Four-dimensional cone beam CT reconstruction image enhancement algorithm based on N-net and CycN-net network structures

A technology for reconstructing images and network structures, which is applied in the field of X-ray imaging and can solve problems such as loss of detailed information and false artifact structures.

Active Publication Date: 2021-05-18
XI AN JIAOTONG UNIV
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Problems solved by technology

Although U-net has been successfully applied in many natural image restoration tasks, there are still limitations when facing 4D-CBCT image restoration tasks
A large number of e

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  • Four-dimensional cone beam CT reconstruction image enhancement algorithm based on N-net and CycN-net network structures
  • Four-dimensional cone beam CT reconstruction image enhancement algorithm based on N-net and CycN-net network structures
  • Four-dimensional cone beam CT reconstruction image enhancement algorithm based on N-net and CycN-net network structures

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Embodiment

[0044] see Figure 9 , the present invention comprises the following steps based on a deep neural network:

[0045] 1. Construct the training data set

[0046] The ground-truth images in network training come from the 4D-CBCT reconstruction challenge dataset organized publicly by AAPM in 2019. Seventeen sets of high-quality 4D-CT images were selected, and each set of data contained reconstructed CT volume data in 10 respiratory phases. The dimension of 4D-C data in each respiratory phase is 512×512×210, and the voxel size is 1×1×1mm 3 . In practical situations, due to the limitations of scanning hardware and dose, it is difficult to obtain the true image of the 4D-CBCT reconstructed image sequence. As mentioned in the introductory chapter, before the patient undergoes radiation therapy, 4D-CT imaging is required and the radiation therapy plan is designed based on the image. 4D-CT can obtain a set of high-quality reconstructed image sequences due to its fast scanning speed...

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Abstract

The invention discloses a four-dimensional cone beam CT (Computed Tomography) reconstructed image enhancement algorithm based on N-net and CycN-net network structures. The method is designed on the basis of fully mining inherent characteristics of four-dimensional cone beam CT reconstructed images, the deep neural network CycN-net takes analysis reconstructed images of five continuous motion phases and corresponding prior images as network input, and takes prior knowledge in four-dimensional cone beam CT image sequences and space-time correlation among the image sequences into consideration; the extracted convolutional feature maps are stitched and fused to serve image restoration. The deep neural network CycN-net in the invention has excellent performance for recovering the four-dimensional cone beam CT image sequence with high temporal-spatial resolution, and can recover and maintain detail structure information in the reconstructed image to the greatest extent while effectively inhibiting serious bar artifacts.

Description

technical field [0001] The invention belongs to the technical field of X-ray imaging, in particular to a four-dimensional cone-beam CT reconstruction image enhancement algorithm based on N-net and CycN-net network structures. Background technique [0002] Radiation therapy is one of the main means of treating malignant tumors (commonly known as cancer). Cone Beam Computed Tomography (CBCT) is widely used in the field of radiation therapy, especially in Image Guided Radiation Therapy (IGRT). The main function of cone beam CT is to position the patient and locate the treatment target according to the reconstruction results, and at the same time provide real-time update of patient information for the radiotherapy process. Generally, the detectors of cone-beam CT in this field have high spatial resolution, which is very convenient for positioning the head, but it faces great challenges for lung imaging. The imaging time of cone beam CT is 1 minute to 4 minutes per week. During...

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Application Information

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IPC IPC(8): G06T11/00G06T5/50G06T5/00G06T3/40G06N3/04G06N3/08
CPCG06T11/005G06T11/008G06T5/001G06T5/50G06T3/4038G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/20221G06T2207/30096G06T2207/30061G06N3/045
Inventor 牟轩沁职少华
Owner XI AN JIAOTONG UNIV
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