Synthetic 4D computed tomography image generation

Abstract

A medical system (100, 300, 500) is disclosed herein, comprising a processor (104) configured to control 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 imaging data (122). The four-dimensional Dixon magnetic resonance imaging data is T1-weighted. The four-dimensional Dixon magnetic resonance imaging data is synchronized with a respiratory signal (124). Execution of the instructions also causes the processor to reconstruct (202) synthetic four-dimensional computed tomography image data (128) based on the four-dimensional Dixon magnetic resonance imaging data. The four-dimensional Dixon magnetic resonance imaging data is synchronized with the respiratory signal.

Description

Technical Field

[0001] The present invention relates to magnetic resonance imaging and, in particular, to the generation of synthetic computed tomography images from magnetic resonance images. Background Art

[0002] Computed tomography (CT) images are very useful for radiation therapy planning. CT images measure the body's three-dimensional absorption of X-rays. The images can be represented in terms of Hounsfield units or spatially correlated electron density maps. Radiation therapy planning software typically uses one of these values ​​to estimate the absorption of ionizing radiation for radiation therapy. The difficulty with using CT for radiation therapy planning is primarily that it exposes the subject to additional radiation. Another difficulty is that CT is excellent at imaging hard or dense tissue (e.g., bone), but it cannot distinguish between different types of soft tissue, as can magnetic resonance imaging (MRI). Typically, during radiation therapy planning, both CT and MRI images are acquired. The combination of these two allows for a good estimate of the radiation absorbed by the subject and the ability to distinguish between different types of tissue (e.g., healthy tissue and tumors).

[0003] International patent application publication WO 2015 / 103184 A1 discloses systems and methods for adaptive radiotherapy planning. In some aspects, the disclosed systems and methods include using a relaxation map to generate a synthetic image based on magnetic resonance data. The method includes applying a correction to the data and generating a relaxation map accordingly. In other aspects, a method for adjusting a radiotherapy plan is disclosed. The method includes determining an objective function based on a dose gradient from an initial dose distribution, and using an aperture deformation and gradient maintenance algorithm to generate an optimized plan based on an updated image without outlining the risk organ. In other aspects, a method for obtaining 4D MR imaging using temporal reorganization of data acquired during normal breathing, a method for performing deformable image registration using a sequentially applied semi-physical model regularization method for multimodal images, and a method for generating a 4D plan based on 4D CT or 4D MR imaging using an aperture deformation algorithm are disclosed. Summary of the Invention

[0004] The 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 4D computed tomography (CT) data from T1-weighted 4D Dixon MRI data. The 4D Dixon MRI data is synchronized with a respiratory signal, resulting in synthetic 4D CT data. The respiratory signal can be, for example, a respiratory phase or amplitude. The respiratory signal can then be used, for example, to improve control of a radiotherapy system.

[0006] Dixon MRI acquires MRI data at various phases, enabling the generation of water and fat images. This enables superior image segmentation, thus producing higher-quality resultant CT images.

[0007] WO 2015 / 103184 A1 teaches against the use of Dixon MRI images and states in paragraph 33 that incorrect registration of multi-echo Dixon images can lead to image segmentation errors, 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 self-navigation in k-space data, a navigator, a respiratory balloon measurement device (a measurement device for an oxygen mask reservoir), optical measurement of respiration using a camera system, and / or a respiratory belt. In one example, there can be a measurement signal referenced to the four-dimensional Dixon magnetic resonance imaging data. In other examples, the four-dimensional Dixon magnetic resonance imaging data can be divided into data bins or groups, and then a specific respiratory signal is referenced to the data bins or groups.

[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 imaging data. The four-dimensional Dixon magnetic resonance imaging data is synchronized with the respiration signal. The reconstruction of the synthetic four-dimensional computed tomography image data can be performed, for example, using an algorithm or model capable of converting a magnetic resonance image into a pseudo-radiographic image.

[0010] Using 4D Dixon MRI data to produce synthetic 4D computed tomography image data can be beneficial because the 4D Dixon MRI data includes data that primarily describes water and fat tissue, respectively. This can facilitate reconstruction of the synthetic 4D computed tomography image data.

[0011] In another embodiment, the synthesized four-dimensional computed tomography image data is synchronized with the respiratory signal. In another embodiment, the execution of the machine executable instructions further causes the processor to receive treatment planning data that is registered to the synthesized four-dimensional computed tomography image data. The treatment planning data can be provided, for example, by an automated algorithm. In other examples, the treatment planning data may have been input or provided by a healthcare provider via, for example, a graphical user interface. The treatment planning data can, for example, indicate an area of ​​the subject to which a desired radiation radiotherapy dose is to be delivered. The treatment planning data can also indicate an area (for example, a sensitive organ or structure) to which it is desired to minimize radiation exposure.

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

[0014] In another embodiment, the medical system further includes a magnetic resonance imaging system. The memory further includes simulation pulse sequence commands configured to control the magnetic resonance imaging system to acquire simulated magnetic resonance imaging data from an imaging volume according to a four-dimensional magnetic resonance imaging protocol. The term "simulation pulse sequence command" is a label that refers to a specific pulse sequence command. During radiation therapy, the term "simulation" is used to refer to a planning phase. The term "simulation" is used to indicate a specific pulse sequence command used to acquire data for radiation therapy simulation.

[0015] Similarly, the simulated MRI data is MRI data acquired for the purpose of performing a radiation therapy simulation. The four-dimensional MRI protocol is a T1-weighted Dixon MRI protocol. The pulse sequence commands are further configured to acquire self-navigated k-space data within the simulated MRI data. Using the navigator k-space data can be beneficial because, for example, it can be useful for deriving a respiratory signal and / or measuring subject motion during acquisition of the simulated MRI data. As an alternative to the navigator k-space data, any of the above-described techniques can be used to measure the respiratory signal.

[0016] Execution of the machine-executable instructions further causes the processor to control the magnetic resonance imaging system using the simulated pulse sequence commands to acquire simulated magnetic resonance data and a subject respiration signal. Execution of the machine-executable instructions further causes the processor to determine the respiration signal using the navigator k-space data. The navigator k-space data is data within the simulated magnetic resonance imaging data.

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

[0018] In another embodiment, the medical system further comprises the radiotherapy system. The radiotherapy system is configured to irradiate a target area within an irradiation zone. The irradiation zone is within the imaging zone. This embodiment is advantageous because a simulation of the radiotherapy treatment can be performed while the subject is in the radiotherapy system. This can be beneficial because the subject is less likely to move. Another benefit is that the magnetic resonance imaging system is used to generate synthetic four-dimensional computed tomography image data. This 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 radiotherapy system is an X-ray radiotherapy system.

[0021] In another embodiment, the radiotherapy system is a cobalt radiotherapy 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 using the radiation therapy control instructions to irradiate the target volume. This embodiment may be beneficial because the radiation therapy control instructions may provide a higher quality radiation therapy system in which irradiation of the target volume is more precisely controlled.

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

[0025] Execution of the machine-executable instructions further causes the processor to use the monitoring magnetic resonance imaging data to determine a current respiration signal. Execution of the machine-executable instructions further causes the processor to use the monitoring respiration signal to select current magnetic resonance image data from the four-dimensional Dixon magnetic resonance image data. Execution of the machine-executable instructions further causes the processor to use the current magnetic resonance image data to adjust irradiation of the target region. This may include gating delivery of irradiation when the target region is in a correct position and / or tracking movement of the target region to adjust targeting of a radiation therapy system. This embodiment may be beneficial because it eliminates the need for external monitoring of subject motion.

[0026] In another embodiment, the monitoring pulse sequence commands are based on the T1-weighted Dixon magnetic resonance imaging protocol. Execution of the machine-executable instructions further causes the processor to repeatedly update a cumulative radiation dose map using the current respiration signal, the current magnetic resonance image data, and the radiotherapy control instructions. This embodiment may be beneficial because it may provide 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 simulated MRI data. This embodiment can be beneficial because the central k-space region can, for example, be used to determine the current respiratory signal. This can also reduce blurring of the 4D Dixon MRI data.

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

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

[0030] In another embodiment, the memory further includes two-dimensional monitoring pulse sequence commands, the two-dimensional monitoring pulse sequence commands being configured to acquire two-dimensional monitoring magnetic resonance imaging data. Execution of the machine-executable instructions further causes the processor to acquire the two-dimensional monitoring magnetic resonance imaging data during the irradiation of the target area, and execution of the machine-executable instructions further causes the processor to use the two-dimensional monitoring magnetic resonance imaging data to adjust the irradiation of the target area. In this embodiment, a two-dimensional magnetic resonance image (e.g., conventionally used) is acquired and then used to change the irradiation. For example, the current position of the subject can be registered to the synthesized four-dimensional computed tomography image data to change the control of the radiotherapy system.

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

[0032] In another embodiment, the k-space sampling pattern is a star-pile 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 four-dimensional Dixon magnetic resonance image data is reconstructed according to a compressed sensing magnetic resonance imaging protocol. Compressed sensing reconstruction can be used even when not all magnetic resonance imaging data is acquired. This is true even if compressed sensing is not used to set up the MR acquisition. If the acquisition of magnetic resonance imaging data is performed with time constraints (which may be the case in real-time operation), it may happen that not all k-space lines are fully acquired for each respiratory phase. Compressed sensing analysis can be useful to compensate for missing k-space data.

[0035] In another embodiment, the simulated pulse sequence commands are in accordance with a compressed sensing magnetic resonance imaging protocol. This may be beneficial, for example, because it may enable the magnetic resonance imaging protocol to be executed more quickly.

[0036] In another embodiment, the four-dimensional Dixon magnetic resonance imaging data includes a first Dixon image and a second Dixon image for each respiratory phase in a set of discrete respiratory phases. The reconstruction of the synthetic four-dimensional computed tomography image data based on the four-dimensional Dixon magnetic resonance imaging data includes constructing a water Dixon image using the first Dixon image and the second Dixon image for each respiratory phase in the set of discrete respiratory phases. The reconstruction of the synthetic four-dimensional computed tomography image data based on the four-dimensional Dixon magnetic resonance imaging data also includes constructing a fat Dixon image using the first Dixon image and the second Dixon image for each respiratory phase in the set of discrete respiratory phases.

[0037] The reconstruction of the synthetic four-dimensional computed tomography image data based on the four-dimensional Dixon magnetic resonance imaging data also includes constructing an in-phase Dixon image using the first Dixon image and the second Dixon image. The reconstruction of the synthetic four-dimensional computed tomography image data based on the four-dimensional Dixon magnetic resonance imaging data also includes the following steps: using the in-phase Dixon image to segment a body mask for each respiratory phase in the set of discrete respiratory phases; and using the in-phase Dixon image to segment a bone mask for each respiratory phase in the set of discrete respiratory phases. The body mask can be useful, for example, for identifying areas outside the object. For example, areas that are not surrounded by fat areas or water areas. The bone mask can be represented by areas within the body of the object where signal is absent as defined by the body mask.

[0038] Reconstructing the synthetic 4D computed tomography image data from the 4D Dixon MRI data further includes 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. Using the fat Dixon image and the water Dixon image can be useful for improving segmentation quality and distinguishing different types of soft tissue.

[0039] The reconstruction of the synthetic four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance imaging data further includes assigning soft tissue Hounsfield Unit values ​​to the soft tissue regions for each respiratory phase in the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model. Finally, the reconstruction of the synthetic four-dimensional computed tomography image data from the four-dimensional Dixon magnetic resonance imaging data further includes assigning bone tissue Hounsfield Unit values ​​within the bone mask for each respiratory phase in the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model. This embodiment can be beneficial because it provides an efficient means of generating synthetic radiographic images from Dixon magnetic resonance images.

[0040] In another aspect, the present invention provides a computer program product comprising 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 based on the four-dimensional Dixon magnetic resonance imaging data. The four-dimensional Dixon magnetic resonance imaging data is synchronized with a respiratory signal. The benefits of this embodiment have been discussed previously.

[0041] In another aspect, the present invention provides a method of operating a medical system. The method includes receiving four-dimensional Dixon magnetic resonance imaging data. The four-dimensional Dixon magnetic resonance imaging data is T1-weighted and 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 imaging data. The four-dimensional Dixon magnetic resonance imaging data is synchronized with the respiration signal. The benefits of this embodiment have been discussed above.

[0043] It should be understood that one or more of the aforementioned embodiments of the present invention may be combined as long as the combined embodiments are not mutually exclusive.

[0044] Those skilled in the art will appreciate that aspects of the present invention may be implemented as devices, methods, or computer program products. Thus, aspects of the present invention may take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects, all generally referred to herein as "circuits," "modules," or "systems." Additionally, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable media having computer executable code embodied thereon.

[0045] Any combination of one or more computer-readable media may be utilized. A 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 that can store instructions that can be executed by a processor of a computing device. A computer-readable storage medium may be referred to as a computer-readable non-transitory storage medium. A computer-readable storage medium may also be referred to as a tangible computer-readable medium. In some embodiments, a computer-readable storage medium may also store data that can be accessed by a 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 drives, flash memory, USB thumb drives, random access memory (RAM), read-only memory (ROM), optical disks, magneto-optical disks, and a processor's register file. Examples of optical disks include compact disks (CDs) and digital versatile disks (DVDs), such as CD-ROMs, CD-RWs, CD-Rs, DVD-ROMs, DVD-RWs, 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 via a network or communication link. For example, data can be retrieved over 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 appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0046] A computer-readable signal medium may include a propagated data signal having 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 may be any computer-readable medium that is not a computer-readable storage medium and that is capable of conveying, propagating, or transmitting 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 by a processor. "Computer storage" or "storage" is another example of a computer-readable storage medium. Computer storage is any non-volatile computer-readable storage medium. In some embodiments, computer storage may also be computer memory, or vice versa.

[0048] As used herein, "processor" encompasses an electronic component capable of running a program or machine-executable instructions or computer-executable code. References to a computing device that includes a "processor" should be interpreted as potentially including more than one processor or processing core. A processor may, for example, be a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed across multiple computer systems. The term "computing device" should also be interpreted as potentially referring to a collection or network of multiple computing devices, each of which includes one or more processors. Computer-executable code may be executed by multiple processors that may be within the same computing device or even distributed across multiple computing devices.

[0049] Computer executable code can include machine executable instructions or programs that cause a processor to perform an aspect of the present invention. The computer executable code for performing operations for various aspects of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(e.g., Java, Smalltalk, C++, etc.) and conventional procedural programming languages ​​(e.g., "C" programming language or similar programming languages), and compiled into machine executable instructions. In some instances, the computer executable code can be in the form of a high-level language or in a precompiled form and can be used in conjunction with an interpreter that generates machine executable instructions during operation.

[0050] The computer executable code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0051] Various aspects of the present invention have been described with reference to the flowchart illustrations and / or the block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each frame or part of the frame of flowchart, diagram and / or block diagram can be implemented by the computer program instructions in the form of computer executable code when appropriate. It should also be understood that, when not mutually exclusive, the frames in different flowcharts, diagrams and / or block diagrams can be combined. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that the instruction executed via the processor of a computer or other programmable data processing device creates a unit for implementing the function / action specified in one or more frames of flowchart and / or block diagram.

[0052] These computer program instructions may also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other device to function in a specific manner such that the instructions stored in the computer-readable medium produce an article of manufacture that includes instructions for implementing the functions / actions specified in the flowchart and / or one or more block diagram blocks.

[0053] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer-implemented process, such that the instructions running on the computer or other programmable apparatus provide a process for implementing the functions / actions specified in the flowchart and / or block diagram blocks.

[0054] "User interface" as used herein is an interface that allows a user or operator to interact with a computer or computer system. "User interface" can also be 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 can enable the input from the operator to be received by the computer, and can provide output to the user from the computer. In other words, a user interface can allow an operator to control or manipulate a computer, and the interface can allow the computer to indicate the effect 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 by a keyboard, mouse, trackball, touchpad, pointing stick, graphic input tablet, joystick, game controller, webcam, head-mounted device, foot pedal, wired gloves, remote controller and accelerometer is all examples of user interface components that enable receiving information or data from an operator.

[0055] As used herein, a "hardware interface" encompasses an interface that enables a processor of a computer system to interact with and / or control an external computing device and / or apparatus. A hardware interface can allow a processor to send control signals or instructions to an external computing device and / or apparatus. A hardware interface can also enable a processor to exchange data with an external computing device and / or apparatus. 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, a "display" or "display device" encompasses an output device or user interface suitable for displaying images or data. A display may output visual, auditory, 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), memory tubes, bi-stable displays, electronic paper, vectorscopes, 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 measurement of radio frequency signals emitted by atomic spins, recorded using the antenna of a magnetic resonance device 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 two-dimensional or three-dimensional visualization reconstructed from anatomical data contained within the magnetic resonance imaging data. This visualization can be performed using a computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Preferred embodiments of the present invention will hereinafter be described, by way of example only, with reference to the accompanying drawings, in which:

[0059] Figure 1 An example of a medical instrument is illustrated;

[0060] Figure 2 The diagram shows the operation Figure 1 A flow chart of a method of a medical instrument;

[0061] Figure 3 Another example of a medical instrument is illustrated;

[0062] Figure 4 The diagram shows the operation Figure 3 A flow chart of a method of a medical instrument;

[0063] Figure 5 illustrates additional examples of medical instruments; and

[0064] Figure 6 The diagram shows the operation Figure 5 Flowchart of a method of a medical instrument.

[0065] Reference Signs List

[0066] 100 Medical System

[0067] 102 Computer

[0068] 104 processors

[0069] 106 Hardware Interface

[0070] 108 User Interface

[0071] 110 Memory

[0072] 120 machine-executable instructions

[0073] 122 4D Dixon MRI data

[0074] 124 Respiration signal

[0075] 126 Synthetic Computed Tomography Module

[0076] 128 Synthetic 4D Computed Tomography Image Data

[0077] 200 Receive 4D Dixon MRI data

[0078] 202 Reconstructing synthetic 4D computed tomography image data from 4D Dixon magnetic resonance imaging data

[0079] 300 Medical System

[0080] 302 Magnetic Resonance Imaging System

[0081] 304 magnet

[0082] 306 Magnet bore

[0083] 308 Imaging Area

[0084] 309 Field of View

[0085] 310 Magnetic Field Gradient Coil

[0086] 312 Magnetic Field Gradient Coil Power Supply

[0087] 314 RF Coil

[0088] 316 transceiver

[0089] 318 objects

[0090] 320 Object Support

[0091] 330 Simulate pulse sequence command

[0092] 332 simulated MRI data

[0093] 334 Self-navigation k-space data

[0094] 336 Sets of Discrete Respiratory Phases

[0095] 400 Using analog pulse sequence commands to control a magnetic resonance imaging system to acquire analog magnetic resonance data and a subject's breathing signal

[0096] 402 Using Planning MRI Data to Determine Respiration Signals

[0097] 404 Using respiratory phase to classify simulated magnetic resonance data into a set of discrete respiratory phases

[0098] 406 Reconstructing 4D Dixon MRI data based on the classified simulated MRI data

[0099] 500 Medical Systems

[0100] 502 Radiation Therapy System

[0101] 504 rack

[0102] 506 Radiotherapy Source

[0103] 508 Collimator

[0104] 510 beam path

[0105] 512 Irradiation Zone

[0106] 514 Target Area

[0107] 516 Rotation Axis

[0108] 518 Cryostat

[0109] 520 superconducting coil

[0110] 530 Processing planning data

[0111] 532 Radiotherapy Control Instructions

[0112] 534 Monitor pulse sequence command

[0113] 536 Monitoring MRI Data

[0114] 538 Current respiratory signal

[0115] 540 Current MRI Data

[0116] 542 Cumulative dose graph

[0117] 600 Receive treatment planning data registered to synthetic 4D computed tomography image data

[0118] 602 Calculating radiotherapy control instructions configured to control a radiotherapy system using the treatment planning data, the synthetic 4D computed tomography image data, and the respiration signal

[0119] 604 Acquiring monitoring magnetic resonance imaging data during irradiation of the target area

[0120] 606 Using monitoring magnetic resonance imaging data to determine the current respiratory signal

[0121] 608 Selecting current magnetic resonance image data from four-dimensional Dixon magnetic resonance image data using a monitored respiratory signal

[0122] 610 Using current magnetic resonance image data to adjust irradiation of the target area

[0123] 612 Use the current respiratory signal to repeatedly update the cumulative dose map DETAILED DESCRIPTION

[0124] In the drawings, components with the same reference numerals are either equivalent elements or perform the same function. If the function is equivalent, it will not be necessary to discuss the previously discussed elements in subsequent drawings.

[0125] Figure 1 An example of a medical system 100 is illustrated. The medical system 100 is shown as including 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 across 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 enables the processor 104 to communicate with and / or control additional components of the medical system 100. The processor 104 is also shown as being connected to a user interface 108. The user interface 108 may, for example, optionally include a display, a user interface device (e.g., a mouse or keyboard). The user interface 108 may also include a graphical user interface.

[0126] Processor 104 is shown as being further connected to memory 110. Memory 110 can be any combination of memory accessible to processor 104. This can include memory such as main memory, buffer memory, and can also include non-volatile memory (e.g., flash RAM, hard drive, or other storage device). In some examples, memory 110 can be considered a non-transitory computer-readable medium.

[0127] Memory 110 is shown as containing machine-executable instructions 120. Machine-executable instructions 120 enable processor 104 to perform various control and data analysis techniques. For example, machine-executable instructions 120 may enable processor 104 to control other components via hardware interface 106. Machine-executable instructions 120 also enable processor 104 to perform various data and image processing techniques. Memory 110 is also shown as containing four-dimensional Dixon magnetic resonance imaging data 122.

[0128] Four-dimensional Dixon magnetic resonance imaging data 122 is provided for a plurality of respiratory phases 124. For example, there may be a plurality of discrete respiratory phases, and for each of these discrete respiratory phases, there is a set of four-dimensional Dixon magnetic resonance imaging data 122. Memory 110 is also shown as containing a synthetic computed tomography module 126. This module 126 enables processor 104 to calculate a synthetic computed tomography image from the Dixon magnetic resonance image. For example, module 126 may include segmentation functionality and other functionality that enables the segmented regions to be assigned Hounsfield units.

[0129] The memory 110 is also shown as containing synthesized 4D computed tomography image data 128 reconstructed from the 4D Dixon magnetic resonance imaging data 122 using a synthetic computed tomography module 126. The respiration signal 124 is also referenced or synchronized to the synthesized 4D computed tomography image data 128.

[0130] Figure 2 Illustration of providing operation Figure 1 Flowchart of a method of the medical system 100. First, in step 200, 4D Dixon MRI data 122 is received. The 4D Dixon MRI data 122 is T1-weighted. As described above, the 4D Dixon MRI data is synchronized with the respiration signal 124. Next, in step 202, synthesized 4D computed tomography image data 128 is reconstructed based on the 4D Dixon MRI data 122. This can be achieved, for example, by inputting the 4D Dixon MRI data 122 into a synthesized computed tomography module 126. The synthesized 4D computed tomography image data 128 is also synchronized with the respiration signal 124.

[0131] Figure 3A further example of a medical system 300 is illustrated. Figure 3 The medical system 300 is shown with Figure 1 The medical system 300 additionally includes a magnetic resonance imaging system 302 .

[0132] Magnetic resonance imaging system 302 includes magnet 304. Magnet 304 is a superconducting cylindrical magnet with a bore 306 extending therethrough. Different types of magnets can also be used; for example, split cylindrical magnets and so-called open magnets can also be used. Split cylindrical magnets are similar to standard cylindrical magnets, except that the cryostat has been divided into two sections to allow access to the magnet's isoplane; such magnets can be used, for example, in conjunction with charged particle beam therapy. Open magnets have two magnet sections, and some open magnet configurations have one magnet section positioned above the other with sufficient space between them to accommodate a subject. In another configuration, the two magnet sections are arranged adjacent to each other. The area arrangement of the two sections resembles a Helmholtz coil. Open magnets are popular because the subject is less confined. Inside the cryostat of the cylindrical magnet is a collection of superconducting coils.

[0133] Within the bore 306 of the cylindrical magnet 304 is an imaging zone 308 in which the magnetic field is sufficiently strong and uniform to perform magnetic resonance imaging. A field of view 309 is shown within the imaging zone 308. Magnetic resonance imaging data is typically acquired for the field of view 309. A subject 318 is shown supported by a subject support 320 such that at least a portion of the subject 318 is within the imaging zone 308 and the field of view 309.

[0134] Also within the bore 306 of the magnet is a set of magnetic field gradient coils 310 for acquiring preliminary magnetic resonance imaging data 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 also be split to allow radiation to pass through. The magnetic field gradient coils 310 are intended to be representative. Typically, the magnetic field gradient coils 310 include three independent coil sets, which are used to perform 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 over time and can be ramped or pulsed.

[0135] Adjacent to the imaging zone 308 is a radio frequency coil 314, which is used to manipulate the orientation of magnetic spins within the imaging zone 308 and to receive radio transmissions from spins also within the imaging zone 308. An RF antenna may include multiple coil elements. An RF antenna may also be referred to as a channel or antenna. The RF coil 314 is connected to an RF transceiver 316. The RF coil 314 and RF transceiver 316 may be replaced by separate transmit and receive coils, or separate transmitters and receivers. It should be understood that the RF coil 314 and RF transceiver 316 are representative. The RF 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 RF coil 314 may also have multiple transmit / receive elements, and the RF transceiver 316 may have multiple transmit / receive channels. For example, if a parallel imaging technique such as SENSE is implemented, the RF coil 314 will have multiple coil elements. The RF coil 314 may be designed to be effectively radiation transparent: the electronics and other structures are designed to minimize the amount of radiation scattered or absorbed by the RF coil 314 .

[0136] The transceiver 316 and the gradient controller 312 are shown connected to the hardware interface 106 of the computer system 102. The memory 110 is also shown as containing simulated pulse sequence commands 330. The simulated pulse sequence commands 330 are configured to acquire magnetic resonance data according to the Dixon magnetic resonance imaging protocol. The Dixon images are further T1-weighted. The memory 110 is also shown as containing simulated magnetic resonance imaging data 332 acquired by controlling the magnetic resonance imaging system 302 using the simulated pulse sequence commands 330. Self-navigation k-space data 334 is embedded in the simulated magnetic resonance imaging data 332.

[0137] For example, this can be the central k-space region used to acquire the simulated magnetic resonance imaging data 332. The respiratory signal 124 can be determined using the self-navigated k-space data 334. The respiratory signal 124 can then be used to generate a set 336 of discrete respiratory phases. The respiratory phases determined from the self-navigated k-space data 334 can then be used to sort the simulated magnetic resonance imaging data 332 into the set 336 of discrete respiratory phases. After the simulated magnetic resonance imaging data 332 have been sorted into the set 336 of discrete respiratory phases, the four-dimensional Dixon magnetic resonance imaging data 122 can be reconstructed for each of these respiratory phases.

[0138] Figure 4 The diagram shows the operation Figure 3Flowchart of a method of a medical system 300 for performing a respiration. First, in step 400, the magnetic resonance imaging system 302 is controlled using simulated pulse sequence commands 330 to acquire simulated magnetic resonance imaging data 332. Next, in step 402, the respiratory signal 124 is determined using self-navigation k-space data 334 taken from the simulated magnetic resonance imaging data 332. Next, in step 404, the simulated magnetic resonance imaging data 332 is classified into a set 336 of discrete respiratory phases. Next, in step 406, four-dimensional Dixon magnetic resonance imaging data 122 is reconstructed based on the simulated magnetic resonance imaging data 332 that has been classified into the set 336 of discrete respiratory phases. After step 406, the method then proceeds to steps 200 and 202, as Figure 2 shown.

[0139] Figure 5 A further example of a medical system 500 is illustrated. Figure 5 The medical system 500 in is similar to Figure 3 , except that medical system 500 additionally includes a radiotherapy system 502. Radiotherapy system 502 is intended to represent one of many different types of radiotherapy systems, such as a cobalt radiotherapy system, an X-ray radiotherapy system, and a LINAC. In this example, radiotherapy system 502 includes a gantry 504 having a radiotherapy source 506. A collimator 508 can be used to shape a beam path 510. Volume 512 is the irradiation area and represents the volume to which a target volume 514 can be directed. For example, collimator 508 can be used to adjust beam path 510. Gantry 504 has a rotation axis 516 about which radiotherapy source 506 rotates.

[0140] The medical system 500 further comprises a subject support 320 for supporting the subject 318. The subject support 320 is configured such that it can support a ventral area of ​​the subject 318 in the irradiation zone 512, for example.

[0141] The medical system may also include a respiratory monitoring system, which is not depicted in the figures. The respiratory monitoring system may include a camera or infrared camera, an additional MR navigator, a respiratory balloon measurement device (a measurement device for the oxygen mask reservoir), and / or a respiratory belt. Movement of the chest of subject 318 may be used to generate a motion signal.

[0142] The radiation therapy system 502 and the subject support 320 are also shown connected to the hardware interface 106 of the computer 102. The subject support 320 may, for example, contain actuators or motors for adjusting the height and position of the subject 318 relative to the axis of rotation 516.

[0143] exist Figure 5In FIG. 3 , magnetic resonance imaging system 302 and radiotherapy system 502 are integrated. Irradiation zone 512 is within imaging zone 308. In this example, radiation beam 510 is shown passing through cryostat 518 of magnet 302. The beam avoids superconducting coil 520. This is intended to be representative. Magnet 304 could also be replaced with a split coil or open magnet, so that radiation beam 510 does not pass through the split coil or open magnet.

[0144] The memory 110 is also shown as containing treatment planning data 530. The treatment planning data 530 is registered to the synthesized 4D computed tomography image data 128 and / or the 4D Dixon magnetic resonance image data 122. The treatment planning data 530 can be used to indicate regions to be irradiated and also to indicate regions where the amount of radiation is to be minimized. The treatment planning data 530, together with the synthesized 4D computed tomography image data 128, is used to calculate a set of radiotherapy control instructions 532. The radiotherapy control instructions 532 contain commands for controlling the radiotherapy system 502 to irradiate the regions indicated in the treatment planning data 530. The memory 110 is also shown as containing monitoring pulse sequence commands 534.

[0145] Monitoring pulse sequence commands 534 are configured to acquire monitoring MRI data. Monitoring MRI data 536 includes self-navigation k-space data. This k-space data can be used to determine a current respiration signal 538. Current respiration signal 538 can be used to retrieve current MRI data 540 from the 4D Dixon MRI data 122. This MRI data can then be used to modify acquisition or irradiation of the target area using the current MRI data.

[0146] In some examples, the monitoring pulse sequence commands are based on a T1-weighted Dixon MRI protocol.Monitoring MRI data 536 can then be repeatedly acquired during irradiation of the target volume and can be used to calculate a very accurate cumulative dose map 542.

[0147] Figure 6 The diagram shows the operation Figure 5 Flowchart of the method of the medical system 500. First, perform Figure 4 The method then proceeds to step 600. In step 600, treatment planning data 630 registered to the synthesized 4D computed tomography image data 128 is received. Next, in step 602, radiation therapy control instructions 632 for controlling the radiation therapy system 502 are calculated using the treatment planning data 630, the synthesized 4D computed tomography image data 128, and the respiration signal 124.

[0148] Because the radiotherapy control instructions 632 are used to control the radiotherapy system 502, the processor controls the magnetic resonance imaging system 302 using the monitoring pulse sequence commands 634 to acquire monitoring magnetic resonance imaging data 636. A current respiration signal 638 can be extracted from the monitoring magnetic resonance imaging data 636. In step 606, the current respiration signal is determined using the self-navigated k-space data within the monitoring magnetic resonance imaging data 636. Next, in step 608, the monitoring respiration signal 638 is used to select current magnetic resonance image data 640 from the four-dimensional Dixon magnetic resonance image data 122. Then, in step 610, the current magnetic resonance image data 640 is used to adjust the irradiation of the target area. For example, the current magnetic resonance image data 640 can be registered to the planned plan, and then a model can be used to generate changes to the radiotherapy control instructions 632. Next, in step 612, the cumulative dose map 612 is updated using the current respiration signal, the current magnetic resonance image data, and the radiotherapy control instructions.

[0149] Over the past decade, four-dimensional magnetic resonance imaging (4D MRI) has been developed for treatment planning to obtain dose calculation information for areas affected by respiratory motion. An example can provide a method by which MR images can be obtained during radiation to monitor the motion of tumors and organs at risk and to generate 4D MRI images. In addition, the CT information obtained from these images is used 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. In this case, MRCAT images (synthetic computed tomography images) can be generated using the different image contrasts obtained using this technology. The benefit of the example can be the generation of 4D MRI images and MRCAT images in the abdomen and chest that can be used directly for MR simulation only, motion management, post-treatment dose delivery verification, thereby optimizing future treatment courses.

[0150] 4D MRI images (four-dimensional Dixon magnetic resonance imaging data) can be acquired using star stack (SOS) radial (e.g., 3D Vane XD) acquisition and XD-GRASP reconstruction (compressed sensing reconstruction). This method uses the self-navigation properties of radial imaging to classify dynamic data into an additional motion state dimension and uses compressed sensing (XD-GRASP) to reconstruct a multidimensional dataset. This produces 3D volumes for several respiratory phases and can be used for motion management in treatment planning in radiation oncology.

[0151] Due to the mDixon sequences (water only 4D MRI, fat only 4D MRI, in-phase 4D MRI), it is also possible to combine the SOS radial sequence with the mDixon XD sequence (3D Vane mDixon XD-T1 weighted) to acquire 3D volumes with different image contrast for each respiratory phase (4D MRI).

[0152] For example, a source image obtained using an m-Dixon sequence (water only, fat only, in-phase) can be used to classify tissue into five categories. A CT number (Honnig value) is then assigned to each category to generate a synthetic CT image (referred to as MRCAT). Alternatively, the CT numbers can be assigned using the per-pixel water / fat and cancellous / cortical bone ratios. Another alternative is to use an artificial intelligence module (e.g., a trained neural network) to assign CT numbers.

[0153] Example 3D Vane m Dixon XD sequence can be used to obtain 4D MRI images for water, fat and in-phase contrast, and they are used as source scans to generate MRCAT images (similar to prostate MRCAT). The benefit of this technology is to generate 4D MRI images and MRCAT images in the abdomen and chest, which can be used directly for MR-only simulation on the MR-LINAC system. In addition, this 4D MRI data can be collected as a motion monitoring technique and MRCAT images can be created during radiation delivery. The combination of motion monitoring information and MRCAT images can be used for dose delivery verification to ensure that the prescribed dose obtained using the MR simulation treatment course is accurately delivered to the moving organs during treatment. Alternatively, these images can be used for dose planning for future treatment courses.

[0154] 4D MRI images can be used for treatment planning in MR-LINAC to assess the motion of the tumor and organs at risk (OARs), thereby designing accurate treatment plans to reduce toxicity to OARs and more effectively target the tumor.

[0155] Integrating 4D MRI with MRCAT using a 3D Vane m-Dixon XD sequence generates 4D MRI MRCAT images, which overcome the need for CT scans in MR-LINAC. This enables MR-only simulations of the abdomen and chest with complementary motion information from 4D MRI MRCAT. Furthermore, the CT information generated using 4D MRI MRCAT can be used for dose delivery verification after treatment delivery.

[0156] To implement this particular example, the first step was to design and optimize a 3D Vane m Dixon XD sequence with reasonable image quality and scan time (less than 5 minutes). The second step was to incorporate and utilize XD-GRASP-based reconstructions, binning these reconstructions into 4D MRI images for water, fat, and in-phase images.

[0157] 4D MRI MRCAT can be useful for any anatomical structure affected by respiratory motion, particularly the upper abdomen (liver, pancreas) and chest (lungs, esophagus). 4D MRI images can be used to detect the degree of motion of tumors and OARs and accurately delineate and outline the tumor and clinical target volume (CTV) (target area 514). MRCAT can be used to perform accurate dose calculations using the contours drawn on the 4D MRI images. Dose delivery can also be verified after treatment delivery using motion information in the 4D MRI images and MRCAT images.

[0158] 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; the invention is not limited to the disclosed embodiments.

[0159] A person skilled in the art will be able to understand and implement other variations to the disclosed embodiments when practicing the claimed invention by studying the drawings, the disclosure and the claims. In the claims, the word "comprising" does not exclude other components or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may implement the functions of several items recited in the claims. Although certain measures are recited in mutually different dependent claims, this does not indicate that a combination of these measures cannot be used to advantage. The computer program may be stored / distributed on a suitable medium, for example, an optical storage medium or solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, for example, via the Internet or other wired or wireless telecommunications systems. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. A medical system (100, 300, 500), comprising: a processor (104) configured to control the medical system; a memory (110) for storing machine-executable instructions, wherein execution of the instructions causes the processor to: receiving (200) four-dimensional Dixon magnetic resonance image data (122), wherein the four-dimensional Dixon magnetic resonance image data is T1-weighted, wherein the four-dimensional Dixon magnetic resonance image data is synchronized with a respiratory signal (124); and reconstructing (202) synthetic four-dimensional computed tomography image data (128) from the four-dimensional Dixon magnetic resonance image data, wherein the four-dimensional Dixon magnetic resonance image data includes a first Dixon image and a second Dixon image for each respiratory phase in a set of discrete respiratory phases, The reconstructing of the synthesized 4D computed tomography image data based on the 4D Dixon magnetic resonance image data comprises: constructing a water Dixon image using the first Dixon image and the second Dixon image for each respiratory phase in a set of discrete respiratory phases; constructing a fat Dixon image using the first Dixon image and the second Dixon image for each respiratory phase in the set of discrete respiratory phases; constructing an in-phase Dixon image for each respiratory phase in the set of discrete respiratory phases using the first Dixon image and the second Dixon image; segmenting a body mask for each respiratory phase in the set of discrete respiratory phases using the in-phase Dixon image; segmenting a bone mask for each respiratory phase in the set of discrete respiratory phases using the in-phase Dixon image; segmenting a region between the body mask and the bone mask into soft tissue regions for each respiratory phase in the set of discrete respiratory phases using the fat Dixon image and the water Dixon image; assigning a soft tissue Hounsfield Unit value to the soft tissue region for each respiratory phase in the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model; and Bone tissue Hounsfield Unit values ​​are assigned within the bone mask for each respiratory phase in the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model.

2. The medical system according to claim 1, wherein Execution of the machine-executable instructions further causes the processor to: receiving (600) treatment planning data (530) registered to the synthetic four-dimensional computed tomography image data; and Radiotherapy control instructions (532) configured for controlling a radiotherapy system are calculated (602) using the treatment planning data, the synthesized four-dimensional computed tomography image data, and the respiration signal.

3. The medical system according to claim 2, wherein: The medical system further comprises a magnetic resonance imaging system (302), wherein the memory further comprises simulated pulse sequence commands (330), the simulated pulse sequence commands being configured to control the magnetic resonance imaging system to acquire simulated magnetic resonance imaging data from an imaging region (308) according to a four-dimensional magnetic resonance imaging protocol, wherein the four-dimensional magnetic resonance imaging protocol is a T1-weighted Dixon magnetic resonance imaging protocol, wherein the pulse sequence commands are further configured to acquire self-navigated k-space data (334) within the simulated magnetic resonance imaging data, and wherein execution of the machine-executable instructions further causes the processor to: controlling (400) the magnetic resonance imaging system using the simulated pulse sequence command to acquire simulated magnetic resonance data and a subject's breathing signal; determining (402) the respiratory signal using the self-navigated k-space data; using the respiratory signal to classify (404) the simulated magnetic resonance data into a set of discrete respiratory phases; and The four-dimensional Dixon magnetic resonance image data is reconstructed (406) based on the classified simulated magnetic resonance data.

4. The medical system according to claim 3, wherein: The medical system further comprises the radiation therapy system (502), wherein the radiation therapy system is configured to controllably irradiate a target region (514) within an irradiation zone (512), and wherein the irradiation zone is within the imaging zone.

5. The medical system according to claim 4, wherein The radiotherapy system is any one of the following: LINAC, X-ray radiotherapy system, Gamma Knife, and Cobalt radiotherapy system.

6. The medical system according to claim 4 or 5, wherein: Execution of the machine-executable instructions further causes the processor to control the radiation therapy system using the radiation therapy control instructions to irradiate the target volume.

7. The medical system according to claim 6, wherein: The memory further includes monitoring pulse sequence commands (534) configured to acquire monitoring magnetic resonance imaging data (536), wherein the monitoring pulse sequence commands are configured to measure monitoring self-navigation k-space data within the monitoring magnetic resonance imaging data, wherein execution of the machine-executable instructions further causes the processor to: acquiring (604) the monitoring magnetic resonance imaging data during the irradiation of the target area; determining (606) a current respiration signal (538) using the monitoring magnetic resonance imaging data; Selecting (608) current magnetic resonance image data from the four-dimensional Dixon magnetic resonance image data using the current respiration signal; and Irradiation of the target zone is adjusted (610) using the current magnetic resonance image data.

8. The medical system according to claim 7, wherein: The monitoring pulse sequence commands are in accordance with the T1-weighted Dixon magnetic resonance imaging protocol, wherein execution of the machine-executable instructions further causes the processor to repeatedly update (612) a cumulative radiation dose map (542) using the current respiratory signal, the current magnetic resonance image data, and the radiotherapy control instructions.

9. The medical system according to claim 6, 7 or 8, wherein: The memory further includes two-dimensional monitoring pulse sequence commands configured to acquire two-dimensional monitoring magnetic resonance imaging data, wherein execution of the machine-executable instructions further causes the processor to: acquiring the two-dimensional monitoring magnetic resonance imaging data during the irradiation of the target area; and The two-dimensional monitoring magnetic resonance imaging data is used to adjust irradiation of the target volume.

10. The medical system according to any one of claims 3 to 9, wherein The pulse sequence commands are configured to rotate a k-space sampling pattern between acquisitions of the simulated magnetic resonance imaging data.

11. The medical system according to claim 10, wherein: The k-space sampling pattern is any one of the following: a star-pile k-space sampling pattern, and a spiral k-space sampling pattern.

12. The medical system according to any one of claims 3 to 11, wherein The four-dimensional Dixon magnetic resonance image data is reconstructed.

13. A computer program product comprising machine executable instructions for a processor controlling a medical system (100, 300, 500), wherein: Execution of the instructions causes the processor to: receiving (200) four-dimensional Dixon magnetic resonance image data (122), wherein the four-dimensional Dixon magnetic resonance image data is T1-weighted, wherein the four-dimensional Dixon magnetic resonance image data is synchronized with a respiratory signal (124); and reconstructing (202) synthetic four-dimensional computed tomography image data (128) from the four-dimensional Dixon magnetic resonance image data, wherein the four-dimensional Dixon magnetic resonance image data comprises a first Dixon image and a second Dixon image for each respiratory phase in a set of discrete respiratory phases, wherein the reconstruction of the synthetic 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 respiratory phase in a set of discrete respiratory phases; constructing a fat Dixon image using the first Dixon image and the second Dixon image for each respiratory phase in the set of discrete respiratory phases; constructing an in-phase Dixon image for each respiratory phase in the set of discrete respiratory phases using the first Dixon image and the second Dixon image; segmenting a body mask for each respiratory phase in the set of discrete respiratory phases using the in-phase Dixon image; segmenting a bone mask for each respiratory phase in the set of discrete respiratory phases using the in-phase Dixon image; segmenting a region between the body mask and the bone mask into soft tissue regions for each respiratory phase in the set of discrete respiratory phases using the fat Dixon image and the water Dixon image; assigning a soft tissue Hounsfield Unit value to the soft tissue region for each respiratory phase in the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model; and Bone tissue Hounsfield Unit values ​​are assigned within the bone mask for each respiratory phase in the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model.

14. A method of operating a medical system (100, 300, 500), wherein: The method comprises: receiving (200) four-dimensional Dixon magnetic resonance image data (122), wherein the four-dimensional Dixon magnetic resonance image data is T1-weighted, wherein the four-dimensional Dixon magnetic resonance image data is synchronized with a respiratory signal (124); and reconstructing (202) synthetic four-dimensional computed tomography image data (128) from the four-dimensional Dixon magnetic resonance image data, wherein the four-dimensional Dixon magnetic resonance image data comprises a first Dixon image and a second Dixon image for each respiratory phase in a set of discrete respiratory phases, wherein the reconstruction of the synthetic 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 respiratory phase in a set of discrete respiratory phases; constructing a fat Dixon image using the first Dixon image and the second Dixon image for each respiratory phase in the set of discrete respiratory phases; constructing an in-phase Dixon image for each respiratory phase in the set of discrete respiratory phases using the first Dixon image and the second Dixon image; segmenting a body mask for each respiratory phase in the set of discrete respiratory phases using the in-phase Dixon image; segmenting a bone mask for each respiratory phase in the set of discrete respiratory phases using the in-phase Dixon image; segmenting a region between the body mask and the bone mask into soft tissue regions for each respiratory phase in the set of discrete respiratory phases using the fat Dixon image and the water Dixon image; assigning a soft tissue Hounsfield Unit value to the soft tissue region for each respiratory phase in the set of discrete respiratory phases using non-rigid registration or using a soft tissue classification model; and Bone tissue Hounsfield Unit values ​​are assigned within the bone mask for each respiratory phase in the set of discrete respiratory phases using the non-rigid registration or using a bone tissue classification model.

Images