DE102010019015A1 - Method for reconstructing image data of human heart from measurement data of computed tomography system, involves reconstructing image data by utilizing iterative algorithm, where pixel values of image data is not smaller than zero - Google Patents

Abstract

The method involves detecting measurement data with a relative rotational motion between a radiation source i.e. X-ray tube (C2), of a computed tomography (CT) system (C1) and a moving object under examination i.e. human heart. The detected measurement data is analyzed with respect to motionless components of the moving object and moving parts of the moving object to provide image data determined from incomplete measurement data set. The image data is reconstructed by utilizing an iterative algorithm, where pixel values of the image data is not smaller than zero. Independent claims are also included for the following: (1) a control and computing unit for reconstruction of image data (2) a computer program product comprising a machine-readable data carrier with a program code having instructions for reconstructing image data of a moving object under examination from measurement data.

Description

The invention relates to a method for reconstructing CT images of a moving examination object from measured data.

Tomographic imaging methods are characterized by the fact that internal structures of an examination subject can be examined without having to perform any surgical procedures on it. One possible type of tomographic imaging is to take a number of projections from different angles from the object to be examined. From these projections, a two-dimensional sectional image or a three-dimensional volume image of the examination object can be calculated.

An example of such a tomographic imaging method is computed tomography. Methods for scanning an examination subject with a CT system are well known. In this case, for example, circular scans, sequential circular scans with feed or spiral scans are used. Also other types of scans that are not based on circular motions are possible, such. B. scans with linear segments. With the aid of at least one X-ray source and at least one opposing detector, absorption data of the examination object are taken from different exposure angles and these absorption data or projections thus collected are offset by means of appropriate reconstruction methods to form sectional images through the examination subject.

For the reconstruction of computed tomographic images from X-ray CT data sets of a computed tomography (CT) device, d. H. From the recorded projections, a so-called filtered back projection (FBP) method is nowadays used as the standard method. After data acquisition, a so-called "rebinning" step is usually performed in which the data generated with the fan-shaped beam from the source is rearranged to be in a shape as if the detector were converging from parallel to the detector X-rays would hit. The data is then transformed into the frequency domain. In the frequency domain, filtering takes place, and then the filtered data is transformed back. With the help of the thus sorted and filtered data, a backprojection then takes place on the individual voxels within the volume of interest.

Recently, iterative reconstruction techniques have been developed. In such an iterative reconstruction method, a reconstruction of initial image data from the projection measurement data first takes place. For this purpose, for example, a convolution-back projection method can be used. From this initial image data, synthetic projection data are then generated with a "projector", a projection operator, which should map the measuring system mathematically as well as possible. The difference to the measurement signals is then backprojected with the adjoint to the projector operator and it is reconstructed so a residual image, with which the initial image is updated. The updated image data can in turn be used to generate new synthetic projection data in a next iteration step with the aid of the projection operator, again to form the difference to the measurement signals and to calculate a new residual image, which again improves the image data of the current iteration stage etc. Such a method can be used to reconstruct image data which has relatively good image sharpness and still low image noise.

A drawback of these generally known calculation methods is that motion blurs can arise in the image in the case of a moving examination object or an examination object that is at least partially moved, since during the time of a scan operation for the data required for an image, a positional offset of the examination object or a part of the examination object may be present, so that the basic data that lead to an image, not all spatially identical situation of the examination object reflect. This motion blur problem arises particularly intensified when performing cardio-CT examinations of a patient, in which due to the heart movement a strong motion blur in the heart area can occur or for examinations, in which relatively fast changes in the examination object are to be measured.

The invention has for its object to provide a method for the reconstruction of CT images of a moving examination object. Furthermore, a corresponding control and processing unit, a CT system, a computer program and a computer program product are to be shown.

This object is achieved by methods with the features of claim 1, and by a control and processing unit, a CT system, a computer program and a computer program product with features of independent claims. Advantageous embodiments and further developments are the subject of dependent claims.

In the inventive method for the reconstruction of image data of a moving For examination objects from measured data, the measured data were previously acquired during a relative rotational movement between a radiation source of a computed tomography system and the examination subject. There is a decomposition of the measurement data in a first, immobile components of the examination subject corresponding part, and a second, moving parts of the examination subject corresponding part. Based on the first part of the measurement data, first image data are reconstructed, and second image data are reconstructed based on an incomplete data set of the second part of the measurement data. In this case, the reconstruction of the second image data is carried out by an iterative algorithm in which it is required that pixel values should not be less than zero.

The acquired measurement data preferably comprises a complete measurement data set that can be used for a conventional image reconstruction. Complete means in computer tomography that at least measurement data of a half-round is present. A half round corresponds to a projection angle range covered by the measurement of 180 ° in parallel beam geometry and 180 ° plus the fan opening angle in fan beam geometry.

The measurement data are first decomposed, in a first and a second part. The first part contains measured data values which, after image reconstruction, exclusively or at least mainly depict the immobile components of the section of the examination object captured by the measurement data. The second part contains measured data values which, after image reconstruction, exclusively or at least mainly depict the moving components of the section of the examination object detected by the measurement data. By joining the two partial images, one obtains a complete image, which includes both the moving and the stationary. Contains ingredients. A distinction between moving and immobile components is not always clearly possible; accordingly, the measurement data decomposition does not have to be a sharp separation.

If one considers the measurement data in the sinogram space, then after the decomposition a first sinogram with first measured data values and a second sinogram with second measured data values are present, whereby some of these measured data values can also be zero.

The first part of the measurement data is used to calculate the first image data. Preferably, a complete measurement data set is used for this purpose. The calculation of the second image data, on the other hand, takes place on the basis of an incomplete measurement data set of the second part of the measurement data. In particular, this incomplete data record may be a subset of the second part of the measurement data. The advantage of using the incomplete data set is that it increases the time resolution, which is essential for improving the image quality in the case of moving examination objects. Because the time required to capture an incomplete measurement data set is smaller than that for acquiring a complete measurement data set.

The first and the second image data may be a two-dimensional sectional image or a three-dimensional volume image of the examination object.

An iterative algorithm is used to calculate the second image data. There are several examples for this, such as the algebraic reconstruction technique (ART), the simultaneous algebraic reconstruction technique (SART), the iterated filtered backprojection (IFBP), or statistical iterative image reconstruction techniques. The principle of the iterative reconstruction is that a CT image is calculated which is used as the input image for the calculation of the iteration image of the next stage. This step-by-step attempt is made to adapt the iteration image as well as possible to the measured data.

If one were to apply an iterative algorithm unchanged to an incomplete measurement data set, the resulting image would contain artifacts due to this incompleteness. To avoid or at least reduce this, it is required that pixel values should not be less than zero. This requirement concerns the iteration images calculated in the course of the iterative reconstruction and thus also the result image of the iterative reconstruction. It can refer to all pixels, or to only a subset of the pixels. It is particularly advantageous to use the requirement as a boundary condition in the iterative reconstruction. Furthermore, it is advantageous if, according to the requirement, negative pixel values are set to zero. Following the demand, in this case, the values are modified for some pixels, those with values less than zero. Instead of the value zero, the pixel values smaller than zero can also be set to another value.

In a development of the invention, the first and the second image data are added. This can be a simple or a weighted addition. The summation image can be output as a result image.

According to a development of the invention, the decomposition of the measured data takes place in such a way that neither the first part nor the second part is negative Contains data values. Negative data values may lead to nonsensical or incorrect image data values.

In addition or as an alternative to the condition of avoiding negative data values during the decomposition, it can also be demanded during the decomposition that the image data corresponding to the second part have no values smaller than zero. Such negative target image data would eventually result in nonsensical or incorrect image data values in the actual reconstruction.

It is particularly advantageous if a low-pass filtering and / or a high-pass filtering of the measured data takes place for the decomposition of the measured data. If a low-pass filtering is performed, the first part corresponds to the low-pass filtered measured data; If a high-pass filtering is performed, the second part corresponds to the high-pass filtered measured data.

The low-pass filtering and / or high-pass filtering can be done by applying a morphological filter, ie a shape filter, to the measured data.

In a further development of the invention, low-pass filtered measurement data are determined for the decomposition of the measurement data, which are scaled and subtracted from the measurement data, the first part corresponding to the scaled low-pass filtered measurement data and the second part of the difference between the measurement data and the scaled low-pass filtered measurement data. The scaling serves to avoid negative values in the difference between the measured data and the low-pass filtered measured data. If no negative values occur, scaling is scaled by a factor of 1. The low-pass filtered measurement data can be determined in various fields, some of which are mentioned below.

In an embodiment of the invention, a median formation with respect to the measurement data takes place for the decomposition of the measurement data, and at least some of the measurement data are changed as a function of the median formed. Depending on how the measurement data changes, this may correspond to low-pass or high-pass filtering.

It is particularly advantageous if, for the decomposition of the measurement data, low-pass filtered measurement data are obtained by forming the median in the respective measurement data environment relative to a measurement data value and replacing the measurement data value by the median, if the median is smaller than the measurement data value. If the median is greater than the measured data value, preferably no change in the measured data value takes place. In this way, it is possible to proceed with respect to each measurement data value in order to low-pass-filter the entire measurement data in this way.

In an embodiment of the invention, low-pass filtered measurement data can be obtained for decomposing the measurement data by reconstructing image data based on the measurement data, lowpass filtering the image data, and projecting the lowpass filtered image data into the data space. In this case, the calculation of the low-pass filtering is shifted from the measurement data space into the image data space.

The control and computing unit according to the invention serves to reconstruct image data of an examination subject from measured data of a CT system. It comprises a program memory for the storage of program code, wherein - if appropriate inter alia - there is program code which is suitable for carrying out a method of the type described above or for effecting or controlling this embodiment. The CT system according to the invention comprises such a control and computing unit. Furthermore, it may contain other ingredients which z. B. needed for the acquisition of measurement data.

The computer program according to the invention has program code which is suitable for carrying out the method of the type described above when the computer program is executed on a computer.

The computer program product according to the invention comprises program code stored on a computer-readable data medium which is suitable for carrying out the method of the type described above when the computer program is executed on a computer.

In the following the invention will be explained in more detail with reference to an embodiment. Showing:

1 FIG. 1 shows a first schematic illustration of an embodiment of a computer tomography system with an image reconstruction component, FIG.

2 FIG. 2 shows a second schematic illustration of an embodiment of a computer tomography system with an image reconstruction component, FIG.

3 : the cycle of movement of the human heart,

4 : a flowchart.

In 1 First, a first computer tomography system C1 with an image reconstruction device C21 is shown schematically. This is a so-called third-generation CT apparatus, to which the invention is not limited. In the gantry housing C6 there is a closed gantry, not shown here, on which a first x-ray tube C2 with an opposite detector C3 are arranged. Optionally, in the CT system shown here, a second X-ray tube C4 is arranged with an opposing detector C5 so that a higher time resolution can be achieved by the additionally available radiator / detector combination, or by using different X-ray energy spectra in the radiator / Detector systems also "dual-energy" studies can be performed.

The CT system C1 also has a patient couch C8, on which a patient can be pushed into the measuring field during the examination along a system axis C9, also referred to as a z-axis, the scan itself being both a pure circular scan without advancing the patient can only take place in the interested field of investigation. The movement of the patient couch C8 relative to the gantry is effected by a suitable motorization. During this movement, the X-ray sources C2 and C4 respectively rotate about the patient. At the same time, the detector C3 or C5 runs parallel to the X-ray source C2 or C4 in order to acquire projection measurement data, which are then used for the reconstruction of sectional images. As an alternative to a sequential scan, in which the patient is pushed step by step between the individual scans through the examination field, there is of course also the possibility of a spiral scan in which the patient during the continuous scan with the X-ray continuously along the system axis C9 through the examination field between X-ray tube C2 or C4 and detector C3 or C5 is pushed. The movement of the patient along the axis C9 and the simultaneous circulation of the X-ray source C2 or C4 results in a helical scan for the X-ray source C2 or C4 relative to the patient during the measurement, a helical trajectory. This path can also be achieved by moving the gantry along the axis C9 when the patient is still moving. Further, it is possible to reciprocate the patient continuously and periodically between two points.

The CT system is controlled 10 by a control and processing unit C10 with computer program code Prg ₁ to Prg _n present in a memory. It should be noted that, of course, these computer program codes Prg ₁ to Prg _{n can} also be contained on an external storage medium and can be loaded into the control and processing unit C10 as required.

From the control and processing unit C10 can via a control interface 24 Acquisition control signals AS are transmitted to drive the CT system C1 according to certain measurement protocols. The acquisition control signals AS in this case relate z. For example, the X-ray tubes C2 and C4, with specifications on their performance and the times of their switching on and off can be made, and the gantry, where specifications can be made to their rotational speed, and the table feed.

Since the control and processing unit C10 has an input console, measurement parameters can be input by a user or operator of the CT apparatus C1, which then control the data acquisition in the form of acquisition control signals AS. Information about currently used measuring parameters can be displayed on the screen of the control and processing unit C10; In addition, further information relevant to the operator can be displayed.

The projection measurement data p or raw data acquired by the detector C3 or C5 is transferred to the control and processing unit C10 via a raw data interface C23. These raw data p are then further processed, if appropriate after suitable preprocessing, in an image reconstruction component C21. The image reconstruction component C21 is implemented in this embodiment in the control and processing unit C10 in the form of software on a processor, for. In the form of one or more of the computer program codes Prg ₁ to Prg _n . With regard to the image reconstruction, as already explained with reference to the control of the measuring process, the computer program codes Prg ₁ to Prg _{n can} also be contained on an external storage medium and can be loaded into the control and processing unit C10 as required. Furthermore, it is possible that the control of the measuring process and the image reconstruction are performed by different computing units.

The image data f reconstructed by the image reconstruction component C21 are then stored in a memory C22 of the control and processing unit C10 and / or output in the usual way on the screen of the control and computing unit C10. You can also have an in 1 not shown interface in a connected to the computed tomography system C1 network, such as a radiological information system (RIS), fed and stored in an accessible there mass storage or output as images.

In addition, the control and computing unit C10 can also perform the function of an ECG, wherein a line C12 is used to derive the ECG potentials between the patient and the control and processing unit C10. In addition, this has in the 1 shown CT system C1 also has a contrast agent injector C11, via the additional contrast agent can be injected into the bloodstream of the patient, so that z. B. the vessels of the patient, in particular the heart chambers of the beating heart, can be better represented. In addition, it is also possible to perform perfusion measurements for which the proposed method is also suitable.

The 2 shows a C-arm system, in contrast to the CT system of 1 the housing C6 carries the C-arm C7, on the one hand the X-ray tube C2 and on the other hand the opposite detector C3 are fixed. The C-arm C7 is also swiveled around a system axis C9 for one scan so that a scan can take place from a plurality of scan angles and corresponding projection data p can be determined from a multiplicity of projection angles. The C-arm system C1 of the 2 Like the CT system, it has the 1 via a control and processing unit C10 to 1 described type.

The invention is in both of the in the 1 and 2 shown systems applicable. Furthermore, it is basically also applicable to other CT systems, for. B. for CT systems with a complete ring forming detector.

As far as parts of the body of a patient are to be taken that can not move or be immobilized, there are no significant problems with movement artifacts for the recording of the projections and the subsequent image reconstruction. On the other hand, this is critical for moving examination objects. In the following, the situation is considered that a CT scan of a moving examination object should be made.

An example of a periodically moving examination object is the human heart. The invention will be explained in more detail below with reference to cardio-CT, ie a CT scan of the beating heart. Of course, it is not limited to this application. As is known, the human heart essentially carries out a periodic movement. The periodic movement consists of an alternating sequence of a rest or relaxation phase and a movement or impact phase. The resting phase has a duration of usually between 500 to 800 ms, the beating phase a duration of 200 to 250 ms. This is off 3 can be seen, in which the level L of ECG ECG signal of a patient over the time t is plotted. The ECG signal illustrates the periodic movement of the patient's heart, wherein the beginning of a cardiac cycle is indicated by an R-wave R and the duration of the respective cardiac cycle by the RR interval T _RR , ie the distance of the R-wave initiating the respective cardiac cycle R from the following cardiac cycle initiating R wave R, is determined. A heart phase starts with an R wave at 0% and ends with the next R wave R at 100%. A conversion between the dimension of time and the heart phase is possible at any time; For this purpose, the ECG data can be used, which can be taken at any time, which heart phase is currently present. The resting phase of the heart, ie the phase of minimal heart movement, is indicated by hatching.

In addition to the requirements for the quality of CT images, which also exist for immobile examination objects, heart imaging aims to achieve a high temporal resolution of the images. The time resolution is inversely proportional to the time required to capture the projections. The more time passes during the data acquisition, the more the heart moves during this measurement time. This movement leads to unwanted motion artifacts in the CT images. The informative value of the CT images is thereby drastically reduced.

Usually, for CT image reconstructions, when measuring in parallel beam geometry, a data interval, i. H. a series of successive projections, each projection corresponding to a measurement at a given projection angle, being available to at least one half-round of the x-ray source around the examination subject, i. H. eifern projection angle range of 180 °, corresponds. For a fan beam geometry, the projection angle range must be 180 ° plus the fan aperture angle. Both cases are summarized below under the name "data of a half-cycle" or "complete data set". This minimum data interval is necessary in order to be able to reconstruct each pixel in the measurement field. In the center of rotation, even in fan beam geometry, a projection angle range of 180 ° is sufficient. The best possible temporal resolution in such a reconstructed CT image is thus just half the rotation time of the CT device in the vicinity of the center of rotation. In the case of a rotation speed of the gantry of 0.5 seconds per revolution, so a maximum time resolution of 0.25 seconds can be erriecht.

A common approach to reducing motion artifacts is to increase the gantry rotation speed. However, this requires high and sometimes unsolvable requirements for Mechanics of the gantry and is therefore very expensive.

Another hardware-based approach to increasing the time resolution is the use of a second tube / detector system which is offset by 90 ° from the first system. This is a so-called dual source system. The two systems offset by 90 ° simultaneously record measured data, so that a projection angle coverage of 180 ° is already reached after a Gantrydrehung of 90 °, which corresponds to a doubling of the time resolution.

Another approach, which is mainly used in cardiac imaging, is the splitting of the data acquisition into several sections, each less than 180 °, which in total result in a projection angle range of 180 ° and which respectively map the same state of motion of the heart, but in successive heart beats , This is the so-called multi-segment reconstruction.

Recently, in the publication G. Chen, J. Tang, J. Hsieh: Temporal resolution improvement using PICCS in MDCT cardiac imaging, Med. Phys. 36 (6), June 2009, pp. 2130-2135 described an algorithm with the help of which under certain conditions from a limited projection angle range CT images are reconstructed, whereby the time resolution is increased.

In the following, another method for increasing the time resolution will be described, which u. a. operated an iterative reconstruction. An incomplete data set is used for this iterative reconstruction. In order to obtain a largely artifact-free image despite the incompleteness of the data set, an appropriate boundary condition is introduced within the framework of the iterative algorithm.

The method is described with reference to the flow chart of 4 explained. First, the data SIN is detected. These data SIN are also called sinograms. A sinogram represents a two-dimensional space per detector row, which is spanned on the one hand by the projection angle, ie the angular position of the x-ray source relative to the examination subject, and on the other by the fan angle within the x-ray beam, ie by the position of the detector pixel in the channel direction. The sinogram space thus represents the domain of the measurement data, while the image space represents that of the image data. The data record SIN is complete, ie at least half-circulation data has been recorded.

In step ZER, a decomposition of the data SIN into a component SIN UB, which contains only the immobile portions of the examination subject, and a portion SIN B, which contains only the moving portions of the examination subject. In cardio-CT, the moving parts are in particular the coronary arteries.

The decomposition ZER of the data SIN can take place in various ways, which are presented by way of example below. In each case, a splitting of the data SIN into a low-frequency component, which corresponds to the stationary components of the examination object, and into a high-frequency component, which corresponds to the moving parts of the examination subject. "Low frequency" and "high frequency" refers to the Fourier space.

In image space, moving structures are typically smaller than stationary structures, so an image can be roughly decomposed into its still or moving parts by low-pass or high-pass filtering. Smaller structures in the object analogously also form smaller structures in the sinogram space, so that this decomposition into stationary and moving object portions is possible at least approximately also in the sinogram space.

The data SIN should in each case be divided into the two subinograms SIN B and SIN UB such that either both subinograms SIN B and SIN UB or at least the subinogram SIN B contains no negative components.

According to a first procedure, low-pass filtering of the entire data SIN is first performed in the frequency domain. To ensure the positivity of the two sub-indices SIN B and SIN UB, the low-pass filtered data is first scaled, and then subtracted from the data SIN. The scaling takes place in such a way that no negative values result from the subtraction. This difference corresponds to the data after being subjected to high-pass filtering. This difference is used as data SIN B, and the data SIN UB is the scaled low-pass filtered data.

According to a second approach, a morphological filter, ie a shape filter, is applied to the data SIN, which decomposes them into a non-negative low-frequency component SIN UB and a non-negative high-frequency component SIN B. The morphological filter can be applied directly to the data SIN, without the need for a Fourier transformation. The term "morphological filter" here refers to a filtering operation in which a given signal is adapted to a given structural element; for example For example, a one-dimensional function is smoothed so that a circular feature can be "rolled" along the smoothed function. Typical morphological filter operations are, for example, "opening" and "closing."

According to a third approach, a conditional local median filtering is applied. If there are a set of values, they are sized to determine the median, with the value in the middle of the ordered row representing the median. For conditional local median filtering, the procedure is as follows: The median is formed in a specific neighborhood of a data point of the sinogram SIN. This neighborhood can be z. B. extend over 9 or 11 data values. Due to the property of the median, the use of an odd number is suitable; Of course, however, even an even number can be used. If this median value lies below the data value of the respective data point, the value for the low-pass filtered data is entered as the median instead of the original data value. On the other hand, if the median exceeds the data value of the respective data point, then the original data value is entered as the value for the low-pass filtered data. In this way, for each data point of the sinogram SIN is proceeded to finally obtain the complete low-pass filtered data. Furthermore, proceeding as described for the first procedure, d. H. there is a scaling of the low-pass filtered data (where the scaling can also be done with the factor 1) and the difference between this scaled low-pass filtered data and the data SIN. The data SIN B corresponds to this difference, and the data SIN UB to the scaled low-pass filtered data.

According to a fourth approach, a filtered backprojection based on the data SIN is first performed, thereby obtaining an image. In the image space, a low-pass filtering takes place. The strength of this low-pass filtering should be chosen so that no motion artifacts are recognizable in the low-pass filtered image. Subsequently, a forward projection is applied to the low-pass filtered image to return to the data space. These data correspond to the lowpass filtered data. Based on these low-pass filtered data, the procedure of the first and fourth procedures is followed to arrive at the data SIN B and SIN UB.

Alternatively, in the fourth approach after the filtered backprojection on the data SIN in the image space, a high-pass filtering, wherein the strength of the high-pass filtering should be selected so that as far as possible all motion artifacts are included in the high-pass filtered image. It should then be ensured that the high-pass filtered image contains only those components of low zero attributable to motion artifacts. On the thus filtered image, a forward projection is performed to get to the data SIN UB. The data SIN B are then obtained by subtraction between the data SIN and the data SIN UB.

From the data SIN UB, which contain only the stationary parts of the examination object, a CT image PIC UB is calculated by a conventional image reconstruction REKON. For this example, a classic FBP can be used. The image PIC UB contains the stationary parts of the examination subject, which is why no attention must be paid to the time resolution in the calculation REKON.

In contrast, the time resolution for image reconstruction in relation to the moving parts plays a major role. In order to avoid motion artifacts, the procedure is as follows: An incomplete data set is used to reconstruct the image of the moving parts. This incomplete data set represents a subset of the complete data set SIN. The time resolution of the image PIC B obtained therefrom directly depends on the angular range over which the data record extends. If, for example, the angular range of the incomplete data set is 120 °, then the time resolution of the image PIC B corresponds to 120 ° / 180 ° = 2/3 of the time resolution of the image PIC UB.

For the reconstruction of the image PIC B based on the incomplete data set, an iterative image reconstruction algorithm IT REKON is used. For this purpose, z. A startup image, d. H. an initial image of the iterative reconstruction, in the classical way, eg. B. using an FBP based on the complete data set SIN B, and subsequently only a limited part of the data set SIN B be used for the further iteration steps. Alternatively, the restricted part of the data set SIN B can also be used to calculate the start image.

A conventional iterative reconstruction based on an incomplete data set would not give a satisfactory result because the resulting image has so-called "limited angle artifacts" due to the incompleteness of the data set. These are due to the fact that not all projection angles of the half-circle have been detected.

To avoid these "limited angle artifacts", the iterative reconstruction IT REKON is performed using a suitable constraint. For this purpose, the a priori knowledge is used that all pixel values must be positive. This is built into the iterative algorithm as a requirement.

Dieser kann gemäß dieser Definition selbstverständlich negative Werte aufweisen, wenn der Schwächungskoeffizient μ des betreffenden Materials kleiner als derjenigen von Wasser ist.In this context, the pixel values are understood to be the attenuation coefficient μ of the object to be examined at the respective pixel. In contrast, the CT value or the CT number in Hounsfield units is defined as

This may, of course, according to this definition have negative values if the attenuation coefficient μ of the material in question is smaller than that of water.

If pixel values smaller than zero are present, they are set to zero. The reason for this constraint is the following:
The moving parts of the examination object are usually only small regions within the entire image or examination object. Thus, looking at the CT image of the moving parts, the pixel values should be almost zero everywhere, except for those small regions within which the moving parts are located. It is therefore known that in the image of the moving parts, if this has been calculated correctly, there are very many pixels with zero pixel value.

The limited-angle artifacts have the effect that in the respective image some of the low CT values are too small, that is to say incorrectly shifted downwards, and some of the high CT values are too large, ie incorrectly shifted upwards. Applying this finding to the moving CT image reveals that the limited-angle artifacts shift some of the many pixel values, which should actually be zero, to negative values. Negative values within a CT image, however, are meaningless and must not occur; they would mean that there is an X-ray source within the examination subject.

Therefore, to avoid limited angle artifacts, it is useful to require that no negative pixel values exist. This condition is used as a constraint in the iterative reconstruction IT REKON. A further advantage here is that, due to the displacement of the negative pixel values forced by the boundary condition towards larger values, a shift from large pixel values to somewhat smaller pixel values is inevitably effected at the same time. This means a reduction of the limited angle artifacts also for this range of the large pixel values.

There is a modification of conventional iterative reconstruction methods by introducing the described boundary condition. For an algebraic iterative reconstruction z. This is done, for example, by adding a boundary condition term as a regularization functional to the cost function, which is to be minimized in the course of the iterative reconstruction. Algebraic iterative reconstruction algorithms minimize the quadratic error between the measured projections Y and calculated projections. If a calculated image X is present, the calculated projections are obtained by applying a projection operator A to image X. A is an operator that emulates the measurement process as well as possible. The cost function K of the algebraic reconstruction can be written as follows: K = || Y - AX || ² (1)

It is therefore attempted to match the calculated image X as well as possible to the measurement data Y.

The application of iterative algorithms - such as the Algebraic Reconstruction Technique (ART) or the simultaneous Algebraic Reconstruction Technique (SART) to an incomplete data set leads to unsatisfactory solutions due to the incompleteness of the data set Y without the introduction of constraints. The limited angle artifacts occur in the result image.

In order nevertheless to find a satisfactory solution, a regularization of the cost function K is used. This means that the cost function K is extended by a second term which includes a regulation term R (X) K = || Y - AX || ² + β · R (X) (2)

The regulation term R (X) penalizes negative values within the image X. β is a regulatory parameter intended to strike a balance between the difference term and the regulation term.

A simple variant for the formulation of R (X) is:
R (X) = minus infinity if X <0; R (X) = 0, otherwise.

It should be emphasized that the invention is not limited to the use of algebraic iterative reconstruction algorithms. In an analogous manner, the cost functions of other iterative reconstruction methods, such. B. the statistical reconstruction can be modified.

After reconstruction of the images PIC B and PIC UB these are combined into a result image PIC. This can be done by a simple or weighted addition.

The result image PIC is largely free of motion artifacts. Because the moving components were reconstructed with an increased time resolution. The advantage of the decomposition of the sinogram SIN into a moving and a stationary part SIN B and SIN UB lies in the fact that for the reconstruction of the moving image PIC B techniques can be used which would not be possible for the entire image. This is based on the use of a suitable constraint that the pixel values must be greater than zero, which is relevant because many pixel values must be zero in the moving image.

The invention has been described above by means of an embodiment. It is understood that numerous changes and modifications are possible without departing from the scope of the invention.

For example, the measurement data decomposition can be omitted if the measurement data are such that SIN = SIN B can be assumed. This is z. As is the case when very few objects have been recorded, which is particularly relevant for industrial CT.

QUOTES INCLUDE IN THE DESCRIPTION

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Cited non-patent literature

• G. Chen, J. Tang, J. Hsieh: Temporal resolution improvement using PICCS in MDCT cardiac imaging, Med. Phys. 36 (6), June 2009, pp. 2130-2135 [0051]

Claims (16)

Method for reconstructing image data (PIC) of a moving examination object from measurement data (SIN), wherein the measurement data (SIN) were acquired during a relative rotational movement between a radiation source (C2, C4) of a computer tomography system (C1) and the examination object, a decomposition (ZER) of the measured data (SIN) takes place in a first part (SIN UB) corresponding to the unmoved parts of the examination subject, and a second part (SIN B) corresponding to the moving parts of the examination subject, based on the first part (SIN UB) of the measurement data (SIN), the first image data (PIC UB) is reconstructed (REKON), based on an incomplete data set of the second part (SIN B) of the measurement data (SIN), second image data (PIC B) are reconstructed (IT REKON), wherein the reconstruction (IT REKON) of the second image data (PIC B) is performed by an iterative algorithm which requires that pixel values should not be less than zero. Method according to Claim 1, in which the requirement is used as a boundary condition in the case of iterative reconstruction (IT REKON). The method of claim 1 or 2, wherein according to the requirement, negative pixel values are set to zero. Method according to one of Claims 1 to 3, in which the incomplete data record is a subset of the second part (SIN B) of the measurement data (SIN). Method according to one of Claims 1 to 4, in which an addition of the first (PIC UB) and the second (PIC B) image data takes place. Method according to one of Claims 1 to 5, in which the decomposition (ZER) of the measured data (SIN) is performed in such a way that neither the first part (SIN UB) nor the second part (SIN B) contains negative data values. Method according to one of Claims 1 to 6, in which low-pass filtering and / or high-pass filtering of the measured data (SIN) takes place for the decomposition (ZER) of the measured data (SIN), wherein the first part (SIN UB) contains the low-pass filtered measured data or the second Part (SIN B) corresponds to the high-pass filtered measured data. Method according to Claim 7, in which the low-pass filtering and / or high-pass filtering is performed by applying a morphological filter to the measured data (SIN). Method according to one of claims 1 to 8, in which for the decomposition (ZER) of the measured data (SIN) low-pass filtered measured data are determined, which are scaled and subtracted from the measured data (SIN), wherein the first part (SIN UB) corresponds to the scaled low-pass filtered measurement data and the second part (SIN B) to the difference. Method according to one of claims 1 to 9, wherein for decomposition (ZER) of the measurement data (SIN) a median formation with respect to the measurement data (SIN) takes place, and at least some of the measurement data (SIN) are changed depending on the median formed. Method according to one of Claims 1 to 10, in which low-pass filtered measured data are obtained for decomposition (ZER) of the measured data (SIN) by forming the median in the respective measured data environment relative to a measured data value, and replacing the measured data value by the median if the median is less than the measured value. Method according to one of claims 1 to 11, in which for the decomposition (ZER) of the measurement data (SIN), low-pass filtered measurement data are obtained by reconstructing image data based on the measurement data (SIN), the image data are low-pass filtered, and projecting the lowpass filtered image data forward into the data space. Control and computing unit (C10) for reconstructing image data (PIC) of an examination object from measurement data (SIN) of a CT system (C1), comprising a program memory for storing program code (Prg ₁ -Prg _n ), program code being stored in the program memory (FIG. Prg ₁ -Prg _n ) is present, which carries out a method according to any one of claims 1 to 12. CT system (C1) with a control and processing unit (C10) according to claim 13. Computer program with program code (Prg ₁ -Prg _n ) to perform the method according to one of claims 1 to 12, when the computer program is executed on a computer. A computer program product comprising program code (Prg ₁ -Prg _n ) of a computer program stored on a computer-readable medium for carrying out the method according to one of claims 1 to 12 when the computer program is executed on a computer.

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