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Residual error learning-based CT sparse reconstruction artifact correction method and system

A sparse reconstruction and correction method technology, applied in the field of medical image processing, to speed up the training process, reduce unnecessary injuries, and shorten the training time

Inactive Publication Date: 2018-04-03
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: Aiming at the problem of sparse angle CT reconstruction, a post-processing method of CT reconstruction artifacts using deep learning is proposed to improve the quality of CT reconstruction images

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  • Residual error learning-based CT sparse reconstruction artifact correction method and system
  • Residual error learning-based CT sparse reconstruction artifact correction method and system

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specific Embodiment

[0069] 2.1 Experimental data

[0070] The experimental data is a CT scan of the lungs provided by a hospital. The experimental equipment is Siemens SOMATOM Sensation 16-slice CT for clinical use, the exposure is 100mAs, the tube voltage is 120kVp, the distance between the radiation source and the detector array is 1040mm, and the distance from the radiation source to the center of rotation is 570mm. The reconstructed CT image size is 512 pixels × 512 pixels, and the size of each pixel is 1.2 mm × 1.2 mm.

[0071] Set different scan angles at intervals, obtain the original data of full projection (720 projections), 120 projections and 60 projections from CT scans, and use FBP method to reconstruct these data to obtain corresponding reconstructed images. The image reconstructed by the full projection is used as a complete image for comparison with the image reconstructed by the sparse projection. For a reconstructed image with a projection number, 2000 images of 512*512 are ra...

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Abstract

The invention discloses a residual error learning-based CT sparse reconstruction artifact correction method and system. The method comprises the steps of firstly, reconstructing sparse projection datagenerated by CT through an FBP (Filtered Back-Projection) method, wherein a image after reconstruction has a serious artifact; secondly, learning features of the artifact by establishing a residual error neural network structure to obtain an artifact image; and finally, recovering a clear CT image by performing residual error operation on the sparse reconstruction image and the artifact image. Based on sparse reconstruction, a residual error learning-based convolutional neural network structure framework is introduced; by adopting GPU acceleration, the training time is shortened, the trainingprocess of an experiment is accelerated, and very high CT reconstruction image quality can be realized at a very low CT projection angle; and therefore, the unnecessary injury of radiation to a humanbody is effectively reduced.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a CT sparse angle reconstruction and post-processing method. Background technique [0002] CT (Computed Tomography), that is, electronic computer tomography, it uses precisely collimated X-ray beams, γ-rays, ultrasound, etc., together with highly sensitive detectors, to conduct cross-sectional scans one after another around a certain part of the human body. It has the characteristics of fast scanning time and clear images, and can be used for the examination of various diseases; according to the different rays used, it can be divided into: X-ray CT (X-CT), ultrasonic CT (UCT) and γ-ray CT (γ-CT) )Wait. The problem of radiation dose has become a hot issue in the field of CT to be solved urgently. The current research mainly focuses on reducing the current and reducing the number of projections. Although the method of reducing the current reduces the amount of X-...

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Application Information

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IPC IPC(8): G06T11/00G06T5/00
CPCG06T11/008G06T2207/10081G06T5/80
Inventor 谢世朋
Owner NANJING UNIV OF POSTS & TELECOMM
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