CN1188947A - Method and apparatus for simplified pre-processing of data in computed tomography system - Google Patents

Abstract

The present invention, in one embodiment, is a system for performing image reconstruction from raw projection data acquired in a tomographic scan.The system implements an estimation algorithm for raw projection data which does not require fully pre-processing all raw projection data for image reconstruction. Particularly, raw projection for a base view is pre-processed to generate pre-processed projection data for the base view. A linear calibration algorithm generates a linearization vector using at least a portion of the fully pre-processed projection data for the base view. The linearization vector is then applied to raw projection data for subsequent views to generate estimated pre-processed projection data for such subsequent views.

Description

The simplification preprocess method and the device of data in the computed tomography system

In general, the present invention relates to Computerized chromatographic (CT) imaging, more particularly, the present invention relates to the pretreated simplification of data in a CT system.

In at least a known CT system configuration, an x-ray source is launched a fan-ray beam, and this beam line makes the X-Y plane that is arranged in a Ka Dier coordinate system, this plane be commonly called " one-tenth image plane through collimation.X-ray beam passes by the object of imaging, for example a patient.Beam shines a radiation detector array after by this object decay.The intensity through the beam of overdamping that is received in detector array is relevant to the attenuation degree of X-ray beam with object.Each detector cells in the detector array produces an independently electric signal, and this signal is exactly that in the beam decay of this detector position one measures.Obtaining the attenuation measurements that all detectors obtain respectively distributes to obtain transmission.

In known third generation CT system, x-ray source and detector array along with a frame in becoming image plane and round treating that imaging object rotates, thereby make angle that X-ray beam and object intersect always in variation.Resulting one a group of X ray attenuation measurements of frame angle position sensor array, promptly data for projection is called as one " view " (view).The single pass of object (scan) is included in one group of view that a plurality of different frame angle in x-ray source and the sonde rotary one circle process obtains.In axial scan, the data for projection that is obtained is handled to constitute the frame image corresponding to a two-dimensional slice that intercepts in this object.A kind of method according to one group of data for projection reconstruct one frame image is called as filtering background plane technology in the art.The attenuation measurements that this disposal route will obtain in single pass converts the integer that is called as " CT number " or " Hounsfield unit " to, and these data are used for the brightness of a corresponding pixel on the control cathode ray tube display.

In order reducing a plurality of sections to be scanned required total sweep time, can to carry out " spiral " scanning.For carry out " spiral " scanning, in the data procedures of the section of obtaining predetermined number, patient along Z-direction with the same moved further of the rotation of frame.Such system is owing to fan-ray beam spiral scan forms a helix.This helix that is mapped out by fan-ray beam forms data for projection, can each predetermined image of cutting into slices of reconstruct according to these data.

Restructing algorithm for spiral scan uses spiral weighting (" HW ") algorithm usually, and this algorithm is weighted the data of gathering as the function that scans visual angle and detector channels index.Particularly, before the filtering background plane, according to a spiral weighting factor data are weighted, said weighting factor is the function of scanning visual angle and probe angle.Specifically, exactly to data for projection carry out filtering, the weighted sum background plane is handled to constitute every frame image.

In known CT system, " original " data for projection of each view of pre-service is to generate " pretreated fully " data for projection.Known pre-service scaling step comprises logarithm operation one time, and carries out QCAL (for example, the X ray position on the calibration z axle), Θ location, XCAL (for example going here and there around proofreading and correct), air calibration and the matrix convolution algorithm that disappears before carrying out this computing.After logarithm operation, other pre-treatment step comprises X ray sclerosis and z axle slant correction.Each pre-treatment step all is complicated, needs a large amount of computing time and cost.

The image reconstruction time, promptly scanning and time of reconstructed image are relevant with the system pre-service.Specifically, by reducing system's pre-treatment step, can shorten the image reconstruction time.But up to the present, it is believed that and reduce or cancel any pre-treatment step and all can reduce image quality significantly.

So need shorten image reconstruction time in the CT system by reducing pre-treatment step to the original projection data.Also need under the prerequisite of whole image quality of not obvious reduction and not obvious the cost that increases known CT system, shorten reconstitution time.But also need to reduce pre-service original projection required cost and the time of data.

These purposes and other purpose can realize in such system that in one embodiment, this system carries out a kind of estimating algorithm to the original projection data, and this algorithm does not need all data for projection of image reconstruction are carried out pre-service completely.Specifically, and according to one embodiment of present invention, the original projection data of a reference view are carried out pre-service to generate the complete pretreated data for projection of reference view.Utilize the pre-service data for projection of the complete pretreated data for projection estimation subsequent view of at least a portion of reference view then.More particularly, utilize original projection data to produce a linearization factor through pretreated data for projection and reference view.This linearization factor is applied to the original projection data of subsequent view to estimate the pre-service data for projection of these subsequent view by a kind of linear scaled algorithm then.

Utilize above-mentioned this estimating algorithm to reduce the processing time, and can carry out pre-service need not that the original projection data of each view are carried out under the independently complete pretreated prerequisite original projection data to view.In addition, reduced required the assessing the cost and expense of reconstructed image.This algorithm has also shortened the processing time, and can believe and obviously not reduce image quality.

Fig. 1 is the synoptic diagram of a CT imaging system.

Fig. 2 is the block diagram of system shown in Figure 1.

Fig. 3 represents the pretreatment time line of the data of pre-service original projection according to one embodiment of present invention.

See figures.1.and.2, a computerized tomography (CT) imaging system 10 as shown in the figure comprises a frame 12, and it has represented " third generation " CT scan equipment.Comprise an x-ray source 14 in the frame 12, this x-ray source projects an X-ray beam 16 on the detector array 18 that is positioned at frame 12 opposite sides.Detector array 18 is made of some detector cells 20, and these detector cells 20 common detections are passed a projection X ray of accepting patient's 22 healths of diagnosis.Each detector cells 20 produces an electric signal, the intensity of this signal indication incident X-rays bundle and the decay when beam passes through patient's 22 healths.In the scanning process of obtaining the X ray data for projection, frame 12 and parts mounted thereto rotate around a rotation center 24.

The operation of the rotation of frame 12 and x-ray source 14 is handled by a control gear 26 of CT system 10.Control gear 26 comprises an X ray controller 28 and a frame motor controller 30, and controller 28 provides power supply and clock signal to x-ray source 14, and controller 30 is used to control the velocity of rotation and the position of frame 12.32 pairs of simulated datas from detector cells 20 outputs of a data acquisition system (DAS) in the control gear 26 are sampled, and these data-switching are become numerical data, in order to the usefulness of subsequent treatment.An image reconstruction device 34 receives through over-sampling and digitized X ray data from DAS32, and carries out quick image reconstruction.The image of reconstruct is transferred to a computing machine 36 as an input signal, computing machine 36 with this image storage in a mass storage 38.

Computing machine 36 also receives order and the sweep parameter that an operator sends from the control desk 40 that comprises a keyboard.A continuous cathode-ray tube display 42 can make the operator observe the image of reconstruct and other data that computing machine 36 produces.Order and parameter that computing machine 36 utilizes the operator to send transmit control signal and information to DAS32, X ray controller 28 and frame motor controller 30.In addition, table top motor controller 44 of computing machine 36 operations, this controller 44 is controlled driven table tops 46 or is patient table, is used for patient 22 is positioned in frame 12.Specifically, table top 46 makes the part of patient body move through frame openings 48.Sometimes again frame openings 48 is called the frame hole in this application.

When reconstruct one frame image, want pre-service original projection data usually.Convolution that known pre-treatment step need be carried out QCAL (the calibration X ray is along the position of detector 18 on the z axle), Θ location, XCAL (crosstalking between the output valve of correction adjacent detector unit 20), air is calibrated and matrix disappears.As mentioned above, it is very consuming time and complicated the original projection data of each view being carried out pre-service.

Below be that the CT system of special adopting by reference axial scan carries out sometimes for the discussion of estimating algorithm.But this estimating algorithm is not limited to only be applied to this system, but can be applied to other CT system, for example spiral scan CT system, Dynamic CT system and CT fluorescing system.In addition, in one embodiment, estimating algorithm can be applied in the computing machine 36, and can handle, and for example, is stored in the data in the mass storage 38.Many other embodiments also are possible certainly.

According to an embodiment, a kind of estimating algorithm is applied to the original projection data with generation pre-service data for projection, and need not these original projection data are carried out complete pre-service.Specifically, as is known, carry out a high picture quality scanning, to obtain the original projection data of a relevant patient 22 or object to be detected.According to known preconditioning technique the original projection data of the reference view of this object to be detected are carried out pre-service, to produce the complete pre-service data for projection of this reference view.Utilize at least a portion of this reference view the original projection data of subsequent view to be carried out pre-service then through pretreated data for projection.

More particularly, not to carry out complete pre-service to the original projection data of subsequent view, but use a kind of linear scaled algorithm to produce the estimation pre-service data for projection of these views the original projection data of subsequent view.As a concrete example, and only consider a channel of CT system 10, suppose I ₁Be a projection value of the original projection data of reference view, and supposition  ₁A projection value for the complete pre-service data for projection of this reference view.Can estimate a subsequent view according to following formula, in other words the pre-service data for projection of second view: ${\hat{I}}_2=\frac{{\hat{I}}_1}{I_1}*I_2-----\left(1\right)$

Wherein: I ₂A projection value for the original projection data of subsequent view;

 ₂A projection value for the estimation pre-service data for projection of subsequent view; And

 ₁/ I ₁It is a linearization factor.

So, can need not original projection data I to these views ₂Carry out complete pre-service and obtain the pre-service data for projection  of subsequent view ₂Similarly, can estimate again the pre-service data for projection  of the same channel of next view ₃,  ₄...  _n

Though foregoing is only with respect to a channel, in fact this linear scaled algorithm is applied to all detector channels.Therefore, this linear scaled algorithm is not to apply a linearization factor for the original projection data of all channels of each subsequent view, but applies a linearization vector {  ₁/ I ₁, to estimate the pre-service data for projection of these views.More particularly, linearization vector {  ₁/ I ₁Be for all channels definition of reference view, this vector is applied to the original projection data of all channels of each subsequent view according to following formula: ${\hat{I}}_2=\left\{\frac{{\hat{I}}_1}{I_1}\right\}*I_2-----\left(2\right)$

Wherein: I ₂A projection value for the original projection data of subsequent view;

 ₂A projection value for the estimation pre-service data for projection of subsequent view; And

{  ₁/ I ₁It is a linearization vector.

Fig. 3 represents to adopt before implementing logarithm operation according to the foregoing description a pretreatment time line of pre-treatment step pre-service original projection data.Specifically, in order to produce pre-service data for projection  ₁, utilize the original projection data I ₁Carry out each following pre-treatment step: QCAL, Θ location, ACAL (air calibration) and the matrix convolution (perhaps MatrixDecon) that disappears.In these pre-treatment step each all is well-known in the art, and as mentioned above, needs a large amount of computing time and cost.But above-mentioned estimating algorithm has been cancelled more such treatment steps, that is, can need not the original projection data I ₂Carry out QCAL, Θ location, ACAL, or the matrix convolution algorithm that disappears just estimates pre-service data for projection  ₂

Above-mentioned estimating algorithm has reduced common pre-treatment step required when the data for projection of one group of view of pre-service.Therefore, shortened the image reconstruction time.Certainly, can also reduce pre-treatment step by improving above-mentioned algorithm.

For example, according to another embodiment of the invention, not that the data for projection to a reference view carries out complete pre-service, but to first and second reference view,  ₁And  _M, the original projection data carry out complete pre-service. ₁And  _MCan be any selected view, and can be a plurality of reference view  in arbitrary given scanning ₁And  _MUtilize the pre-service data for projection  of these reference view then ₁And  _MPre-service medial view I ₂-I _M-1The original projection data.Specifically, utilize a kind of linear scaled algorithm according to formula (1) with these original projection data linearizations.More particularly, determine medial view  according to following linear relation ₂- _M-1The pre-service data for projection: ${\hat{I}}_j=\frac{{\hat{I}}_1}{I_M-I_1}*(I_M-I_j)+\frac{{\hat{I}}_M}{I_M-I_1}(I_j-I_1)$ J=2 wherein ..., M-1 (3)

The same with above-mentioned linear scaled algorithm, formula (3) is applied to all channels of detector 18.

The foregoing description makes that can need not that the original projection data are carried out pre-service just produces through pretreated data for projection.So, shortened reconstitution time.In addition, it is believed that above-mentioned algorithm can not reduce image quality significantly, and do not need to increase implementation cost.

In order further to suppress the decline of image quality, can constitute a reference view new or that upgrade by single pass.Specifically, periodically the original projection data are carried out complete pre-service to produce a new reference view, this reference view is used for pre-service estimation thereafter.

In addition, in order to improve image quality, can adopt a kind of regularization method.Specifically, can determine medial view  according to following formula ₂- _M-1The pre-service data for projection: ${\hat{I}}_j=\frac{1}{2}*\{{\hat{I}}_1+{\hat{I}}_M\}$ If $\left|\frac{{\hat{I}}_1-{\hat{I}}_M}{{\hat{I}}_1}\right|\≤T,$ And (4) ${\hat{I}}_j=\left\{\frac{{\hat{I}}_1}{I_M-I_1}\right\}*(I_M-I_j)+\left\{\frac{{\hat{I}}_M}{I_M-I_1}\right\}*(I_j-I_1)$ Other situation

J=2 wherein ..., M-1, T are a threshold value.Threshold value T can select and store in advance, for example, exists in the mass storage 38.In one embodiment of the invention, threshold value T is 0.05.

Perhaps, can determine medial view  according to following formula ₂- _M-1The pre-service data for projection: ${\hat{I}}_j=\frac{{\mathrm{<figure-callout\; id="2"\; label="I\; j"\; filenames="A9712559100141.png"\; state="\{\{state\}\}">I</figure-callout>}}_j}{2}*\{\&lsqb;\frac{{\hat{I}}_1}{{\mathrm{<figure-callout\; id="1"\; label="I"\; filenames="A9712559100141.png"\; state="\{\{state\}\}">I</figure-callout>}}_1}\&rsqb;+\&lsqb;\frac{{\hat{I}}_M}{I_M}\&rsqb;\}$ If $\left|\frac{{\hat{I}}_1-{\hat{I}}_M}{{\hat{I}}_1}\right|\&le;T,$ And (5) ${\hat{I}}_j=\left\{\frac{{\hat{I}}_1}{I_M-I_1}\right\}*(I_M-I_j)+\left\{\frac{{\hat{I}}_M}{I_M-I_1}\right\}*(I_j-I_1)$ Other situation

J=2 wherein ..., M-1, T are a threshold value.

From top description, obviously realized purpose of the present invention to each embodiment of the present invention.Although described and illustrated the present invention in detail, be to be understood that this just describes the present invention in the mode of diagram and example, rather than with formal description the present invention of restriction.For example, above-mentioned algorithm is to combine with pre-treatment step before logarithm operation to implement.This algorithm can also combine enforcement (for example combining with Z axle tilt correction) with the pre-treatment step after the logarithm operation.In addition, CT system described here is " third generation " system, and x-ray source and detector all rotate round frame in this system.Can also use many other the CT systems that comprise " the 4th generation " system, their detector is a kind of complete annular fixed detector, has only x-ray source to center on the frame rotation.Similarly, the foregoing description can be used for many slice systems.So design of the present invention and scope only are limited only by the accompanying claims.

Claims (20)

1. method that is used for utilizing the image of an object of original projection data reconstruction that in single pass, obtains CT system, said CT system has an x-ray source and a detector array that is used to throw X ray, said detector array comprises one group of detector, and said method may further comprise the steps:

The original projection data of at least one reference view of pre-service are to produce the pre-service data for projection of this reference view;

Utilize the preprocessed data of at least a portion of said reference view through pretreated data for projection estimation subsequent view.

2. a kind of method as claimed in claim 1 is characterized in that estimating that the step of the pre-service data for projection of subsequent view comprises the step of the original projection data of subsequent view being implemented a kind of linear scaled algorithm.

3. a kind of method as claimed in claim 2 is characterized in that the pre-service data for projection of said subsequent view is estimated according to following formula: ${\hat{I}}_2=\frac{{\hat{I}}_1}{I_1}*I_2$

Wherein:

 ₁A projection value for the pre-service data for projection of this reference view;

I ₁A projection value for the original projection data of this reference view;

 ₂A projection value for the pre-service data for projection of said subsequent view; With

I ₂A projection value for the original projection data of said subsequent view.

4. a kind of method as claimed in claim 1 is characterized in that estimating that the step of the pre-service data for projection of subsequent view comprises the step that the original projection data of said subsequent view is applied a linearization vector.

5. a kind of method as claimed in claim 4 is characterized in that said linearization vector is: {  ₁/ I ₁}

Wherein:

 ₁A projection vector for the pre-service data for projection of said reference view;

I ₁A projection vector for the original projection data of said reference view.

6. method that is used for utilizing the image of an object of original projection data reconstruction that in single pass, obtains CT system, said CT system has an x-ray source and a detector array that is used to throw X ray, said detector array comprises one group of detector, and said method may further comprise the steps:

The original projection data of two reference view of pre-service are to produce the pre-service data for projection of these two reference view;

Utilize at least a portion of these two reference view to pass through the preprocessed data that pretreated data for projection is estimated a medial view between these two reference view.

7. a kind of method as claimed in claim 6 is characterized in that estimating that the step of the pre-service data for projection of said medial view comprises the step of the original projection data of said medial view being implemented a kind of linear scaled algorithm.

8. a kind of method as claimed in claim 7 is characterized in that at least a portion pre-service data for projection of said medial view estimates according to following formula: ${\hat{I}}_j=\frac{{\hat{I}}_1}{I_M-I_1}*(I_M-I_j)+\frac{{\hat{I}}_M}{I_M-I_1}*(I_j-I_1)$ J=2 wherein ..., M-1

Wherein:

 ₁It is a projection value of the pre-service data for projection of a reference view;

I ₁A projection value for the original projection data of a said reference view;

 _MA projection value for the pre-service data for projection of another reference view;

I _MA projection value for the original projection data of said another reference view;

 _jA projection value for the pre-service data for projection of said medial view; With

I _jA projection value for the original projection data of said medial view.

9. a kind of method as claimed in claim 8 is characterized in that the pre-service data for projection of said medial view estimates according to following formula: ${\hat{I}}_j=\frac{1}{2}*\{{\hat{I}}_1+{\hat{I}}_M\}$ If $\left|\frac{{\hat{I}}_1-{\hat{I}}_M}{{\hat{I}}_1}\right|\&le;T,$ And ${\hat{I}}_j=\left\{\frac{{\hat{I}}_1}{I_M-I_1}\right\}*(I_M-I_j)+\left\{\frac{{\hat{I}}_M}{I_M-I_1}\right\}*(I_j-I_1)$ Other situation

Wherein T is a threshold value.

10. a kind of method as claimed in claim 8 is characterized in that the pre-service data for projection of said medial view estimates according to following formula: ${\hat{I}}_j=\frac{I_j}{2}*\{\&lsqb;\frac{{\hat{I}}_1}{I_1}\&rsqb;+\&lsqb;\frac{{\hat{I}}_M}{I_M}\&rsqb;\}$ If $\left|\frac{{\hat{I}}_1-{\hat{I}}_M}{{\hat{I}}_1}\right|\&le;T,$ And ${\hat{I}}_j=\left\{\frac{{\hat{I}}_1}{I_M-I_1}\right\}*(I_M-I_j)+\left\{\frac{{\hat{I}}_M}{I_M-I_1}\right\}*(I_j-I_1)$ Other situation

Wherein T is a threshold value.

11. the system of the image of the object of original projection data reconstruction that a utilization is obtained in a tomographic scanning, said system constitutes like this, makes it:

The original projection data of at least one reference view of pre-service are to produce the pre-service data for projection of this reference view;

Utilize the preprocessed data of at least a portion of said reference view through pretreated data for projection estimation subsequent view.

12. a kind of system as claimed in claim 11 is characterized in that said system constitutes like this in order to estimate the pre-service data for projection of subsequent view, makes its original projection data to subsequent view implement a kind of linear scaled algorithm.

13. a kind of system as claimed in claim 12 is characterized in that the pre-service data for projection of said subsequent view is estimated according to following formula: ${\hat{I}}_2=\frac{{\hat{I}}_1}{I_1}*I_2$

Wherein:

 ₁A projection value for the pre-service data for projection of this reference view;

I ₁A projection value for the original projection data of this reference view;

 ₂A projection value for the pre-service data for projection of said subsequent view; With

I ₂A projection value for the original projection data of said subsequent view.

14. a kind of system as claimed in claim 11 is characterized in that said system constitutes like this in order to estimate the pre-service data for projection of subsequent view, makes its original projection data to said subsequent view apply a linearization vector.

15. a kind of system as claimed in claim 14 is characterized in that said linearization vector is: {  ₁/ I ₁}

Wherein:

 ₁A projection vector for the pre-service data for projection of said reference view;

I ₁A projection vector for the original projection data of said reference view.

16. a kind of system as claimed in claim 11, it also constitutes like this, the original projection data that make two reference view of its pre-service are producing the pre-service data for projection of said two reference view, and wherein said subsequent view is positioned in the middle of two reference view.

17. a kind of method as claimed in claim 16 is characterized in that estimating that the step of the pre-service data for projection of subsequent view comprises the step of the original projection data of subsequent view being implemented a kind of linear scaled algorithm.

18. a kind of method as claimed in claim 17 is characterized in that at least a portion pre-service data for projection of said subsequent view estimates according to following formula: ${\hat{I}}_j=\frac{{\hat{I}}_1}{I_M-I_1}*(I_M-I_j)+\frac{{\hat{I}}_M}{I_M-I_j}*(I_j-I_1)$ J=2 wherein ..., M-1 wherein:

 ₁It is a projection value of the pre-service data for projection of a reference view;

I ₁A projection value for the original projection data of a said reference view;

 _MA projection value for the pre-service data for projection of another reference view;

I _MA projection value for the original projection data of said another reference view;

 _jA projection value for the pre-service data for projection of said medial view; With

I _jA projection value for the original projection data of said medial view.

19. a kind of method as claimed in claim 18 is characterized in that the pre-service data for projection of said medial view estimates according to following formula: ${\hat{I}}_j=\frac{1}{2}*\{{\hat{I}}_1+{\hat{I}}_M\}$ If $\left|\frac{{\hat{I}}_1-{\hat{I}}_M}{{\hat{I}}_1}\right|\&le;T,$ And ${\hat{I}}_j=\left\{\frac{{\hat{I}}_1}{I_M-I_1}\right\}*(I_M-I_j)+\left\{\frac{{\hat{I}}_M}{I_M-I_1}\right\}*(I_j-I_1)$ Other situation

Wherein T is a threshold value.

20, a kind of method as claimed in claim 18 is characterized in that the pre-service data for projection of said medial view estimates according to following formula: ${\hat{I}}_j=\frac{I_j}{2}*\{\&lsqb;\frac{{\hat{I}}_1}{I_1}\&rsqb;+\&lsqb;\frac{{\hat{I}}_M}{I_M}\&rsqb;\}$ If $\left|\frac{{\hat{I}}_1-{\hat{I}}_M}{{\hat{I}}_1}\right|\&le;T,$ And ${\hat{I}}_j=\left\{\frac{{\hat{I}}_1}{I_M-I_1}\right\}*(I_M-I_j)+\left\{\frac{{\hat{I}}_M}{I_M-I_1}\right\}*(I_j-I_1)$ Other situation wherein T is a threshold value.

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