Reconstruction of an image using one or more imaging modalities

Abstract

Method for reconstructing an image of an object under investigation comprising the following steps: - Acquisition of a first image data set of the examination object with a first imaging modality (110), - providing at least one further image data set of the examination object to at least one further imaging modality (120), - reconstruction of at least one first image based on the first image data set using the at least one further image data set by a reconstruction unit (130), wherein the first image data set has a subsampling, wherein the reconstruction comprises an iterative reconstruction, wherein the at least one further image data set is used as a priori knowledge for the iterative reconstruction, wherein the at least one further image data set comprises contrast information which is used as a priori knowledge for the iterative reconstruction.

Description

[0001] The invention relates to a method for reconstructing an image of an examination object, a medical imaging device and a computer program product.

[0002] Various modalities are used in medical imaging. Important modalities include ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). Ideally, the most appropriate imaging modality is used depending on the clinical application and the question being investigated.

[0003] Imaging modalities use different methods to acquire image data. Typically, a measurement data space is filled with the data needed to compute an image during acquisition, and the image is then computed.

[0004] In MRI, for example, a Fourier space, which can also be referred to as k-space or spatial frequency space, is filled with data, and an image is created from it using Fourier transformation. The size of the measurement data space, i.e., the number of data points to be acquired, is usually determined by parameters such as resolution, matrix size, slice thickness, and number of slices. The measurement time required for acquisition is usually proportional to the size of the measurement data space.

[0005] For many applications, it is advantageous to keep the measurement time as short as possible, for example to keep the dose low or to be able to represent fast-running processes.

[0006] The publication US 2015 / 0 011 865 A1 describes an image acquisition module configured to acquire image information using a non-MRI modality. The publication DE 10 2011 081 411 A1 describes an iterative reconstruction of an MR image based on an input estimate. The publication DE 10 2011 006 188 A1 describes the creation of tomographic image representations using at least two source-detector systems. The publication US 2013 / 0 267 841 A1 describes segmentation in multimodal reconstruction. The publication US 2011 / 0 286 646 A1 describes image generation using a previous image.

[0007] The present invention is intended to provide a method, a device and a computer program product which enables an advantageous, in particular faster and / or higher quality, generation of an image of an examination object.

[0008] The problem is solved by the features of the independent claims. Advantageous embodiments are described in the subclaims.

[0009] Accordingly, a method for reconstructing an image of an examination object is proposed, which comprises the following steps: a first image data set of the examination object is acquired using a first imaging modality. Furthermore, at least one further image data set of the examination object is provided to at least one further imaging modality. Preferably, the acquired first image data set and the provided at least one further image data set are transmitted to a reconstruction unit. Based on the first image data set, the reconstruction unit reconstructs at least one first image using the at least one further image data set, ie when reconstructing the at least one first image from the data of the first image data set, the data of the at least one further image data set is also used.

[0010] This allows for the reconstruction of at least one first image to be based on a larger database than would be possible using only the first image dataset. This, in turn, allows for the calculation of images of higher quality, for example, by showing fewer artifacts.

[0011] The object under examination can, for example, be a part of a patient's body. The acquisition of the first image dataset is typically performed using a first medical imaging device configured to perform the first imaging modality.

[0012] Advantageously, the first imaging modality differs from the at least one further imaging modality, ie the first imaging modality is a different imaging modality than the at least one further imaging modality.

[0013] Possible imaging modalities include, for example, magnetic resonance imaging (MRI) and / or an X-ray image and / or computed tomography (CT) and / or digital volume tomography (DVT), in particular cone-beam CT (CBCT), and / or mammography and / or magnetic resonance / positron emission tomography (MR / PET) and / or positron emission tomography / computed tomography (PET / CT) and / or scintigraphy and / or sonography and / or thermography and / or electrical impedance tomography (EIT).

[0014] The at least one further imaging modality may comprise the first imaging modality. The at least one further imaging modality may also comprise one or more imaging modalities different from the first. The at least one further imaging modality may also comprise the first imaging modality and one or more imaging modalities different from the first.

[0015] An image dataset can contain values ​​that reflect information about a structure and / or condition of the object under examination. The values ​​can be measured values ​​acquired using a medical imaging device. For example, in the case of an MRI, the image dataset can comprise a Fourier space filled with measured values.

[0016] Providing the at least one additional image data set may comprise acquiring the at least one additional image data set. This acquisition typically occurs with at least one additional medical imaging device configured to perform the at least one additional imaging modality. However, it is also conceivable for this acquisition to occur with the first medical imaging device, provided it is configured to perform the at least one additional imaging modality.

[0017] The acquisition of at least one additional image data set can occur before or after the acquisition of the first image data set. However, simultaneous acquisition of the image data sets is also conceivable, particularly if the acquiring medical imaging device is configured to perform multiple imaging modalities.

[0018] Furthermore, the provision of the at least one additional image data set can include reading in an existing image data set. For example, a further image data set can first be generated using a CT scan, which can then be stored on a data storage medium and later provided by loading it into the reconstruction unit.

[0019] When reconstructing the at least one first image based on the first image data set, for example by Fourier transformation of a Fourier space in MRI, the provided at least one further image data set, which may include CT data, for example, is processed in addition to the first image data set.

[0020] One embodiment provides that the first image data set has a first orientation, the at least one further image data set has at least one further orientation, and the first orientation and the at least one further orientation are aligned.

[0021] In particular, the first image data set is linked to a first spatial coordinate system, and the at least one further image data set is linked to at least one further coordinate system. By transforming the coordinate systems, the orientations can be aligned so that the data sets have the same spatial reference system. This allows the at least one further image data set to be easily used in the reconstruction of the at least one first image.

[0022] The first image data set is undersampled, meaning that the data set actually required for reconstruction is not completely filled.

[0023] For example, by subsampling the first image data set, a sparse measurement data matrix is ​​obtained, for example a sparse Fourier space matrix in MRI. This allows the acquisition of the measurement data to be faster, since only a portion of the measurement data matrix is ​​no longer recorded, as was previously the case, but rather all entries. Ideally, with a subsampling factor of n, i.e. only every nth matrix element is sampled on average, an equally large acceleration factor n results, i.e. only the nth part is required to acquire the measurement data. Advantageously, the subsampling factor used in this method is at least two, preferably at least three, and particularly preferably at least four. Parallel acquisition techniques (PAT), for example, can also be used to acquire the measurement data in MRI.Such methods allow the generation of largely or completely artifact-free images despite undersampled acquisition.

[0024] Reconstruction involves iterative reconstruction. Especially with undersampled image datasets, iterative reconstruction can produce high-quality images.

[0025] In iterative reconstruction, prior knowledge, so-called a priori knowledge, is typically used to iteratively determine missing measurement data from the image dataset as accurately as possible. For example, assumptions can be made as a priori knowledge, such as that the object under investigation has a head shape.

[0026] For the iterative reconstruction, the at least one additional image data set is used as a priori knowledge. Thus, useful information can be derived from the at least one additional image data set for the iterative reconstruction in order to improve the quality of the resulting at least one first image.

[0027] The at least one further image data set includes contrast information, which is used as a priori knowledge for the iterative reconstruction. The contrast information can be determined, in particular, from at least one further image that can be derived from the at least one further image data set. The fact that contrasts between images from different imaging modalities can vary greatly can be advantageously exploited here.

[0028] Preferably, the contrast information can comprise at least an outline of the examination object. In particular, external boundaries of the examination object, such as a head shape, can be used. Furthermore, the contrast information can comprise at least one edge of a tissue structure of the examination object. An edge of a tissue structure usually separates two flat tissue regions, i.e., it characterizes a transition region from one tissue region to another. In particular, high-frequency components of the additional images can be taken into account, which are generally characteristic of edges within the additional images, while the low frequencies of the additional images, which generally determine a contrast intensity, are not taken into account.

[0029] One embodiment provides that the a priori knowledge includes representation information for evaluating the contrast information. Such representation information can, for example, include prior knowledge about contrast representations dependent on the imaging modality, e.g., that bone tissue is typically displayed as dark in MRI, whereas it is typically displayed as bright in CT.

[0030] It is further proposed that a segmentation be performed based on the at least one further image data set, and that segmentation information be derived from the segmentation, which is used as a priori knowledge. For example, at least one further image can be generated from the at least one further image data set, to which the segmentation is applied.

[0031] When segmenting the further image, regions with related content, especially body regions, are typically identified, for example, by combining neighboring pixels or voxels. This can be done using a homogeneity criterion and / or a threshold, among other things. For example, all pixels in the further image that lie below a certain threshold can be assigned a value of "0," while all pixels above a certain threshold can be assigned a value of "1."

[0032] The segmentation information preferably includes an assignment of segments to tissue types. This allows different tissue types of an examination object to be identified, to which, for example, signal intensities and / or density values ​​can then be assigned.

[0033] It is further proposed that the first image data set comprises measurement data from a first field of view (FOV), and that the at least one further image data set comprises measurement data from at least one further image field, wherein the at least one further image field comprises at least one additional region that is not encompassed by the first image field. In particular, the further image field can completely encompass the first image field, such that the further image field represents a true superset of the first image field and is thus larger than the first image field. Preferably, the at least one additional image field directly borders the first image field.

[0034] If the additional image field comprises at least one additional region, data from the at least one additional image field can also be used to reconstruct the at least one first image based on the first image data set. This allows for higher image quality to be achieved, especially in the outer regions of the at least one first image.

[0035] One embodiment provides that the method includes an additional step in which the at least one image is displayed. In particular, additional image data generated from the at least one additional image data set is displayed together with the at least one first image. This can increase the informative value of the resulting display.

[0036] For example, at least one first image can be displayed in the center of the display and peripherally image data based on at least one further image data set.

[0037] Furthermore, a medical imaging device of a first imaging modality is proposed, which is configured to carry out a method according to the invention for reconstructing an image of an examination subject. Advantageously, the medical imaging device comprises an acquisition unit for acquiring a first image data set using the first imaging modality, a provision unit for providing at least one further image data set of at least one further imaging modality, and a reconstruction unit for reconstructing at least one first image based on the first image data set using the at least one further image data set.

[0038] The advantages of the medical imaging device according to the invention essentially correspond to the advantages of the method according to the invention for reconstructing an image of an examination subject, which have been described in detail above. Features, advantages, or alternative embodiments mentioned herein can also be applied to the other claimed subject matter, and vice versa.

[0039] Furthermore, a computer program product is proposed which comprises a program and can be loaded directly into a memory of a programmable system control unit of a medical imaging device and has program means, e.g. libraries and auxiliary functions, for carrying out a method according to the invention when the computer program product is executed in the system control unit of the medical imaging device. The computer program product can comprise software with source code that still needs to be compiled and linked or that only needs to be interpreted, or executable software code that only needs to be loaded into the system control unit for execution. The computer program product enables the method according to the invention to be carried out quickly, identically repeatably and robustly. The computer program product is configured such that it can carry out the method steps according to the invention by means of the system control unit.The system control unit must have the necessary prerequisites, such as appropriate RAM, a suitable graphics card, or a suitable logic unit, so that the respective method steps can be executed efficiently. The computer program product is stored, for example, on a computer-readable medium or on a network or server, from where it can be loaded into the processor of a local system control unit, which can be directly connected to the medical imaging device or embodied as part of the medical imaging device. Furthermore, control information of the computer program product can be stored on an electronically readable data carrier.The control information of the electronically readable data carrier can be configured such that it carries out a method according to the invention when the data carrier is used in a system control unit of a medical imaging device. Examples of electronically readable data carriers are a DVD, a magnetic tape, or a USB stick on which electronically readable control information, in particular software, is stored. If this control information is read from the data carrier and stored in a system control unit of the medical imaging device, all embodiments of the methods described above can be carried out. Thus, the invention can also be based on said computer-readable medium and / or said electronically readable data carrier.

[0040] Further advantages, features, and details of the invention will become apparent from the exemplary embodiments described below and from the drawings. Corresponding parts are provided with the same reference numerals in all figures.

[0041] They show: Fig. 1 a schematic representation of a medical imaging device according to the invention, Fig. 2 a schematic block diagram of a method according to the invention, Fig. 3 a schematic block diagram of an extended method according to the invention, Fig. 4 a schematic cross-sectional view of various projection covers.

[0042] For an exemplary imaging modality, Fig. 1 schematically shows a magnetic resonance device 10 as a medical imaging device for performing magnetic resonance imaging (MRI). Other possible imaging modalities include, for example, an X-ray image and / or a computed tomography (CT) and / or a digital volume tomography (DVT, in particular CBCT) and / or a mammography and / or a magnetic resonance positron emission tomography (MR / PET) and / or a positron emission tomography / computed tomography (PET / CT) and / or a scintigraphy and / or a sonography and / or a thermography and / or an electrical impedance tomography (EIT).

[0043] The magnetic resonance apparatus 10 comprises a magnet unit 11 having a superconducting main magnet 12 for generating a strong and, in particular, temporally constant main magnetic field 13. Furthermore, the magnetic resonance apparatus 10 comprises a patient receiving area 14 for receiving a patient 15. In the present exemplary embodiment, the patient receiving area 14 is cylindrical and is surrounded in a circumferential direction by the magnet unit 11. In principle, however, a different design of the patient receiving area 14 is conceivable at any time. The patient 15 can be pushed into the patient receiving area 14 by means of a patient support device 16 of the magnetic resonance apparatus 10. For this purpose, the patient support device 16 has a patient table 17 designed to be movable within the patient receiving area 14.

[0044] The magnet unit 11 further comprises a gradient coil unit 18 for generating magnetic field gradients used for spatial encoding during imaging. The gradient coil unit 18 is controlled by a gradient control unit 19 of the magnetic resonance device 10. The magnet unit 11 further comprises a radio-frequency antenna unit 20, which in the present exemplary embodiment is designed as a body coil permanently integrated into the magnetic resonance device 10. The radio-frequency antenna unit 20 is designed to excite atomic nuclei that arise in the main magnetic field 13 generated by the main magnet 12. The radio-frequency antenna unit 20 is controlled by a radio-frequency antenna control unit 21 of the magnetic resonance device 10 and radiates radio-frequency magnetic resonance sequences into an examination room, which is essentially formed by a patient receiving area 14 of the magnetic resonance device 10.The radio frequency antenna unit 20 is further designed to receive magnetic resonance signals.

[0045] The magnetic resonance apparatus 10 has a system control unit 22 for controlling the main magnet 12, the gradient control unit 19, and the radio-frequency antenna control unit 21. The system control unit 22 centrally controls the magnetic resonance apparatus 10, such as performing a predetermined imaging gradient echo sequence. Furthermore, the magnetic resonance apparatus 10 comprises a user interface 23 connected to the system control unit 22. Control information, such as imaging parameters, as well as reconstructed magnetic resonance images, can be displayed on a display unit 24, for example, on at least one monitor, of the user interface 23 for medical personnel. Furthermore, the user interface 23 has an input unit 25, by means of which information and / or parameters can be entered by the medical personnel during a measurement process.

[0046] The magnetic resonance apparatus further comprises an acquisition unit 31 for acquiring a first image data set using the first imaging modality. The acquisition unit 31 comprises several units, which were previously explained in detail.

[0047] The magnetic resonance apparatus further comprises a provision unit 32 for providing at least one further image data set of at least one further imaging modality and a reconstruction unit 33 for reconstructing at least one first image based on the first image data set using the at least one further image data set.

[0048] The provision unit 32 can, for example, be an interface to a data network, in particular a hospital information system (HIS) and / or a radiology information system (RIS), from which the at least one additional image data set can be retrieved. It can also be a local data storage device on which the at least one additional image data set is stored. Transmission of the at least one additional image data set to the reconstruction unit 33 can, for example, be wired and / or wireless.

[0049] The reconstruction unit 33 is comprised in this example by the system control unit 22. To carry out the reconstruction, the reconstruction unit 33 can access a program memory unit (not shown in detail) and a processor unit, by means of which software and / or computer programs stored in the program memory unit are executed. In particular, a computer program product can be executed by means of which, among other things, a method according to Fig. 2 and / or 3 can be carried out.

[0050] Fig. Figure 2 shows a method in which, in a step 110, an acquisition of a first image data set is performed using a first imaging modality, such as MRI. In a step 120, at least one further image data set is provided using at least one further imaging modality, such as CT, which advantageously differs from the first imaging modality. The generation of the at least one further image data set can, for example, occur before or after step 110. In Fig. 3, an acquisition step 100 is added by way of example, in which the at least one further image data set is acquired. However, particularly for combined imaging modalities such as MR / PET and / or PET / CT, it is also conceivable that the acquisition of the first image data set and the at least one further image data set takes place simultaneously.

[0051] In a step 130, a reconstruction, in particular an iterative reconstruction, of at least a first image is carried out based on the first image data set using the at least one further image data set. In a step 140, as exemplarily shown in Fig. 3, at least one first image can be displayed.

[0052] A priori knowledge can be derived from the at least one additional image data set, which is then used for iterative reconstruction based on the first image data set. This allows the quality of the at least one first image to be optimized and / or the acquisition of the first image data set to be accelerated. Acceleration can be achieved through subsampling, for example, by sampling a Fourier space matrix only intermittently, rather than continuously.

[0053] At least one further image can usually be generated from the at least one further image data set. This at least one further image typically exhibits contrasts, from which contrast information can be derived as a priori knowledge for the reconstruction of the at least one first image based on the first image data set.

[0054] For example, outlines and / or external boundaries of the patient 15, e.g., the shape of the head or a leg, can be used. Furthermore, edges, in particular those derived from high-frequency components of the at least one further image in contrast to low frequencies of the image, which primarily determine contrast intensity, can be taken into account. Using the edges, tissue structures within the patient's external boundaries can be determined and made available as a priori knowledge.

[0055] Further a priori knowledge can be previous knowledge about specific contrasts, such as that bones are generally dark in an MRI image, whereas a bright image is common in a CT image.

[0056] Furthermore, to generate a priori knowledge, the at least one further image can be segmented, i.e., segmentation information is generated as a priori knowledge based on the at least one further image data set. This allows different tissue types to be identified, to which, for example, signal intensities and / or density values ​​can then be assigned.

[0057] For example, an image dataset can be acquired from an MRI, based on which an image is generated. In this image, for example, bones, air, lungs, fat and / or soft tissue can be identified and assigned initial values, e.g. +1000 Hounsfield units (HU), -1000 HU, -500 HU, -75 HU and +40 HU, for a reconstruction of CT measurement data, which are then iteratively corrected. In this example, CT represents the first imaging modality and MRI the further imaging modality. Segmentation information is derived from the MRI image dataset, i.e. the further image dataset, and used as a priori knowledge for the iterative reconstruction of the CT image dataset, i.e. the first image dataset.

[0058] If at least one additional image dataset covers a larger image field than the first image dataset, data from the image field that is covered by the additional image field but not by the first image field can be used to generate a priori knowledge. Fig. Figure 4 illustrates such a case using a cone-beam CT (CBCT), which is a form of digital volume tomography (DVT). Ideally, when performing a CBCT, measurement data is acquired over a 360° circumference of a patient 15, shown here in cross-section, i.e., several cone-beam projections are performed over the entire circumference of the patient. To generate a cone-beam projection, the patient 15 is irradiated with X-rays, with the X-rays covering a cone-shaped irradiation volume. Fig. 4 shows two differently oriented radiation volumes as examples, namely a frontal radiation volume 401 and a lateral radiation volume 402.

[0059] To acquire an image data set, a detector 410 records a signal distribution of the transmitted X-rays in different orientations. The detector has a limited dimension L, e.g., 40×40 cm. 2, so that the radiation volumes 401, 402 are also limited. This leads to the frontal radiation volume 401 not covering a part of the body of the patient 15, which is shown hatched here, i.e., no 360° projections are available for parts of the patient. Any images cannot be exactly reconstructed from the acquired image data set due to this undercoverage. Although methods are known with the aid of which images can be reconstructed from this incomplete image data set, these images are always subject to errors. These errors can be corrected using another image data set from another imaging modality.

[0060] For example, MRI image datasets often have a larger field of view than CBCT image datasets. Fig.4, for example, it is conceivable that an MRI image dataset covers an image field encompassing the entire volume of the patient 15. This allows for better image quality, especially in outdoor areas, here in particular in the hatched areas of the patient 15.

[0061] Finally, the method is further illustrated by exemplary embodiments. For example, in the context of radiation therapy of a patient, an MRI can first be performed in step 100 for radiation planning. To carry out irradiation of the patient in a radiation device, this comprises a CBCT device for patient positioning. With the help of the CBCT device, a first image data set can be acquired in step 110. In order to improve the quality and / or the acquisition time of the CBCT, in this example, in step 130 at least a first image is reconstructed based on the first image data set by means of iterative reconstruction. In this case, a further image data set from the MRI performed in step 100 is used as a priori knowledge, which is provided in a step 120.

[0062] In another example, an MR / PET image is generated using iterative reconstruction from an MR / PET image dataset, with attenuation correction being performed. If a CT scan is available as an additional image dataset, bone segmentation can be performed using this. The resulting segmentation information can then be used as a priori knowledge to improve and / or accelerate the attenuation correction and thus to reconstruct the MR / PET image.

[0063] Finally, it should be noted once again that the methods described in detail above, as well as the medical imaging device illustrated, are merely exemplary embodiments that can be modified in a variety of ways by those skilled in the art without departing from the scope of the invention. Furthermore, the use of the indefinite articles "a" or "an" does not exclude the possibility that the respective features may be present in multiple instances. Likewise, the term "unit" does not exclude the possibility that the respective component may consist of several interacting subcomponents, which may also be spatially distributed.

Claims

[1] Method for reconstructing an image of an object under investigation comprising the following steps: - Acquisition of a first image data set of the examination object with a first imaging modality (110), - providing at least one further image data set of the examination object to at least one further imaging modality (120), - reconstruction of at least one first image based on the first image data set using the at least one further image data set by a reconstruction unit (130), wherein the first image data set has a subsampling, wherein the reconstruction comprises an iterative reconstruction, wherein the at least one further image data set is used as a priori knowledge for the iterative reconstruction, wherein the at least one further image data set comprises contrast information which is used as a priori knowledge for the iterative reconstruction. [2] The method of claim 1, wherein the first image data set has a first orientation, the at least one further image data set has at least one further orientation, and the first orientation and the at least one further orientation are aligned. [3] Method according to claim 1, wherein the contrast information comprises at least one outline of the examination object and / or at least one edge of a tissue structure of the examination object. [4] Method according to one of claims 1 or 3, wherein the a priori knowledge comprises representation information for evaluating the contrast information. [5] Method according to one of the preceding claims, wherein a segmentation is carried out on the basis of the at least one further image data set and by means of the segmentation a segmentation information is derived which is used as a priori knowledge. [6] The method of claim 5, wherein the segmentation information comprises an assignment of segments to tissue types. [7] Method according to one of claims 5 or 6, wherein a further segmentation is carried out on the basis of the first image data set using the segmentation information derived from a segmentation on the basis of the at least one further image data set. [8] Method according to one of the preceding claims, wherein the first image data set comprises measurement data from a first image field and the at least one further image data set comprises measurement data from at least one further image field, wherein the at least one further image field comprises at least one additional region which is not covered by the first image field. [9] A method according to any one of the preceding claims, wherein the method comprises an additional step: - Representation of at least one first image (140) [10] Method according to claim 9, wherein, together with the at least one first image, further image data are displayed which are generated on the basis of the at least one further image data set. [11] Method according to one of the preceding claims, wherein the first and / or second imaging modality comprises an X-ray image and / or a computed tomography (CT) and / or a digital volume tomography (DVT) and / or a mammography and / or a magnetic resonance imaging (MRI) and / or a magnetic resonance positron emission tomography (MR / PET) and / or a positron emission tomography / computed tomography (PET / CT) and / or a scintigraphy and / or a sonography and / or a thermography and / or an electrical impedance tomography (EIT). [12] A medical imaging device of a first imaging modality, comprising: - an acquisition unit for acquiring a first image data set with the first imaging modality, - a provision unit for providing at least one further image data set of a further imaging modality, - a reconstruction unit for reconstructing at least a first image based on the first image data set using the at least one further image data set, wherein the medical imaging device is designed to carry out a method according to one of the preceding claims. [13] Computer program product comprising a program and being directly loadable into a memory of a programmable evaluation unit of a medical imaging device, with program means for executing a method according to one of claims 1 to 11 when the program is executed in the evaluation unit.

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