CN104700377A - Method and device for acquiring beam hardening correction parameters for performing beam hardening correction on computer tomography data - Google Patents
Method and device for acquiring beam hardening correction parameters for performing beam hardening correction on computer tomography data Download PDFInfo
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Abstract
The invention provides a method and device for acquiring beam hardening correction parameters for performing beam hardening correction on computer tomography data. The method includes firstly according to an object of the specific size, acquiring original reconstructed images and original sinograms; acquiring error-reduced sinograms by reducing the errors of the original reconstructed images; sampling and calculating the average value of the original sinograms and the average value of the error-reduced sinograms; optimizing the original sinograms according to the original sinograms to determine the parameter vectors of the optimization function of the object of the specific size; finally fitting the parameter vectors of the optimization function of the original sinograms, and acquiring the beam hardening correction parameters of the object of the specific size.
Description
Technical field
Present invention relates in general to computed tomography (Computed Tomography, CT), relate more particularly to the method and apparatus obtaining beam hardening correction coefficient computed tomography data being carried out to beam hardening correction.
Background technology
Auxiliary diagnostic equipment comprises magnetic resonance (Magnetic Resonance, MR) system, ultrasonic system, calculating x-ray tomography system, positron emission tomography X (PET) system, core medical treatment and the imaging system of other types.
Such as, carry out in CT x-ray imaging in employing CT system to patient, X ray is used for carrying out imaging to the inner structure of patient body and the feature of region of interest (ROI).This imaging is completed by CT scanner.During operation, photography target being scanned and collects raw data, pre-service being carried out to raw data, then reconstructed image, for improving picture quality, also carrying out aftertreatment.
Due to the spectral correlations of the ray attenuation performance of real-world object, will observe when the X ray of polychrome and offset to higher energy value by by the average energy penetrating the X ray that object transmits.This effect is called as " beam hardening ".In the reconstructed image of object by observable relative to the gray-scale value skew of theoretical case linearly, with the ray attenuation of spectral correlation.This makes the image disruption after reconstruct to the correct judgement of image especially by gray-scale value skew---or beam hardening virtual image---had in reconstructed image that high nuclear charge number and highdensity material (such as bone) cause, and may cause being the doctor that checks in the worst case to the explanation of error of image.
In pre-service, carry out beam hardening correction and at least can partly get rid of this virtual image.Some existing beam hardening technology have shown the homogeneity of improvement for centering scanning, but for eccentric scanning, image also present band artifacts.
Summary of the invention
One embodiment of the present of invention provide a kind of acquisition computed tomography data to be carried out to the method for the beam hardening correction coefficient of beam hardening correction.The method comprises the steps:, first for the object of specific dimensions, to obtain its original reconstructed image and initial sinusoids figure; Then original reconstructed image is carried out obtaining error after error reduces process and reduce sinogram; Then sample and calculate the mean value of initial sinusoids figure and the mean value of error minimizing sinogram; Then reduce sinogram according to error to be optimized initial sinusoids figure, to determine the coefficient vector of the majorized function of the object for specific dimensions; Finally matching is carried out to the coefficient vector of the majorized function of initial sinusoids figure, to obtain the beam hardening correction coefficient of the object for specific dimensions.
A kind of device obtaining beam hardening correction coefficient computed tomography data being carried out to beam hardening correction of an alternative embodiment of the invention.This device comprises acquisition device, error reduces device, equilibration device, optimization device and matching device.Wherein, acquisition device, for the object of specific dimensions, obtains its original reconstructed image and initial sinusoids figure; Error reduces device and carries out obtaining error minimizing sinogram after error reduces process to original reconstructed image; Equilibration device is sampled and is calculated the mean value of initial sinusoids figure and the mean value of error minimizing sinogram; Optimization device reduces sinogram according to error and is optimized initial sinusoids figure, to determine the coefficient vector of the majorized function of the object for specific dimensions; Matching device carries out matching to the coefficient vector of the majorized function of initial sinusoids figure, to obtain the beam hardening correction coefficient of the object for specific dimensions.
A kind of method of computed tomography data being carried out to beam hardening correction, it is characterized in that, adopt the beam hardening correction coefficient obtained as the method in claim 1-6 as described in any one to carry out beam hardening correction to the computed tomography data of other object of specific dimensions.
Of the present inventionly provide a kind of device computed tomography data being carried out to beam hardening correction an embodiment, it comprises the device and the correction calculation device that obtain as above and computed tomography data is carried out to the beam hardening correction coefficient of beam hardening correction, and this correction calculation device utilizes described beam hardening correction coefficient to carry out beam hardening correction to the computed tomography data of other object of specific dimensions.
The fourth embodiment of the present invention provides a kind of ct apparatus, and it comprises scanister and processor.Wherein scanister is used for utilizing X ray to scan to obtain raw data to object, to generate original reconstructed image; Processor can operate and be couple to described scanister, and able to programme to realize: for the object of specific dimensions, obtain its original reconstructed image and initial sinusoids figure; Original reconstructed image is carried out obtaining error after error reduces process and reduces sinogram; Sample and calculate the mean value of initial sinusoids figure and the mean value of error minimizing sinogram; Reduce sinogram according to error to be optimized initial sinusoids figure, to determine the coefficient vector of the majorized function of the object for specific dimensions; Matching is carried out to the coefficient vector of the majorized function of initial sinusoids figure, to obtain the beam hardening correction coefficient of the object for specific dimensions.
The fifth embodiment of the present invention provides a kind of computer program, comprises the instruction be stored on nonvolatile recording medium, when this instruction performs within a processor, implements the step of the method disclosed in the embodiment of the present invention.
The sixth embodiment of the present invention, provides a kind of non-volatile memory medium, which stores the instruction of the step implementing the method disclosed in the embodiment of the present invention when performing within a processor.
Accompanying drawing explanation
In order to more thoroughly understand content of the present disclosure, below with reference to following description carried out by reference to the accompanying drawings, in the accompanying drawings:
Fig. 1 is the structural map according to CT imaging system of the present disclosure;
Fig. 2 is the schematic block diagram of the system shown in Fig. 1;
Fig. 3 is the processing flow chart of the beam hardening correction according to disclosure embodiment;
Fig. 4 is the method schematic diagram according to disclosure embodiment deckle circle view really;
Fig. 5 is an image of the water mould reconstructed after adopting existing beam hardening correction;
Fig. 6 is another image of the water mould reconstructed after adopting existing beam hardening correction;
Fig. 7 is an image of the water mould reconstructed after adopting the beam hardening correction according to disclosure embodiment;
Fig. 8 is another image of the water mould reconstructed after adopting the beam hardening correction according to disclosure embodiment;
Fig. 9 is an image of the head reconstructed after adopting existing beam hardening correction;
Figure 10 is an image of the head mould reconstructed after adopting the beam hardening correction according to disclosure embodiment;
Figure 11 is the block diagram of the device for obtaining beam hardening correction coefficient according to disclosure embodiment.
Figure 12 is the block diagram of the device for beam hardening correction according to disclosure embodiment.
Embodiment
In the following detailed description, with reference to the accompanying drawing as its part, wherein diagrammatically show and wherein can realize specific embodiment of the present disclosure.With enough details, these embodiments are described, those skilled in the art are made to realize the disclosure, and should be appreciated that when not departing from the scope of each embodiment of the disclosure, can combine embodiment, or other embodiments can be utilized and can make structure, logic and electrically on change.Therefore, detailed description below should not be considered restrictive, and should be illustrative.Scope of the present invention is by the claims of enclosing and equivalents thereof.
With reference to Fig. 1 and 2, CT (computer tomography) (CT) imaging system 10 is depicted as and comprises scanning support 12.In a non-restrictive example, system 10 comprises " third generation " CT scanner.Scanning support 12 has x-ray source 14, and X-ray beam 16 is incident upon on the opposition side of scanning support 12 towards detector set piece installing 18 by it.Detector set piece installing 18 is formed by multiple detecting device 20 and data-acquisition system (DAS) 32.Described multiple detecting device 20 senses the X ray of the projection through medical patient 22, and wherein each detecting device 20 produces analog electrical signal, and it represents the intensity of impinging x-ray beam and the attenuated beam thus when it passes through patient 22.Detecting device 20 generally include for make the collimating apparatus of the X-ray beam collimation received at detecting device, contiguous collimating apparatus for X ray is converted to luminous energy scintillator (scintillator) and for receiving the luminous energy coming from contiguous scintillator and the photodiode producing electric signal from it.Usually, X ray is converted to luminous energy by each scintillator of scintillator arrays.Luminous energy is released into the photodiode being close to it by each scintillator.Each photodiode detects luminous energy and each detecting device 20 generating corresponding electrical signal detection device array 18 produces independent electric signal, and it is decay of radiation beam that the intensity and therefore may be used for that this electric signal represents impact radiation bundle (such as X-ray beam) to be estimated at radiation beam through object or patient 22.
Obtaining the scan period of X-ray projection data, scanning support 12 and the assembly that it is installed rotate around rotation center 24.The rotation of scanning support 12 and the operation of x-ray source 14 carry out management and control by the control gear 26 of CT system 10.Control gear 26 comprises X-ray controller 28, and it provides electric power and timing signal to x-ray source 14 and gantry motor controller 30, the rotational speed of this gantry motor controller 30 gantry 12 and position.Data acquisition system 32 sampling in control gear 26 carrys out the simulated data of self-detector 20 and these data is converted to digital signals for subsequent process.DAS32 exports the projected dataset being included in the attenuation measurement that the particular chassis anglec of rotation (such as visual angle) obtains.When scanning support 12 rotates, multiple view can be obtained during single rotation.Single rotation is 360 degree of rotations that of scanning support 12 is complete.Each view has corresponding visual angle, and the ad-hoc location on scanning support 12.
The image applications of reconstruct is the input to computing machine 36, and this computing machine 36 stores the image in high-capacity storage 38.
Computing machine 36 also receives order from operator and sweep parameter through operator's control desk 40, and operator's control desk 40 has the operator interface of certain form, the controller of such as keyboard, mouse, voice activation or any other input equipment be applicable to.The display 42 of association allows operator to observe the image of other data from computing machine 36 and reconstruct.The order that operator provides and parameter can by computing machine 36 for providing control signal and information to DAS32, X-ray controller 28 and gantry motor controller 30.In addition, computing machine 36 operator's console electric machine controller 44, it controls vehicularized 46 with patient 22 and scanning support 12.Especially, patient 22 is moved through the frame openings 48 of Fig. 1 by platform 46 in whole or in part.
In one embodiment, computing machine 36 comprises equipment 50, such as, floppy disk, CD-ROM drive, DVD driver, magneto-optic disk (MOD) equipment, or comprise any other digital device of network access device of such as ethernet device, for from computer-readable medium 52 reading command and/or data, another digital source of described computer-readable medium 52 such as floppy disk, CD-ROM, DVD or such as network or internet, and the digital device that will develop.In another embodiment, computing machine 36 performs the instruction be stored in firmware (not shown).In some configurations, computing machine 36 and/or image reconstructor 34 are programmed to perform function described herein.
Fig. 3 is the processing flow chart of the beam hardening correction according to disclosure embodiment.We adopt the water mould of all size to calculate beam hardening correction coefficient.But the invention is not restricted to only adopt water mould and be applicable to any mould of employing.The scan vision (Scan Field ofView, SFOV) of the corresponding various sizes that may scan of water mould of all size.Suppose the water mould that we have selected N kind and vary in size.
For the water mould of some sizes, first, in step 302, obtain original reconstructed image I
origwith initial sinusoids figure I
origsin, this original reconstructed image I
origwith initial sinusoids figure I
origsincan be input after the data for projection of DAS32 is reconstructed in image reconstructor 34, also can obtain from high-capacity storage 38, alternatively, also can obtain from computing machine 36.
In step 304, orthogonal projection is carried out to original reconstructed image, wherein need that error is carried out to projection value and reduce process, obtain error and reduce sinogram I
unifsin.Error reduces the mode of process such as, but be not limited to: by view, for each detecting device, the CT value of the pixel passed by the X ray detected by it is sued for peace, then divided by the number of pixel, the set of the value obtained is designated as P ', and the set finding out projection value corresponding with it in initial sinusoids figure is designated as P, adopts following formulae discovery:
Wherein a is fixing coefficient, such as, be the value that system defines water mould, obtains error thus and reduces sinogram I
unifsin.
Then, in step 306, determine border view.At initial sinusoids figure I
origsinin, find out the view Orig_View1 that detector channel is crossing with water mould first from top to bottom, such as the 400th view, find out the view Orig_View2 that on another direction, detector channel is crossing with water mould first from bottom to up simultaneously, such as the 200th view, as shown in Figure 4, horizontal ordinate represents view, and ordinate represents the passage of detecting device.Line is first horizontal line crossing with initial sinusoids figure from above above, the horizontal ordinate that intersection point is corresponding represents the 400th view, line is first horizontal line crossing with initial sinusoids figure from below below, and the horizontal ordinate that intersection point is corresponding represents the 200th view.Equally, sinogram I is reduced in error
unifsinin find out the view Unif_View1 (such as 400th view) corresponding with Orig_View1 (such as the 400th view) and Orig_View2 (such as the 200th view) and Unif_View2 (such as the 200th view).
Next in step 308, the average sine value of computation bound view.Need to reduce sinogram to initial sinusoids figure and error to calculate respectively.First to sample, in initial sinusoids figure, the orthogonal projection value of two border views and several views of front and back thereof be averaged together.Such as Orig_View1, find out 20 views before and after it, to Orig_View2, find out 20 views before and after it, the mean value calculating the orthogonal projection value of these 42 views altogether obtains average view Orig_Aver, sinogram is reduced to error and carries out same operation, for Unif_View1, find out 20 views before and after it, for Unif_View2, also find out 20 views before and after it, the mean value calculating the orthogonal projection value of these 42 views altogether obtains average view Unif_Aver.Wherein, average view number can be understood as parameter, and it depends on the size of noise, can experimentally set, and can be 10,20 or 40, but the number of preferably getting in two sinograms is equal.
Then in step 310, with average view for initial sinusoids figure I optimized by sample
origsin, make initial sinusoids figure I
origsinpress close to error as far as possible and reduce sinogram I
unifsin.The mode optimized illustrates with reference to following formula:
Wherein, Q and Q ' is Orig_Aver and Unif_Aver respectively, we with
as desirable orthogonal projection value, wherein a is fixing coefficient, the value that to be such as system define water mould as target, by one group of basis function B
j(j=1,2 ... n, n are the number of adopted basis function, also artificially determine by experiment) original orthogonal projection value P is optimized, i=1,2, ... m, m are self-defining top step number, and the object of this step determines the coefficient vector b of majorized function.
For the water mould of different size, need repetition step 302-310, thus determine N number of coefficient vector b of the water mould varied in size for N kind.
In step 312, carry out matching facing to N number of coefficient vector b, to obtain beam hardening correction coefficient c.Matching such as can adopt formula as follows:
Wherein h=1,2 ... h, h are self-defining top step number, can be identical from m or different.
After this, beam hardening correction coefficient vector c just can be utilized to carry out beam hardening correction to the computed tomography data of other object of different size.In the formula corrected and above-mentioned matching, the formula mentioned is similar, the data for projection P after correction
newfor:
It should be noted that as a system, need that there is the ability of the object of all size being carried out to beam hardening correction.But with single object, from step 302 to 312, only carry out one time, just can obtain the beam hardening correction coefficient for this single object.
Fig. 5 is an image of the water mould reconstructed after adopting existing beam hardening correction, and this scans at eccentric 5.6mm the result obtained, and can find out an existence black circle between water mould and border, this undesirably occurs.Fig. 6 is another image of the water mould reconstructed after adopting existing beam hardening correction, and this scans at eccentric 5cm the result obtained, and can find out, along with the increase of eccentric distance, problem is more serious, and the band artifacts that existence one is wider in the picture, this obviously also undesirably occurs.Fig. 7 is an image of the water mould reconstructed after adopting the beam hardening correction according to disclosure embodiment, and obtain in eccentric 5.6mm scanning equally, black circle obviously disappears.Fig. 8 is another image of the water mould reconstructed after adopting the beam hardening correction according to disclosure embodiment, and this obtains in eccentric 5cm scanning, and band artifacts disappears, and obtain relatively uniform image, this is desired, realistic image.Refer now to the result of in practice patients head being carried out to imaging.Fig. 9 is an image of the head reconstructed after adopting existing beam hardening correction, and be obtain in 5cm eccentric scanning situation, can find out, the image in skull exists band artifacts, and this can affect the diagnosis of doctor.Figure 10 is an image of the head mould reconstructed after adopting the beam hardening correction according to disclosure embodiment, and be obtain in 5cm eccentric scanning situation equally, can find out, the band artifacts problem of the image in skull is obviously alleviated.In fact, adopt method of the present invention, can eliminate or alleviate on issuable when eccentric scanning slightly " black circle ", and when obvious eccentric scanning issuable band artifacts.
Figure 11 is the block diagram of the device for obtaining beam hardening correction coefficient according to disclosure embodiment.Device 1100 wherein for obtaining beam hardening correction coefficient comprises: acquisition device 1101, error reduce device 1102, equilibration device 1103, optimization device 1104 and matching device 1105.Wherein acquisition device 1101 at least reduces device 1102, equilibration device 1103 and optimization device 1104 and couples mutually with error, error reduces device 1102 and at least couples mutually with equilibration device 1103 and optimization device 1104, and optimization device 1104 also is at least wanted to couple with equilibration device 1103 and matching device 1105.In fig. 11, in order to illustrate conveniently, each device is coupled mutually.It is noted, however, that each device can couple mutually with any other connected mode, as long as each function as described below can be realized.Further, the function of multiple device may be incorporated in a device and realizes, and each device also can become more device to realize by Further Division, and same device quantity in systems in which can be greater than 1.
Acquisition device 1101 is mainly used in obtaining original reconstructed image and initial sinusoids figure.Error reduces device 1102 and is mainly used in the sinogram obtaining relative ideal.Equilibration device 1103 is mainly used in calculating the average orthogonal projection value of the more neighbouring views of border view of water mould.Optimization device 1104 is mainly used in the basis function coefficient carrying out calculation optimization according to the result of equilibration device 1103 and initial sinusoids figure.Matching device 1105 is mainly used in carrying out matching beam hardening correction coefficient based on the result of optimization device more than 1104 time.
Suppose the water mould that we have selected N kind and vary in size.For the water mould of some sizes, first, acquisition device 1101 obtains original reconstructed image I
origwith initial sinusoids figure I
origsin, this original reconstructed image I
origwith initial sinusoids figure I
origsincan be input after the data for projection of DAS32 is reconstructed in image reconstructor 34, also can obtain from high-capacity storage 38, alternatively, also can obtain from computing machine 36.
Then error reduces device 1102 pairs of original reconstructed image and carries out orthogonal projection, wherein needs to carry out error to projection value and reduces process, obtain error and reduce sinogram I
unifsin.Error reduces the mode of process such as, but be not limited to: by view, for each detecting device, the CT value of the pixel passed by the X ray detected by it is sued for peace, then divided by the number of pixel, the set of the value obtained is designated as P ', and the set finding out projection value corresponding with it in initial sinusoids figure is designated as P, adopts following formulae discovery:
Wherein a is fixing coefficient, such as, be the value that system defines water mould, obtains error thus and reduces sinogram I
unifsin.
Then, equilibration device 1103 determines border view by its border determining device 11031.At initial sinusoids figure I
orirsinin, find out the view Orig_View1 that detector channel is crossing with water mould first from top to bottom, such as the 400th view, find out the view Orig_View2 that on another direction, detector channel is crossing with water mould first from bottom to up simultaneously, such as the 200th view, as shown in Figure 4, horizontal ordinate represents view, and ordinate represents the passage of detecting device.Line is first horizontal line crossing with initial sinusoids figure from above above, the horizontal ordinate that intersection point is corresponding represents the 400th view, line is first horizontal line crossing with initial sinusoids figure from below below, and the horizontal ordinate that intersection point is corresponding represents the 200th view.Equally, sinogram I is reduced in error
unifsinin find out the view Unif_View1 (such as 400th view) corresponding with Orig_View1 (such as the 400th view) and Orig_View2 (such as the 200th view) and Unif_View2 (such as the 200th view).
Following equilibration device 1103 is by the average sine value of its average computation device 11032 computation bound view.Need to reduce sinogram to initial sinusoids figure and error to calculate respectively.First to sample, in initial sinusoids figure, the orthogonal projection value of two border views and several views of front and back thereof be averaged together.Such as Orig_View1, find out 20 views before and after it, to Orig_View2, find out 20 views before and after it, the mean value calculating the orthogonal projection value of these 42 views altogether obtains average view Orig_Aver, sinogram is reduced to error and carries out same operation, for Unif_View1, find out 20 views before and after it, for Unif_View2, also find out 20 views before and after it, the mean value calculating the orthogonal projection value of these 42 views altogether obtains average view Unif_Aver.Wherein, average view number can be understood as parameter, and it depends on the size of noise, can experimentally set, and can be 10,20 or 40, but the number of preferably getting in two sinograms is equal.
Then optimization device 1104 with average view for initial sinusoids figure I optimized by sample
origsin, make initial sinusoids figure I
origsinpress close to error as far as possible and reduce sinogram I
unifsin.The mode optimized illustrates with reference to following formula:
Wherein, Q and Q ' is Orig_Aver and Unif_Aver respectively, we with
as desirable orthogonal projection value, wherein a is fixing coefficient, the value that to be such as system define water mould as target, by one group of basis function B
j(j=1,2 ... n, n are the number of adopted basis function, also artificially determine by experiment) original orthogonal projection value P is optimized, i=1,2 ... m, m are self-defining top step number.The object of this step determines the coefficient vector b of majorized function.
For the water mould of different size, need the operation repeated above, thus determine N number of coefficient vector b of the water mould varied in size for N kind.
Matching device 1105 carries out matching to this N number of coefficient vector b, to obtain beam hardening correction coefficient c.Matching such as can adopt formula as follows:
Wherein h=1,2 ... h, h are the highest self-defining end, can be identical or different with m.
After this, beam hardening correction coefficient c just can be utilized to carry out beam hardening correction to the computed tomography data of other object of different size.
Figure 12 is the block diagram of the device for beam hardening correction according to disclosure embodiment, wherein for the device of beam hardening correction comprise as shown in figure 11 for the device 1100 that obtains beam hardening correction coefficient and the correction calculation device 1202 be attached thereto.After the device 1100 for obtaining beam hardening correction coefficient obtains correction coefficient vector c, correction calculation device 1202 is applied this coefficient vector c and is carried out correction to data for projection P and obtain P
new:
In this article, term "a" or "an" comprises each or more than one a plurality of of odd number.That term "or" is used to refer to not get rid of or (nonexclusive or), unless otherwise stated.
Also as used herein, word " reconstructed image " does not intend to get rid of the data that wherein produce and represent image and does not produce the embodiment of the present disclosure of visual image.Therefore, term used herein " image " refers to visual image widely and represents the data of visual image.But, many embodiment generations (or being configured to produce) at least one visual image.
Operating environment of the present disclosure describes relative to 16 layers of X-ray computed tomography (CT) system.But those skilled in the art will understand, the disclosure can be equally applicable to the system of multi-layer configuration, and be applicable to move during operation or the system of ability of " shake " focus.And, the disclosure by relative to X ray detection and conversion describe.But those skilled in the art will understand further, the disclosure can be equally applicable to detection and the conversion of other high frequency electromagnetic energy.Although specific embodiment with reference to third generation CT system, method as herein described is applied to forth generation CT system (such as with the silent oscillation detecting device in rotational x-ray source) and the 5th generation CT system (such as silent oscillation detecting device and x-ray source) equally.In addition, expect that benefit of the present disclosure extends to other imaging patterns except CT, such as MRI, SPECT and PET.
The part that various embodiment or its parts can be used as computer system realizes.This computer system can comprise computing machine, input equipment, display unit and such as the interface of access the Internet.Microprocessor can be connected to communication bus.Computing machine can also comprise storer.This storer can comprise random access memory (RAM) and ROM (read-only memory) (ROM).This computer system can also comprise memory device, and it can make the movable memory equipment of hard disk drive or such as floppy disk, CD drive etc.This memory device can also be used in Load Computer program or other instructions to the similar device of other in computer system.
In various embodiment of the present disclosure, create acquisition beam hardening correction coefficient described herein method can processor form embody.The typical case of processor comprises multi-purpose computer, the microprocessor of programming, digital signal processor (DSP), microcontroller, peripheral integrated circuit element, and can realize other equipment of step or the layout of equipment of method described herein.
As used herein, term " computing machine " is not limited to those integrated circuit being called as computing machine in the art, but can comprise any based on processor or based on untreated system, comprise use microcontroller, reduced instruction set circuits (RISC), special IC (ASIC), logical circuit and any other circuit of function described herein or the system of processor can be performed.Above-mentioned example is exemplary, and does not intend to limit by any way definition and/or the implication of term " computing machine ".Such as these terms of computing machine, processor, microcontroller, microcomputer, programmable logic controller (PLC), special IC and other programmable circuits and so on are used interchangeably herein.
Processing mechanism performs one group of instruction (such as, the method step described in correspondence), and this instruction is stored in a multiple memory element of work (being also called computer usable medium).The form of memory element can be database or the physical memory element be present in processor.Memory element can also hold data or other information as required.Physical storage can be, such as but not limited to: electronics, magnetic, optical, electrical magnetic, infrared or semiconductor system, device, equipment or propagation medium.The more specifically example of physical storage includes but not limited to lower example: random access memory (RAM), ROM (read-only memory) (ROM), EPROM (Erasable Programmable Read Only Memory) (EPROM or flash memory), hard disk drive (HDD) and compact disc-ROM (CDROM).Above-mentioned type of memory is exemplary, and the type therefore for the storer that can be used for storage computer program is not restrictive.
Described instruction group can comprise various order, and this order instruction processing machine performs specific operation, the process of the various embodiment of the such as disclosure.The form of instruction group can be software program.Software can be the various forms of such as system software or application software.In addition, the form of software can be single program, in the set compared with the program module in large program or a part of program module.It is the modeled programming of form that software also comprises with object based programming.Inputting data by processor process can be order corresponding to user, or corresponding to the result of first pre-treatment, or corresponding to the request made by another processor.
Various embodiment of the present disclosure, the method obtaining beam hardening correction coefficient can be realized by software, hardware or its combination.Such as by using standard programming language (such as C, C++, Java etc.) can the method that provided by various embodiment of the present disclosure of software simulating.As used herein, term " software " and " firmware " can exchange, and comprise the storage any computer program for being performed by computing machine in memory.
In addition, although method described here describes in the medical scene of X-ray computed tomography (CT) system, it is expected to these benefits and also help magnetic resonance (MR) system, ultrasonic system, positron emission tomography X (PET) system, core medical treatment and the imaging system of other types.Can operate for certain organs or structure, this comprises biologic-organ, such as brain, stomach, heart, lung or liver; Biological structure, such as diaphragm, the wall of the chest, thoracic cavity, rib, spine, breastbone or pelvis; Tumour or damage or wound (sore), such as compression fracture.
Claims (17)
1. obtain a method for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction, comprise step:
For the object of specific dimensions, obtain its original reconstructed image and initial sinusoids figure;
Original reconstructed image is carried out obtaining error after error reduces process and reduces sinogram;
Sample and calculate the mean value of initial sinusoids figure and the mean value of error minimizing sinogram;
Reduce sinogram according to error to be optimized initial sinusoids figure, to determine the coefficient vector of the majorized function of the object for specific dimensions;
Matching is carried out to the coefficient vector of the majorized function of initial sinusoids figure, to obtain the beam hardening correction coefficient of the object for specific dimensions.
2. obtain the method for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 1, it is characterized in that, can for the object of multiple different size, the coefficient vector of calculation optimization function respectively, and described matching carries out matching to the coefficient vector of all majorized functions.
3. obtain the method for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 1, it is characterized in that, described error reduces process and comprises step further:
By view, for each detecting device, the CT value of the pixel passed by the X ray detected by it is sued for peace, and then divided by the number of passed pixel, obtains the set P ' of value;
Find out the set P of projection value corresponding with it in initial sinusoids figure, adopt following formulae discovery:
, obtain error and reduce sinogram, wherein a is the coefficient that system defines described object.
4. obtain the method for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 1, it is characterized in that, described sampling also calculates the mean value that the mean value of initial sinusoids figure and error reduce sinogram and comprises step further:
Determine the border view of initial sinusoids figure and contiguous view thereof, as the first sampling view, and determine that error reduces the border view of sinogram and contiguous view thereof, as the second sampling view;
Calculate the mean value of the first sampling view and the second sampling view respectively, as the mean value of initial sinusoids figure and the mean value of error minimizing sinogram.
5. obtain the method for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 1, it is characterized in that, describedly reduce sinogram according to error and initial sinusoids figure is optimized comprises step further:
Calculating makes
as far as possible close to zero coefficient vector b, wherein Q and Q ' is the mean value that the mean value of initial sinusoids figure and error reduce sinogram respectively, B
jbasis function used, j=1,2 ... n, n are the number of adopted basis function, i=1,2 ..., m, m are the top step number of definition.
6. obtain the method for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 5, it is characterized in that, the coefficient vector of the described majorized function to initial sinusoids figure carries out matching and comprises further:
According to
calculate beam hardening correction coefficient vector c, wherein k=1,2 ... h, h are the top step number of definition.
7. obtain a device for beam hardening correction coefficient computed tomography data being carried out to beam hardening correction, comprising:
Acquisition device, it obtains its original reconstructed image and initial sinusoids figure for the object of specific dimensions;
Error reduces device, and it carries out obtaining error after error reduces process to original reconstructed image and reduces sinogram;
Equilibration device, samples and calculates the mean value of initial sinusoids figure and the mean value of error minimizing sinogram;
Optimization device, it reduces sinogram according to error and is optimized initial sinusoids figure, to determine the coefficient vector of the majorized function of the object for specific dimensions;
Matching device, it carries out matching to the coefficient vector of the majorized function of initial sinusoids figure, to obtain the beam hardening correction coefficient of the object for specific dimensions.
8. obtain the device of beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 7, it is characterized in that, can for the object of multiple different size, the coefficient vector of calculation optimization function respectively, and described matching device carries out matching to the coefficient vector of all majorized functions.
9. obtain the device of beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 7, it is characterized in that, described error reduces device and is further used for:
By view, for each detecting device, the CT value of the pixel passed by the X ray detected by it is sued for peace, and then divided by the number of passed pixel, obtains the set P ' of value;
Find out the set P of projection value corresponding with it in initial sinusoids figure, adopt following formulae discovery:
, obtain error and reduce sinogram, wherein a is the coefficient that system defines described object.
10. obtain the device of beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 7, it is characterized in that, described equilibration device is further used for:
Determine the border view of initial sinusoids figure and contiguous view thereof, as the first sampling view, and determine that error reduces the border view of sinogram and contiguous view thereof, as the second sampling view;
Calculate the mean value of the first sampling view and the second sampling view respectively, as the mean value of initial sinusoids figure and the mean value of error minimizing sinogram.
11. devices obtaining beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 7, it is characterized in that, optimization device is further used for:
Calculating makes
as far as possible close to zero coefficient vector b, wherein Q and Q ' is the mean value that the mean value of initial sinusoids figure and error reduce sinogram respectively, B
jbasis function used, j=1,2 ... n, n are the number of adopted basis function, i=1,2 ..., m, m are the top step number of definition.
12. devices obtaining beam hardening correction coefficient computed tomography data being carried out to beam hardening correction as claimed in claim 11, it is characterized in that, matching device is further used for:
According to
calculate beam hardening correction coefficient vector c, wherein k=1,2 ... h, h are the top step number of definition.
13. 1 kinds are carried out the method for beam hardening correction to computed tomography data, it is characterized in that, adopt the beam hardening correction coefficient obtained as the method in claim 1-6 as described in any one to carry out beam hardening correction to the computed tomography data of other object of specific dimensions.
14. 1 kinds are carried out the device of beam hardening correction to computed tomography data, it is characterized in that, comprising:
As the acquisition in claim 7-12 as described in any one computed tomography data carried out to the device of the beam hardening correction coefficient of beam hardening correction; And
Correction calculation device, utilizes described beam hardening correction coefficient to carry out beam hardening correction to the computed tomography data of other object of specific dimensions.
15. 1 kinds of ct apparatus, comprising:
Scanister, for utilizing X ray to scan to obtain raw data to object, to generate original reconstructed image;
Processor, can operate and be couple to described scanister, and able to programme to realize:
For the object of specific dimensions, obtain its original reconstructed image and initial sinusoids figure;
Original reconstructed image is carried out obtaining error after error reduces process and reduces sinogram;
Sample and calculate the mean value of initial sinusoids figure and the mean value of error minimizing sinogram;
Reduce sinogram according to error to be optimized initial sinusoids figure, to determine the coefficient vector of the majorized function of the object for specific dimensions;
Matching is carried out to the coefficient vector of the majorized function of initial sinusoids figure, to obtain the beam hardening correction coefficient of the object for specific dimensions.
16. 1 kinds of computer programs, comprise the instruction be stored on nonvolatile recording medium, when this instruction performs within a processor, implement as the method in claim 1-6 as described in any one.
17. 1 kinds of non-volatile memory mediums, which stores and implement according to the instruction as the method in claim 1-6 as described in any one when performing within a processor.
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