X-ray computed tomography energy spectrum estimation method based on convolutional neural network

Abstract

The invention provides an X-ray computed tomography energy spectrum estimation method based on a convolutional neural network. The method comprises the following steps: step 1, designing a convolutional neural network for describing a coupling relationship between a CT image and a photon distribution probability in an energy spectrum; step 2, training the convolutional neural network by using theconstructed training data set to obtain a trained convolutional neural network; and step 3, performing energy spectrum estimation on the CT image by using the trained convolutional neural network to obtain CT image energy spectrum information. The method is mainly used for estimating the X-ray energy spectrum information through the characteristics of the CT reconstructed image from the perspective of the image domain, does not need additional hardware or a special working process, can be directly applied to an existing CT scanning system, and is beneficial to improving the efficiency.

Description

technical field [0001] The invention relates to the technical field of CT imaging, in particular to a convolutional neural network-based X-ray computed tomography energy spectrum estimation method. Background technique [0002] X-ray computed tomography (Computed Tomography, CT) is a technology to reverse the attenuation distribution of an object from the X-ray projection, covering nuclear physics, mathematics, computers, precision instruments and other disciplines. This technology uses the X-ray transmission information of the object at different angles, and obtains the attenuation characteristic distribution of the object through the image reconstruction algorithm, so as to realize the three-dimensional internal structure of the object without loss. Since Hounsfield successfully developed the first CT, CT has been widely used in non-destructive testing, medical diagnosis, material analysis and other fields. Among them, the X-rays generated by the tube in conventional CT ha...

AI Technical Summary

Problems solved by technology

The model-based method mainly uses empirical formulas or semi-empirical physical models to generate energy spectra, but for specific different ray sources and different energy intervals, the adaptability of the model method is limited

Measurement-based methods mainly estimate the used ray energy spectrum by indirectly measuring phantom attenuation information, which is relatively simple and has been implemented in a wide range of X-ray CT systems, but it requires auxiliary hardware, such as wedge models, resulting in the entire CT System scans require the addition of dedicated workflows

[0004] In 2008, Zhang Peng and others attributed the indirect measurement of energy spectrum to an optimization problem with regularization terms, but the regularization parameters in the model need prior information, otherwise the estimation results will be large

However, this method requires additional scanning of the auxiliary phantom, which makes the process of energy spectrum estimation more complicated, which is not conducive to direct application to existing imaging systems

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