Device-free motion assessment

Abstract

A method and system for reconstructing a CT image of an object, an imaging device for acquiring the CT images, and a computer program for carrying out the method are provided. A method for reconstructing a CT image includes receiving projection image data, selecting successive portions of the data, and reconstructing each of the portions into a volumetric slab image to form a sequence of images. The method also includes registering each slab image to another slab image spaced in the sequence by a spacing parameter to obtain a difference metric, selecting a sequence of difference metrics, and extracting a motion function from the sequence by forming a motion function using the selected sequence. The method further includes identifying trigger points in the motion function to define motion states, grouping the projection image data into these motion states, and reconstructing a volumetric image for each motion state.

Description

[Technical field]

[0001] The present invention relates generally to medical image reconstruction, and in particular, but not exclusively, to motion compensation in computed tomography image reconstruction. [Background technology]

[0002] In medical imaging, techniques such as computed tomography (CT) are used to visualize a patient's anatomy. A CT imaging system typically has a radiation source, such as an X-ray radiation, mounted on a rotatable gantry opposite a detector array having one or more rows of detector pixels. The X-ray source rotates about an examination region located between the X-ray tube and the detector array and emits radiation that traverses the examination region and a subject disposed therein. The detector array detects the radiation that traverses the examination region and generates projection data indicative of the examination region and an object or subject disposed therein. The projection data is reconstructed to generate volumetric image data indicative of the subject. The volumetric image data may be processed to generate one or more images including the scanned portion of the subject. Summary of the Invention [Problem to be solved by the invention]

[0003] 4D-CT imaging is routinely used in radiation oncology. In 4D-CT imaging, a series of 3D volumetric images of a subject are acquired over a period of time. This type of image can support the visualization of the motion of the patient's anatomy. This is particularly useful when the region of interest, such as a tumor, undergoes regular and involuntary motion, such as respiratory and cardiac motion. Based on gating based on respiratory phase, the motion of a lung tumor can be tracked over respiratory time.

[0004] For 4D-CT imaging, data acquisition takes several minutes. During this time, involuntary regular movements such as respiratory movements occur. The patient cannot hold his / her breath during this period and therefore can breathe freely or must follow a breathing protocol. To obtain sufficient image quality, data from corresponding motion states are combined to generate groups for image reconstruction. These groups are also commonly called "gates" or "bins". To define the bins, the motion signal needs to be measured during image acquisition.

[0005] The most common way to measure respiratory signals through image acquisition is to use a belt worn on the patient's chest or abdomen. The belt attaches a pressure sensor to the patient. Pressure fluctuations are caused by abdominal movements and these are thought to represent respiratory signals. However, such belts are uncomfortable for the patient.

[0006] An alternative option to using a pressure belt is to place marker blocks on the patient's torso and monitor their movement with a camera-based system, however camera-based systems require an unobstructed line of sight to the markers, which in practice is not always available.

[0007] Furthermore, both of these approaches to measuring respiratory motion have the drawback of requiring additional hardware, setup, calibration, and disinfection after use, which increases the complexity of the imaging workflow and makes their use expensive and sensitive to errors.

[0008] The present invention seeks to provide an approach for 4D-CT image reconstruction that does not require separate measurement of the motion signal. [Means for solving the problem]

[0009] A method and system for reconstructing a computed tomography image of an object, an imaging device for acquiring a computed tomography image of an object, and a computer program for carrying out said method are provided.

[0010] A method for reconstructing a computed tomography image of an object comprises the steps of receiving projection image data of the object, selecting successive portions of the projection image data, and reconstructing each selected portion into a volumetric slab image to form a sequence of slab images. The method further comprises the steps of extracting a motion function from the sequence of slab images, the step of extracting comprising the steps of: for each slab image in the sequence, registering the slab image to at least one other slab image in the sequence that is spaced from the slab image by a spacing parameter to obtain at least one difference metric; selecting a sequence of difference metrics from the obtained difference metrics for the sequence of slab images; and forming a motion function using the selected sequence of difference metrics. The method further comprises the steps of identifying at least one trigger point in the motion function to define motion states; grouping the projection image data into defined motion states; and for each defined motion state, reconstructing the grouped projection data into a volumetric image of the object. The method is preferably implemented by a computer or other suitable computing means.

[0011] In one embodiment of the method, the extraction of the motion function from the sequence of slab images, the identification of at least one trigger point, and the grouping of the projection image data into defined motion states are performed at least partially simultaneously with receiving the projection data, which has the advantage that image reconstruction can begin during image acquisition without having to wait for a full CT scan to be completed.

[0012] Preferably, the volumetric slab image is reconstructed at a lower resolution than the volumetric image in motion: the lower image quality is sufficient to extract the motion function, allowing a faster and more efficient reconstruction of the slab image.

[0013] According to one aspect, the motion function is formed by fitting a periodic function to the sequence of selected differential metrics. By fitting a periodic function to the sequence of differential metrics, a smooth and regular motion function is obtained. For example, cos 4 The (x) function provides a good approximation of human respiratory motion.

[0014] According to another alternative, forming the motion function comprises analyzing and correcting outliers. For example, if the subject is coughing, this may result in a large disruption of the motion function. Such outliers can be identified by analyzing the sequence of selected difference metrics or by analyzing the initially calculated motion function. Optionally, a corresponding group of image projection data can be removed to correct the motion function and improve the quality of the reconstructed image.

[0015] In an alternative or additional option, the spacing parameter has at least one preset value. Using one preset value allows for fast and robust extraction of the motion functions. If one preset value of the spacing parameter is used, a single sequence of difference metrics is obtained for the sequence of slabs. This sequence is automatically selected as the sequence of difference metrics. In another option, the spacing parameter has two or more preset values. If two or more preset values ​​are used, the sequence of difference metrics can be selected by using an optimal value of the spacing value for each slab image. Alternatively, if two or more preset values ​​are used, the sequence of difference metrics can be selected using a majority vote.

[0016] Registering the slab images can include applying one or more of elastic and rigid deformations. Preferably, rigid deformations such as translation and / or rotation are applied. Applying rigid deformations can be of sufficient quality, require less computation and are more robust.

[0017] In one embodiment, the difference metric indicates the amount and / or direction of movement of the region of interest, which has the advantage of providing improved image quality relative to the motion conditions of the actual region of interest itself.

[0018] Preferably, the projection image data of the subject is received from one or more of a computed tomography imaging device, an imaging data storage device, and a database, such as a dedicated imaging data storage database, a hospital database, or a patient record database.

[0019] A system for reconstructing a computed tomography image of an object comprises an input configured to receive projection image data of the object, a slab image reconstructor, a motion extraction unit, and a reconstruction processor. The slab image reconstructor is configured to select successive portions of the projection image data and reconstruct each selected portion into a volumetric slab image to form a sequence of slab images. The motion function extraction unit is configured to perform the following steps for each slab image in the sequence: registering the slab image to at least one other slab image in the sequence spaced from the slab image by a spacing parameter to obtain at least one difference metric; selecting from the obtained difference metrics a sequence of difference metrics for the sequence of slab images; and forming a motion function using the selected sequence of difference metrics. The reconstruction processor is configured to identify at least one trigger point in the motion function to define motion states; grouping the projection data into defined motion states; and for each defined motion state reconstructing the grouped projection data into a volumetric image of the object. Preferably, the system further comprises a display for displaying at least one of the volumetric images. Displaying the images can assist a physician in diagnosing a patient and preparing a treatment plan.

[0020] In one embodiment, the system further comprises a user interface configured to display to a user the obtained differential metrics calculated for at least one spacing parameter, said user interface further preferably configured to enable a user to select at least one of the sequence of differential metrics and the value of the spacing parameter.

[0021] An apparatus for acquiring a computed tomography image of an object comprises a computed tomography imaging device for acquiring projection image data of the object and the above-mentioned system for reconstructing a computed tomography image of the object.

[0022] The computer program product comprises instructions which, when executed, cause a processor to carry out the above-mentioned methods.

[0023] An advantage of the invention is that 4D-CT images are acquired without the need to acquire regular motion signals from the subject. In particular, it is no longer necessary to use belts and other instruments to measure respiratory and / or cardiac motion. Such devices may increase the complexity of the procedure and cause discomfort to the patient.

[0024] Another advantage is that the motion signal can be determined more accurately. The invention allows for direct determination of the motion of the anatomical region of interest. If an external device such as a belt device is used, the respiratory phase detected at the scanned axial position may be different from the axial position where the belt is located. [Brief description of the drawings]

[0025] [Figure 1] 1 shows a schematic and exemplary diagram of an apparatus for acquiring a CT image of an object, the apparatus comprising a system for reconstructing a CT image of the object; [Diagram 2] FIG. 2 illustrates a schematic of projection data acquisition and reconstruction of a volumetric slab image. [Diagram 3] 1A and 1B illustrate an example of a method for reconstructing a computed tomography image of an object; [Figure 4] 2 illustrates diagrammatically an example of method steps for extracting a motion function from a sequence of slab images; [Diagram 5] FIG. 4 illustrates diagrammatically another example of method steps for extracting a motion function from a sequence of slab images; [Figure 6] FIG. 2A is a diagram illustrating an example of a user interface configured to allow a user to select at least one value of a spacing parameter. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] In the following examples, computed tomography images are acquired of a subject. Such a subject may be a human being, in particular a human patient who requires imaging for medical purposes. However, alternative objects are also envisaged, for example animals such as pets or livestock. The approach according to the invention can also be applied to inanimate objects that have an inherent periodic motion that cannot be paused during image acquisition.

[0027] FIG. 1 shows an imaging device 100 for acquiring computed tomography images of a subject.

[0028] The imaging system 100 includes an imaging device 110, such as a computed tomography (CT) scanner. The computed tomography imaging system 110 includes a stationary gantry 102 and a rotating gantry 104 rotatably supported by the stationary gantry 102. When the system is in operation, the rotating gantry 104 rotates about a longitudinal or z-axis around an examination region 106. A radiation source 108, such as an x-ray tube, is supported by and rotates with the rotating gantry 104 and emits radiation that traverses the examination region 106. A source collimator 109 collimates the emitted radiation to form a generally fan-, wedge-, or cone-shaped radiation beam that traverses the examination region 106.

[0029] A support 118, such as a couch, is provided to support a subject or object to be imaged in the examination region 106. The support 118 is movable along the z-axis in coordination with the rotation of the rotating gantry 104 to facilitate a desired scan trajectory, preferably a helical scan trajectory.

[0030] A radiation sensitive detector array 112 detects radiation emitted by the radiation source 108 traversing the examination region 106 and generates projection image data 120 indicative of the detected radiation. The illustrated radiation sensitive detector array 112 has one or more rows of radiation sensitive photosensor pixels aligned along the z-axis.

[0031] The imaging device 100 further comprises an image reconstruction system 130 that reconstructs the projection data to generate volumetric image data indicative of the examination region 106, the volumetric image data including structures of an object or subject disposed in the examination region. One or more images can be generated from the volumetric image data. In this example, the imaging device 100 further comprises an optional display 140 for displaying the one or more generated images.

[0032] The system 130 for reconstructing a CT image comprises an input 132 for receiving projection image data 120 of the object, a slab image reconstructor 134 , a motion extraction unit 136 , and a reconstruction processor 138 .

[0033] The input 132 is configured to receive the projection image data 120 directly from the CT imager 110 while the data is being acquired or after it has been acquired. Alternatively, the projection image data can be stored by an imaging data store or a database, such as a dedicated imaging data storage database, a hospital database, or a patient record database. In this option, the reconstruction system 130 can operate independently of the imaging device 110, and the input is configured to receive the data from the data store or database.

[0034] The slab image reconstructor 134 is configured to select successive portions of the projection image data 120 and reconstruct each selected portion into a volumetric slab image to form a sequence of slab images. The motion function extraction unit 136 is configured to, for each slab image in the sequence, register the slab image to at least one other slab image in the sequence that is spaced from the slab image by a spacing parameter to obtain at least one difference metric, select a sequence of difference metrics from the obtained difference metrics for the sequence of slab images, and form a motion function using the selected sequence of difference metrics.

[0035] The reconstruction processor 138 is configured to identify at least one trigger point in the extracted motion function to define a motion state, group the projection data into the defined motion states, and reconstruct the grouped projection data into a volumetric image of the object for each defined motion state. In the case of respiratory motion, the trigger points are, for example, end-expiration state, end-inspiration state, mid-expiration state, and mid-inspiration state. In the case of 4D-CT images, preferably at least one, more preferably at least two trigger points are selected to indicate the motion of the patient's region of interest in different motion phases.

[0036] FIG. 2 shows a schematic of the projection data acquisition and volumetric image reconstruction of a CT image. Data is acquired as the subject on the support passes through the imaging device. A commonly used method to do this is by a helical scan trajectory. In a helical scan trajectory, the support moves the subject continuously through the examination region while the radiation source and the detector array rotate around the subject. Alternatively, the table motion can be stopped at each scan position, and the radiation source and the detector array are then rotated with the subject in a stationary position. However, such a step-and-shoot approach is more cumbersome to perform and less comfortable for the subject. In FIG. 2, the support position z on the horizontal axis is shown as the distance to the end of the support. Time t is shown on the vertical axis and increases upwards.

[0037] The projection data are collected in a portion of the angular rotation of the source and detector around the subject. If the most significant subject motion that needs to be compensated for is respiratory motion, such a portion is preferably at least 180°. Having a portion of data of at least 180° has the advantage that each portion can be selected and reconstructed into an image of sufficient quality to display to the user. However, having a portion of data in this rotational range is not essential for the current reconstruction approach. If a higher time resolution is required, for example if the most significant subject motion that needs to be compensated for is cardiac motion, the portion may be less than 180°. The portions of projection data can be adjacent within the acquisition angle, but can also partially overlap. Such portions of projection data represent the data acquired for the subject at the support position and instantaneous time during the acquisition time. Alternatively, the portions of projection data can be defined using a time window within the acquisition time span, or a time window covering the average position of the support.

[0038] FIG. 2 shows a grid of volumetric image data 210 divided into slices 220. As described with reference to FIG. 1, the radiation sensitive detector array of the imaging device has one or more rows of light sensitive pixels along the z-axis. Each pixel acquires projection data as the detector rotates around the subject, and upon completion of a given rotation, preferably greater than 180°, this projection data can be reconstructed into a 2D cross-sectional volumetric image of the subject. This image is called a slice 220. Adjacent pixels of the detector array each provide an adjacent slice. The detector field of view 230 is determined primarily by the number of pixels that span the length of the examination region. By moving the support and acquiring data at each subsequent position of the support, the total coverage of the subject in the CT image 240 is increased. Preferably, multiple slices of data are acquired for each position of the subject support. Such data acquisition is referred to as oversampled image acquisition. Acquiring more slices per support position improves image quality but requires a slower speed of the subject support, increasing acquisition time. Thus, in practice, a tradeoff is made between the amount of projection data acquired and the scan speed.

[0039] From the acquired portions of projection image data, each can be selected and reconstructed into a separate volumetric image. This 3D CT image is a volumetric slab image 250. A slab image is a volumetric image of the subject for a selected support position and instantaneous point in time of acquisition. In FIG. 2, the slab images are shown as rows of slices 220. Here, the slabs are shown as adjacent in time, although alternatively the slabs may partially overlap or there may be time gaps between the slabs.

[0040] The volumetric slab images form a time-continuous sequence of 3D images 260. The time sequence starts with the first slab image S1, followed by the second slab image S2, and so on until the last slab image SN. The total number of slab images N shown in the schematic diagram of Fig. 2 is 16, but in practice this is typically higher. The total number of slab images is determined by imaging parameters such as the detector width of the CT imager, the rotation speed of the detector, the translation speed of the couch, and the required field of view 240.

[0041] Consecutive slabs Si and Si+D are spaced apart in the sequence by a spacing parameter D. In the example shown in FIG. 2, the spacing parameter D has a value of 4, but can have any integer value starting from 1. In practice, however, there is an upper limit to the values ​​that can be used for the spacing parameter. For the slab images Si and Si+D to be recorded, a spatial overlap in the transport direction z is required. This constraint determines an upper limit for D. Preferably, the region of interest from which the motion function is extracted should be at least partially present in both images. If all slab images overlap sufficiently, in principle an upper limit of N-1 for D is possible. In practice, however, the maximum value for D that results in a motion function of sufficient quality for 4D-CT image reconstruction depends on the frequency of the object motion. It is substantially less than N, for example less than N / 10, or even less than N / 20.

[0042] 3 shows a schematic diagram of an example of a method for reconstructing a computed tomography image 300 of an object. The method for reconstructing a CT image begins with receiving projection image data 310 of the object. This data can be received, for example, from a computed tomography imaging device, an imaging data storage device, or a database, such as a dedicated imaging data storage database, a hospital database, or a patient record database. Successive portions of the projection image data are then selected (320). In a next step, each of the selected portions of the image projection data is reconstructed into a volumetric slab image to form a sequence of slab images 330.

[0043] The volumetric slab images can be reconstructed to the same resolution as the volumetric images in motion provided by the method, but this is not necessary. A sequence of slab images is used to extract the motion function. For this purpose, it is not necessary to have diagnostic or display quality images. It is even preferred that the slab images are reconstructed at a lower resolution, as this allows faster and more efficient calculations.

[0044] Once the sequence of slab images is formed, a motion function is extracted (340). To extract the motion function, for each slab image in the sequence, the slab image is registered to at least one other slab image in the sequence, the other slab images being spaced apart from the slab image in the sequence by a spacing parameter. Each slab image in the sequence is registered to at least one other slab image to obtain at least one difference metric 342. The difference metric may characterize and / or quantify differences in the subject's anatomy between the two slab images.

[0045] The slab images can be registered by applying elastic and / or rigid deformations. Optionally, additional techniques such as automatic masking or intensity clipping can additionally be used during registration to improve robustness. Elastic deformations can be applied by calculating a deformation vector field. A difference metric can then be determined by analyzing this vector field. The rigid deformations can be translations and / or rotations. Preferably, rigid deformations are applied. For respiratory motion, and even more preferably, a rigid registration optimized for anterior-posterior translations is applied. This has the advantage of being more robust and easier to compute. Difference metrics that can be obtained from rigid deformations are, for example, the amount of rotation, the angle of rotation, the amount of translation, the direction of translation, or combinations thereof. In these examples, the difference metric can be a scalar indicating the amount of change, or a vector indicating both the amount and direction of change. To extract the motion function, the difference metric can be sufficient as a number that represents the amount of movement of the region of interest between the slab images. Having additional information, such as direction of movement, may also be of interest to the clinician and therefore provide additional information to aid in diagnosis or treatment planning.

[0046] From the difference metrics obtained for the sequence of slab images, a sequence of difference metrics is selected 344. The selected sequence of difference metrics 346 is then used to form a motion function.

[0047] The method further comprises identifying at least one trigger point in the motion function 350 to define a motion state, and grouping the projection image data 360 into the defined motion states. During the grouping process, the projection data can be assigned to one or more motion states. Optionally, the projection data can be grouped by sorting the projection data into the motion states. By sorting, the projection data is assigned to the most appropriate or closest motion state. In an alternative option, the projection data can be assigned to one or more of the defined motion states by applying a smooth weighting function. In this approach, a weighting function is calculated based on the defined motion states and applied to the projection data to match the data to the motion state. The use of such a smooth weighting function has the advantage that the amount of data used for image reconstruction is minimized while retaining full coverage and timing resolution of the subject. The use of a smooth weighting function also allows smooth transitions between the motion states, thereby avoiding artifacts.

[0048] Further, for each of the defined motion states, the grouped projection data is reconstructed 370 into a volumetric image of the object, and these volumetric images for the defined motion states form a 4D-CT image.

[0049] As shown by the example of FIG. 3, the CT image reconstruction method 300 according to the invention can be used to reconstruct the projection image data after all data has been acquired. However, it is not necessary to first complete the image acquisition before the method is started. In particular, extracting the motion function from the sequence of slab images 340, identifying at least one trigger point 350, and grouping the projection image data 360 into defined motion states can be performed at least partially simultaneously with receiving the projection image data 310. Once sufficient projection data has been acquired and received to form a first portion of data, this portion can be reconstructed into a volumetric slab image while the next successive portion of data is being received, and so on. When at least the first two slab images of the sequence are available, the extraction of the motion function 340 can begin. Also, the motion function can be formed while the sequence of difference metrics is being constructed. As soon as an initial portion of the motion function is available, the identification of the trigger points and the grouping of the projection data into motion states can begin. While the sequence of slab images continues to be formed, the extraction of the motion function, the identification of the trigger points, and the grouping of the projection data continue until all data has been received and these processing steps are completed.

[0050] Both Figures 4 and 5 show schematic examples of method steps for extracting a motion function from a sequence of slab images. The embodiments of Figures 4 and 5 show the combination of several optional additional method steps. Although the steps are shown all in combination, this is not necessary and merely represents an advantageous option. Any step can also be performed separately or in a combination of two or more.

[0051] 4 shows an embodiment for extracting a motion function from a sequence of slab images 440. In this embodiment, the spacing parameter has one preset value. Having a single preset value for D allows fast and robust computation of a sequence of difference metrics from a sequence of slab images.

[0052] In this example, the difference metric is calculated iteratively in order according to the sequence of slabs. The method starts by registering (441) the image S1+D to the first image S1. From this registration, the difference metric is calculated (442). In this embodiment, a rigid registration is applied and the difference metric is the shift of the anatomical structure of interest. For example, a motion signal can be extracted for respiratory motion. When the diaphragm moves upwards, the difference metric is a positive number indicating the size of the movement, and when the diaphragm moves downwards, the difference metric is a negative number indicating the size of the movement. In this way, both the amount and direction of the movement can be captured.

[0053] After the differential metric is calculated for the current slab Si, the method moves to the next slab Si+1 (443) and returns to registering slab Si+1 to the slab spaced D from slab Si+1 in the sequence (441), calculating the differential metric (442), etc. Once the final slab is registered, a single sequence of numbers is calculated and this is automatically selected as the sequence of differential metrics (444).

[0054] A motion function is then formed using the sequence of difference metrics. In this example, options are used to analyze and correct the motion function for outliers.

[0055] From the sequence of difference metrics, an initial motion function is formed (445). The initial motion function may be the same as the sequence of difference metrics. Alternatively, it is possible to form the initial motion function by applying some mathematical operations to the sequence of difference metrics, such as subtracting the mean value, interpolating additional data points, integrating the sequence, and / or applying a smoothing filter. Outliers in the patient motion may be caused by sudden large irregular movements such as coughing. The initial motion function is analyzed to identify the presence of any such deviations (446). If outliers are identified, the corresponding difference metrics are removed from the sequence to correct for them (447). Although not shown here, it may also be advantageous to remove portions of the projection data of the corresponding slab images from the final CT image reconstruction. This improves image quality.

[0056] The corrected sequence of differential metrics forms a motion function (448). The motion function can be formed in a similar manner as described for the initial motion function above. Additionally, a periodic function can be fitted to the sequence of differential metrics to form the motion function. Fitting such a function to the corrected sequence provides a smoother and more robust motion function. Alternatively, outliers can be removed directly from the initial motion function to form the motion function.

[0057] 5 shows another embodiment for extracting a motion function from a sequence of slab images 540. In this embodiment, the spacing parameter has two or more preset values. Using multiple values ​​for D has the advantage that the robustness of the calculation is increased. It also has the advantage that the extraction of the motion function can be adapted if artefacts are present in one of the slab images or if the subject's motion pattern changes during image acquisition.

[0058] First, a value for D is set (541). Then, in this example, the difference metrics are calculated sequentially according to the order of the slabs, similar to the example described in FIG. 4. The slab image Si+D is registered (542) to the current image Si, the difference metrics are calculated (543), and the next slab is selected (544). These steps are repeated until the final slab image is reached. This iterative process results in a sequence of difference metrics for set values ​​of D (545). Once the sequence of difference metrics is completed, the next value of the separation parameter is selected (546) and the process is repeated. This is repeated again until a sequence of difference metrics is obtained for each value of D.

[0059] In an alternative option, the iterative loop can be reversed: for each value of D, each slab image S+D is registered (542) to the current image S, a difference metric is calculated (543), the next slab is selected (544), and the process is repeated until the final slab is reached.

[0060] A sequence of difference metrics 547 is then selected which is used to form a motion function 548. The value or values ​​of D can be selected by a user, for example using the interface shown in Figure 6, but can also be selected automatically.

[0061] Alternatively, the sequence of difference metrics can be selected using a majority vote for several values ​​of the spacing parameter. For example, five consecutive values ​​of D can be used, such as 1, 2, 3, 4, and 5. Each value of D gives its own sequence of difference metrics. The sequences are similar in shape enough to allow them to be overlaid on one selected sequence. The overlay also includes an appropriate shift for the sequences to match. This can be, for example, the first sequence, here for D=1, or the middle sequence, here for D=3. If all the overlaid values ​​are close to a particular slab image in the sequence, all are averaged to provide a selected value of the difference metric for that position in the sequence. If one of the slab images is corrupted or has artifacts, this can cause outliers or missing data. Outliers are present in all sequences, but occur at different positions in each. The sequence for the value of D with the outlier is ignored and the other values ​​are averaged to provide a selected value for the difference metric. In this way, forming the motion function further includes automatically analyzing and correcting the outliers.

[0062] Once the sequence of differential metrics is established (547), a motion function is formed from this sequence (548). In this example, the motion function is a periodic function applied to this sequence of differential metrics, specifically the function cos 4 (x), where x corresponds to the sequence position number. This function is fitted with respect to the human breathing pattern.

[0063] FIG. 6 illustrates diagrammatically an example of a user interface configured to allow a user to select at least one value of the spacing parameter. The user interface is configured to display the obtained difference metric calculated for at least one value of D. In this example, the difference metric is displayed as a difference metric plot 610. The horizontal axis of the plot indicates slab number. In this example, 180 slab images are selected and reconstructed. The vertical axis of the plot indicates the value of D, increasing from 1 at the top to a value of 10 at the bottom. The difference metric plot 610 illustrates greyscale values ​​of the difference metric. Advantageously, rather than greyscale, colour coding can be used for the difference metric plot 610.

[0064] The differential metric plot 610 is overlaid with a distance parameter selector 620. In this example, the distance parameter selector is a horizontal line that allows the user to select a single value by moving the selector up or down. Alternatively, the user could draw an angled or curved line to allow multiple values ​​of D to be selected. Once the distance parameter selector 620 is positioned to the user's satisfaction, a confirm button 630 can be used to select the sequence of differential metrics accordingly.

[0065] As another option, the selector 620 may have two horizontal straight or rectangular boxes to select a smaller sub-range of values ​​for D. This range may then be used to apply the majority voting scheme described in connection with Figure 5 to create a sequence of differential metrics.

[0066] In addition to the differential metric plot 610, the user interface 600 also displays a motion function plot 640. The motion function plot visualizes a sequence 650 of a selected differential metric and two possible motion functions 660 and 670 as a function of the slab image number in the sequence. In an advantageous option, the sequence of different metrics is displayed in real time as the parameter selector 620 moves across the differential metric plot. This can help the user to select the optimal sequence of metrics. A confirm button can be used to confirm the selection for forming the motion function. In the example of FIG. 6, two options for forming the motion function are displayed in addition to the sequence of metrics 650. In the first option 660, shown by a solid line, the motion function is formed by integrating the sequence of differential metrics. In the second option 670, shown by a dashed line, the motion function is formed by integrating the sequence of differential metrics and subtracting the average value. Both options allow easy identification of trigger points corresponding to the subject's respiratory phase.

[0067] Any of the method steps disclosed herein may be recorded in the form of a computer program containing instructions that, when executed on a processor, cause the processor to perform such method steps. The instructions may be stored on a computer program product. The computer program product may be provided by dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, functions may be provided by a single dedicated processor, a single shared processor, or by multiple individual processors, some of which may be shared. Furthermore, embodiments of the invention may take the form of a computer program product accessible from a computer usable or computer readable storage medium that provides program code for use by or in connection with a computer or any instruction execution system. For purposes of this description, a computer usable or computer readable storage medium may be any apparatus that can contain, store, transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory "RAM", a read-only memory "ROM", a solid-state magnetic disk, and an optical disk. Current examples of optical disks include compact disk-read only memory "CD-ROM", compact disk-read / write "CD-R / W", Blu-Ray and DVD. An example of a propagation medium is the Internet or other wired or wireless telecommunications system.

[0068] Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. It is noted that various embodiments can be combined to achieve further advantageous effects.

[0069] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

[0070] A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0071] Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. 1. A method for reconstructing a computed tomography image of an object, comprising: receiving projection image data of a subject; selecting a contiguous portion of the projection image data; reconstructing each of the selected portions into a volumetric slab image to form a sequence of slab images; extracting a motion function from the sequence of slab images, the extracting comprising the steps of: for each slab image in the sequence, registering the slab image to at least one other slab image in the sequence that is spaced from the slab image by a spacing parameter to obtain at least one difference metric; selecting a sequence of difference metrics from the obtained difference metrics for the sequence of slab images; and forming a motion function using the selected sequence of difference metrics. and the method further comprises: identifying at least one trigger point within the motion function to define the motion state; grouping said projection image data into said defined motion states; reconstructing the grouped projection image data into a volumetric image of the subject for each of the defined motion states; The method according to claim 1,

2. The method of claim 1 , wherein the steps of extracting the motion function from the sequence of slab images, identifying the at least one trigger point, and grouping the projection image data into the defined motion states are performed at least partially simultaneously with receiving the projection image data.

3. The method of claim 1 or 2, wherein the volumetric slab image is reconstructed at a lower resolution than the volumetric image for the motion states.

4. The method of claim 1 , wherein the motion function is formed by fitting a periodic function to the sequence of selected difference metrics.

5. The method according to claim 1 , wherein the step of forming the motion function comprises analysing and correcting outliers.

6. The method of claim 1 , wherein the spacing parameter has at least one preset value.

7. The method of claim 6 , wherein the spacing parameter has two or more preset values, and the sequence of difference metrics is selected using a majority vote.

8. The method of claim 1 , wherein the registration of the slab images comprises applying one or more of an elastic deformation and a rigid deformation.

9. The method of claim 1 , wherein the difference metric indicates an amount and / or direction of movement of a region of interest.

10. 10. The method of claim 1, wherein the projection image data of the subject is received from one or more of a computed tomography imaging device, an imaging data storage device, a database, a dedicated imaging data storage database, a hospital database, or a patient record database.

11. 1. A system for reconstructing a computed tomography image of an object, comprising: an input for receiving projection image data of a subject; a slab image reconstructor performing the steps of selecting successive portions of the projection image data and reconstructing each of the selected portions into a volumetric slab image to form a sequence of slab images; a motion function extraction unit performing, for each slab image in the sequence, the steps of registering the slab image with at least one other slab image in the sequence that is spaced from the slab image by a spacing parameter to obtain at least one difference metric; selecting, for the sequence of slab images, a sequence of difference metrics from the obtained difference metrics; and forming a motion function using the selected sequence of difference metrics; and wherein the system further comprises: a reconstruction processor that is configured to identify at least one trigger point in the motion function to define a motion state, group the projection image data into the defined motion states, and reconstruct the grouped projection image data into a volumetric image of a subject for each of the defined motion states; A system having

12. 12. The system of claim 11, further comprising a user interface configured to display the obtained difference metrics calculated for at least one spacing parameter and to allow a user to select at least one of a sequence of difference metrics and at least one value for the spacing parameter.

13. 13. The system according to claim 11 or 12, further comprising a display for displaying at least one volumetric image of the subject.

14. 1. An imaging apparatus for acquiring a computed tomography image of a subject, comprising: a computed tomography imaging device; A system according to any one of claims 11 to 13 for reconstructing a computed tomography image of an object, An imaging device having the above configuration.

15. A computer program comprising instructions which, when executed by a processor, cause the processor to carry out a method according to any one of claims 1 to 10.

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