MVCT image texture enhancement method based on double regular constraints

Abstract

The invention discloses an MVCT image texture enhancement method based on double regular constraints, and mainly solves the problem that MVCT image enhancement cannot be carried out in the prior art.According to the scheme, the method comprises the following steps: 1) acquiring a plurality of KVCT and MVCT images from the same part of a human body; 2) normalizing the obtained CT image data set, and taking blocks from each pair of CT images to obtain a CT image block data set; 3) establishing a 13-layer MVCT image texture enhancement network, using the CT image block data set as training data,and optimizing the network by using a gradient descent algorithm to obtain a trained network; and 4) inputting a complete MVCT image into the trained network, and outputting the enhanced MVCT image.According to the invention, while the image texture is enhanced, the edge and details of the image can be well maintained, the image quality is improved, a doctor can conveniently read and diagnose the MVCT image, the focus position error is corrected, and the radiotherapy accuracy is ensured.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, in particular to a method for MVCT image texture enhancement, which can be used to improve the CT image quality and the visual effects of imaging organs and tissues. Background technique [0002] Megavoltage computed tomography (MVCT) and kilovolt computed tomography (KVCT) are two common forms of X-ray CT. Compared with KVCT, the voltage of the imaging tube of MVCT equipment is higher, which can find cancerous areas in tissues and organs, and is widely used in preoperative radiation therapy of tumors and cancers. However, MVCT is noisy and not suitable for tracking treatment. The current mainstream method is to use MVCT imaging before treatment, register with the KVCT image of the radiotherapy plan, and correct the error of the lesion position to ensure the accuracy of radiotherapy. As people pay more attention to CT radiation, the utilization rate of MVCT, which is less harmf...

AI Technical Summary

Problems solved by technology

[0003] At present, MVCT image enhancement mainly focuses on the direction of image denoising. There are two types of methods, one is based on projection domain, which includes bilateral filtering, static wavelet transform, maximum a posteriori probability estimation, etc. The signal dependence is strong, and the resolution of the image after denoising will be reduced to a certain extent; the other is the method based on neural network, which includes RED-CNN, denoising autoencoder, DnCNN, etc. After denoising, these methods There is no significant improvement in the visual effect and contrast of the image, and there will be soft tissue edge blurring and other phenomena

[0004] In addition, the biggest shortcoming of these two types of methods is: since the enhancement task of the MVCT image is only simplified to the denoising task, this simple denoising can neither greatly improve the image quality, but also lose the contrast of the MVCT image and details

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