Method for processing computed tomography imaging data of a suspect's respiratory system

Abstract

A method for processing CT imaging data includes providing CT imaging data obtained at two x-ray energy levels in a first respiratory phase, preferably in an inhalation phase, of the subject and providing second CT imaging data obtained at two x-ray energy levels in a second respiratory phase, preferably in an exhalation phase, of the subject. The method may include reconstructing first regional perfusion blood volume (PBV) imaging data from the provided first CT imaging data, reconstructing second regional PBV imaging data from the provided second CT imaging data, reconstructing first virtual non-contrast (VNC) imaging data from the provided first CT imaging data, reconstructing second VNC imaging data from the provided second CT imaging data, determining a transformation function for registering the first and second reconstructed VNC imaging data, and registering the first and second reconstructed VNC imaging data by applying the transformation function.

Claims

1. A method for processing computed tomography (CT) imaging data (D) of a subject's respiratory system, wherein the subject has a CT-contrast enhancing agent distributed in its blood vessels, comprising: providing first CT imaging data obtained at least at two x-ray energy levels (E.sub.1, E.sub.2) in a first respiratory phase of the subject; providing second CT imaging data obtained at least at two x-ray energy levels (E′.sub.1, E′.sub.2) in a second respiratory phase, of the subject; reconstructing first regional perfusion blood volume (PBV) imaging data from the provided first CT imaging data; reconstructing second regional PBV imaging data from the provided second CT imaging data; reconstructing first virtual non-contrast (VNC) imaging data from the provided first CT imaging data; reconstructing second VNC imaging data from the provided second CT imaging data; determining a transformation function T for registering the first and second reconstructed VNC imaging data; registering the first and second reconstructed VNC imaging data by applying the transformation function T; and calculating regional ventilation imaging data using at least one of the registered first and second reconstructed VNC imaging data and the determined transformation function T.

2. Method according to claim 1, further comprising the step of: registering the first and second reconstructed regional PBV imaging data by applying said transformation function T.

3. Method according to claim 1, further comprising the step of: adjusting the first and/or second reconstructed regional PBV imaging data for different blood volume fractions caused by the different respiratory phases.

4. Method according to claim 1, further comprising at least one of: a) reconstructing first morphological imaging data from the provided first CT imaging data; and b) reconstructing second morphological imaging data from the provided second CT imaging data.

5. Method according to claim 1, wherein the first CT imaging data obtained at least at the two x-ray energy levels (E.sub.1, E.sub.2) in an inhalation phase of the first respiratory phase of the subject is provided by acquiring the CT imaging data using a multi-energy CT scanner.

6. Method according to claim 5, wherein the second CT imaging data obtained at least at the two x-ray energy levels (E′.sub.1, E′.sub.2) in an exhalation phase of the second respiratory phase of the subject is provided by acquiring the CT imaging data using the multi-energy CT scanner.

7. Method according to claim 1, wherein in the step of determining the transformation function T, the first and second reconstructed VNC imaging data are used as reference data and floating data to determine the transformation function T which maps an image point in the reference data to the corresponding image point in the floating data.

8. Method according to claim 1, wherein the second CT imaging data of the second respiratory phase is obtained in a different respiratory phase, but in the same respiratory cycle with respect to the first respiratory phase.

9. Method according to claim 1, wherein the second CT imaging data of the second respiratory phase is obtained between 2 and 15 minutes after the first respiratory phase.

10. Method according to claim 1, wherein the first and second CT imaging data are obtained each at exactly the two x-ray energy levels (E.sub.1 and E.sub.2, E′.sub.1 and E′.sub.2) by using a dual-energy CT scanner.

11. Method according to claim 1, wherein the first and second CT imaging data are obtained each at more than the two x-ray energy levels (E.sub.1, E.sub.2, . . . , and E.sub.n, E′.sub.1, E′.sub.2, . . . , and E′.sub.n) by using a multi-energy CT scanner.

12. Method according to claim 1, wherein the two x-ray energy levels (E.sub.1, E.sub.2) of the first CT imaging data and the two x-ray energy levels (E′.sub.1, E′.sub.2) of the second CT imaging data are one of a) identical (E.sub.1=E′.sub.1, E.sub.2=E′.sub.2); b) partly identical (E.sub.1=E′.sub.1, E.sub.2≠E′.sub.2) and c) different (E1≠E′.sub.1, E.sub.2≠E′.sub.2).

13. Method according to claim 1, further comprising the steps of: providing third CT imaging data obtained at least at two x-ray energy levels (E″.sub.1, E″.sub.2) in a third respiratory phase of the subject; reconstructing third regional PBV imaging data from the provided third CT imaging data of the third respiratory phase; reconstructing third VNC imaging data from the provided third CT imaging data of the third respiratory phase; determining a second transformation function T.sub.2 for registering the third VNC imaging data with one of the first and second reconstructed VNC imaging data; registering the third and one of the first and second reconstructed VNC imaging data by applying said second transformation function T.sub.2, and calculating second regional ventilation imaging data using at least one of the registered third and the one of the first and second reconstructed VNC imaging data and the determined second transformation function T.sub.2.

14. Method according to claim 13, wherein the spatial resolution of the first and second reconstructed regional PBV imaging data and the calculated regional ventilation imaging data is below 5 mm.

15. A non-transitory computer readable medium on which a computer program product is stored, the computer program product comprising a sequence of machine instructions that, when executed by a computer, causes the computer to execute the method according to claim 1.

16. A computer on which a computer program product is stored, the computer program product being processable by the computer, the computer program product comprising a sequence of machine instructions that, when executed by a computer, causes the computer to execute the method according to claim 1.

17. Computer according to claim 16, wherein the computer is formed as a control device for a CT scanning unit.

18. A Multi-energy CT scanning unit comprising a control device which is configured to perform a method according to claim 1.

19. A Multi-energy CT scanning unit comprising a control device which is configured to perform a method according the computer of claim 17.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1: A flowchart illustrating a method for processing CT imaging data according to an embodiment of the invention;

(2) FIG. 2: Schematic illustration (processing pipeline) of an embodiment of the CT image processing method;

(3) FIG. 3: Schematic illustration (processing pipeline) of the embodiment as shown in FIG. 2, including exemplary measured and processed morphological and functional CT imaging data of a human lung;

(4) FIG. 4: Schematic illustration of a dual-source dual-energy CT scanning unit according to an embodiment of the invention.

DETAILED DESCRIPTION

(5) According to FIG. 1, the method of the invention comprises the steps of providing first (S1) and second (S2) imaging data, reconstructing first (S3) and second (S4) regional PBV imaging data, reconstructing first (S5) and second (S6) VNC imaging data, determining a transformation function T (S7), registering the first and second reconstructed VNC imaging data (S8), calculating regional ventilation imaging data (S9), registering the first and second reconstructed PBV imaging data (S10), and adjusting the first reconstructed regional PBV imaging data for blood volume variations (S11).

(6) It should be noted that although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of some operations may be re-arranged. The processes may be terminated when their operations are completed, but may also have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, etc.

(7) In the following the individual steps of FIG. 1 are described with further details.

(8) In steps S1 and S2 first and second CT imaging data, e.g. series of 2D CT images which might be measured and stored beforehand, are provided, whereby each of said first and second CT imaging data was obtained at least at two x-ray energy levels and in a certain respiratory phase of the subject. Furthermore, the subject had a CT-contrast enhancing agent distributed in its blood vessels, preferably in the pulmonary blood pool, while said CT imaging data was obtained. This can be achieved by administering an contrast agent, e.g. iodine, containing infusion. Preferably both CT imaging data were obtained by using a dual-source dual-energy CT scanner, enabling a quasi-simultaneously acquisition of a high and low photon energy spectrum of the same anatomic location within a single CT scan. Particularly preferred said first and second CT imaging data were obtained in different respiratory phases, e.g. first CT imaging data was obtained in an inhalation phase, while the second CT imaging data was obtained in an exhalation phase.

(9) In steps S3 and S4 first and second regional pulmonary perfusion blood volume imaging data are reconstructed from the respective first and second CT imaging data. For this purpose the first and second CT imaging data, each including spectra of at least two x-ray energy levels, are respectively analysed for specific x-ray energy-dependent changes in attenuation caused by the presence of the contrast agent. The resulting distribution map of the contrast agent in the first and second CT imaging data is then represented in the respective PBV imaging data, reflecting the regional distribution of the contrast agent in the pulmonary system.

(10) In steps S5 and S6 first and second virtual non-contrast imaging data are reconstructed from the respective first and second CT imaging data. These first and second VNC imaging data might provide information equivalent to that obtainable from unenhanced images measured before the administration of the contrast agent. For this purpose the attenuation of the contrast agent is removed from the original imaging data.

(11) In step S7 a transformation function T for registering the first and second VNC imaging data is determined. Thereby, T might include a combination of basic transformations such as rotations, translations, scalings, and/or shearings, in order to align the first and second reconstructed VNC imaging data. For determining said transformation function T, for example, a feature-based registration can be used, including the steps of detecting feature points in both data sets, finding corresponding pairs of feature points and constructing a transformation function T which minimizes a measure of mismatch between corresponding pairs of feature points in both data sets. However, also other methods as described in reference [8] can be used.

(12) In step S8 the first and second VNC imaging data are registered by applying the determined transformation function T. Depending how the transformation function T was determined, either the first reconstructed VNC imaging data might be registered on the second reconstructed VNC imaging data, or vice versa. However, in the end two registered VNC imaging data sets are available which can be regionally compared voxel by voxel.

(13) In step S9 regional ventilation data is calculated using the registered first and second VNC data and/or said transformation function T. In order to calculate this ventilation data which shows the change in air content due to ventilation, for example, fractional air content in corresponding voxels of the first and second registered VNC imaging data can be compared. Alternatively, the Jacobian of the transformation function T which measures the differential expansion or contraction at a position in the imaging data might be used.

(14) In step S10 the first and second PBV imaging data are registered by applying said determined transformation function T. Using here the same transformation function T for registering as in the case of the VNC imaging data is possible since the first and second reconstructed regional PBV imaging data were reconstructed from the same CT imaging data sets as the first and second reconstructed VNC imaging data. Therefore, once said transformation function T is determined, it can be used to register the first and second reconstructed VNC imaging data, as well as the first and second reconstructed regional PBV imaging data respectively.

(15) In step S11 the first reconstructed and registered regional PBV imaging data is adjusted for different blood volume fractions caused by the different respiratory phases. For this correction in a first step the regional change of the blood volume fraction between corresponding voxels of the first and second registered VNC imaging data is determined by comparing the image CT values of the respective voxels. Based on this regional change of the blood volume the first reconstructed regional PBV imaging data is then scaled accordingly to compensate for this change.

(16) In the end, the method presented in FIG. 1 results in five registered imaging data sets (1 ventilation, 2 PBV from two respiratory phases, and 2 VNC from two respiratory phases), including three types of information, which can be regionally compared voxel by voxel. By this a more precise evaluation of or correlation between the morphological and functional information of the respiratory system is possible.

(17) FIG. 2 schematically illustrates an embodiment of the image processing method. For a better understanding the operations are illustrated in an more figurative manner (processing pipeline), rather than showing a sequential process. Initially first and second (raw) CT imaging data D.sub.1 and D.sub.2 are provided. Thereby, D.sub.1 was obtained in a first respiratory phase of the subject, e.g. in end-inspiration, and includes two, preferably co-recorded, data sets of the same anatomic location obtained with x-ray energy levels E.sub.1 and E.sub.2. Accordingly, D.sub.2 was obtained in a subsequent second respiratory phase of the subject, e.g. in end-expiration, and includes two, preferably co-recorded, data sets of again the same anatomic location obtained with x-ray energy levels E′.sub.1 and E′.sub.2. In order to facilitate the data evaluation E′.sub.1 and E.sub.1, as well as E′.sub.2 and E.sub.2 might have the same value, e.g. E′.sub.1=E.sub.1=80 kVp and E′.sub.2=E.sub.2=120 kVp. From each of the first and second CT imaging data first and second PBV as well as VNC imaging data are reconstructed respectively, resulting in four reconstructed data sets PBV.sub.1, VNC.sub.1, PBV.sub.2 and VNC.sub.2 which include functional (perfusion) as well as anatomical information.

(18) In a next step a transformation function T is determined which matches, when applied on the first VNC imaging data VNC.sub.1, said first VNC imaging data with the second VNC imaging data VNC.sub.2. Furthermore, this transformation function T can also be used to register said first PBV imaging data PBV.sub.1 with the second PBV imaging data PBV.sub.2, since they were reconstructed from the same CT imaging data sets D.sub.1 and D.sub.2 as the first and second reconstructed VNC imaging data VNC.sub.1 and VNC.sub.2. Additionally, when applying said transformation function T to the first PBV imaging data PBV.sub.1 also a blood volume correction is performed which accounts for different blood volume content in the different respiratory phases. As a result, the four data sets PBV.sub.1(registered), VNC.sub.1(registered), PBV.sub.2 and VNC.sub.2 are now registered and provide quantitative anatomical as well as functional pulmonary information which can be directly compared on the regional level.

(19) In a last step regional ventilation imaging data is calculated based on the registered first and second VNC imaging data VNC.sub.1(registered) and VNC.sub.2. For this purpose the fractional air content in corresponding voxels of VNC.sub.1(registered) and VNC.sub.2 are compared. Alternatively, the regional ventilation data can also be calculated from the determined transformation function T by the Jacobian determinant of T.

(20) In the end the imaging processing method provides five registered imaging data sets (1 ventilation, 2 PBV from two respiratory phases, and 2 VNC from two respiratory phases), including three types of information (ventilation, perfusion, morphology), which can be regionally compared voxel by voxel. This enables a more precise evaluation of or correlation between the morphological and functional information of the respiratory system.

(21) FIG. 3 shows the embodiment of the image processing method of FIG. 2 with exemplary measured and processed imaging data (PBV.sub.1, PBV.sub.1(registered), PBV.sub.2, VNC.sub.1, VNC.sub.1(registered), VNC.sub.2, and ventilation data). Since the sequence of imaging processing operations is identical to that of FIG. 2, in the following only the features and/or changes of the exemplary imaging data will be discussed. From the provided first and second (raw) CT imaging data D.sub.1 and D.sub.2 non-contrast coronal reformatted CT images of a human lung were reconstructed VNC.sub.1 and VNC.sub.2. Since D.sub.1 and D.sub.2 were obtained in two different respiratory phases of the subject consequently the shape of the lung differs in VNC.sub.1 and VNC.sub.2. Additionally, also the first and second regional reconstructed PBV imaging data PBV.sub.1 and PBV.sub.2 are shown. Thereby, the regional volume of blood to which fresh blood is being delivered is reflected in the regional contrast agent concentration (in the present case: iodine). Also in the PBV imaging data the different respiratory phases are notable in the different overall lung shape as well in the different regional iodine distribution. After the registration the local features of the PBV and VNC imaging data of the first respiratory phase PBV.sub.1 and VNC.sub.1 are transformed in order to match with the respective imaging data of the second respiratory phase PBV.sub.2 and VNC.sub.2. As can be seen in the images PBV.sub.1(registered) and VNC.sub.1(registered) the overall lung shape has changed compared to the not registered case before. Therefore, the morphological and functional information of the images of the first and second respiratory phase can be regionally compared voxel by voxel which enables a precise evaluation of the data in any following diagnostics. Finally, regional ventilation data were calculated based on the registered VNC.sub.1(registered) and VNC.sub.2 imaging data. The ventilation data highlights regionally the rate at which new air reaches the gas exchange area of the lungs.

(22) FIG. 4 schematically shows an example of an dual-energy CT scanning unit 10 with a dual-source dual-energy CT scanner 2, including two separate x-ray tubes 3a and 3b and two corresponding detectors 4a and 4b which are placed orthogonally to each other within a single rotating gantry. Each tube can be operated at their own kilovoltage and miliampere settings, allowing for pairs of images of a subject 1 to be generated simultaneously, utilizing appropriate x-ray spectra for the separation of the material of interest (i.e. iodine). The pairs of CT images can be reconstructed from the multiple angles of view generated for each detector respectively. The dual-source dual-energy CT scanner 2 is connected to a control device 5, being adapted for controlling the dual-source dual-energy CT scanner 2, i.e. sending machine instructions to the scanner, and configured to perform a method according to the invention. The connection between the dual-source dual-energy CT scanner 2 and the control device 5 can be a wired, wireless, or any other type of data communication line which allows for a transfer of information between the dual-source dual-energy CT scanner 2 and the control device 5. Via the data communication line series of sets of raw imaging data D.sub.1 and D.sub.2 collected by the dual-source dual-energy CT scanner 2 are transferred to the control device 5 and subsequently processed with a method according to the invention. However, it is also possible that the imaging data D.sub.1 and D.sub.2 acquired by the dual-energy CT scanning unit 10 is transferred via a medium 6 or data network to another distant computer and processed there.

(23) The features of the invention disclosed in the above description, the drawing and the claims can be of significance both individually as well as in combination or sub-combination for the realisation of the invention in its various embodiments.

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