IPC분류정보
국가/구분 |
United States(US) Patent
등록
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국제특허분류(IPC7판) |
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출원번호 |
US-0313077
(2011-12-07)
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등록번호 |
US-8650014
(2014-02-11)
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발명자
/ 주소 |
- Vija, Alexander Hans
- Yahil, Amos
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출원인 / 주소 |
- Siemens Medical Solutions USA, Inc.
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대리인 / 주소 |
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인용정보 |
피인용 횟수 :
0 인용 특허 :
13 |
초록
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An iterative reconstruction method to reconstruct an object includes determining, in a series of iteration steps, updated objects, wherein each iteration step includes determining a data model from an input object, and determining a stop-criterion of the data model on the basis of a chi-square-gamma
An iterative reconstruction method to reconstruct an object includes determining, in a series of iteration steps, updated objects, wherein each iteration step includes determining a data model from an input object, and determining a stop-criterion of the data model on the basis of a chi-square-gamma statistic. The method further includes determining that the stop-criterion of the data model has transitioned from being outside the limitation of a preset threshold value to being inside the limitation, ending the iterations, and selecting one of the updated objects to be the reconstructed object.
대표청구항
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1. An iterative reconstruction method for updating an input object, the method comprising: providing an initial image object representing radiation from a patient;reconstructing, in a series of iteration steps by a processor, updated image objects, wherein each iteration step includes determining a
1. An iterative reconstruction method for updating an input object, the method comprising: providing an initial image object representing radiation from a patient;reconstructing, in a series of iteration steps by a processor, updated image objects, wherein each iteration step includes determining a data model from an input image object, the input image object of a first iteration being the initial image object and the input image objects of subsequent iterations being the updated image objects from previous iterations, and determining a stop-criterion, Q(χγ2), of the data model on the basis of a chi-square-gamma statistic, χγ2;determining that the stop-criterion of the data model has remained outside the limitation of a preset threshold value;wherein determining the stop-criterion comprises calculating the chi-square-gamma statistic, χγ2, as a sum of ratios calculated over data points, dj, defining a data space, wherein for each data point a denominator of the ratio is a square of a residual, and the numerator of the ratio is the sum of a measured value of that data point, dj, and a statistical-data-offset number, y, and wherein the residual is the difference between a corrected measured value of that data point and a modeled value, mj, of that data point, wherein the corrected measured value is the sum of the measured value, dj, and the minimum of the measured value and one, Min (dj,1), the chi-square-gamma statistic, χγ2, calculating represented as: χγ2=Σ(dj+Min(dj,1)−mj)2/(dj+y);and wherein determining the stop-criterion, Q(χγ2), comprises setting the stop-criterion, Q(χγ2), as a function of a statistical value, A(χγ2), of the chi-square statistic, χγ2; anddisplaying an image of the updated image object from one of the iterations. 2. The method of claim 1, further comprising setting the stop-criterion, Q(χγ2), to be a ratio of the difference between a value of the chi-square-gamma statistic and an expectation value, E(χγ2), of that chi-square-gamma value, and a standard deviation, σ(χγ2), of that chi-square gamma value, the setting represented as Q(χγ2)=(χγ2−E(χγ2))/σ(χγ2) and continuing the iteration for a stopping criterion greater than one, the statistical value being the expectation value or the standard deviation. 3. The method of claim 1, further comprising setting the stop-criterion, Q(χγ2), to be a value of the chi-square-gamma statistic, χγ2, and continuing the iteration when the stop-criterion is greater than a sum of an expectation value, E(χγ2), of that chi-square-gamma value and the product of an assigned factor, n, and the standard deviation, σ(χγ2), of that chi-square-gamma value, the sum represented as E(χγ2)+nσ(χγ2), the statistical value being the expectation value, E(χγ2), or the standard deviation, σ(χγ2). 4. The method of claim 1, wherein a region of interest is defined within an object space, and wherein calculating comprises forward projecting the region of interest in a data space and calculating the chi-square-gamma statistic as a function of weights derived from the forward projecting of the region of interest. 5. The method of claim 1, wherein the iteration step is an iteration step of an algorithm selected from the group consisting of algorithms based on maximum likelihood, algorithms based on an ordered subset expectation maximization, algorithms based on a non-negative least square fit, algorithms based on an ordered subset non-negative least square fit, and algorithms based on a pixon method. 6. The method of claim 1 wherein determining the stop-criterion comprises varying the stop-criterion by spatial location and being weighted toward a region of interest within the volume. 7. In a non-transitory computer readable medium having included software thereon, the software including instructions to reconstruct a reconstruction image object by a processor, the instructions comprising: providing an initial image object representing radiation from a patient;reconstructing, in a series of iteration steps by a processor, updated image objects, wherein each iteration step includes determining a data model from an input image object, the input image object of a first iteration being the initial image object and the input image objects of subsequent iterations being the updated image objects from previous iterations, and determining a stop-criterion, Q(χ2), of the data model on the basis of a chi-square-gamma statistic, χγ2; anddetermining that the stop-criterion of the data model has remained outside the limitation of a preset threshold value;wherein determining comprises calculating the chi-square-gamma statistic as a sum of ratios calculated over data points, dj, defining a data space, wherein for each data point a denominator of the ratio is a square of a residual, and the numerator of the ratio is the sum of a measured value of that data point, dj, and a statistical-data-offset number, y, and wherein the residual is the difference between a corrected measured value of that data point and a modeled value, mj, of that data point, wherein the corrected measured value is the sum of the measured value, mj, and the minimum, Min(dj,1) of the measured value, dj, and one, the chi-square-gamma statistic, χγ2, calculating represented as: χγ2=Σ(dj+Min(dj,1)−mj)2/(dh+y);and wherein determining the stop-criterion, Q(χγ2), comprises setting the stop-criterion as a function of a statistical value, A(χγ2), of the chi-square gamma statistic, χγ2. 8. The non-transitory computer readable medium of claim 7, further comprising setting the stop-criterion to be the ratio of the difference between a value of the chi-square-gamma statistic and an expectation value, E(χγ2), of that chi-square-gamma value, and a standard deviation, σ(χγ2), of that chi-square-gamma value, the setting represented as Q(χγ2)=(χγ2−E(χγ2))/σ(χγ2), and continuing the iteration for a stopping criterion greater than one. 9. The non-transitory computer readable medium of claim 7, further comprising setting the stop-criterion to be the value of the chi-square-gamma statistic, and continuing the iteration when the stop-criterion is greater than the sum of an expectation value, E(χγ2), of that chi-square-gamma value and the product of an assigned factor, n, and the standard deviation, σ(χγ2), of that chi-square-gamma value, the sum represented as E(χγ2)+nσ(χγ2). 10. The non-transitory computer readable medium of claim 7, wherein a region of interest is defined within an object space, and wherein calculating comprises forward projecting the region of interest in a data space and calculating the chi-square-gamma statistic as a function of weights derived from the forward projecting of the region of interest. 11. The non-transitory computer readable medium of claim 7, wherein the iteration step is an iteration step of an algorithm selected from the group consisting of algorithms based on maximum likelihood, algorithms based on an ordered subset expectation maximization, algorithms based on a non-negative least square fit, algorithms based on an ordered subset non-negative least square fit, and algorithms based on a pixon method. 12. The non-transitory computer readable medium of claim 7, further comprising: displaying an image of the updated image object from one of the iterations. 13. The non-transitory computer readable medium of claim 7, wherein determining the stop-criterion comprises varying the stop-criterion by spatial location and being weighted toward a region of interest within the volume.
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