CN102982582A - Method for obtaining three-dimensional X-ray image data set for periodically moving object - Google Patents
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Abstract
The invention relates to a method for obtaining three-dimensional X-ray image data set for a self-periodically moving image object. Disclosed is obtaining information on stages in periodical movement in the mapping of the self-periodically moving image object. This is used for multiple two-dimensional X-ray image data sets, and three-dimensional X-ray image data sets can be obtained respectively for stage intervals. A movement field is obtained by a registration method, and the movement field enables the three-dimensional X-ray image data sets to be totally transformed and as a whole integrated to an average volume I (S20). In the invention, the method is continued subsequently, i.e., average volumes I (S22a, S22n) based an optimized movement field. Therefore, a suitable objective function is adopted which, for example, contains the entropy of the average volume I.
Description
Technical field
The present invention relates to a kind of method that is used for the image object of cyclical movement is obtained the 3 D X-ray image data set of preamble according to claim 1.
Background technology
Such method is known by prior art.
The 3 D X-ray image data set has comprised, with the volume element in three dimensions in the scope of image object corresponding to data value or gray-scale value, this data value or gray-scale value are the tolerance to the decay of the X ray that passes image object in volume element.Such 3 D X-ray image data set obtains from two-dimensional x-ray images data group.This two-dimensional x-ray images data group obtains by the radioscopic image filming apparatus with x-ray source and X-ray detector, and this radioscopic image filming apparatus is around the turning axle rotation.Take respectively two-dimensional x-ray images data group in different angle positions.Then, back projection's (typically comprising filtering) is carried out in this so-called projection, in order to obtain the 3 D X-ray image data set.
In the image object of cyclical movement own, not all two-dimensional x-ray images data group all is suitable for each other.For this reason when taking respectively two-dimensional x-ray images data group, determine that image object laid respectively in which of cyclical movement in stage.For example, can be in the stage of measuring the cyclical movement of heartbeat when having the cardiac imaging of the heart coronary artery (cardiac muscle) that centers on.Then, two-dimensional x-ray images data group can be divided into group according to the determined stage.Total cycle is divided into the interval for this reason.Stipulated in each stage thus which interval it is positioned at, thereby relevant two-dimensional x-ray images data group can be corresponding to corresponding group.Carry out now (filtering) back projection by group, namely obtain the 3 D X-ray image data set by group.These 3 D X-ray image data set are called as 3 D X-ray group image data set now.Then, be used as the reference image data group with one group in this 3 D X-ray group image data set.Derive so-called sports ground (Bewegungsfeld) for each group (group that does not namely belong to the reference image data group) of other group now with respect to this reference image data group.Sports ground has illustrated which spatial point certain position of the image object of motion moves to from which spatial point.In order to derive sports ground, carry out so-called registration (Registrieren).In registration, determine the mapping ruler from an image data set to another image data set, carry out thus the 3D-3D registration.That is, an image data set correct position and dimension correctly can be mapped to another image data set by this registration.In other words, can find a kind of mapping ruler (Abbildungsvorschrift), this mapping ruler is mapped to each point of 3 D X-ray image data set the point of reference image data group.Then carry out this mapping, namely calculate the 3 D X-ray group image data set of revising.For example, if image object " heart " just shrinks in the reference image data group and then enlarges, the calculating of the 3 D X-ray group image data set of then revising corresponding to, with the situation that not corresponding to the stage of the heart that shrink be mapped to the heart of contraction of view data from cyclical movement.
But the 3 D X-ray group image data set behind all modifications can be similar to the reference image data group thus.Can the 3 D X-ray group image data set behind all modifications and reference image data group is comprehensive (namely average) be the first 3 D X-ray image data set thus.
This 3 D X-ray image data set, as known according to prior art, the result who normally moves.
At present, in this 3 D X-ray image data set that from each 3 D X-ray image data set comprehensive, obtains, also there is pseudo-shadow.
Summary of the invention
Therefore, the technical problem to be solved in the present invention is, reduces or suppresses this pseudo-shadow fully.
This technical matters is that the method by a kind of feature with according to claim 1 solves.
According to the present invention, conversion sports ground at least in part, use conversion the situation of sports ground under calculate the 3 D X-ray image data set that remodifies, and the 3 D X-ray image data set that will revise like this and reference image data group comprehensively are new 3 D X-ray image data set.When the sports ground conversion, determine mapping parameters as the registration basis in conjunction with the comprehensive 3 D X-ray image data set of previous calculations.
In other words, at first calculate the first 3 D X-ray image data set, and then can go out to quantize with any one formal grammar from this 3 D X-ray image data set, it is how good that (at least according to predetermined standard) this calculating has.Then quantizing by this also can the correction maps parameter.
The present invention is based on following knowledge aspect this, namely the step of registration needn't work best.When registration, use similarity measurement, in order to find a kind of mapping ruler.If but used subsequently this mapping ruler, in order to finally obtain the first 3 D X-ray image data set, then the weakening in the step of registration could cause the degradation of 3 D X-ray image data set.Now, proofread and correct intervention here by the present invention.
Preferably carry out iteratively conversion, but the highest iteration specific times.Conversion by iteration can be carried out specific algorithm, such as Gradient Descent method (Gradientenabstiegsverfahren) etc.If the highest iteration specific times has been carried out in conversion, then guaranteed: for only inappreciable improvement of final acquisition, do not use too many computing time.
Of the present invention preferred embodiment in, determine like this mapping parameters, so that improve (namely for example minimizing) objective function according to specific standard, this objective function comprises the item relevant with the comprehensive 3 D X-ray image data set of previous calculations, this for example can be the tolerance of imbalance, namely entropy item (Entropieterm).If the use objective function is then considered so respectively specific standard in suitable structure, i.e. adjustment, find the best in this balance: standard is satisfied which kind of degree and which kind of degree is another standard be satisfied.If objective function comprises the item relevant with the comprehensive 3 D X-ray image data set of previous calculations thus, then at least one standard of considering in objective function is the quality of 3 D X-ray image data set.
If item is the entropy item, the tolerance of namely lacking of proper care, suppose that then correct " image " would rather be low noise and be (geordnet) of classification, so that need only as far as possible accurately mapping by the image object of this cyclical movement of 3 D X-ray image data set own, the entropy item is exactly little.
Preferably carry out iteratively in this embodiment conversion, and have following target: objective function satisfies predetermined standard, that is, it not only improves in each step, and reaches at any time specific target.
The conversion of iteration can be carried out always, until reach predetermined standard.The highest number of times of being scheduled to of conversion can be set best as emulative interrupt criteria.
Description of drawings
Preferred embodiment be further described of the present invention below in conjunction with accompanying drawing, in the accompanying drawing:
Fig. 1 shows for the process flow diagram of explaining according to this embodiment of method of the present invention.
Embodiment
This method begins as follows, obtains a plurality of two-dimensional x-ray images data groups in step S10.Image object is current should to be human heart.During image taking, take simultaneously cardiogram.Heart beat cycle is divided into a plurality of stage t
1, t
2... t
nQuantity n for example can be 10.The data group that obtains in step S10 in this way can be divided into group, namely is divided into stage t
1Group S12a, stage t
2Group S12b and stage t
nGroup S12n.
Now can obtain 3 D X-ray (group) image data set from the projection of each group, this radioscopic image data group is called as " volume (Volumen) ".In step S14a thus to stage t
1Projection obtain volume 1, in step S14b, obtain volume 2, in step S14n, obtain volume n.
At this, each registration carries out for itself.The volume behind the so-called registration can be determined in the based on motion field in step S18a to S18n: for example be out of shape like this (or " correction ") volume 2 by sports ground 2, so that the heart of mapping to a certain degree is mapped to stage t virtually
1, namely come from stage t
2Image calculated back virtually stage t
1Volume 2 behind the registration is thus finally corresponding to volume 1.Volume n behind the registration is equally also corresponding to volume 1.Because now the volume behind the registration is all corresponding to volume 1, thus its can be comprehensive in step S20, be the new volume of average out to.
In the step S20 conventional method that is through with.At present, we with average external volume only as intermediate result: counter i has improved quantity 1, and in step S22a and S22b with sports ground Θ
j(I
j) optimize.Define new objective function or cost function for this reason:
In this summation function, at first considered the cost function K(I that all are known
1, Θ
j(I
j)).But these cost functions and be not again accurately to draw inevitably same sports ground Θ
j(I
j).But characteristics are, because a β E(I) comprised the entropy item, this entropy item with as a whole on average after volume I be associated.Have thus a kind of " feedback " herein, namely in average external volume I, considered sports ground, but sports ground is adjusted based on the average external volume I that obtains in iteration i=i+1 subsequently again thus.The frequency that the entropy item comprises particular data value multiply by this frequency logarithm and.
E(I)=∑h(k)·log(h(k))
Can in step 28, calculate again now volume S24a, S24n behind the registration by sports ground S22a, S22n after optimizing.Whether the number of times that checks now iteration in step 26 has reached desired value N.If so, then in step S28, use the average external volume that drawn by the volume 1 according to step S14a and according to step S24a, the last registration volume that obtains of the final circulation of S24n.As long as the number of times of iteration does not also reach N, just continue to carry out iteratively the method, the volume I after namely at first will be average is used as intermediate result in step S20, and then optimizes sports ground, and then calculates volume behind the new registration etc.
Alternatively, also can be with following standard inspection in step S26, for example whether objective function has reached the specific objective value, and perhaps whether the variation of objective function has been lower than desired value etc. from last iteration.
Thus, volume I after as a result of in step S28, obtaining on average, the latter is fixed from now on as net result, and cost function J(I wherein) according to predetermined standard (as given in advance by method), namely for example in iteration optimised after the times N.
The net result that obtains in step S28 has less pseudo-shadow than the intermediate result that obtains usually in step S20.Reach thus elementary object of the present invention.
Claims (4)
1. method that is used for the image object of cyclical movement own is obtained the 3 D X-ray image data set, wherein obtain a plurality of two-dimensional x-ray images data groups by the radioscopic image filming apparatus, wherein, determine that respectively described image object is arranged in which of cyclical movement in stage, wherein, described two-dimensional x-ray images data group is divided into group (S12a according to the determined stage, S12b, S12n), and wherein, from the two-dimensional x-ray images data group of each group, obtain respectively 3 D X-ray group image data set (S14a, S14b, S14n), one in these 3 D X-ray group image data set is used as the reference image data group, derive sports ground (S16a for each of other group adopting in the situation that 3 D X-ray group image data group is registrated to the reference image data group with respect to described reference image data group, S16n), calculate amended 3 D X-ray group image data set (S18a by described sports ground, S18n), wherein, comprehensively be the first 3 D X-ray image data set (S20) with the 3 D X-ray group image data set behind described reference image data group and all modifications, it is characterized in that
With described sports ground conversion (S22a at least in part, S22n), and calculate the 3 D X-ray image data set (S24a that remodifies in the situation of the sports ground after using conversion, S24n) and with described reference image data group comprehensively be new 3 D X-ray image data set, wherein, the comprehensive 3 D X-ray image data set (I) of calculating in conjunction with the front when described sports ground conversion is determined the mapping parameters as the registration basis.
2. method according to claim 1 is characterized in that, the highest iteration pre-determined number of described conversion N.
3. method according to claim 1 and 2, it is characterized in that, determine so described mapping parameters, so that improve objective function according to specific standard, described objective function comprises the item (β E(I) that the comprehensive 3 D X-ray image data set calculated with the front is relevant), entropy item particularly.
4. method according to claim 3 is characterized in that, carries out iteratively described conversion and has following target, so that described objective function satisfies predetermined standard.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102011075287.0A DE102011075287B4 (en) | 2011-05-05 | 2011-05-05 | A method for obtaining a 3D X-ray image data set to a periodically moving image object |
| DE102011075287.0 | 2011-05-05 |
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| Publication Number | Publication Date |
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| CN102982582A true CN102982582A (en) | 2013-03-20 |
| CN102982582B CN102982582B (en) | 2017-01-18 |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104424659A (en) * | 2013-08-30 | 2015-03-18 | 西门子公司 | Image-based motion compensation of image data |
| CN107206252A (en) * | 2014-12-11 | 2017-09-26 | 医科达有限公司 | Sport Administration in nuclear magnetic resonance guiding linear accelerator |
| CN109389655A (en) * | 2013-06-25 | 2019-02-26 | 西门子保健有限责任公司 | The reconstruction of time-variable data |
| CN111544020A (en) * | 2020-04-17 | 2020-08-18 | 北京东软医疗设备有限公司 | Geometric correction method and device for X-ray imaging equipment |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101797042B1 (en) | 2015-05-15 | 2017-11-13 | 삼성전자주식회사 | Method and apparatus for synthesizing medical images |
| DE102021201600A1 (en) | 2021-02-19 | 2022-08-25 | Siemens Healthcare Gmbh | Improvement of a set of imaging parameters in transmission X-ray imaging |
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| US6775405B1 (en) * | 2000-09-29 | 2004-08-10 | Koninklijke Philips Electronics, N.V. | Image registration system and method using cross-entropy optimization |
| US20060067459A1 (en) * | 2004-09-30 | 2006-03-30 | Siemens Aktiengesellschaft | Method and device for generating a three-dimensional image data record of a moving object by means of X-ray tomography |
| CN1846618A (en) * | 2005-04-15 | 2006-10-18 | 西门子公司 | Method for mathematical compensation of a periodic movement of an organ and image shooting system |
| CN101028196A (en) * | 2005-12-21 | 2007-09-05 | 西门子公司 | Method and tomography unit for carrying out an analysis of a movement of an object |
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- 2011-05-05 DE DE102011075287.0A patent/DE102011075287B4/en not_active Expired - Fee Related
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US6775405B1 (en) * | 2000-09-29 | 2004-08-10 | Koninklijke Philips Electronics, N.V. | Image registration system and method using cross-entropy optimization |
| US20060067459A1 (en) * | 2004-09-30 | 2006-03-30 | Siemens Aktiengesellschaft | Method and device for generating a three-dimensional image data record of a moving object by means of X-ray tomography |
| CN1846618A (en) * | 2005-04-15 | 2006-10-18 | 西门子公司 | Method for mathematical compensation of a periodic movement of an organ and image shooting system |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109389655A (en) * | 2013-06-25 | 2019-02-26 | 西门子保健有限责任公司 | The reconstruction of time-variable data |
| CN109389655B (en) * | 2013-06-25 | 2021-09-03 | 西门子保健有限责任公司 | Reconstruction of time-varying data |
| CN104424659A (en) * | 2013-08-30 | 2015-03-18 | 西门子公司 | Image-based motion compensation of image data |
| US9619869B2 (en) | 2013-08-30 | 2017-04-11 | Siemens Aktiengesellschaft | Image-based motion compensation of image data |
| CN107206252A (en) * | 2014-12-11 | 2017-09-26 | 医科达有限公司 | Sport Administration in nuclear magnetic resonance guiding linear accelerator |
| CN107206252B (en) * | 2014-12-11 | 2020-03-10 | 医科达有限公司 | Motion management in a nuclear magnetic resonance guided linear accelerator |
| US10806368B2 (en) | 2014-12-11 | 2020-10-20 | Elekta, Inc. | Motion management in MRI-guided Linac |
| CN111544020A (en) * | 2020-04-17 | 2020-08-18 | 北京东软医疗设备有限公司 | Geometric correction method and device for X-ray imaging equipment |
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| Publication number | Publication date |
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| DE102011075287A1 (en) | 2012-11-08 |
| DE102011075287B4 (en) | 2016-07-21 |
| CN102982582B (en) | 2017-01-18 |
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