CN109272472A - Noise and artifact eliminating method towards medical power spectrum CT image - Google Patents
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Abstract
The present invention relates to computed tomography fields, influence brought by beam hardening, random noise to reduce, export more accurate medical image, reduce injury suffered by human body.For this purpose, of the invention, noise and artifact eliminating method towards medical power spectrum CT image, steps are as follows: Step1: building Dummy mould；Step2: power spectrum CT imaging is simulated by body mould with a large amount of x-ray photons；Step3: power spectrum CT imaging is simulated by body mould with relatively low-dose x-ray photon；Step4: reconstruction image is matched；Step5: training convolutional neural networks complete training；Step6: test network training effect.Present invention is mainly applied to the design and manufacture occasions of medical CT equipment.
Description
Technical field
The present invention relates to the fields computed tomography (Computed Tomography), for power spectrum CT different-energy
The lower problem of signal-to-noise ratio caused by the number of photons collected in section is less, using convolutional neural networks model to each energy section
Reconstruction image with noise and artifact is handled, and reduces dose of radiation while improving the precision of medical image.Specifically,
It is related to noise and artifact eliminating method towards medical power spectrum CT image.
Background technique
The key of Medical CT imaging is broadly divided into two o'clock: first is that the precision of images is promoted, second is that reducing dose of radiation.With monochromatic light
Sub-count type detector and energy product parting detector are that the multi-power spectrum CT of representative is capable of providing more accurately image information, are improved
The precision of medical imaging, and the much narrower of energy interval division will can be imaged closer to single, can preferably eliminate and penetrate
Beam hardening artifact provides more fully human body information, increases advantage to improve the accuracy of diagnosis.But it is reducing
Under conditions of overall radiation dosage, the number of photons that each energy section is covered is reduced, and causes the noise of image relatively low, is generated
Low dosage noise and artifact, it is difficult to meet the accuracy requirement of medical image.
In general, X-ray interacted with matter energy during include complicated energetic interaction mechanism, and charge-trapping
Generate and collect the event that process is randomization.Single-photon counting module is to the more demanding of count rate, energy product parting
Detector layer-to-layer signal transfer is serious, they are difficult to pass through tradition under the conditions of number of photons is insufficient for image bring noise and artifact
Method is eliminated.Recently as the development of machine learning techniques, convolutional neural networks show big advantage, by power spectrum
Each energy section reconstruction image of CT is handled by convolutional neural networks, in the actual clinical needs for meeting high-precision power spectrum CT
While further decrease the dose of radiation of X-ray.
Summary of the invention
In order to overcome the deficiencies of the prior art, the problem of being directed to low dosage noise present in power spectrum CT imaging and artifact,
The present invention is directed to propose a kind of method of the medical X-ray power spectrum CT image procossing based on convolutional neural networks, by primary low
The X-ray scanning of dosage, by preparatory trained convolutional neural networks model to the figure of each energy section Noise and artifact
As being handled, reduction becomes with the reconstruction image with phase homogenous quantities under the conditions of high dose x-ray bombardment, can reduce beam
It is influenced brought by hardening, random noise, exports more accurate medical image, reduce injury suffered by human body.For this purpose, this
Invention adopts the technical scheme that noise and artifact eliminating method towards medical power spectrum CT image, steps are as follows:
Step1: building Dummy mould imitates human body constituent and builds body mould, simulates bone, blood, fat, soft tissue
Institutional framework and including liver, lung, mammary gland vitals；
Step2: simulating power spectrum CT imaging by body mould with a large amount of x-ray photons, selects an individual mould, uses clinical criteria
The X-ray of dosage is irradiated, using software emulation detector physical process and carry out image reconstruction, obtain different-energy area
Between medical image, replace body mould, repeat the above process, obtain label image of the data set as neural network；
Step3: power spectrum CT imaging is simulated by body mould with relatively low-dose x-ray photon, with the X-ray weight of relatively low-dose
Detection and reconstruction process in multiple Step2, obtain input picture of the data set as neural network；
Step4: reconstruction image is matched.It will be one group with image setting obtained by Integral mold different-energy section, with
Just convolutional neural networks can learn to the information that all can be composed, and by various dose X-ray by same Integral mold the institute in Step2
It obtains label image to be matched with input picture obtained in Step3, makes its one-to-one correspondence, repeat the above process, until completing
The matching of reconstruction image under the conditions of all body moulds；
Step5: training convolutional neural networks.After defining a suitable convolutional neural networks structure, Step4 is randomly choosed
The N group input picture of middle arrangement is trained neural network with corresponding N group label image, by back-propagation algorithm until
Loss function converges to minimum, completes training；
Step6: test network training effect is instructed using Step5 is removed in data set matched in Step4 for neural network
Input picture is inputted network as test data set by the remainder outside experienced image, is examined using image quality evaluation standard
The effect of network training is looked into, if qualified, network training is completed, if unqualified, repeatedly Step5 and Step6.
A large amount of x-ray photons are Standard Ratio dosage used in clinically normal CT scan, and relatively low-dose is in Step2
The a quarter of used number of photons.
The features of the present invention and beneficial effect are:
The invention proposes a kind of noises and artifact eliminating method towards medical power spectrum CT image.Pass through convolutional Neural net
Network model handles the low signal-to-noise ratio (SNR) images in power spectrum CT different-energy section, improves while reducing X-ray radiation dosage
Picture quality provides advantage for accurate medical treatment.Trained neural network has faster processing speed simultaneously, compared to
The saving such as iterative reconstruction algorithm many times, improve the efficiency of image reconstruction.
Detailed description of the invention:
A kind of removing method frame diagram of noise and artifact towards medical X-ray energy spectrum CT image of Fig. 1.
The preparation method flow chart of Fig. 2 neural network model.
Specific embodiment
The problem of being directed to low dosage noise present in above-mentioned power spectrum CT imaging and artifact, the invention proposes a kind of bases
In the method for the medical X-ray power spectrum CT image procossing of convolutional neural networks, as shown in Figure 1.By the X-ray of a low dosage
Scanning is handled the image of each energy section Noise and artifact by preparatory trained convolutional neural networks model,
Reduction becomes with the reconstruction image with high dose x-ray bombardment phase homogenous quantities, can reduce beam hardening, random noise institute band
The influence come exports more accurate medical image, reduces injury suffered by human body.
The invention proposes a kind of noises and artifact eliminating method towards medical power spectrum CT image, pass through power spectrum CT first
The image in resulting different-energy section is rebuild under the conditions of system acquisition low dose X-ray, it is then logical as input data
Preparatory trained convolutional neural networks model is crossed, obtains having identical as resulting image is rebuild under high dose X-ray
The image for being suitable for the precisely different-energy section of medical treatment of quality.Convolutional neural networks model is by simulating low dosage X respectively
Obtained by the image training in the different-energy section reconstructed under ray and high dose X-ray as same Integral mold, this mistake
Journey frame diagram as shown in Figure 1, the specific preparation method flow chart of convolutional neural networks model as shown in Fig. 2, specific embodiment
It is as follows:
Step1: building Dummy mould.It imitates human body constituent and builds body mould, it should simulation bone, blood as far as possible
The vitals such as the institutional frameworks such as liquid, fat, soft tissue and liver, lung, mammary gland.
Step2: power spectrum CT imaging is simulated by body mould with a large amount of x-ray photons.The individual mould of selection one, uses clinical criteria
The X-ray of dosage is irradiated, using software emulation detector physical process and carry out image reconstruction, obtain different-energy area
Between medical image.Body mould is replaced, repeats the above process, obtains label image of the data set as neural network.
Step3: power spectrum CT imaging is simulated by body mould with a small amount of x-ray photon.(about with the X-ray of relatively low-dose
The a quarter of number of photons used in Step2) repeat Step2 in detection and reconstruction process, obtain data set as mind
Input picture through network.
Step4: reconstruction image is matched.It will be one group with image setting obtained by Integral mold different-energy section, with
Just convolutional neural networks can learn to the information that all can be composed, and by various dose X-ray by same Integral mold the institute in Step2
It obtains label image to be matched with input picture obtained in Step3, makes its one-to-one correspondence.It repeats the above process, until completing
The matching of reconstruction image under the conditions of all body moulds.
Step5: training convolutional neural networks.After defining a suitable convolutional neural networks structure, Step4 is randomly choosed
The N group input picture of middle arrangement is trained neural network with corresponding N group label image, by back-propagation algorithm until
Loss function converges to minimum, completes training.
Step6: test network training effect.It is instructed using Step5 is removed in data set matched in Step4 for neural network
Input picture is inputted network as test data set by the remainder outside experienced image, is examined using image quality evaluation standard
The effect of network training is looked into, if qualified, network training is completed, if unqualified, repeatedly Step5 and Step6.
High dose X-ray mentioned by the present invention is clinically Standard Ratio dosage used in normal CT scan, with sweeping
It retouches the difference of body positions and changes, low dose X-ray dosage is usually a quarter of standard dose.
The volume that can restore the image with noise and artifact that low dose X-ray reconstructs is generated in above-mentioned steps
Under conditions of product neural network model, so that it may for being clinically directed to the processing of low dosage power spectrum CT image.By power spectrum CT at
Image as system acquisition low dosage different-energy section with noise and artifact inputs in convolutional neural networks model, defeated
Out with the medical image of the reconstruction image equal quality in high dose different-energy section.
The present invention is further illustrated below by example, but does not therefore limit the invention to the example ranges
Interior, those of ordinary skill in the art conceive according to the present invention, and the simple change made, should be claimed in the present invention
In range.Below in conjunction with attached drawing, it is described as follows:
Used body mould should meet human body reality when present invention training neural network model, with human body vitals height
It is similar.The X-ray source of proposed adoption is generated by simulation GE Maxiray125, and wherein peak tube voltage and electric current are respectively set to
EMax=120keV, 0.5mAs and EMax=120keV, 2mAs simulates low dose X-ray and high dose X-ray, Qi Tacan respectively
Number is default setting.By layer-stepping energy product parting detector, it is grouped the charge number in each energy section of adding up, carries out energy
Reconstruction image after parsing forms the data set of neural network.
N group image in data set is randomly selected as training set, using low dosage different-energy section reconstruction image as
Input picture, high dose different-energy section reconstruction image are inputted in convolutional neural networks and are trained, directly as label image
Minimum is converged to loss function, completes the training of network.Non-training collection part is used as test set in data set, by input picture
Network is inputted, output image is evaluated with indexs such as PSNR (Y-PSNR), CNR (comparison noise ratio), if reaching institute
Required standard then completes the training of convolutional neural networks, if not up to standard, chooses N group image again and is trained, adjust
Whole network structure and its parameter, until output image meets the requirements.After the training for completing neural network model, it can be used for low dosage
The processing for the medical image with noise and artifact rebuild under X-ray.
Claims (2)
1. a kind of noise and artifact eliminating method towards medical power spectrum CT image, characterized in that steps are as follows:
Step1: building Dummy mould imitates human body constituent and builds body mould, the group of simulation bone, blood, fat, soft tissue
Knit structure and including liver, lung, mammary gland vitals；
Step2: simulating power spectrum CT imaging by body mould with a large amount of x-ray photons, an individual mould is selected, with clinical criteria dosage
X-ray be irradiated, using software emulation detector physical process and carry out image reconstruction, obtain different-energy section
Medical image replaces body mould, repeats the above process, obtain label image of the data set as neural network；
Step3: power spectrum CT imaging is simulated by body mould with relatively low-dose x-ray photon, is repeated with the X-ray of relatively low-dose
Detection and reconstruction process in Step2, obtain input picture of the data set as neural network；
Step4: reconstruction image is matched.It will be one group with image setting obtained by Integral mold different-energy section, to roll up
Product neural network can learn to the information that all can be composed, and by various dose X-ray as same Integral mold in Step2 gained mark
Label image is matched with input picture obtained in Step3, is made its one-to-one correspondence, is repeated the above process, all until completing
The matching of reconstruction image under the conditions of body mould；
Step5: training convolutional neural networks.After defining a suitable convolutional neural networks structure, randomly choose whole in Step4
The N group input picture of reason is trained neural network with corresponding N group label image, by back-propagation algorithm until loss
Function convergence completes training to minimum；
Step6: test network training effect is used for neural metwork training using Step5 is removed in data set matched in Step4
Input picture is inputted network as test data set by the remainder outside image, checks net using image quality evaluation standard
The effect of network training, if qualified, network training is completed, if unqualified, repeatedly Step5 and Step6.
2. noise and artifact eliminating method as described in claim 1 towards medical power spectrum CT image, characterized in that a large amount of X
Ray photons are Standard Ratio dosage used in clinically normal CT scan, and relatively low-dose is number of photons used in Step2
A quarter.
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| CN110246199A (en) * | 2019-05-26 | 2019-09-17 | 天津大学 | A kind of projection domain data noise minimizing technology towards power spectrum CT |
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| CN110728727B (en) * | 2019-09-03 | 2023-04-18 | 天津大学 | Low-dose energy spectrum CT projection data recovery method |
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| CN110742635A (en) * | 2019-10-08 | 2020-02-04 | 南京安科医疗科技有限公司 | Composite energy spectrum CT imaging method |
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| CN113689359B (en) * | 2021-09-23 | 2024-05-14 | 上海联影医疗科技股份有限公司 | Image artifact removal model and training method and system thereof |
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