Multi-cycle dynamic CT imaging

Abstract

A dynamic CT imaging method is provided. With the method, projection measurement data for a region of an examination object to be imaged is captured, with simultaneous correlated capture of the respiratory movement of the examination object. A phase of the respiratory movement, for which image data is to be reconstructed, is selected. Phase projection measurement data assigned to the selected phase is also determined. Transition regions of partial images of the region to be imaged between successive respiratory cycles are then reconstructed on a trial basis based on a part of the phase projection measurement data, and a standard reconstruction is performed using parts of the phase projection measurement data for each of the successive respiratory cycles assigned to an optimum reconstruction.

Claims

1. A dynamic CT imaging method comprising: capturing projection measurement data for a region of an examination object to be imaged, with simultaneous correlated capture of respiratory movement of the examination object; selecting a phase of the respiratory movement, for which image data is to be reconstructed; determining phase projection measurement data assigned to the selected phase; multiple reconstructing transition regions of subregions of the region to be imaged between successive respiratory cycles of the respiratory movement based on candidate projection measurement data sets that are part of the phase projection measurement data; determining the candidate projection measurement data sets, which correspond to an optimum reconstruction, as target projection measurement data; and performing a standard reconstruction using the target projection measurement data for each of the successive respiratory cycles.

2. The method of claim 1, wherein the determining of the phase projection measurement data assigned to the selected phase comprises: determining at least one start projection index interval assigned to the assigned phase projection measurement data; determining a plurality of different candidate start projection indices for each start projection index interval; and performing the reconstruction of transition regions between successive respiratory cycles based on a part of the phase projection measurement data, the multiple reconstruction starting with the plurality of determined different candidate start projection indices.

3. The method of claim 2, further comprising determining, for optimum reconstruction, optimum start projection indices, the optimum start projection indices including the plurality of different candidate start projection indices, for which the reconstructed transition regions between successive respiratory cycles match one another best, calculating a criterion for how closely reconstructed transition regions between successive respiratory cycles match one another in each instance as a displacement value of an artifact metric, or a combination thereof.

4. The method of claim 3, wherein the respective candidate start projection indices, for which the displacement value of the artifact metric is a minimum, are determined as the optimum start projection indices.

5. The method of claim 2, wherein the standard reconstruction is performed using the determined optimum start projection indices for a plurality of different phases.

6. The method of claim 1, wherein selecting the phase comprises determining corresponding phase time points.

7. The method of claim 2, further comprising determining start projection indices that are assigned to a plurality of successive respiratory movement cycles and correspond to selected phase time points with the aid of a binning method.

8. The method of claim 7, further comprising determining each of the at least one start projection index interval as an interval around the determined start projection indices.

9. The method of claim 1, wherein the multiple reconstructing of transition regions between successive respiratory cycles is performed with interpolation-free assignment to the respective respiratory cycle.

10. The method of claim 1, wherein the multiple reconstructing of transition regions between successive respiratory cycles is performed sequentially for each of the successive respiratory cycles, starting with a first respiratory cycle that is part of the reconstruction, up to a last respiratory cycle.

11. The method of claim 1, wherein freely selectable parameters are adapted in a phase-specific manner with the aid of the simultaneous correlated capture of the respiratory movement of the examination object.

12. The method of claim 1, further comprising: determining a region in which artifacts are to be reduced based on a previously performed respiration-correlated standard image data reconstruction; and for the determined region in which artifacts are to be reduced: capturing projection measurement data with simultaneous correlated capture of respiratory movement of the examination object; selecting a phase of the respiratory movement, for which image data is to be reconstructed; determining phase projection measurement data assigned to the selected phase; multiple reconstructing transition regions of subregions between successive respiratory cycles of the respiratory movement based on candidate projection measurement data sets that are part of the phase projection measurement data; determining the candidate projection measurement data sets, which correspond to an optimum reconstruction, as target projection measurement data; and performing a standard reconstruction using the target projection measurement data for each of the successive respiratory cycles.

13. An image data reconstruction device comprising: an input interface configured to capture projection measurement data for a region of an examination object to be imaged, with simultaneous correlated capture of respiratory movement of the examination object; a processor configured to: capture selection information relating to a selected phase of the respiratory movement, for which image data is to be reconstructed; determine the projection measurement data assigned to the selected phase; multiple reconstruct of transition regions of subregions of the region to be imaged between successive respiratory cycles based on candidate projection measurement data sets of a part of the phase projection measurement data; determine the candidate projection measurement data sets, which correspond to an optimum reconstruction, as target projection measurement data; and perform a standard reconstruction using the target projection measurement data for each of the successive respiratory cycles.

14. A computed tomography system comprising: a control system comprising: an image data reconstruction device comprising: an input interface configured to capture projection measurement data for a region of an examination object to be imaged, with simultaneous correlated capture of respiratory movement of the examination object; a processor configured to: capture selection information relating to a selected phase of the respiratory movement, for which image data is to be reconstructed; determine the projection measurement data assigned to the selected phase; multiple reconstruct of transition regions of subregions of the region to be imaged between successive respiratory cycles based on candidate projection measurement data sets of a part of the phase projection measurement data; determine the candidate projection measurement data sets, which correspond to an optimum reconstruction, as target projection measurement data; and perform a standard reconstruction using the target projection measurement data for each of the successive respiratory cycles.

15. In a non-transitory computer-readable storage medium storing instructions executable by a computer for dynamic CT imaging, the instructions comprising: capturing projection measurement data for a region of an examination object to be imaged, with simultaneous correlated capture of respiratory movement of the examination object; selecting a phase of the respiratory movement, for which image data is to be reconstructed; determining phase projection measurement data assigned to the selected phase; multiple reconstructing transition regions of subregions of the region to be imaged between successive respiratory cycles of the respiratory movement based on candidate projection measurement data sets that are part of the phase projection measurement data; determining the candidate projection measurement data sets, which correspond to an optimum reconstruction, as target projection measurement data; and performing a standard reconstruction using the target projection measurement data for each of the successive respiratory cycles.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows a flow diagram illustrating an imaging method according to a first exemplary embodiment;

(2) FIG. 2 shows a diagram illustrating an exemplary reconstruction of image data assigned to a selected phase;

(3) FIG. 3 shows a flow diagram illustrating a planning method according to an exemplary embodiment;

(4) FIG. 4 shows representations of conventional image acquisitions and image acquisitions produced with the aid of a method according to an exemplary embodiment;

(5) FIG. 5 shows a block diagram with an image data reconstruction facility according to an exemplary embodiment; and

(6) FIG. 6 shows a schematic representation of a computed tomography system according to an exemplary embodiment.

DETAILED DESCRIPTION

(7) FIG. 1 shows a dynamic CT imaging method 100 according to an exemplary embodiment.

(8) In act 1.I, raw data is acquired with the aid of an external respiratory surrogate. The respiratory surrogate may be implemented with the aid of a respiration monitoring system (e.g., a camera or respiration belt). Use of the respiratory surrogate serves to provide a first correlation between a respiratory phase PH to be reconstructed (see FIG. 2) and the acquired raw data PMD(t), also referred to as projection measurement data.

(9) In act 1.II, the phases PH to be reconstructed are selected, for example, by a user. In other words, the phase PH of the respiratory cycle for which image data is to be generated is determined. In act 1.III, the projections or projection measurement data sets to be used for the selected phase are selected from the captured projection measurement data PMD(t) using an algorithm (e.g., a predefined binning algorithm). There are various binning algorithms, which are known in the literature as “phase-based”, “local amplitude-based” or “global amplitude-based”. Any of these approaches may be used for the described method. In act 1.IV, the start projection indices IND.sub.SPn,1 for each respiratory cycle Z.sub.n corresponding to the selected projections or projection measurement data sets are identified. Also in act 1.V, the algorithm is used to determine a start projection index interval INT.sub.INDSPn around the respective start projection index IND.sub.SPn,1 in a respiratory-cycle-specific manner.

(10) In acts 1.VI to 1.XIV, a sequential search is performed for the optimum selection of start projection indices IND.sub.SPn,k, IND.sub.SPn+1,m within permitted adjacent start projection index intervals INT.sub.INDSPn, INT.sub.INDSPn+1 of successive respiratory cycles. The aim is to find start projection indices IND.sub.SPn,kmin, IND.sub.SPn+1,mmin (binning points) and thus projection measurement data sets for the final reconstruction RK.sub.WFBP(IND.sub.SPn,kmin, IND.sub.SPn+1,mmin) in act 1.XIV, so that this final reconstruction has minimal artifacts.

(11) More specifically in act 1.VI, the transition region between two respiratory cycles is reconstructed without overlap (e.g., with unambiguous interpolation-free assignment to the respective respiratory cycle) for a first start projection index IND.sub.SP1,1 and the following start projection index IND.sub.SP2,1 for the next respiratory cycle (this process is shown in FIG. 1 as RK(IND.sub.SPn,k, IND.sub.SPn+1,m)). Two such respiratory cycles Z.sub.1, Z.sub.2 are shown in a partial diagram 202 on the right in FIG. 2. The binning regions assigned to the start projection index intervals INT.sub.INDSP1, INT.sub.INDSP2 of the two respiratory cycles are also shown there as vertical bars. On the left of FIG. 2, there is also a partial diagram 201 showing image data for a reconstructed transition region 203 between the two respiratory cycles Z.sub.1 and Z.sub.2. When the start projection indices have been determined, it may be seen from the technical parameters of the imaging system and the basic conditions of an image acquisition, which include, for example, the slice thickness of the slices of the image acquisition and the width of the detector of the imaging system, up to which projection index reconstruction takes place from the respective start projection index for a respiratory cycle.

(12) In act 1.VII, it is checked whether a predefined number k.sub.max of start projection indices IND.sub.Sp1,k has been generated for the first respiratory cycle and used for reconstruction in act 1.VI. If not, as shown in FIG. 1 by “n”, in act 1.VIII, the running index k is increased by the value 1, and in act 1.VI, a reconstruction is performed with new start projection indices IND.sub.SP1,2 or IND.sub.SP2,2. If it is determined in act 1.VII that reconstructions have been performed for all k=k.sub.max predefined start projection indices of the first respiratory cycle Z.sub.1 for the transition region 203 between the first two respiratory cycles Z.sub.1 and Z.sub.2, as shown in FIG. 1 by “y”, it is asked in act 1.IX whether all the reconstructions have also been performed for all the m=m.sub.max start projection indices IND.sub.SP2,m of the second respiratory cycle. If this is not yet the case, as shown in FIG. 1 by “n”, in act 1.X, a running index m is increased by the value 1, and in act 1.VI, a reconstruction is performed with correspondingly amended start projection indices. If it is determined in act 1.IX that all the reconstructions have been performed, as shown in FIG. 1 by “y”, in act 1.XI, the optimum start projection indices IND.sub.SP1,kmin, IND.sub.SP2,mmin are determined for the transition region 203 between the first two respiratory cycles Z.sub.1 and Z.sub.2. An artifact metric is calculated, which quantifies how closely the adjacent reconstructed slices from the two respiratory cycles Z.sub.1, Z.sub.2 match, and the transition with the smallest artifact metric value is selected (e.g., with the start projection indices IND.sub.SP1,kmin, IND.sub.SP2,mmin), the assigned reconstruction of which gives the smallest artifact—metric value. In act 1.XII, it is determined whether all the optimum start projection indices have been determined for all the n=n.sub.max, respiratory cycles. If not, as shown in FIG. 1 by “n”, in act 1.XIII, a running index n is increased by the value 1, and in act 1.VI, a reconstruction is performed again for the next transition region (e.g., between the second and third respiratory cycles Z.sub.2, Z.sub.3). Acts 1.VI to 1.XIII are also performed for each transition region between all successive respiratory cycles Z.sub.n, Z.sub.n+1. If it is determined in act 1.XII that the reconstructions have been performed for all the n.sub.max-1 transition regions (n.sub.max is the number of respiratory cycles), in act 1.XIV, a standard WFBP reconstruction is performed for each phase PH to be reconstructed with the previously adapted binning points (e.g., the determined optimum start projection indices IND.sub.SPn,kmin, IND.sub.SPn,mmin). This reconstruction is shown as RK.sub.WFBP(IND.sub.SPn,kmin, IND.sub.SPn+1,min) in FIG. 1.

(13) The freely selectable parameters of the method 100 may be adjusted in a phase-specific manner, in that prior knowledge of the different respiratory phases acquired with the aid of the external respiratory surrogate is also incorporated. This serves to provide the stability of the algorithm and to achieve maximum artifact reduction.

(14) The diagram 200 in FIG. 2, as mentioned above, shows a partial diagram 201 of a reconstruction based on projection measurement data for two respiratory cycles Z.sub.1, Z.sub.2. A partial diagram 202 on the right in FIG. 2, corresponding to the partial diagram 201 on the left showing the selected phase, shows corresponding binning regions as vertical bars in a respiratory curve AK. The binning regions are assigned start projection indices IND.sub.SP1,1, IND.sub.SP2,1. As described in relation to FIG. 1, start projection index intervals INT.sub.INDSP1, INT.sub.INDSP1 are also determined around the start projection indices IND.sub.SP1,1, IND.sub.SP2,1, in which further candidates for start projection indices may be set. The binning regions are assigned projection measurement data sets, from which in the method of one or more of the present embodiments, by selecting the matching candidates for the start projection indices, a subset of the projection measurement data sets, with which the reconstruction of partial images is finally performed to provide an overall image, is selected. The partial images have transition regions that only match closely when optimum start projection indices are selected.

(15) To illustrate this problem, the partial diagram 201 shows a transition region 203 marked with a broken line between the two respiratory cycles Z.sub.1 and Z.sub.2. In this transition region, the two edge segments of the first and second subregions or partial images 204, 205 facing one another do not match precisely (e.g., the two edge segments are displaced slightly in the horizontal direction). The edge segments are assigned to a respective respiratory cycle Z.sub.1, Z.sub.2 and belong to first and second partial images 204, 205. The first and second partial images are, for example, each assigned to the same selected phase PH and together form the overall image 201.

(16) FIG. 3 shows a comparison between a conventional respiration-correlated 4D CT reconstruction without optimum selection of the binning points and an artifact-reduced 4D CT reconstruction according to an exemplary embodiment. The left-hand partial drawings 31, 33 each show the result of a conventional reconstruction, and by contrast, the right-hand partial drawings 32, 34 show image acquisitions performed with the aid of an artifact-reduced 4D CT reconstruction according to an exemplary embodiment. The left-hand partial drawings 31, 33 each show step-type artifacts within a subregion ROI, marked with a circular line. The artifacts are no longer present in the corresponding subregions ROI in the two right-hand partial drawings 32, 34.

(17) FIG. 4 shows one embodiment of a method 400 for performing radiation planning for a tumor. In act 4.I, a conventional respiration-correlated reconstruction is used to determine a region ROI of an examination object O, in which the tumor is located. In act 4.II, the method 100 is applied to the image of the tumor but the region to be imaged is limited to the region ROI determined in act 4.I. The image generated in act 4.I is supplemented in act 4.III by the image generated in act 4.II in the region ROI. In other words, the region ROI present in the conventional image is replaced by the image generated in act 4.II. In act 4.IV, radiation of the tumor is planned in synchronicity with the dynamic 4D image generated in act 4.III. For example, a corresponding protocol is produced, according to which subsequent dynamic imaging is performed.

(18) FIG. 5 shows a schematic diagram of an image data reconstruction facility 50 (e.g., an image data reconstruction device) according to an exemplary embodiment. The image data reconstruction facility 50 includes an input interface 51 that captures projection measurement data PMD(t) for a region VOI of an examination object O to be imaged (see FIG. 6). The projection measurement data PMD(t) is captured with the simultaneous correlated capture of the respiratory movement of the examination object O. The capture of the respiratory movement may be performed, for example, with the aid of a respiratory surrogate (see FIG. 6). A respiratory curve generated with the aid of the respiratory surrogate is then used to determine phases PH for which an image data determination or image data reconstruction is to be performed.

(19) The selected phases PH are transferred to a selection unit 52 of the image data reconstruction facility 50, which receives such selection information relating to the selected phases PH of the respiratory movement, for which image data is to be reconstructed. Based on this information, a start projection index determination unit 53 determines a start projection index IND.sub.SPn,1 for each of N respiratory cycles. A start projection index interval determination unit 54 then determines corresponding start projection index intervals INT.sub.INDSPn around the start projection indices IND.sub.SPn,1, to which projection measurement data PMD.sub.PHn, the projection indices of which are within the start projection index intervals INT.sub.SPn, are assigned. Additional start projection indices IND.sub.SPn,k, INDP.sub.SPn+1,m, which are part of the start projection index intervals INT.sub.ISPn, are also determined by the start projection index determination unit 53 based on the start projection index intervals INT.sub.INDSPn determined by the start projection index interval determination unit 54.

(20) Based on the captured projection measurement data PMD and the start projection indices IND.sub.SPn,k, IND.sub.SPn+1,m determined by the start projection index determination unit 53, to which candidate projection measurement data sets PMD.sub.K correspond in each instance, a reconstruction unit 55 reconstructs image data BD.sub.n,k, BD.sub.n+1,m for each of the transition regions between successive respiratory cycles Z.sub.n, Z.sub.n+1. The image data BD.sub.n,k, BD.sub.n+1, m is examined by a target projection measurement data determination unit 56 to determine which of the image data BD.sub.n,mmin, BD.sub.n+1,mmin shows the smallest artifacts. The start projection indices IND.sub.SPn,kmin, IND.sub.SPn+1,mmin assigned to the image data BD.sub.n,kmin, BD.sub.n+1,mmin, which correspond to assigned target projection measurement data PMD.sub.Z, are then transferred to the reconstruction unit 55. The reconstruction unit 55 performs a standard WFBP reconstruction using the optimum start projection indices IND.sub.SPn,kmin, IND.sub.SPn+1,mmin for each of the successive respiratory cycles Z.sub.nZ.sub.n+1, so that optimized image data BD.sub.opt is generated. The optimized image data BD.sub.opt determined during the reconstruction is transferred to an output interface 57, which forwards the image data BD.sub.opt to external units, such as storage units or terminals, for example.

(21) FIG. 6 shows a schematic diagram of a computed tomography system (CT system) 1 with one embodiment of an image data reconstruction facility 50.

(22) The CT system 1 essentially includes a scanner 10, in which a projection data acquisition unit 5 with a detector 16 and an x-ray source 15 opposite the detector 16 travels around a measurement space 12 on a gantry 11. Located in front of the scanner 10 is a patient support facility 3 or patient table 3, the upper part 2 of which holds a patient O and may be moved together with the patient O toward the scanner 10 in order to move the patient O through the measurement space 12 relative to the detector system 16. The scanner 10 and patient table 3 are activated by a control facility 20, from which acquisition control signals AS are output via a standard control interface 23 to activate the entire system according to predefined measurement protocols in the conventional manner. The movement of the patient O along the z direction, which corresponds to the system axis z longitudinally through the measurement space 12, and the simultaneous circulation of the x-ray source 15 provide that the x-ray source 15 describes a helical path relative to the patient O during measurement. The detector 16 always runs in a parallel manner opposite the x-ray source 15 in order to capture projection measurement data PMD, which is then used to reconstruct volume and/or slice image data.

(23) A sequential measuring method, in which a fixed position is approached in the z direction and during a circuit, a partial circuit or a number of circuits the required projection measurement data PMD is captured at the relevant z position, may also be performed in order to reconstruct a slice image at the z position or in order to reconstruct volume image data from the projection data from a number of z positions.

(24) The method may also be used in principle on other CT systems (e.g., with a number of x-ray sources and/or detectors and/or with a detector forming a complete ring).

(25) The projection measurement data PMD acquired by the detector 16 (also referred to in the following as raw data) is transferred via a raw data interface, which in this exemplary embodiment is the input interface 51 of the image data reconstruction facility 50, to the control facility 20 or the image data reconstruction facility 50 contained therein. The raw data is further processed in the manner described above in the image data reconstruction facility 50. In this exemplary embodiment, the image data reconstruction facility 50 is implemented in the form of software on a processor in the control facility 20.

(26) After processing in the image data reconstruction facility 50, the determined image data BD.sub.opt is output to a storage unit 22 and, for example, to an output unit of the control facility 20 of the CT system.

(27) Before image reconstruction, a respiratory curve generated with the aid of the respiratory surrogate is used to determine phases PH, for which an image data determination or image data reconstruction is to be performed.

(28) The image data reconstruction facility 50 also includes, as mentioned above in relation to FIG. 5, a selection unit 52 that receives the selection information relating to the selected phases PH of the respiratory movement, for which image data is to be reconstructed, from an input unit 21 of the control facility 20.

(29) The methods and devices described above are only exemplary embodiments, and the invention may be varied by the person skilled in the art without departing from the scope of the invention, as defined by the claims. The invention is not restricted to applications in the medical field but may also be used for the acquisition of CT images for other purposes. The use of the indefinite article “a” or “an” does not exclude the possibility of more than one of the relevant features being present. Similarly the term “unit” does not exclude the possibility of the present embodiments including a number of components that may also be distributed spatially.

(30) The elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present invention. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent. Such new combinations are to be understood as forming a part of the present specification.

(31) While the present invention has been described above by reference to various embodiments, it should be understood that many changes and modifications can be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.