Generation of synthetic 4D computed tomography images

Abstract

Disclosed herein are medical systems 100, 300, 500 comprising a processor 104 configured for controlling the medical system and a memory 110 for storing machine-executable instructions. Execution of the instructions causes the processor to receive (200) four-dimensional Dixon magnetic resonance image data 122. The four-dimensional Dixon magnetic resonance image data is T1-weighted. The four-dimensional Dixon magnetic resonance image data is synchronized with a respiratory signal 124. Execution of the instructions further causes the processor to reconstruct (202) synthetic four-dimensional computed tomography image data 12 from the four-dimensional Dixon magnetic resonance image data. The four-dimensional Dixon magnetic resonance image data is synchronized with the respiratory signal 124.

Description

[Technical Field]

[0001] The present invention relates to magnetic resonance imaging, and more particularly to generating synthetic computed tomography images from magnetic resonance images. [Background technology]

[0002] Computed tomography (CT) images are useful for radiation therapy planning. CT images measure the three-dimensional absorption of X-rays by the body. The images can be expressed in Hounsfield units or as spatially dependent electron density maps. Typically, one of these values ​​is used by radiation therapy planning software to estimate the absorption of ionizing radiation for radiation therapy. The primary difficulty with using CT for radiation therapy planning is that it exposes the subject to additional radiation. Another difficulty is that CT, while excellent at imaging hard or dense tissues such as bone, does not distinguish between different types of soft tissue as well as magnetic resonance imaging (MRI). Typically, both CT and MRI images are acquired during radiation therapy planning. The combination of the two allows for a superior estimation of the radiation absorbed by the subject and the ability to distinguish between different types of tissue, such as distinguishing healthy tissue from tumors.

[0003] International Patent Application Publication No. WO 2015 / 103184 (A1) discloses systems and methods directed to adaptive radiation therapy planning. In some aspects, the disclosed systems and methods include generating a composite image from magnetic resonance data using a relaxometry map. The method includes applying corrections to the data and generating a relaxometry map therefrom. In another aspect, a method for adaptive radiation therapy planning is disclosed. The method includes determining a dose gradient-based objective function from an initial dose distribution and generating an optimized plan based on the updated image using aperture morphing and gradient preservation algorithms without the need for organ-at-risk contouring. In yet another aspect, a method for obtaining 4D MR imaging using time-series reordering of data acquired during normal breathing, a method for deformable image registration using a sequentially applied quasi-physical model normalization method for multi-modality images, and a method for generating a 4D plan using an aperture morphing algorithm based on 4D CT or 4D MR imaging are disclosed. Summary of the Invention

[0004] The present invention provides a medical system, a computer program product and a method in the independent claims. Embodiments are given in the dependent claims.

[0005] Embodiments provide an improved method for generating synthetic four-dimensional computed tomography data from T1-weighted four-dimensional Dixon magnetic resonance data. The four-dimensional Dixon magnetic resonance data is synchronized with a respiratory signal, resulting in synthetic four-dimensional computed tomography data. The respiratory signal may be, for example, a respiratory phase or a respiratory amplitude. The respiratory signal may then be used to improve control of a radiation therapy system.

[0006] Dixon magnetic resonance imaging acquires magnetic resonance image data at various phases to generate both water and fat images, allowing for superior image segmentation and therefore the creation of higher quality composite CT images.

[0007] WO2015 / 103184 A1 teaches away from the use of Dixon magnetic resonance images, stating in paragraph 33 that misalignment of multi-echo Dixon images can confound image segmentation, resulting in blurred structure boundaries.

[0008] In one aspect, the present invention provides a medical system comprising a processor configured to control the medical system. The medical system further comprises a memory for storing machine-executable instructions. Execution of the instructions causes the processor to receive four-dimensional Dixon magnetic resonance image data. The four-dimensional Dixon magnetic resonance image data is T1-weighted. The four-dimensional Dixon magnetic resonance image data is synchronized with a respiratory signal. Synchronization with the respiratory signal can be achieved in different ways. For example, the respiratory signal can be measured using automatic navigation within k-space data, a navigator, respiratory balloon measurement (oxygen mask reservoir measurement), optical measurement of breathing with a camera system, and / or a respiratory belt. In one example, the measured signal can be referenced to the four-dimensional Dixon magnetic resonance image data. In another example, the four-dimensional Dixon magnetic resonance image data is divided into bins or groups of data, which are then referenced by a particular respiratory signal.

[0009] Execution of the machine-executable instructions further causes the processor to reconstruct synthetic four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, the four-dimensional Dixon magnetic resonance image data being synchronized with a respiratory signal. The reconstruction of the synthetic four-dimensional computed tomography image data is performed, for example, using an algorithm or model capable of converting magnetic resonance images into pseudoradiography images.

[0010] The use of four-dimensional Dixon magnetic resonance image data to create composite four-dimensional computed tomography image data can be beneficial because the four-dimensional Dixon magnetic resonance image data contains data that primarily represents water and fat tissue separately, which can aid in the reconstruction of the composite four-dimensional computed tomography image data.

[0011] In another embodiment, the composite 4D computed tomography image data is synchronized with a respiratory signal. In another embodiment, execution of the machine-executable instructions further causes the processor to receive treatment planning data registered with the composite 4D computed tomography image data. The treatment planning data may be provided, for example, by an automated algorithm. In another example, the treatment planning data may be input or provided by a healthcare provider, for example, via a graphical user interface. The treatment planning data may indicate, for example, regions of the subject to which it is desired to deliver a desired radiotherapy dose of radiation. The treatment planning data may also indicate regions, such as sensitive organs or structures, to which it is desired to minimize exposure to radiation.

[0012] Execution of the machine-executable instructions further causes the processor to calculate radiation therapy control commands configured to control a radiation therapy system using the treatment planning data, the synthetic 4D computed tomography image data, and the respiratory signal. The radiation therapy control commands are calculated, for example, using a model that simulates the radiation therapy system. The synthetic 4D computed tomography image data is useful for simulating the absorption of radiation by the subject. This embodiment therefore provides an improved means of providing radiation therapy control commands that can be synchronized to the subject's respiratory signal.

[0013] In another embodiment, execution of the machine-executable instructions further causes the processor to first receive treatment planning data and then register the treatment planning data with the composite four-dimensional computed tomography image data. Execution of the machine-executable instructions further causes the processor to calculate radiation therapy control commands configured to control the radiation therapy system using the treatment planning data, the composite four-dimensional computed tomography image data, and the respiratory signals.

[0014] In another embodiment, the medical system further comprises a magnetic resonance imaging system. The memory further comprises simulation pulse sequence commands configured to control the magnetic resonance imaging system to acquire simulation magnetic resonance image data from an imaging zone according to a four-dimensional magnetic resonance imaging protocol. The term simulation pulse sequence commands is a label that refers to specific pulse sequence commands. During radiation therapy, the term simulation is used to refer to the planning stage. And, use of the word simulation refers to the specific pulse sequence commands used to acquire data for radiation therapy simulation.

[0015] Similarly, the simulation magnetic resonance image data is magnetic resonance image data acquired for the purpose of performing a radiation therapy simulation. The four-dimensional magnetic resonance imaging protocol is a T1-weighted Dixon magnetic resonance imaging protocol. The pulse sequence command is further configured to acquire automatic navigation k-space data within the simulation magnetic resonance image data. Use of the navigation k-space data may be beneficial because, for example, it may be useful for deriving a respiratory signal and / or measuring subject movement during acquisition of the simulation magnetic resonance image data. As an alternative to navigation k-space data, the respiratory signal may also be measured using any of the techniques described above.

[0016] Execution of the machine-executable instructions further causes the processor to control a magnetic resonance imaging system with simulation pulse sequence commands to acquire simulated magnetic resonance data and a subject's respiratory signal. Execution of the machine-executable instructions further causes the processor to determine the respiratory signal using the navigation k-space data, where the navigation k-space data is within the simulated magnetic resonance image data.

[0017] Execution of the machine-executable instructions further causes the processor to sort the simulated magnetic resonance data into a set of discrete respiratory phases using a respiratory signal. Execution of the machine-executable instructions further causes the processor to reconstruct four-dimensional Dixon magnetic resonance image data from the sorted simulated magnetic resonance data. In this embodiment, simulated magnetic resonance image data is acquired and then sorted using a respiratory signal determined by the navigation k-space data.

[0018] In another embodiment, the medical system further comprises a radiation therapy system. The radiation therapy system is configured to irradiate a target zone within an irradiation zone. The irradiation zone is within the imaging zone. This embodiment is advantageous because a simulation of radiation therapy can be performed while the subject is within the radiation therapy system. This can be beneficial in that the subject is less likely to have moved. Another benefit is the use of a magnetic resonance imaging system to create synthetic four-dimensional computed tomography image data, which can reduce the amount of radiation to which the subject is exposed.

[0019] In another embodiment, the radiation therapy system is a LINAC system.

[0020] In another embodiment, the radiation therapy system is an X-ray radiation therapy system.

[0021] In another embodiment, the radiation therapy system is a cobalt radiation therapy system.

[0022] In another embodiment, the radiation therapy system is a gamma knife.

[0023] In another embodiment, execution of the machine-executable instructions further causes the processor to control the radiation therapy system with the radiation therapy control instructions to irradiate the target zone. This embodiment may be beneficial because the radiation therapy control instructions result in a higher quality radiation therapy system in which irradiation of the target zone is more precisely controlled.

[0024] In another embodiment, the memory further includes monitor pulse sequence commands configured to acquire monitor magnetic resonance image data. The monitor pulse sequence commands are configured to measure monitor automated navigation k-space data within the monitoring magnetic resonance image data. Execution of the machine-executable instructions further causes the processor to acquire the monitor magnetic resonance image data during irradiation of the target zone.

[0025] Execution of the machine-executable instructions further causes the processor to determine a current respiratory signal using the monitor magnetic resonance image data. Execution of the machine-executable instructions further causes the processor to select current magnetic resonance image data from the four-dimensional Dixon magnetic resonance image data using the monitor respiratory signal. Execution of the machine-executable instructions further causes the processor to adjust irradiation of the target zone using the current magnetic resonance image data. This may include gating delivery of irradiation when the target zone is in the correct location and / or tracking movement of the target zone to adjust the aim of the radiation therapy system. This embodiment may be advantageous because it eliminates the need for external monitoring of the subject's movement.

[0026] In another embodiment, the monitor pulse sequence commands follow a T1-weighted Dixon magnetic resonance imaging protocol. Execution of the machine-executable instructions further causes the processor to repeatedly update the accumulated radiation dose map using current respiratory signals, current magnetic resonance image data, and radiation therapy control commands. This embodiment can be beneficial because it provides a better means of measuring the dose of radiation delivered to the subject.

[0027] In another embodiment, the pulse sequence commands are configured to rotate the k-space sampling pattern between acquisitions of the simulation magnetic resonance image data. This embodiment can be beneficial because the central k-space region can be used to determine, for example, the current respiratory signal. This can also reduce blurring of the four-dimensional Dixon magnetic resonance image data.

[0028] In another embodiment, the pulse sequence commands are configured to rotate the k-space sampling pattern between acquisitions of the monitor magnetic resonance image data. This embodiment can be beneficial because the central k-space region can be used to determine, for example, the current respiratory signal. This can also reduce blurring of the Dixon magnetic resonance image data.

[0029] In another embodiment, the k-space sampling pattern is a stack of stars k-space sampling pattern or a spiral k-space sampling pattern.

[0030] In another embodiment, the memory further includes two-dimensional monitor pulse sequence commands configured to acquire two-dimensional monitor magnetic resonance image data. Execution of the machine-executable instructions further causes the processor to acquire two-dimensional monitor magnetic resonance image data during irradiation of the target zone, and execution of the machine-executable instructions further causes the processor to adjust irradiation of the target zone using the two-dimensional monitor magnetic resonance image data. In this embodiment, two-dimensional magnetic resonance images, such as those conventionally used, are acquired and then used to modify the irradiation. For example, the subject's current position can be registered with the synthetic four-dimensional computed tomography image data to modify control of the radiation therapy system.

[0031] In another embodiment, the pulse sequence commands are configured to rotate the k-space sampling pattern between acquisitions of the simulation magnetic resonance image data. This embodiment may be advantageous because a central region of the rotating k-space pattern may be used, for example, for automatic navigation. It may also be advantageous because the rotation of the k-space sampling pattern allows the acquired k-space data to be binned by the respiratory signal.

[0032] In another embodiment, the k-space sampling pattern is a stack-of-stars k-space sampling pattern.

[0033] In another embodiment, the k-space sampling pattern is a spiral k-space sampling pattern.

[0034] In another embodiment, the 4D Dixon magnetic resonance image data is reconstructed according to a compressed sensing magnetic resonance imaging protocol. Compressed sensing reconstruction can be used even if not all of the magnetic resonance image data has been acquired. This is true even if the MR acquisition was not configured using compressed sensing. When the magnetic resonance image data acquisition is time-constrained, such as in real-time operation, it may happen that not all k-space lines are completely acquired for each respiratory phase. Compressed sensing analysis can be useful for compensating for missing k-space data.

[0035] In another embodiment, the simulation pulse sequence commands follow a compressed sensing magnetic resonance imaging protocol, which may be beneficial because it may allow, for example, the magnetic resonance imaging protocol to be performed more quickly.

[0036] In another embodiment, the four-dimensional Dixon magnetic resonance image data includes a first Dixon image and a second Dixon image for each of the set of discrete respiratory phases. Reconstructing the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data includes constructing a water Dixon image using the first Dixon image and the second Dixon image for each of the set of discrete respiratory phases. Reconstructing the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data further includes constructing a fat Dixon image using the first Dixon image and the second Dixon image for each of the set of discrete respiratory phases.

[0037] Reconstructing the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data further includes constructing an in-phase Dixon image using the first Dixon image and the second Dixon image. Reconstructing the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data further includes using the in-phase Dixon images to segment a body mask for each of the set of discrete respiratory phases, and using the in-phase Dixon images to segment a bone mask for each of the set of discrete respiratory phases. The body mask is useful, for example, for identifying regions outside the subject, such as regions that are not surrounded by fat or water regions. The bone mask can be represented by a signal-devoid region in the subject's body defined by the body mask.

[0038] The reconstruction of the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data further includes segmenting the region between the body mask and the bone mask into soft tissue regions for each of the set of discrete respiratory phases using the fat Dixon image and the water Dixon image. The use of the fat Dixon image and the water Dixon image can be useful for improving the quality of the segmentation and distinguishing between different types of soft tissue.

[0039] The reconstruction of the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data further includes assigning soft tissue Hounsfield unit values ​​to the soft tissue regions for each of the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model. And finally, the reconstruction of the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data includes assigning bone tissue Hounsfield unit values ​​in the bone mask for each of the set of discrete respiratory phases using non-rigid registration or using a bone tissue classification model. This embodiment can be advantageous because it provides an effective means of creating composite radiographic images from Dixon magnetic resonance images.

[0040] In another aspect, the present invention provides a computer program product including machine-executable instructions for a processor controlling a medical system. Execution of the instructions causes the processor to receive four-dimensional Dixon magnetic resonance image data. The four-dimensional Dixon magnetic resonance image data is T1-weighted. The four-dimensional Dixon magnetic resonance image data is synchronized with a respiratory signal. Execution of the instructions further causes the processor to reconstruct synthetic four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data. The four-dimensional Dixon magnetic resonance image data is synchronized with the respiratory signal. Advantages of this embodiment are described above.

[0041] In another aspect, the present invention provides a method of operating a medical system, the method comprising receiving four-dimensional Dixon magnetic resonance image data, the four-dimensional Dixon magnetic resonance image data being T1 weighted, and the four-dimensional Dixon magnetic resonance image data being synchronized with a respiratory signal.

[0042] The method further comprises reconstructing synthetic four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, the four-dimensional Dixon magnetic resonance image data being synchronized with the respiratory signal. The benefits of this embodiment have been described above.

[0043] It should be understood that one or more of the above-described embodiments of the present invention may be combined, unless the combined embodiments are mutually exclusive.

[0044] As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as an apparatus, a method, or a computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which may be referred to generally herein as a "circuit," "module," or "system." Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable medium(s) having computer-executable code embodied on the computer-readable medium(s).

[0045] Any combination of one or more computer-readable media may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. As used herein, "computer-readable storage medium" encompasses any tangible storage medium capable of storing instructions executable by a processor of a computing device. The computer-readable storage medium may also be referred to as a computer-readable non-transitory storage medium. The computer-readable storage medium may also be referred to as a tangible computer-readable medium. In some embodiments, the computer-readable storage medium may also be capable of storing data accessible by the processor of a computing device. Examples of computer-readable storage media include, but are not limited to, floppy disks, magnetic hard disk drives, solid-state hard disks, flash memory, USB thumb drives, random access memory (RAM), read-only memory (ROM), optical disks, magneto-optical disks, and processor register files. Examples of optical disks include compact discs (CDs) and digital versatile discs (DVDs), e.g., CD-ROM, CD-RW, CD-R, DVD-ROM, DVD-RW, or DVD-R disks. The term computer-readable storage medium also refers to various types of recording media that can be accessed by a computing device over a network or communications link. For example, data may be read by a modem, over the Internet, or over a local area network. Computer-executable code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the above.

[0046] A computer-readable signal medium may include a propagated data signal with computer-executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium and may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0047] "Computer memory" or "memory" is an example of a computer-readable storage medium. Computer memory is any memory that is directly accessible to a processor. "Computer storage" or "storage" is a further example of a computer-readable storage medium. Computer storage is any non-volatile computer-readable storage medium. In some embodiments, computer storage may be computer memory, and vice versa.

[0048] As used herein, "processor" encompasses an electronic component capable of executing a program, machine-executable instructions, or computer-executable code. Reference to a computing device including a "processor" should be interpreted as including two or more processors or processing cores, as the case may be. A processor may be, for example, a multi-core processor. A processor also refers to a collection of processors within a single computer system or distributed among multiple computer systems. The term computing device should be understood to also refer to a collection or network of computing devices, each having one or more processors. Computer-executable code is executed by multiple processors within the same computing device or distributed among multiple computing devices.

[0049] Computer-executable code may include machine-executable instructions or programs that cause a processor to perform aspects of the present invention. Computer-executable code for performing operations related to aspects of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, or C++, and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages, and may be compiled into machine-executable instructions. In some cases, the computer-executable code may be in the form of a high-level language or in pre-compiled form and may be used in conjunction with an interpreter that generates the machine-executable instructions on the fly.

[0050] The computer executable code may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., through the Internet using an Internet Service Provider).

[0051] Aspects of the present invention will be described with reference to flowcharts, illustrations, and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block or portion of the blocks of the flowcharts, illustrations, and / or block diagrams, where applicable, can be implemented by computer program instructions in the form of computer-executable code. It will further be understood that combinations of blocks in different flowcharts, illustrations, and / or block diagrams can be combined, if not mutually exclusive. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to create a machine such that the instructions, executing via the processor of the computer or other programmable data processing apparatus, cause the machine to perform the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0052] These computer program instructions may also be stored on a computer-readable medium that can instruct a computer, other programmable data processing apparatus, or other device to function in a certain way, such that the instructions stored on the computer-readable medium create an article of manufacture including instructions that perform the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0053] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to produce a computer-implemented process by causing a series of operational steps to be performed on the computer, other programmable apparatus, or other device, such that the instructions executing on the computer or other programmable apparatus provide a process for performing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0054] As used herein, a "user interface" is an interface that allows a user or operator to interact with a computer or computer system. A "user interface" is sometimes referred to as a "human interface device." A user interface can provide information or data to an operator and / or receive information or data from an operator. A user interface may allow input from an operator to be received by a computer and may provide output from the computer to a user. That is, a user interface may allow an operator to control or manipulate a computer, and an interface may allow a computer to show the results of the operator's control or manipulation. Displaying data or information on a display or graphical user interface is an example of providing information to an operator. Receiving data via a keyboard, mouse, trackball, touchpad, wand, graphics tablet, joystick, gamepad, webcom, headset, pedals, wired gloves, remote control, and accelerometer are all examples of user interface elements that allow information or data to be received from an operator.

[0055] As used herein, a "hardware interface" encompasses an interface that allows a processor of a computer system to interact with and / or control external computing devices and / or devices. A hardware interface may allow a processor to send control signals or instructions to external computing devices and / or devices. A hardware interface may also allow a processor to exchange data with external computing devices and / or devices. Examples of hardware interfaces include, but are not limited to, a universal serial bus, an IEEE 1394 port, a parallel port, an IEEE 1284 port, a serial port, an RS-232 port, an IEEE 488 port, a Bluetooth connection, a wireless LAN connection, a TCP / IP connection, an Ethernet connection, a control voltage interface, a MIDI interface, an analog input interface, and a digital input interface.

[0056] As used herein, "display" or "display device" encompasses an output device or user interface configured to display images or data. A display may output visual, audio, and / or tactile data. Examples of displays include, but are not limited to, computer monitors, television screens, touch screens, tactile electronic displays, Braille screens, cathode ray tubes (CRTs), storage tubes, bi-stable displays, electronic paper, vector displays, flat panel displays, vacuum fluorescent displays (VFs), light-emitting diode (LED) displays, electroluminescent displays (ELDs), plasma display panels (PDPs), liquid crystal displays (LCDs), organic light-emitting diode displays (OLEDs), projectors, and head-mounted displays.

[0057] Magnetic resonance (MR) data is defined herein as the recorded measurements of radio frequency signals emitted by atomic spins by the antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan. Magnetic resonance data is an example of medical image data. A magnetic resonance imaging (MRI) image or MR image is defined herein as a reconstructed two- or three-dimensional visualization of anatomical data contained in the magnetic resonance image data. This visualization can be performed using a computer.

[0058] In the following, preferred embodiments of the present invention will be described, by way of example only, with reference to the following drawings: [Brief explanation of the drawings]

[0059] [Figure 1] FIG. 1 is a diagram illustrating an example of a medical device. [Figure 2] 2 is a flow chart illustrating a method of operating the medical device of FIG. 1. [Figure 3] FIG. 10 illustrates a further example of a medical device. [Figure 4] 4 is a flow chart illustrating a method of operating the medical device of FIG. 3. [Figure 5] FIG. 10 illustrates a further example of a medical device. [Figure 6] 6 is a flow chart illustrating a method of operating the medical device of FIG. 5. DETAILED DESCRIPTION OF THE INVENTION

[0060] Elements with similar reference numbers in the above figures are either equivalent elements or perform the same function. An element discussed earlier is not necessarily discussed in a later figure if the functionality is equivalent.

[0061] FIG. 1 illustrates an example of a medical system 100. The medical system 100 is shown to include a computer 102. The computer 102 includes a processor 104. The processor 104 is intended to represent one or more processors having one or more processing cores. The processor 104 may, for example, be distributed among multiple computer systems. The processor 104 is connected to an optional hardware interface. The hardware interface 106 may, for example, be a network interface that allows the processor 104 to communicate with and / or control other components of the medical system 100. The processor 104 is further shown to be connected to a user interface 108. The user interface 108 may, for example, optionally include a display, a mouse, a keyboard, and other user interface devices. The user interface 108 may also include a graphical user interface.

[0062] Processor 104 is further shown as being coupled to memory 110. Memory 110 may be any combination of memory accessible by processor 104, including main memory, cache memory, and non-volatile memory such as flash RAM, a hard drive, or other storage device. In some examples, memory 110 may be considered a non-transitory computer-readable medium.

[0063] Memory 110 is shown as including machine-executable instructions 120. The machine-executable instructions 120 enable processor 104 to perform various control and data analysis techniques. For example, the machine-executable instructions 120 enable processor 104 to control other components via hardware interface 106. The machine-executable instructions 120 also enable processor 104 to perform various data and image processing techniques. Memory 110 is further shown as including four-dimensional Dixon magnetic resonance image data 122.

[0064] The 4D Dixon magnetic resonance image data 122 are provided for multiple respiratory phases 124. For example, there may be several discrete respiratory phases, and for each of the discrete respiratory phases, there is a set of 4D Dixon magnetic resonance image data 122. The memory 110 is further shown as including a synthetic computed tomography module 126. This module 126 enables the processor 104 to calculate a synthetic computed tomography image from the Dixon magnetic resonance images. For example, the module 126 may include segmentation functions and other functions that enable Hounsfield units to be assigned to the segmented regions.

[0065] The memory 110 is further shown as containing synthetic 4D computed tomography image data 128 reconstructed from the 4D Dixon magnetic resonance image data 122 using a synthetic computed tomography module 126. The respiratory signal 124 is also referenced to or synchronized with the synthetic 4D computed tomography image data 128.

[0066] 2 shows a flowchart providing a method for operating the medical system 100 of FIG. 1. Initially, in step 200, four-dimensional Dixon magnetic resonance image data 122 is received. The four-dimensional Dixon magnetic resonance image data 122 is T1-weighted. The four-dimensional Dixon magnetic resonance image data 122 is synchronized with the respiratory signal 124, as described above. Next, in step 202, synthetic four-dimensional computed tomography image data 128 is reconstructed from the four-dimensional Dixon magnetic resonance image data 122. This is accomplished, for example, by inputting the four-dimensional Dixon magnetic resonance image data 122 into a synthetic computed tomography module 126. The synthetic four-dimensional computed tomography image data 128 is also synchronized with the respiratory signal 124.

[0067] Figure 3 shows a further example of a medical system 300. The medical system 300 shown in Figure 3 is similar to the one shown in Figure 1. The medical system 300 additionally comprises a magnetic resonance imaging system 302.

[0068] The magnetic resonance imaging system 302 includes a magnet 304. The magnet 304 is a superconducting cylindrical magnet with a bore 306 extending therethrough. Different types of magnets can be used, including both segmented and so-called open magnets. A segmented cylindrical magnet is similar to a standard cylindrical magnet except that the cryostat is split into two sections to allow access to the magnet's isoplane. Such magnets may be used, for example, in conjunction with charged particle beam therapy. An open magnet has two magnet sections. Some open magnet configurations have one magnet section above the other, with a space between them large enough to accommodate a subject. In other configurations, the two magnet sections are positioned adjacent to each other. The arrangement of the two sections may be similar to that of a Helmholtz coil. Open magnets are popular because they provide less confinement for the subject. Inside the cryostat of the cylindrical magnet are a group of superconducting coils.

[0069] Within the bore 306 of the cylindrical magnet 304 is an imaging zone 308 where the magnetic field is strong and uniform enough to perform magnetic resonance imaging. A field of view 309 is shown within the imaging zone 308. Acquired magnetic resonance data is typically acquired relative to the field of view 309. A subject 318 is shown as being supported by a subject support 320 such that at least a portion of the subject 318 is within the imaging zone 308 and field of view 309.

[0070] Also within the magnet bore 306 are a set of magnetic field gradient coils 310 used for preliminary magnetic resonance data acquisition to spatially encode magnetic spins within the imaging zone 308 of the magnet 304. The magnetic field gradient coils 310 are connected to a magnetic field gradient coil power supply 312. The magnetic field gradient coils 310 may be segmented to allow radiation to pass through them. The magnetic field gradient coils 310 are intended to be representative. Typically, the magnetic field gradient coils 310 include three separate sets of coils for spatial encoding in three orthogonal spatial directions. The magnetic field gradient power supply supplies current to the magnetic field gradient coils. The current supplied to the magnetic field gradient coils 310 is controlled as a function of time and may be ramped or pulsed.

[0071] Adjacent to the imaging zone 308 is a radio frequency coil 314 for manipulating the orientation of magnetic spins within the imaging zone 308 and for receiving radio transmissions from the spins also within the imaging zone 308. The radio frequency antenna includes multiple coil elements. The radio frequency antenna is also referred to as a channel or antenna. The radio frequency coil 314 is connected to a radio frequency transceiver 316. The radio frequency coil 314 and the radio frequency transceiver 316 may be replaced with separate transmit and receive coils and separate transmitters and receivers. It should be understood that the radio frequency coil 314 and the radio frequency transceiver 316 are representative. The radio frequency coil 314 is also intended to represent a dedicated transmit antenna and a dedicated receive antenna. Similarly, the transceiver 316 may also represent a separate transmitter and receiver. The radio frequency coil 314 also has multiple receive / transmit coil elements, and the radio frequency transceiver 316 has multiple receive / transmit channels. For example, if a parallel imaging technique such as SENSE is performed, the radio frequency coil 314 has multiple coil elements. The radio frequency coil 314 is designed to be substantially radiotransparent, and the electronic components and other structures are designed to minimize the amount of radiation scattered or absorbed by the radio frequency coil 314.

[0072] The transceiver 316 and gradient controller 312 are shown connected to the hardware interface 106 of the computer system 102. The memory 110 is further shown as containing simulation pulse sequence commands 330 configured to acquire magnetic resonance data according to a Dixon magnetic resonance imaging protocol. The Dixon images are further T1 weighted. The memory 110 is further shown as containing simulated magnetic resonance image data 332 acquired by controlling the magnetic resonance imaging system 302 with the simulation pulse sequence commands 330. Automated navigation k-space data 334 is embedded within the simulated magnetic resonance image data 332.

[0073] For example, this may be the central k-space region for acquisition of the simulation magnetic resonance image data 332. The automated navigation k-space data 334 can be used to determine the respiratory signal 124. The respiratory signal 124 can then be used to create a set of discrete respiratory phases 336. The simulation magnetic resonance image data 332 is then sorted into a set of discrete respiratory phases using the respiratory phases determined by the automated navigation k-space data 334. After the simulation magnetic resonance image data 332 has been sorted into the set of discrete respiratory phases 336, four-dimensional Dixon magnetic resonance image data 122 can be reconstructed for each of the respiratory phases.

[0074] FIG. 4 shows a flowchart illustrating a method for operating the medical system 300 of FIG. 3. First, in step 400, the magnetic resonance imaging system 302 is controlled with simulation pulse sequence commands 330 to acquire simulated magnetic resonance image data 332. Next, in step 402, the respiratory signal 124 is determined using automated navigation k-space data 334 taken from the simulated magnetic resonance image data 332. Next, in step 404, the simulated magnetic resonance image data 332 is sorted into a set of discrete respiratory phases 336. Next, in step 406, four-dimensional Dixon magnetic resonance image data 122 is reconstructed from the simulated magnetic resonance image data 332 sorted into the set of discrete respiratory phases 336. After step 406, the method then proceeds to steps 200 and 202 shown in FIG. 2.

[0075] FIG. 5 illustrates a further example of a medical system 500. The medical system 500 of FIG. 5 is similar to the medical system 300 depicted in FIG. 3, except that the medical system 500 additionally includes a radiation therapy system 502. The radiation therapy system 502 is intended to represent one of many different radiation therapy systems, such as a cobalt radiation therapy system, an X-ray radiation therapy system, and a LINAC. In this example, the radiation therapy system 502 includes a gantry 504 having a radiation therapy source 506. A collimator 508 is used to shape a beam path 510. A volume 512 is an irradiation zone, representing a volume relative to which a target zone 514 can be directed. For example, the collimator 508 is used to adjust the beam path 510. The gantry 504 includes an axis of rotation 516 around which the radiation therapy source 506 rotates.

[0076] The medical system 500 further includes a subject support 320 for supporting the subject 318. The subject support 320 is configured to support, for example, the ventral region of the subject 318 within the irradiation zone 512.

[0077] The medical system may also include a respiratory monitoring system, which is not depicted in the figure. The respiratory monitoring system may be a camera or infrared camera, an additional MR navigator, a respiratory balloon measurement (measurement of the oxygen mask reservoir), and / or a respiratory belt. The chest movement of the subject 318 is used to generate a movement signal.

[0078] The radiation therapy system 502 and subject support 320 are also shown connected to the hardware interface 106 of the computer 102. The subject support 320 contains actuators or motors, for example, to adjust the height and position of the subject 318 relative to the axis of rotation 516.

[0079] 5, the magnetic resonance imaging system 302 and the radiation therapy system 502 are integrated. A radiation zone 512 is located within the imaging zone 308. In this example, the radiation beam 510 is shown passing through the cryostat 518 of the magnet 302. The beam avoids the superconducting coil 520, which is intended to be representative. The magnet 304 may be replaced with a split coil or an open magnet so that the radiation beam 510 does not pass through the magnet.

[0080] The memory 110 is further shown as including treatment planning data 530. The treatment planning data 530 is registered with the synthetic 4D computed tomography image data 128 and / or the 4D Dixon magnetic resonance image data 122. The treatment planning data 530 is used to indicate regions to be irradiated and also to indicate regions where the amount of radiation should be minimized. The treatment planning data 530 together with the synthetic 4D computed tomography image data 128 is used to calculate a set of radiation therapy control instructions 532. The radiation therapy control instructions 532 include commands to control the radiation therapy system 502 to irradiate the regions indicated in the treatment planning data 530. The memory 110 is further shown as including monitor pulse sequence commands 534.

[0081] The monitor pulse sequence command 534 is configured to acquire monitor magnetic resonance image data. The monitor magnetic resonance image data 536 includes auto-navigation k-space data. This k-space data can be used to determine a current respiratory signal 538. The current respiratory signal 538 can be used to derive current magnetic resonance image data 540 from the four-dimensional Dixon magnetic resonance image data 122. This magnetic resonance image data is then used to modify the acquisition or irradiation of the target zone using the current magnetic resonance image data.

[0082] In some examples, the monitor pulse sequence commands follow a T1-weighted Dixon magnetic resonance imaging protocol. Monitor magnetic resonance image data 536 can then be acquired repeatedly during irradiation of the target zone and used to calculate a highly accurate accumulated dose map 542.

[0083] Figure 6 shows a flowchart illustrating a method of operating the medical system 500 of Figure 5. Steps 400-202 shown in Figure 4 are performed first. The method then proceeds to step 600. In step 600, treatment planning data 630 is received. The treatment planning data 630 is registered with the synthetic 4D computed tomography image data 128. Next, in step 602, radiation therapy control commands 632 for controlling the radiation therapy system 502 are calculated using the treatment planning data 630, the synthetic 4D computed tomography image data 128, and the respiratory signal 124.

[0084] As the radiation therapy control instructions 632 are used to control the radiation therapy system 502, the processor controls the magnetic resonance imaging system 302 with monitor pulse sequence commands 634 to acquire monitor magnetic resonance image data 636. A current respiratory signal 638 can be extracted from the monitor magnetic resonance image data 636. In step 606, the current respiratory signal is determined using the automated navigation k-space data in the monitor magnetic resonance image data 636. Next, in step 608, the current magnetic resonance image data 640 is selected from the four-dimensional Dixon magnetic resonance image data 122 using the monitor respiratory signal 638. Then, in step 610, the irradiation of the target zone is adjusted using the current magnetic resonance image data 640. For example, the current magnetic resonance image data 640 is matched with the planned content, and then a model is used to generate changes to the radiation therapy control instructions 632. Next, in step 612, the accumulated dose map 612 is updated using the current respiratory signal, the current magnetic resonance image data, and the radiation therapy control instructions.

[0085] Four-dimensional magnetic resonance imaging (4D MRI) has been under development over the past decade for treatment planning to obtain dose calculation information in regions affected by respiratory motion. An example may provide a method for acquiring MR images during irradiation to monitor motion within tumors and organs at risk and generate 4D MRI images. In addition, CT information can be obtained from the images for treatment planning and dose delivery verification. As a result, 3D volumes with different image contrasts (water-only, fat-only, in-phase) can be obtained for each respiratory signal. These different image contrasts can then be used with this technique to generate MRCAT images (synthetic computed tomography images). An example may have the advantage of generating MRCAT images that can be used directly for MR-only simulation, motion management, and post-treatment dose delivery verification to optimize future treatment sessions, in addition to 4D MRI images of the abdomen and chest.

[0086] 4D MRI images (four-dimensional Dixon magnetic resonance imaging data) are obtained using stack-of-stars (SOS) radial (e.g., 3D Vane XD) acquisition and XD-GRASP reconstruction (compressed sensing reconstruction). This method uses the automated navigation properties of radial imaging to sort dynamic data into an additional motion state dimension and reconstructs a multidimensional dataset using compressed sensing (XD-GRASP). The result is a 3D volume for several respiratory phases, which can be used for motion management in treatment planning in radiation oncology.

[0087] It is also possible to combine the SOS radial sequence with the mDixon XD sequence (3D Vanish mDixon XD-T1 weighted) to acquire 3D volumes (4D MRI) for each respiratory phase with various image contrasts (water-only 4D MRI, fat-only 4D MRI, in-phase 4D MRI) thanks to the mDixon sequence.

[0088] An example could be to use source images (water only, fat only, in-phase) obtained by an mDixon sequence, use them to classify tissue into five categories, and then assign a CT number (Hounsfield value) to each category to generate a composite CT image called MRCAT. As an alternative, the pixel-by-pixel ratios of water / fat and cancellous / cortical bone could be used to assign CT numbers. Another alternative is to use an artificial intelligence module, such as a trained neural network, to assign CT numbers.

[0089] For example, a 3D Vane Dixon XD sequence can be used to obtain 4D MRI images with water, fat, and in-phase contrast, which can then be used as source scans to generate MRCAT images similar to prostate MRCAT. This technique has the advantage of generating MRCAT images that can be used directly for MR-only simulations on the MR-Linac system, in addition to 4D MRI images of the abdomen and chest. Additionally, this 4D MRI data can be acquired during radiation delivery as a motion monitoring technique and to generate MRCAT images. The combination of motion monitoring information and MRCAT images can be used during treatment for dose delivery confirmation, ensuring that the prescribed dose using the MR simulation session is accurately delivered to moving organs. Alternatively, these images can be used for dose planning for future treatment sessions.

[0090] 4D MRI images can be used with MR-Linac for treatment planning to assess motion within the tumor and organs at risk (OARs), which allows for the design of precise treatment plans to reduce toxicity and OARs and more effectively target the tumor.

[0091] Integrating 4D MRI methods with MRCAT using a 3D Vane-Dixon XD sequence results in 4D MRI MRCAT images that can overcome the need for CT scans in MR-Linac. Therefore, MR-only simulations are possible within the abdomen and thorax using the complementary motion information of 4D MRI MRCAT. Additionally, CT information generated using 4D MRI MRCAT can be used to verify dose delivery after treatment administration.

[0092] To achieve this particular example, the first step is to design and optimize a 3D Van Dixon XD sequence with reasonable image quality and scan time (less than 5 minutes). The second step is to incorporate and utilize a binned XD-GRASP-based reconstruction for 4D MRI images corresponding to water, fat, and in-phase images.

[0093] 4D MRI MRCAT can be useful for anatomical structures affected by respiratory motion, especially for the upper abdomen (liver, pancreas) and thorax (lungs, esophagus). 4D MRI images can be used to detect the degree of motion within the tumor and OARs and to accurately delimit and contour the tumor and clinical target volume (CTV) (target zone 514). MRCAT can be used for accurate dose calculations using contours drawn on the 4D MRI images. Dose delivery can also be confirmed after treatment administration using the motion information in the 4D MRI and MRCAT images.

[0094] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive, i.e., the invention is not limited to the disclosed embodiments.

[0095] Other variations of 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. In the claims, the word "comprises" does not exclude other elements or steps, and the singular does not exclude a plurality. A single processor or other unit fulfills the functions of several items recited in the claims. The mere fact that certain means are recited in mutually different dependent claims does not indicate that a combination of these means cannot be used to advantage. A computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, or in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope of the invention. [Explanation of symbols]

[0096] 100 Healthcare Systems 102 Computer 104 processors 106 Hardware Interface 108 User Interface 110 memory 120 machine executable instructions 122 Four-dimensional Dixon magnetic resonance imaging data 124 Breathing Signal 126 Synthetic Computed Tomography Module 128 Synthetic 4D Computed Tomography Image Data Receive 200 four-dimensional Dixon magnetic resonance image data 202 Reconstructing synthetic 4D computed tomography image data from 4D Dixon magnetic resonance image data 300 Healthcare Systems 302 Magnetic Resonance Imaging System 304 Magnet 306 Magnet Bore 308 Imaging Zone 309 Field of view 310 Magnetic Gradient Coil 312 Magnetic field gradient coil power supply 314 Radio Frequency Coil 316 Transceiver 318 Target 320 Subject Support 330 Simulation Pulse Sequence Commands 332 Simulation Magnetic Resonance Image Data 334 Automatic Navigation k-space Data A set of 336 discrete respiratory phases 400 Controlling the magnetic resonance imaging system with simulated pulse sequence commands to acquire simulated magnetic resonance data and subject respiratory signals. 402 DETERMINING RESPIRATORY SIGNALS USING PLANNING MAGNETIC RESONANCE DATA 404 Respiratory Phases are used to sort the simulated magnetic resonance data into a set of discrete respiratory phases. 406 Reconstructing 4D Dixon Magnetic Resonance Image Data from Sorted Simulation Magnetic Resonance Data 500 Healthcare Systems 502 Radiation Therapy System 504 Gantry 506 Radiation Therapy Sources 508 Collimator 510 Beam Path 512 irradiation zone 514 Target Zone 516 Rotation axis 518 Cryostat 520 Superconducting Coil 530 Treatment Planning Data 532 Radiation Therapy Control Order 534 Monitor Pulse Sequence Command 536 Monitor Magnetic Resonance Image Data 538 Current Respiration Signal 540 Current Magnetic Resonance Imaging Data 542 Accumulated Dose Map 600 receiving treatment planning data registered with the composite four-dimensional computed tomography image data; and 602 uses the treatment planning data, the synthetic 4D computed tomography image data, and the respiratory signal to calculate radiation therapy control commands configured to control the radiation therapy system. 604 Acquire monitor magnetic resonance image data during irradiation of the target zone 606 Monitor Magnetic Resonance Image Data to Determine Current Respiratory Signals 608 Selecting current magnetic resonance image data from four-dimensional Dixon magnetic resonance image data using a monitor respiratory signal 610. Adjusting irradiation of the target zone using current magnetic resonance image data 612 Repeatedly update the accumulated dose map using the current respiratory signal

Claims

1. a processor for controlling the medical system; a memory for storing machine-executable instructions, wherein execution of the machine-executable instructions causes the processor to: receiving respiratory signal-synchronized T1-weighted four-dimensional Dixon magnetic resonance image data; reconstructing composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, the first Dixon image and the second Dixon image for each of a set of discrete respiratory phases; Let them do this, the reconstruction of the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, constructing a water Dixon image using the first Dixon image and the second Dixon image for each of a set of discrete respiratory phases; constructing a fat Dixon image using the first Dixon image and the second Dixon image for each of the set of discrete respiratory phases; constructing an in-phase Dixon image for each of the set of discrete respiratory phases using the first Dixon image and the second Dixon image; segmenting a body mask for each of the set of discrete respiratory phases using the in-phase Dixon images; segmenting a bone mask for each of the set of discrete respiratory phases using the in-phase Dixon images; segmenting a region between the body mask and the bone mask into soft tissue regions for each of the set of discrete respiratory phases using the fat Dixon image and the water Dixon image; assigning soft tissue Hounsfield unit values ​​to the soft tissue regions for each of the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model; assigning a bone tissue Hounsfield unit value in the bone mask for each of the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model.

2. Execution of the machine-executable instructions further causes the processor to: receiving treatment planning data registered with the composite four-dimensional computed tomography image data; and calculating radiation therapy control commands for controlling a radiation therapy system using the treatment planning data, the composite four-dimensional computed tomography image data, and the respiratory signal.

3. the medical system further comprises a magnetic resonance imaging system, the memory further comprises simulation pulse sequence commands for controlling the magnetic resonance imaging system to acquire simulation magnetic resonance image data from an imaging zone according to a four-dimensional magnetic resonance imaging protocol, the four-dimensional magnetic resonance imaging protocol being a T1-weighted Dixon magnetic resonance imaging protocol, the simulation pulse sequence commands further for acquiring auto-navigation k-space data within the simulation magnetic resonance image data, and execution of the machine-executable instructions further causes the processor to: controlling the magnetic resonance imaging system with the simulation pulse sequence commands to acquire the simulation magnetic resonance image data and a subject's respiratory signal; determining the respiratory signal using the automated navigation k-space data; and sorting the simulation magnetic resonance image data into a set of discrete respiratory phases using the respiratory signal; and reconstructing the four-dimensional Dixon magnetic resonance image data from the sorted simulation magnetic resonance image data.

4. 4. The medical system of claim 3, further comprising a radiation therapy system that controllably irradiates a target zone within an irradiation zone, the irradiation zone being within the imaging zone.

5. The medical system of claim 4 , wherein the radiation therapy system is one of a LINAC, an X-ray radiation therapy system, a gamma knife, and a cobalt radiation therapy system.

6. 6. The medical system of claim 4 or 5, wherein execution of the machine-executable instructions further causes the processor to control the radiation therapy system with the radiation therapy control instructions to irradiate the target zone.

7. The memory further includes monitor pulse sequence commands for acquiring monitor magnetic resonance image data, the monitor pulse sequence commands for measuring monitor auto-navigation k-space data within the monitoring magnetic resonance image data, and execution of the machine-executable instructions further causes the processor to: acquiring the monitor magnetic resonance image data during irradiation of the target zone; determining a current respiratory signal using the monitored magnetic resonance image data; and selecting current magnetic resonance image data from the four-dimensional Dixon magnetic resonance image data using the respiratory signal; and adjusting irradiation of the target zone using the current magnetic resonance image data.

8. 8. The medical system of claim 7, wherein the monitor pulse sequence commands are in accordance with a T1-weighted Dixon magnetic resonance imaging protocol, and execution of the machine-executable instructions further causes the processor to repeatedly update an accumulated radiation dose map using the current respiratory signal, the current magnetic resonance image data, and the radiation therapy control commands.

9. The memory further includes two-dimensional monitor pulse sequence commands for acquiring two-dimensional monitor magnetic resonance image data, and execution of the machine-executable instructions further causes the processor to: acquiring the two-dimensional monitor magnetic resonance image data during the irradiation of the target zone; adjusting irradiation of said target zone using two-dimensional monitor magnetic resonance image data; The medical system according to claim 6, 7, or 8, wherein the medical system performs the following:

10. A medical system described in any one of claims 3 to 9, wherein the pulse sequence command is for rotating a k-space sampling pattern between acquisitions of the simulation magnetic resonance image data.

11. The medical system of claim 10 , wherein the k-space sampling pattern is one of a stack of stars k-space sampling pattern and a spiral k-space sampling pattern.

12. The medical system according to claim 3 , wherein the four-dimensional Dixon magnetic resonance image data is reconstructed.

13. 1. A computer program product comprising machine-executable instructions for a processor controlling a medical system, wherein execution of the machine-executable instructions causes the processor to: receiving respiratory signal-synchronized T1-weighted four-dimensional Dixon magnetic resonance image data; reconstructing composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, the first Dixon image and the second Dixon image for each of a set of discrete respiratory phases; Let them do this, the reconstruction of the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, constructing a water Dixon image using the first Dixon image and the second Dixon image for each of a set of discrete respiratory phases; constructing a fat Dixon image using the first Dixon image and the second Dixon image for each of the set of discrete respiratory phases; constructing an in-phase Dixon image for each of the set of discrete respiratory phases using the first Dixon image and the second Dixon image; segmenting a body mask for each of the set of discrete respiratory phases using the in-phase Dixon images; segmenting a bone mask for each of the set of discrete respiratory phases using the in-phase Dixon images; segmenting a region between the body mask and the bone mask into soft tissue regions for each of the set of discrete respiratory phases using the fat Dixon image and the water Dixon image; assigning soft tissue Hounsfield unit values ​​to the soft tissue regions for each of the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model; assigning a bone tissue Hounsfield unit value in the bone mask for each of the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model.

14. 1. A method of operating a medical system, the method comprising: receiving respiratory signal-gated T1-weighted four-dimensional Dixon magnetic resonance image data; reconstructing composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data, the four-dimensional Dixon magnetic resonance image data including a first Dixon image and a second Dixon image for each of a set of discrete respiratory phases; and the reconstruction of the composite four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance image data comprises: constructing a water Dixon image using the first Dixon image and the second Dixon image for each of a set of discrete respiratory phases; constructing a fat Dixon image using the first Dixon image and the second Dixon image for each of the set of discrete respiratory phases; constructing an in-phase Dixon image using the first Dixon image and the second Dixon image; segmenting a body mask for each of the set of discrete respiratory phases using the in-phase Dixon images; segmenting a bone mask for each of the set of discrete respiratory phases using the in-phase Dixon images; segmenting a region between the body mask and the bone mask into soft tissue regions for each of the set of discrete respiratory phases using the fat Dixon image and the water Dixon image; assigning soft tissue Hounsfield unit values ​​to the soft tissue regions for each of the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model; assigning a bone tissue Hounsfield unit value in the bone mask for each of the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model.

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