerous clinical applications. In order to reduce the impact of X-rays on patients, low radiation doses are recommended in most clinical applications. In this case, since the signal strength is rel...

AI Technical Summary

Problems solved by technology

However, real 2D projection images taken under low-dose conditions are sometimes contaminated by outliers such as readout noise (impulse noise)

At the same time, although the TV prior can effectively reduce Gaussian noise, it will introduce staircase artifacts

Also, when projection data is lost due to impulsive noise, it is not efficient to recover the lost data

Images