Respiratory state resolved magnetic resonance imaging

Abstract

Disclosed herein is a medical system (100, 400) comprising a magnetic resonance imaging system (102) configured to provide a respiratory signal. The breathing signal describes a breathing location of the subject as well as inspiration and expiration. Operation of machine executable instructions (140) causes a computing system to repeatedly: perform (200) individual acquisition of k-space data using the magnetic resonance imaging system; receiving (202) the respiratory signal; k-space data acquired by an individual is binned (204) into one of a predetermined number of k-space bin elements (148) using the respiratory signal, and abnormal value rejection is performed. Execution of the machine executable instructions also causes the computing system to reconstruct (208) a magnetic resonance image for each k-space bin of the predetermined number of k-space bins to provide a breathing state resolved magnetic resonance image (150).

Description

Technical Field

[0001] The present invention relates to magnetic resonance imaging, and in particular to respiratory state magnetic resonance imaging. Background Art

[0002] As part of the process for producing images within a patient's body, a magnetic resonance imaging (MRI) scanner uses a large static magnetic field to align the nuclear spins of atoms. This large static magnetic field is called the B0 field.

[0003] During an MRI scan, radio frequency (RF) pulses generated by one or more transmitter coils induce a so-called B1 field. Additional applied gradient fields and B1 fields induce perturbations to the effective local magnetic field. The RF signals emitted by the nuclear spins are then detected by one or more receiver coils. These RF signals are recorded as k-space data and are used to reconstruct MR images, typically using Fourier or other sparse transforms. By repeatedly acquiring k-space data, respiratory state-resolved magnetic resonance images can be acquired. If the k-space data is acquired in three dimensions or multi-slice two-dimensional space, the respiratory state-resolved magnetic resonance images are typically referred to as 4D or four-dimensional magnetic resonance images.

[0004] U.S. Patent Application Publication US2020 / 0150206 Al discloses a method for MR imaging of an object. MR imaging uses a star stacking acquisition scheme with enhanced control of the contrast of the reconstructed MR image. The method of the present invention comprises the following steps: generating MR signals by subjecting the object to multiple excitations of a multi-echo imaging sequence, the multi-echo imaging sequence comprising RF pulses and switched magnetic field gradients, wherein a series of echo signals are generated by each excitation; b) acquiring echo signals according to a star stack (i.e., a hybrid radial 3D acquisition scheme, wherein radial sampling is performed in each slice plane and phase encoding is performed along the slice encoding direction) or a spiral stacking scheme, wherein the echo signals are acquired as radial or spiral k-space profiles arranged at different positions in the k-space along the slice direction, wherein echo signals from different k-space slices are acquired in each excitation of the imaging sequence, and wherein the echo signals are acquired from each k-space slice using different relaxation time weightings; and c) reconstructing at least one MR image of desired contrast according to the acquired echo signals using a k-space weighted image contrast (KWIC) filter. Furthermore, the invention relates to an MR device and a computer program for an MR device.

[0005] The disclosure by Grimm R et al.: "Self-gating Reconstructions of Motion and Perfusion for Free-breathing T1-weighted DCE-MRI of the Thorax Using 3DStack-of stars GRE Imaging" (20 April 2012) describes a retrospective self-gating technique that can be used to extract information from a single continuous free-breathing scan acquired using a star stack 3DVIBE sequence. According to the disclosure, the technique can be used to calculate different sliding window reconstructions that show a complete "virtual" breathing cycle at high spatial resolution, or alternatively, a time series of contrast enhancement at a specified inspiratory level and high temporal resolution.

[0006] ROBERT GRIMM et al.'s publication: "Self-gated MRI motion modeling for respiratory motion compensation in integrated PET / MRI" (MEDICAL IMAGE ANALYSIS, DOI: 10.1016 / j.media.2014.08.003) describes how to apply MRI self-gating to perform respiratory gating of MRI data and simultaneously acquired PET raw data. After gated PET reconstruction, the individual gates are fused into a single motion compensated volume with a high signal-to-noise ratio (SNR) using an MRI motion model.

[0007] US Patent Application Publication US2016 / 0324500 A1 describes respiratory motion detection using a continuous spoiled gradient echo sequence with 3D radial trajectory and 1D self-gating. In some embodiments, the acquired data is retrospectively classified into different respiratory phases based on its temporal position within the respiratory cycle, and each phase is reconstructed via a self-calibrated CG-SENSE procedure.

[0008] WIPO patent application publication WO 2020 / 214725 A1 describes a method for performing proton resonance frequency shift (PRF) and T1-based temperature mapping using a magnetic resonance imaging (MRI) system, comprising acquiring a set of magnetic resonance (MR) data from a region of interest of an object using the MRI system by executing a variable flip angle multi-echo gradient echo 3D radial stack pulse sequence. The pulse sequence is configured to acquire radial k-space data in a plurality of segments, each segment being acquired at each of a plurality of flip angles.

[0009] The publication by Stemkens Bjorn et al.: "Optimizing 4-Dimensional Magnetic Resonance Imaging Data Sampling for Respiratory Motion Analysis of Pancreatic Tumors" (INTERNATIONAL JOURNAL OF RADIATION:ONCOLOGY BIOLOGY PHYSICS., DOI:10.1016 / j.ijrobp.2014.10.050) describes a study aimed at determining the best sampling strategy for retrospective reconstruction of 4-dimensional (4D) MR data used for non-rigid motion characterization of tumors and risk organs for radiation therapy purposes. Two alternative signals (external respiratory bellows and internal MRI navigator) and two MR sampling strategies (Cartesian and radial) were compared in terms of image quality and robustness. Summary of the invention

[0010] The invention provides a medical system, a computer program product and a method in the independent claims. Embodiments are given in the dependent claims.

[0011] Embodiments may provide an improved means of making respiratory state resolved magnetic resonance images by binning not only for respiratory position but also using the subject's inhalation or exhalation state to assign bins. The motion of a tumor in the chest, lung, abdomen (liver, pancreas, and kidney), or heart region of a subject may have different positions depending on whether the subject is inhaling or exhaling. This improved binning method may enable reconstruction of improved respiratory state resolved magnetic resonance images.

[0012] Respiratory state resolved magnetic resonance images can be useful, for example, for radiotherapy planning. The motion of the abdomen and chest caused by breathing may hinder the accurate determination of the treatment margins of the target tumor, as may be useful for radiotherapy planning. Techniques using breath holding and abdominal compressions may have an impact on patient comfort and treatment plan reproducibility. To overcome these challenges, 4D MRI has been introduced that uses external or intrinsic respiratory signals during the entire acquisition time to capture organ or tumor motion in the abdomen and chest under free breathing with automatic respiratory state classification in bins. Based on the respiratory state, target areas, risk organs can be better targeted or avoided.

[0013] As used herein, a respiratory state resolved magnetic resonance image encompasses a cycle time resolved magnetic resonance image. In other words, a respiratory state resolved magnetic resonance image is a time series of magnetic resonance images that tracks the entire respiratory cycle of a subject, such that the images are resolved for the temporal respiratory cycle and not for the position of the subject. Thus, a respiratory state resolved magnetic resonance image can capture the motion of the subject during the respiratory cycle.

[0014] In one aspect, the present invention provides a medical system comprising a magnetic resonance imaging system configured to acquire k-space data for a field of view of a subject. The magnetic resonance imaging system is configured to provide a breathing signal. The breathing signal describes the breathing position of the subject. The breathing signal also describes the inhalation and exhalation of the subject. In other words, the breathing signal not only details the position of the subject, but also details whether the subject is in the process of inhaling or exhaling. Having a breathing signal that provides this information can be beneficial because the position of organs may be slightly different when the subject inhales or exhales for a specific breathing position.

[0015] Respiratory state binning using inspiration or expiration state can be accomplished by classifying the amplitude of the respiratory signal using a so-called amplitude binning method or classifying the respiratory phase close to end-expiration or end-inspiration using a so-called phase binning method.

[0016] The medical system also includes a memory storing machine executable instructions and pulse sequence commands. The pulse sequence commands are configured to control the magnetic resonance imaging system to perform the acquisition of the k-space data as multiple acquisitions of k-space data according to a multidimensional k-space sampling pattern. The pulse sequence commands are also configured to cause the multidimensional k-space sampling pattern to change between each individual k-space data acquisition in the multiple acquisitions of the k-space data. Therefore, in other words, the multidimensional k-space sampling pattern is changed whenever an individual k-space data acquisition is performed. Typically, this can be accomplished by rotating the multidimensional k-space sampling pattern. A common way of doing this is to rotate a particular k-space sampling pattern by a so-called golden angle. This enables free acquisition of k-space data, which can then be binned later. Due to the rotation or change of the multidimensional k-space sampling pattern, it is very unlikely that previous acquisitions will overlap exactly with each other. This enables k-space data to be established in various bins.

[0017] The medical system also includes a computing system. The computing system can be configured to control the magnetic resonance imaging system. The execution of the machine executable instructions causes the computing system to repeatedly perform individual acquisitions of k-space data by controlling the magnetic resonance imaging system using the pulse sequence command. The execution of the machine executable instructions also causes the computing system to repeatedly receive the breathing signal during the individual acquisition of the k-space data. For example, the individual acquisition of k-space data can be annotated or marked with a specific breathing signal including the breathing position and whether the subject is in the process of inhaling or exhaling. The execution of the machine executable instructions also causes the computing system to repeatedly use the breathing signal and the subject's inhalation or exhalation to bin the individual acquired k-space data into one of the predetermined number of k-space bins.

[0018] Execution of the machine executable instructions further causes the computing system to reconstruct a magnetic resonance image for each of the k-space bins to provide a temporally respiratory state resolved magnetic resonance image of the field of view. This embodiment may be beneficial, for example, because the subject's inhalation and exhalation represent a lagged movement of the subject. Binning the k-space data based solely on respiratory position may result in lower quality, blurred magnetic resonance images. The improved respiratory state resolved magnetic resonance images may be useful in providing clearer magnetic resonance images that distinguish tumors and risk organs throughout the respiratory cycle for use in radiation therapy planning and treatment.

[0019] The breathing signal can describe the subject's inhalation and exhalation in various ways. For example, the derivative of the breathing signal can be used to indicate inhalation and exhalation. In other cases, a velocity substantially equal to the derivative of the breathing signal can also be used. In addition, detecting the breathing signal in the toe-to-toe and front-to-back directions (side-to-side motion is generally negligible) can more quantitatively determine the inhalation and exhalation hysteresis effects, thereby providing a more accurate estimate of the breathing hysteresis effects and outliers, including the motion vector field.

[0020] The execution of the machine executable instructions also causes the computing system to repeatedly bin the k-space data acquired by the individual into a respiratory position histogram. A predetermined number of k-space bins are distinguished in terms of the position of the object and the inhalation and exhalation of the object. The respiratory position histogram distinguishes the position of the object and does not distinguish between the inhalation and exhalation of the object. The execution of the machine executable instructions also causes the computing system to use the respiratory position histogram to identify respiratory outliers. Correct the respiratory state-resolved magnetic resonance image for respiratory outliers. Respiratory outliers may be caused by sudden changes in breathing, such as sighing, deep breathing and exhalation, and coughing. This correction of the respiratory state-resolved magnetic resonance image can be accomplished in several different ways. In one way, respiratory outliers are removed from a predetermined number of k-space bins. In this case, they do not contribute at all to the reconstruction of the respiratory state-resolved magnetic resonance image. In other cases, the respiratory state-resolved magnetic resonance image can be reconstructed and various k-space samples can be given different weighting factors. One way to minimize the impact of respiratory outliers is to give low or lower weighting values ​​to those particular samples in a predetermined number of k-space bins. In either case, the effect is to improve the quality of the respiratory state resolved magnetic resonance images.

[0021] In another embodiment, the multidimensional k-space sampling pattern is configured to oversample the central k-space region. This may be particularly beneficial because such oversampling of the central k-space region can be used in several different ways. In one example, such oversampling of the central k-space region can be useful for correcting the motion of the object. For example, samples with higher levels of motion can have their central k-space region given a lower weighting factor or even removed. K-space data outside the central k-space region can then be used to reconstruct an image. In another example, the oversampling can be used to perform self-navigation or navigation.

[0022] In another embodiment, the multi-dimensional k-space sampling pattern is a variable density star stack sampling pattern.

[0023] In another embodiment, the multi-dimensional k-space sampling pattern is a variable density spiral sampling pattern.

[0024] In another embodiment, the multidimensional k-space sampling pattern is a (spiral) Cartesian sampling pattern.

[0025] In another embodiment, the multi-dimensional k-space sampling pattern is a variable density sampling pattern.

[0026] In another embodiment, the multi-dimensional k-space sampling pattern is a two-dimensional variable density sampling pattern.

[0027] In another embodiment, the multi-dimensional k-space sampling mode is a 2D Propeller sampling mode. Multi-slice 2D Propeller sampling with, for example, highly desired T2 contrast has an inherently high oversampling of the central k-space. Using respiratory information, the oversampled central k-space can be filtered out using KWIC filtering to extract a 4D respiratory state resolved T2 image within the same multi-slice 2D Propeller overview scan.

[0028] In another embodiment, the multi-dimensional k-space sampling pattern is a Koosh sphere sampling pattern.

[0029] In another embodiment, the multi-dimensional k-space sampling pattern is a Floret sampling pattern.

[0030] In another embodiment, the multi-dimensional k-space sampling pattern is a spiral sampling pattern.

[0031] In another embodiment, execution of the machine executable instructions is configured to cause an intended adaptation of the sampling pattern to obtain a uniform sampling of the respiratory state using, for example, a golden angle scheme.

[0032] In another embodiment, the medical instrument is configured to provide a breathing guide for distribution over breathing states. The breathing guide will guide the patient to breathe consistently, thereby covering all breathing states in a uniform, equally distributed sinusoidal manner, reducing drift and outliers.

[0033] In another embodiment, execution of the machine executable instructions further causes the computing system to perform real-time reconstruction of the individual acquisitions of k-space data to visualize the respiratory motion in real-time alongside the respiratory state resolved magnetic resonance images. By real-time, the visualization of the respiratory motion appears synchronized with the respiratory motion of the subject being imaged. The delay of real-time may be, for example, about 1 / 2 second or the time to perform the acquisition of the k-space data for the individual acquisitions.

[0034] In another embodiment, execution of the machine executable instructions further causes the computing system to apply a k-space weighting filter to the individual acquisitions of k-space data in the predetermined number of k-space bins prior to reconstructing the magnetic resonance image for each of the k-space bins. The k-space filter is configured to apply a higher weighting value to the central k-space region than outside the central k-space region.

[0035] This embodiment can be beneficial because k-space is sampled at a higher rate in the central region. For this region, the samples in the central region can be minimized. This can be beneficial, for example, to reduce the effects of motion within the bin, thereby sharpening and defining the respiratory state.

[0036] In another embodiment, execution of the machine executable instructions further causes the computing system to adjust the boundaries of the predetermined number of k-space bins. The binning of the individually acquired k-space data may be such that each of the predetermined number of k-space bins has the same amount of individually acquired k-space data within a predetermined difference. This embodiment may be beneficial because a bin may have more k-space than other bins, which may, for example, result in a particular bin having a lower signal-to-noise ratio or more artifacts due to undersampling. Equally sized bins may help improve the overall quality of magnetic resonance images that resolve respiratory states. Another example may be that adjacent bins overlap and share contours.

[0037] In another embodiment, the individual acquisitions of k-space data are assigned weights by the k-space weighting filter according to the position determined by the respiratory signal. For example, the center of the bin may be given specific coordinates according to the respiratory signal. The individually acquired k-space data may vary according to its position within the bin according to the coordinates. K-space samples closer to the center of the bin or any other selected position of the bin may be given a higher weighting factor. This may, for example, be useful to reduce the effects of motion within the bin, thereby reducing blur. However, it also allows for the definition of coded respiratory states. End-expiratory and end-inspiratory coding states may be defined at the outermost respiratory states relative to the center of the bin.

[0038] In case of overlapping bins, the contours may be shared with a k-space filter applied only for higher k-space using the shared contours.

[0039] In another embodiment, the k-space weighting filter is a k-space weighted image contrast filter. The k-space weighted image contrast filter is also referred to as a KWIC filter in many literatures. The use of a k-space weighted image contrast filter can be useful, where it minimizes the central region of radial k-space relative to the peripheral region. This can be useful, for example, to construct a magnetic resonance image with reduced motion artifacts or blurring.

[0040] In another embodiment, the k-space weighting filter is configured to perform phase correction to correct for motion and / or increase image sharpness according to a gradient entropy measure. This may be beneficial as it may provide an improved quality of respiratory state resolved magnetic resonance images.

[0041] The image data reconstructed from the k-space data in a predetermined number of bins may be post-processed by different techniques, such as providing a mean value (MIP), a mid-respiration position or a vector field representation.

[0042] In another embodiment, the execution of the machine executable instructions further causes the computing system to calculate extreme respiratory state resolved image data by calculating the modulus average or complex average of the extreme inspiratory and expiratory respiratory states in the respiratory state resolved magnetic resonance image. The extreme inspiratory and expiratory respiratory states can be end-inspiratory, end-expiratory states, but can also be automatic selection of selected extreme states through gradient entropy comparison and / or preselected gating window settings. This can be beneficial because it can provide a model of the end-respiratory state of the object that can be used for radiotherapy planning. This can be useful for constructing radiotherapy control commands that are unlikely to cause irradiation of the object's vital organs. Radiotherapy control commands can be constructed using respiratory state resolved segmentation, and then checking whether the extreme inspiratory state and / or extreme expiratory state leads to undesired irradiation of the object's vital organs or regions.

[0043] Mean values ​​of extreme expiratory and inspiratory positions are proposed. Tumor and risk organ movement during respiration are represented in a simple but effective way, thereby showing clinical information as well as motion margins from respiration for radiation therapy planning (fast ITV). An example of the mean value of extreme expiratory and inspiratory positions can be the mean value of end-expiratory and end-inspiratory respiratory states. Another example is to select extreme states by a gradient entropy metric and the mean value of the extreme states. Another example is to consider preselected gating window settings in the inspiratory / expiratory states and provide the mean value of the extreme respiratory states within the selected gating window settings. Vice versa, the proposed gating settings can be derived from the analysis of these extreme respiratory states for optimizing radiation therapy. MIP, surface rendering and other 3D representations of the averaged data set can be provided for simple intuitive representation. The averaging will preferably be done using modulus averaging, however complex averaging is also possible.

[0044] In another embodiment, the memory also stores a segmentation algorithm, which is configured to output a respiratory state-resolved segmentation in response to receiving the respiratory state-resolved magnetic resonance image. For example, the segmentation algorithm can be applied to each of the individual images that constitute the respiratory state-resolved magnetic resonance image. Then, assembling the segmentation set can result in respiratory state-resolved segmentation. The execution of the machine-executable instructions also causes the computing system to provide the respiratory state-resolved segmentation of the respiratory state-resolved magnetic resonance image by inputting the respiratory state-resolved magnetic resonance image into the segmentation algorithm. This embodiment can be beneficial because segmentation can be useful for applications such as radiation therapy planning when the subject is breathing.

[0045] In another embodiment, the memory further stores a radiation therapy system control module configured to output control commands for a radiation therapy system in response to receiving the respiratory state resolved segmentation and the radiation therapy plan as input. Execution of the machine executable instructions further causes the computing system to provide the radiation therapy control commands in response to inputting the respiratory state resolved segmentation and the radiation therapy plan into the radiation therapy system control module.

[0046] In another embodiment, the medical system further comprises the radiotherapy system. Execution of the machine executable instructions further causes the computing system to control the radiotherapy system to irradiate the subject using the radiotherapy control commands. The respiration signal is used to at least partially control or gate the irradiation of the subject.

[0047] The radiation therapy system can be implemented in various ways. In some instances, the radiation therapy system is integrated with a magnetic resonance imaging system, and the system is capable of performing magnetic resonance imaging before, during, and after the radiation therapy. For example, it can be a magnetic resonance image-guided LINAC. In other examples, the magnetic resonance imaging system and the radiation therapy system can be separate. For example, magnetic resonance imaging can be performed at one location, and then the object can be transported to another location, and the breathing signal can also be measured on the radiation therapy system. Multidimensional segmentation can still be useful at a second location where only the radiation therapy system is present. In the case where there are two separate locations between the magnetic resonance imaging system and the radiation therapy system, constraints and / or vacuum mattresses and / or corsets can be used to keep the object in a specific position.

[0048] In another embodiment, the magnetic resonance image for each of the k-space bins is reconstructed using compressed sensing. This may be particularly beneficial because if there are fewer k-space samples in some bins, this may still enable the construction of a high quality respiratory state resolved magnetic resonance image.

[0049] In another embodiment, the magnetic resonance image for each of the predetermined number of k-space bins is reconstructed using artificial intelligence based reconstruction. Likewise, with this embodiment, if there is undersampling of k-space, using artificial intelligence based reconstruction may be useful for improving image quality.

[0050] In another embodiment, the motion signal is a respiratory zone signal.

[0051] In another embodiment, the motion signal is an intrinsic k-space navigator. An internal source using a start-up period can be used to extract quantitative motion information, allowing accurate motion estimation without the need for respiratory belt placement. Therefore, before each shot, a start-up echo of the central k-space contour allows quantitative motion information to be extracted from the projection data (in AP, RL, and SI, as shown below) using a bandpass filtered centroid calculation.

[0052] In another embodiment, the motion signal is a breathing tube signal. For example, the subject may breathe through a tube, or may be provided with a device to assist breathing. The breathing tube signal may then be a measure of breathing assistance and / or actual breathing measured from the subject.

[0053] In another embodiment, the motion signal is a k-space navigator.

[0054] In another embodiment, the motion signal is an image navigator. For example, an individual acquisition of k-space data may be reconstructed, and although the image may not be of sufficient quality for clinical use, the image may still identify anatomical locations so that it can provide navigation.

[0055] In another embodiment, the motion signal is a noise navigator.

[0056] In another embodiment, the motion signal is based on a camera's motion signal.For example, a camera may be mounted to observe the object during acquisition of k-space data, and the observation of the motion may be used to provide the navigator signal.

[0057] In another embodiment, the respiratory state resolved magnetic resonance image is corrected for respiratory outliers during reconstruction of the magnetic resonance image for each of the predetermined number of k-space bins by removing outliers from the k-space bins or by giving a predetermined weighting value to the respiratory outliers.

[0058] In another aspect, the present invention provides a computer program product comprising machine executable instructions for being run by a computing system controlling a medical system. In some examples, the computer program product may also include pulse sequence commands. The medical system comprises a magnetic resonance imaging system configured to acquire k-space data for a field of view of the subject. The magnetic resonance imaging system is configured to provide a breathing signal. The breathing signal describes a breathing position of the subject. The breathing signal also describes an inhalation and an exhalation of the subject.

[0059] Execution of the machine executable instructions causes the computing system to repeatedly perform individual acquisitions of k-space data by controlling the magnetic resonance imaging system using the pulse sequence commands. The pulse sequence commands are configured to perform acquisitions in the individual acquisitions of k-space data according to a multidimensional k-space sampling pattern. The pulse sequence commands are configured to cause the multidimensional k-space sampling pattern to change between each individual k-space data acquisition in the multiple acquisitions of k-space data. Execution of the machine executable instructions also causes the computing system to repeatedly receive the respiration signal during acquisition in the individual acquisitions of k-space data.

[0060] Execution of the machine executable instructions further causes the computing system to repeatedly bin the k-space data acquired by the individual into one of the predetermined number of k-space bins using the respiration signal and the subject's inhalation or exhalation. Execution of the machine executable instructions further causes the computing system to reconstruct a magnetic resonance image for each of the k-space bins to provide a respiration state resolved magnetic resonance image of the field of view.

[0061] Execution of the machine-executable instructions causes the computing system to repeatedly bin the k-space data acquired by the individual into a respiratory position histogram, wherein execution of the machine-executable instructions causes the computing system to use the respiratory position histogram to identify respiratory outliers, and wherein the respiratory state-resolved magnetic resonance image is corrected for the respiratory outliers.

[0062] In another aspect, the present invention provides a method for operating a medical system. The medical system includes a magnetic resonance imaging system, which is configured to acquire k-space data for the field of view of the object. The magnetic resonance imaging system is configured to provide a breathing signal. The breathing signal describes the breathing position of the object. The breathing signal also describes the inhalation and exhalation of the object. The method includes performing individual acquisitions of k-space data by controlling the magnetic resonance imaging system using the pulse sequence command. The pulse sequence command is configured to perform the acquisition of the k-space data as multiple acquisitions of k-space data according to a multidimensional k-space sampling mode. The pulse sequence command is configured to cause the multidimensional k-space sampling mode to change between each individual k-space data acquisition in the multiple acquisitions of k-space data.

[0063] The method includes receiving the respiratory signal during acquisition in the individual acquisition of the k-space data. The method also includes binning the individual acquired k-space data into one of a predetermined number of k-space bins using the respiratory signal and the subject's inhalation or exhalation. The method also includes reconstructing a magnetic resonance image for each of the k-space bins to provide a respiratory state resolved magnetic resonance image of the field of view.

[0064] The method includes repeatedly binning k-space data acquired by the individual into a respiratory position histogram, identifying respiratory outliers using the respiratory position histogram, and correcting the respiratory state resolved magnetic resonance image for the respiratory outliers.

[0065] 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.

[0066] As will be appreciated by those skilled in the art, aspects of the present invention may be implemented as an apparatus, method, or computer program product. Accordingly, aspects of the present invention may take the form of a fully hardware embodiment, a fully software embodiment (including firmware, resident software, microcode, etc.), or a combination of software and hardware aspects (all of which may be generally referred to herein as "circuits," "modules," or "systems"). In addition, aspects of the present invention may take the form of a computer program product implemented in one or more computer-readable media having computer executable code implemented thereon.

[0067] 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 that can store instructions that can be executed by a processor or a computing system of a computing device. A computer-readable storage medium may be referred to as a computer-readable non-transient 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 be capable of storing data that can be accessed by a computing system 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 memories, USB thumb drives, random access memories (RAM), read-only memories (ROM), optical disks, magneto-optical disks, and register files of computing systems. 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 computer device via a network or communication link. For example, data may be retrieved on a modem, the Internet, or 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.

[0068] A computer readable signal medium may include a propagated data signal having computer executable code implemented 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 is capable of conveying, propagating, or transmitting a program used by or in conjunction with an instruction execution system, apparatus, or device.

[0069] "Computer memory" or "memory" is an example of a computer-readable storage medium. Computer memory is any memory that is directly accessible by a computing system. "Computer storage" or "storage" is another example of a computer-readable storage medium. A computer storage is any non-volatile computer-readable storage medium. In some embodiments, a computer storage may also be computer memory, or vice versa.

[0070] As used herein, "computing system" encompasses an electronic component capable of executing a program or machine executable instructions or computer executable code. References to computing systems including examples of "computing system" should be interpreted as possibly including more than one computing system or processing core. The computing system may be, for example, a multi-core processor. A computing system may also refer to a collection of computing systems within a single computer system or distributed among multiple computer systems. The term computing system should also be interpreted as possibly referring to a collection or network of computing devices that each include a processor or computing system. Machine executable code or instructions may be executed by multiple computing systems or processors that may be within the same computing device or may even be distributed across multiple computing devices.

[0071] Machine executable instructions or computer executable code may include instructions or programs that cause a processor or other computing system to perform aspects of the present invention. Computer executable code for performing operations for various aspects of the present invention may be written in any combination of one or more programming languages ​​and compiled into machine executable instructions, and the one or more programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, C++, etc. and conventional procedural programming languages ​​such as "C" programming language or similar programming languages. In some instances, the computer executable code may take the form of a high-level language or take a precompiled form and be used in conjunction with a decoder that generates machine executable instructions at work. In other instances, the machine executable instructions or computer executable code may take the form of programming for a programmable logic gate array.

[0072] The computer executable code may run entirely on the user's computer, partially on the user's computer (as a stand-alone 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 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).

[0073] Aspects of the present invention are described with reference to the flowchart, diagram and / or block diagram of the method, device (system) and computer program product according to an embodiment of the present invention. It should be understood that, when applicable, each frame or part of the frame of the flowchart, diagram and / or block diagram can be implemented by a computer program instruction in the form of a computer executable code. It should also be understood that, when mutually non-exclusive, the combination of frames in different flowcharts, diagrams and / or block diagrams can be combined. These computer program instructions can be provided to the computing system of a general-purpose computer, a special-purpose computer or other programmable data processing device that produces a machine, so that the instruction running via the computing system of a computer or other programmable data processing device creates a unit for implementing the function / action specified in a flowchart and / or one or more block diagram frames.

[0074] These machine-executable instructions or computer program instructions may also be stored in a computer-readable medium, which can direct a computer, other programmable data processing apparatus, or other device to work in a specific manner, so that the instructions stored in the computer-readable medium produce an article of manufacture including instructions for implementing the functions / actions specified in the flowchart and / or one or more block diagram blocks.

[0075] The machine-executable instructions or computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device, thereby producing a computer-implemented process, so that the instructions running on the computer or other programmable apparatus provide a process for the functions / actions specified in the flowchart and / or one or more block diagram blocks.

[0076] As used herein, "user interface" is an interface that allows a user or operator to interact with a computer or computer system. "User interface" may also be referred to as a "human-machine interface device". A user interface may provide information or data to an operator and / or receive information or data from an operator. A user interface may enable the input from an operator to be received by a computer and may provide output to a user from a computer. In other words, the user interface may allow an operator to control or manipulate a computer, and the interface may allow a computer to indicate the effect of the control or manipulation of an operator. The display of data or information on a display or graphical user interface is an example of providing information to an operator. The reception of data by a keyboard, mouse, tracking ball, touch pad, pointing stick, graphic input board, joystick, game controller, webcam, earphone, pedal, wired gloves, remote controller, and accelerometer are all examples of user interface components that implement the reception of information or data from an operator.

[0077] As used herein, "hardware interface" encompasses an interface that enables a computing system of a computer system to interact with and / or control an external computing device and / or apparatus. A hardware interface may allow a computing system to send a control signal or instruction to an external computing device and / or apparatus. A hardware interface may also enable a computing system 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.

[0078] 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, 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), 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.

[0079] Medical imaging data is defined herein as recorded measurements made by a tomographic medical imaging system describing an object. Medical imaging data can be reconstructed into a medical image. A medical image is defined herein as a reconstructed two-dimensional or three-dimensional visualization of anatomical data contained within the medical imaging data. The visualization can be performed using a computer.

[0080] K-space data is defined herein as the measurements of radio frequency signals emitted by atomic spins recorded using the antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan.Magnetic resonance data is an example of tomographic medical image data.

[0081] A Magnetic Resonance Imaging (MRI) image or MR image is defined herein as a reconstructed two-dimensional or three-dimensional visualization of anatomical data contained within the magnetic resonance imaging data. The visualization may be performed using a computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In the following, preferred embodiments of the present invention will be described by way of example only and with reference to the accompanying drawings, in which:

[0083] Figure 1 An example of a medical system is illustrated;

[0084] Figure 2 The diagram shows the use of Figure 1 A flowchart of a method of a medical system;

[0085] Figure 3 The diagram shows the use of Figure 1 A flowchart of another method of a medical system;

[0086] Figure 4 Another example of a medical system is illustrated;

[0087] Figure 5 The diagram shows the use of Figure 4 A flowchart of a method of a medical system;

[0088] Figure 6 The diagram shows the motion hysteresis effect caused by breathing;

[0089] Figure 7 A graph showing a k-space intrinsic navigator;

[0090] Figure 8 A set of a predetermined number of k-space bins 148 and a respiratory position histogram 152 are illustrated;

[0091] Fig. 9 illustrates a single KWIC filter for weighting k-space data based on position within or relative to a k-space bin;

[0092] Fig.10 A set of KWIC filters for multiple k-space bins is illustrated;

[0093] Fig.11 Examples of a three-dimensional golden angle star stack k-space sampling mode and a three-dimensional variable density radial star stack k-space sampling mode are illustrated;

[0094] Fig.12 A multi-slice Propeller k-space sampling mode 1202 is shown;

[0095] Fig.13 shows two possible bins at different breathing states, where the color code illustrates the KWIC filter strength; contours within the blue edge are strongly weighted, while green and red are much less weighted; and

[0096] Fig.14 Illustration of k-space and leaf components relative to the standard Propeller k-space sampling pattern. Fig.13 The standard Propeller without contrast weighting is shown in 1400 and 1402 for both k-space and leaf components.

[0097] Reference numerals list

[0098] 100 Medical System

[0099] 102 Magnetic Resonance Imaging System

[0100] 104 Magnet

[0101] 106 Magnet bore

[0102] 108 Imaging Area

[0103] 109 Field of view

[0104] 110 Magnetic field gradient coil

[0105] 112 Magnetic field gradient coil power supply

[0106] 114 RF Coil

[0107] 116 Transceiver

[0108] 118 Objects

[0109] 120 Object support

[0110] 122 Breathing belt

[0111] 130 Computer

[0112] 132 Computing Systems

[0113] 134 Hardware Interface

[0114] 136 User Interface

[0115] 138 Memory

[0116] 140 machine executable instructions

[0117] 142 Pulse sequence commands

[0118] Individual acquisition of 144 k spatial data

[0119] 146 Respiration signal

[0120] 148 A set of a predetermined number of k-space bins

[0121] 150 Respiratory state-resolved MRI

[0122] 152 Breathing position histogram

[0123] 154 Abnormal breathing value

[0124] 200 Performing individual acquisitions of k-space data by controlling a magnetic resonance imaging system using pulse sequence commands

[0125] 202 Receiving a respiratory signal when an individual acquires k-space data by a magnetic resonance imaging system

[0126] 204 binning the k-space data acquired by the individual into one of a predetermined number of k-space bins using the breathing signal and the subject's inhalation or exhalation

[0127] 206 Collection completed?

[0128] 208 Reconstructing a magnetic resonance image for each k-space bin in a predetermined number of k-space bins to provide a respiratory state resolved magnetic resonance image of the field of view

[0129] 300 Repeatedly bin the individual acquired k-space data into respiratory position histograms

[0130] 302 Identifying respiratory outliers using a respiratory position histogram, wherein the respiratory state-resolved magnetic resonance image is corrected for the respiratory outliers

[0131] 400 Medical Systems

[0132] 402 Radiation therapy system

[0133] 404 Rack

[0134] 406 Radiotherapy Source

[0135] 408 Collimator

[0136] 410 Cryostat

[0137] 412 Superconducting Coil

[0138] 414 Rotation axis

[0139] 416 Radiation Beam

[0140] 418 Target Area

[0141] 420 Segmentation Algorithm

[0142] 422 Segmentation for respiratory state discrimination

[0143] 424 Radiation therapy system control module

[0144] 426 Radiation Therapy Planning

[0145] 428 Radiotherapy Control Order

[0146] 500 Providing respiratory state resolved segmentation of the respiratory state resolved magnetic resonance image by inputting the respiratory state resolved magnetic resonance image into a segmentation algorithm

[0147] 502 providing radiation therapy control commands in response to inputting the respiratory state resolved segmentation and radiation therapy plan into the radiation therapy system control module

[0148] 504 Use radiotherapy control commands to control the radiation therapy system to irradiate the object

[0149] 600 Magnetic Resonance Images

[0150] 602 Tumor Segmentation

[0151] 604 Movement of the center of gravity in the TM plane

[0152] 700 k space intrinsic navigator

[0153] 701 Respiration state or box element 1

[0154] 702 Respiration state or box element 2

[0155] 703 Respiratory state or box element 3

[0156] 704 Respiration state or box element 4

[0157] 705 breathing state or box element 5

[0158] 706 Respiratory state or box element 6

[0159] 707 Respiratory state or box element 7

[0160] 710 Frame

[0161] 712 Time

[0162] 714 Navigator value

[0163] 900 KWIC filter single box element

[0164] 902 Position in the box (navigator position)

[0165] 904 Weighting Factor

[0166] 1000 Box Yuan Center

[0167] 1100 3D golden angle star stack k-space sampling mode

[0168] 1102 Three-dimensional variable density radial star stack k-space sampling mode

[0169] 1104 Area

[0170] 1106 Radial k-space sampling mode

[0171] 1108 Radial k-space sampling mode

[0172] 1110 The central region of k space

[0173] 1200 Multi-dimensional k-space sampling mode

[0174] 1202 Individual Propeller Sampling Mode

[0175] 1300 Continuous sampling of central k-space

[0176] 1302 Sampling performed when partial filtering of central k-space is performed using a KWIC filter

[0177] 1304 Inner frame

[0178] 1306 Middle frame

[0179] 1308 Frame DETAILED DESCRIPTION

[0180] Like numbered elements in these figures are either equivalent elements or perform the same function. If the function is equivalent, an element that has been discussed previously will not necessarily be discussed in a later figure.

[0181] Figure 1 An example of a medical system 100 including a magnetic resonance imaging system 102 and a computer 130 is illustrated. The magnetic resonance imaging system 102 includes a magnet 104. The magnet 104 is a superconducting cylindrical magnet having a bore 106 passing therethrough. The use of different types of magnets is also possible; for example, it is also possible to use both a split cylindrical magnet and a so-called open magnet. A split cylindrical magnet is similar to a standard cylindrical magnet, except that the cryostat has been split into two sections to allow access to the isoplane of the magnet, such a magnet can be used, for example, in conjunction with charged particle beam therapy. An open magnet has two magnet sections, one above the other, with a space large enough between to receive an object: the arrangement of the two sections is similar to the arrangement of Helmholtz coils. Open magnets are common because the object is less constrained. Inside the cryostat of the cylindrical magnet there is a series of superconducting coils.

[0182] Within the bore 106 of the cylindrical magnet 104 there is an imaging zone 108 in which the magnetic field is strong and uniform enough to perform magnetic resonance imaging. A field of view 109 is shown within the imaging zone 108. Magnetic resonance data is typically acquired with respect to the field of view 109. An object 118 is shown supported by an object support 120 so that at least a portion of the object 118 is within the imaging zone 108 and the field of view 109.

[0183] There is also a set of magnetic field gradient coils 110 in the bore 106 of the magnet, which is used to acquire magnetic preliminary resonance data to spatially encode magnetic spins in the imaging zone 108 of the magnet 104. The magnetic field gradient coils 110 are connected to a magnetic field gradient coil power supply 112. The magnetic field gradient coils 110 are intended to be representative. Typically, the magnetic field gradient coils 110 include three independent 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 110 is controlled as a function of time and can be ramped or pulsed.

[0184] Adjacent to the imaging zone 108 is a radio frequency coil 114 for manipulating the orientation of magnetic spins within the imaging zone 108 and for receiving radio frequency transmissions also from spins within the imaging zone 108. The radio frequency antenna may contain multiple coil elements. The radio frequency antenna may also be referred to as a channel or antenna. The radio frequency coil 114 is connected to a radio frequency transceiver 116. The radio frequency coil 114 and the radio frequency transceiver 116 may be replaced by separate transmit and receive coils and separate transmitters and receivers. It should be understood that the radio frequency coil 114 and the radio frequency transceiver 116 are representative. The radio frequency coil 114 is also intended to represent a dedicated transmit antenna and a dedicated receive antenna. Similarly, the transceiver 116 may also represent a separate transmitter and receiver. The radio frequency coil 114 may also have multiple receive / transmit elements, and the radio frequency transceiver 116 may have multiple receive / transmit channels. For example, if a parallel imaging technique such as SENSE is performed, the radio frequency coil 114 will have multiple coil elements.

[0185] The transceiver 116 and the gradient controller 112 are shown as being connected to the hardware interface 106 of the computer system 102. In this example, the respiratory belt 122 is shown as being connected to the chest of the subject 118. The respiratory belt 122 provides a respiratory signal 146. The respiratory signal 146 not only provides the position of the chest of the subject 118, but also can determine whether the subject 118 is inhaling or exhaling by looking at the trend or speed.

[0186] The medical system 100 is also shown to include a computer 130. The computer 130 is intended to represent one or more computing or calculating devices located at one or more locations. The computer 130 is shown to contain a computing system 132. The computing system 132 is intended to represent one or more computing systems that may be, for example, one or more processing cores located at one or more locations. Various combinations of computing systems 132 and / or computers 130 may be connected using a network and work together collaboratively. The computing system 132 is shown to communicate with a hardware interface 134, a user interface 136, and a memory 138. The hardware interface 134 is an interface that enables the computing system 132 to communicate with and / or control other components of the medical system 100 (such as the magnetic resonance imaging system 102). The user interface 136 is a user interface that enables an operator of the medical system 100 to control and operate the medical system 100. The memory 138 is intended to represent various types of memory that may communicate with the computing system 132.

[0187] The memory 138 is shown as containing machine executable instructions 140. The machine executable instructions contain instructions that enable the computing system 132 to control the magnetic resonance imaging system 102 and perform various data and image processing tasks. The memory 138 is also shown as containing pulse sequence commands 142. The pulse sequence commands 142 are commands or data that can be converted into commands that enable the computing system 132 to control the operation of the magnetic resonance imaging system 102. Typically, the pulse sequence commands 142 are in the form of a timing diagram or timing commands that contain information about which commands are used to control the magnetic resonance imaging system 102 at a particular time. The memory 138 is also shown as containing individual acquisitions of k-space data 144 that have been acquired from the magnetic resonance imaging system 102 using the pulse sequence commands 142. The memory 138 is also shown as containing a respiratory signal 146 that has been measured using a respiratory belt 122. Although a respiratory belt 122 is shown, various other techniques may also be used, such as using a camera image of the subject 118 or using various k-space navigation techniques.

[0188] As individual acquisitions of k-space data 144 are acquired, they may be assigned a specific respiratory signal 146. The respiratory signal 146 is then used to bin the individual acquisitions of k-space data 144 into a set of a predetermined number of k-space bins 148. The predetermined number of k-space bins 148 are resolved in terms of both the position of the subject 118 and the respiratory state. Thus, it records inspiration and exhalation as separate bins, but the positions may be the same. The memory 138 is also shown as containing a respiratory state resolved magnetic resonance image 150 that has been reconstructed by making an image for each k-space bin in the predetermined number of k-space bins 148. This can then be assembled into a respiratory state resolved magnetic resonance image 150.

[0189] The memory is also shown as containing a respiratory position histogram 152. The respiratory position histogram 152 is used to bin the individual acquisitions of the k-space data 144, except in this case the respiratory state is ignored and based purely on the position of the object 118. The respiratory position histogram 152 is used to identify respiratory outliers or when the object is substantially at an extreme position as measured by the respiratory signal 146. Prior to reconstructing the respiratory state resolved magnetic resonance image 150, the respiratory outliers 154 may be removed from the set of a predetermined number of k-space bins 148. Alternatively, a weighting factor may be used to reduce the effect of the respiratory outliers 154 on the respiratory state resolved magnetic resonance image 150.

[0190] Figure 2 The diagram shows the operation Figure 1 Flowchart of a method of a medical system 100 of the present invention. First, in step 200, individual acquisitions of k-space data 144 are acquired by controlling the magnetic resonance imaging system 102 using a pulse sequence command 142. A breathing signal 146 is simultaneously received. The specific individual acquisition of k-space data is then paired with the breathing signal 146 at the time of acquisition. Next, in step 204, the individual acquired k-space data 144 is then binned into one of a predetermined number of k-space bins 148 using the breathing signal 146. The breathing signal indicates the position of the subject 118 who is breathing and the inhalation or exhalation state. The method then proceeds to step 206. Step 206 is a decision box, which is "Is the acquisition complete?" This can be, for example, for a predetermined number of acquisitions, or it can be such that each of the bins has a minimum number of acquisitions. If the answer is no, the method repeats back to step 200, and another individual acquisition of k-space data is acquired and binned. If the answer is yes, the method proceeds to step 208. In step 208 , the computing system reconstructs a magnetic resonance image for each of the k-space bins 148 to provide a respiratory state resolved magnetic resonance image 150 .

[0191] Figure 3 Graphical operation Figure 1 Another approach to the medical system. Figure 3 The method shown is similar to Figure 2 The method shown. Figure 3 In this method, Figure 2 1 and 2. Next, in step 300, the individual acquired k-space data 144 is further binned into the respiratory position histogram 152. The method then proceeds to Figure 2Step 206 is shown. When all acquisitions are completed, the method proceeds to step 302. In step 302, respiratory outliers 154 are identified in the respiratory position histogram 152. After the respiratory outliers 154 are identified, the method proceeds to step 208, wherein a magnetic resonance image for each of the k-space bins is reconstructed again. However, this time the respiratory state resolved magnetic resonance image 150 is corrected for the respiratory outliers 154. This may, for example, involve removing the respiratory outliers 154 from the set of a predetermined number of k-space bins 148, or assigning a reduced weighting to the respiratory outliers 154 so that they affect the respiratory state resolved magnetic resonance image 150 less.

[0192] Figure 4 Another example of a medical system 400 is illustrated. Figure 4 The medical system 400 depicted in FIG. Figure 1 4. The medical system of claim 1, except that it additionally includes a radiation therapy system 402. The radiation therapy system 402 includes a gantry 404 and a radiation therapy source 406. The gantry 404 is used to rotate the radiation therapy source 406 about a gantry rotation axis or rotation axis 414. Adjacent to the radiation therapy source 406 is a collimator 408.

[0193] The magnet 104 shown in this embodiment is a standard cylindrical superconducting magnet. The magnet 104 has a cryostat 410 with superconducting coils 412 within the cryostat 410. A split magnet may alternatively be used.

[0194] Within the subject 118 is a target area 418. The gantry rotation axis 414 is coaxial in this particular embodiment with the axis of the cylindrical magnet 104. The radiation source 406 is pointed toward the rotation axis 414 such that the radiation source has a target volume around the rotation axis 140.

[0195] The object support 120 has been positioned so that the target area 418 is located on the gantry rotation axis 414. The radiation source 406 is shown as generating a radiation beam 416 that passes through the collimator 408 and passes through the target area 418. As the radiation source 406 rotates around the axis 414, the radiation beam 416 will always target the target area 418. The radiation beam 416 passes through the cryostat 410 of the magnet 104. The magnetic field gradient coil can have a gap that divides the magnetic field gradient coil into two sections. If present, the gap reduces the attenuation of the radiation beam 416 by the magnetic field gradient coil 110. In some embodiments, the radio frequency coil 114 can also have a gap or be spaced apart to reduce the attenuation of the radiation beam 416.

[0196] The memory 138 is also shown as containing a segmentation algorithm 420. The segmentation algorithm 420 can be used to segment the individual images that make up the respiratory state resolved magnetic resonance image 150, and to generate a respiratory state resolved segmentation 422. The respiratory state resolved segmentation 422 can, for example, identify target locations within the subject 118 and critical organs that should not be irradiated. Having these in a respiratory state resolved manner can be useful, where it can provide less damage to the subject 118 during irradiation. The memory 138 is shown as containing the respiratory state segmentation 422 after the respiratory state resolved magnetic resonance image 150 is segmented using the segmentation algorithm 420. The memory 138 is also shown as containing a radiation therapy system control module 424. The radiation therapy system control module 424 is configured to output a radiation therapy control command 428 in response to receiving a radiation therapy plan 426 and the respiratory state resolved segmentation 422. The radiation therapy plan 426 and the radiation therapy control command 428 are shown as being stored in the memory 138.

[0197] In this example, the respiratory zone is not shown. However, the respiratory signal 146 may be derived from individual acquisitions of the k-space data 144, for example using self-navigation techniques.

[0198] Figure 5 The diagram shows the operation Figure 4 Flow chart of a method of a medical system 400 . Figure 5 The method is similar to Figure 3 ; in fact, the method continues to execute Figure 3 Steps. Figure 3 When the method in ends in step 208, additional steps are performed. After performing step 208, in step 500, a respiratory state resolved segmentation 422 is provided by inputting the respiratory state resolved magnetic resonance image 150 into the segmentation algorithm 420. Next, in step 502, a radiotherapy control command 428 is generated by inputting the radiotherapy plan 426 and the respiratory state resolved segmentation 422 into the radiotherapy system control module 424. Finally, in step 504, the radiotherapy system 402 is controlled to irradiate the object 118 by controlling the radiotherapy system 402 using the radiotherapy control command 428. It should be noted that in some examples, the respiratory signal 146 is acquired during the execution of the radiotherapy control command 428, and the respiratory signal 146 can be used to gate or adjust the radiotherapy control command, for example, possibly in real time. It may be possible to improve motion robustness through respiratory soft gating.

[0199] One type of multidimensional k-space acquisition can be performed using 4D radial star stacking. 4D radial star stacking based on breath binning offers several promising advantages for radiation therapy planning and motion management, such as significantly higher robustness to motion, benign aliasing artifacts, and retrospective breath binning possibilities.

[0200] Despite the benefits, current technology still does not provide the temporal and spatial fidelity required for 4D motion management, manifested by the inability to distinguish respiratory hysteresis effects shown in streaks and signal fluctuations. Distinguishing respiratory hysteresis effects is important for MR radiotherapy because the treated volume can move differently between inspiration-to-expiration or exhalation-to-inspiration breathing states.

[0201] Some examples may provide improved multi-dimensional or 4D binning schemes.

[0202] Examples may include one or more of the following features:

[0203] - Acquisition of 3D k-space using temporally staggered oversampling of central k-space (preferably using 3D variable density radial star stack sampling)

[0204] - Detect and classify breathing states to distinguish between inspiration to exhalation and exhalation to inspiration breathing states and respiratory hysteresis effects

[0205] - Provides a breathing histogram that separates inspiration to exhalation and exhalation to inspiration breathing states

[0206] - Remove breathing outliers or weight them with low weight

[0207] - Classify and bin the acquired data with respect to detected and classified breathing states

[0208] - Apply a k-space weighting filter in the respiratory state domain, weighting the oversampled central k-space more strongly in isometric respiratory states

[0209] The present invention proposes Figures 4 to 6 The optimal binning scheme shown.

[0210] Figure 6 604 is used to illustrate the hysteresis effect of tumor motion when a subject breathes. A magnetic resonance image 600 is shown. Within the image 600 is a segmentation 602 of a tumor. In this particular example, the position of the tumor 602 is recorded for ten different positions along the phase trajectory of the subject's breathing. A graph 604 positions the center of mass of the tumor 602 in the TM plane as a function of the breathing state. It can be seen that the tumor trajectory has a hysteresis effect and has different positions for inspiration and expiration.

[0211] exist Figure 7 and 8In , the breathing data are divided into eight different breathing states. Figure 7 A graph of a k-space intrinsic navigator 700 is shown. An expanded view of the k-space intrinsic navigator of box 710 is shown below. The k-space intrinsic navigator 700 is shown as a function of time 712 and navigator value 714. The navigator is divided into eight sections or bins: 701 represents breathing state or bin 1, 702 represents breathing state or bin 2, 703 represents breathing state or bin 3, and 704 represents breathing state or bin 4. Bins 701, 702, 703, and 704 represent the subject's inhalation. Breathing state or bin 705 represents breathing state or bin 5, 706 represents breathing state or bin 6, 707 represents breathing state or bin 7, and 708 represents breathing state or bin 8. Bins 705, 706, 707, and 708 represent the subject's exhalation.

[0212] Figure 8 The diagram shows how to use Figure 7 A set of a predetermined number of k-space bins 148 and a respiratory position histogram 152 of navigator classification are shown. The predetermined number of k-space bins 148 can be shown as containing k-space data for each of the eight respiratory states or bins 701-708. In addition, there are some respiratory outliers 154 that are not used to reconstruct the respiratory state resolved magnetic resonance image 150. In order to reconstruct the respiratory state resolved magnetic resonance image 150, an image for each of the bins 701-708 is reconstructed.

[0213] The respiratory position histogram 152 classifies the k-space data according to its position and does not distinguish between respiratory states, in which case states 701 and 708 are grouped together, states 702 and 707 are grouped together, states 703 and 706 are grouped together, and finally states 704 and 705 are grouped together. In other words, the respiratory position histogram 152, the k-space intrinsic navigator 700 is binned based only on the value of the navigator, and the inhalation and exhalation states are binned together. This makes it easy to identify respiratory abnormal values ​​154.

[0214] 3D k-space is acquired using temporal interleaved sampling of the central k-space. In some examples, 3D golden angle variable density radial star stack sampling can be used because it guarantees good k-space sampling distribution, motion robustness with higher k-space density at the central kz space, providing more efficient scanning and optimized filtering capabilities. Acquisition can be combined with irregular sampling patterns optimized for compressed sensing and AI.

[0215] The breathing state can be detected from intrinsic k-space or from outside the camera - classified based on the history taking into account the breathing hysteresis effect with respect to the inspiration to expiration and expiration to inspiration breathing states.

[0216] The acquired 3D k-space data is then classified and binned using a breathing histogram (breathing position histogram 152) with respect to detected and classified breathing states that separate inspiration to exhalation and exhalation to inspiration breathing states.

[0217] Respiratory outliers 154 are removed based on the histogram 152. Starting from the first and last bins, respectively, if the bin is below a certain threshold, the bin is rejected. The threshold is a defined factor (e.g., 0.1) multiplied by the height of the highest bin. Rejection of external bins continues as long as the first bin exceeding the threshold is encountered.

[0218] Fig. 9 A KWIC filter 900 is illustrated for a single k-space bin. This provides a weighting factor 904 as a function of position in the bin 902. In this case, it would likely be the navigator position. The weighting factor has a maximum value within the bin. The value of the KWIC filter can be quickly reduced to reduce the impact of outliers or samples outside of a particular bin.

[0219] Fig.10 A collection of KWIC filters 900 applied to a histogram 1002 of k-space samples in bins that are a function of navigator phase 902 is illustrated. In this example, the KWIC filters 900 are used to define the boundaries and interactions of the various bins. The k-space filter (KWIC) is applied to the acquired 3D k-space data for different respiratory bins. The vertical bars represent the center k-space 1000 that is more heavily weighted in the isometric respiratory state.

[0220] Then a k-space weighting filter (KWIC) in the respiratory state domain is applied. Thus, the oversampled central k-space is more strongly weighted than the more distant k-space contours in the equidistant respiratory state. For illustration purposes, Figure 4 Filtering is illustrated in , without taking into account respiratory hysteresis effects. Ideally, these effects are taken into account by filtering only the oversampled central k-space with correctly classified respiratory states.

[0221] Fig.11 Several k-space sampling patterns are illustrated. Sampling pattern 1100 is a standard three-dimensional golden angle star stack k-space sampling pattern. Sampling pattern 1102 is for a three-dimensional variable density radial star stack k-space sampling pattern. For example, the lines 1104 marked in the three-dimensional variable density radial star stack k-space sampling pattern 1104 indicate how different layers of the sampling pattern 1102 can be reduced by linear or quadratic radial density when sampling. Outside the area marked 1104, the sampling pattern 1102 can be made sparser. Both sampling patterns 1100 and 1102 are examples of multi-dimensional k-space sampling patterns.

[0222] A very simple representation of a KWIC filter is shown, where the peripheral k-space 1106 and 1108 are shared over adjacent overlapping bins, while the central k-space portion 1110 filters out the respiratory state range contour.

[0223] Conventional undersampling, compressed sensing (CS) and artificial intelligence (AI) can be used to reconstruct respiratory state images that identify respiratory hysteresis effects. After spatial CS, a respiratory state-resolved CS reconstruction can be applied using a temporal total variation constraint. AI can be integrated in (non-Cartesian) CS reconstruction for improved denoising and undersampling artifacts, but also for sharpening and providing super-resolution images from different respiratory states.

[0224] Fig.12 , 13 and 14 are used to illustrate the method of tuning to multi-slice Propeller imaging. Fig.12 A multi-slice Propeller k-space sampling 1202 is shown. Multi-slice 2D Propeller sampling with, for example, desired T2 contrast has an inherently high oversampling of the central k-space. Using respiratory information, the oversampled central k-space can be filtered out using KWIC filtering to extract a 4D respiratory state resolved T2 image within the same multi-slice 2D Propeller overview scan.

[0225] Fig.13 Two possible bins 1300 and 1302 are shown for different breathing states with different KWIC filter strengths. Each bin 1300 and 1302 has three boxes indicating the weighting of the KWIC filter: an inner box 1304, a middle box 1306, and an outer box 1308. In both cases, the inner box 1304 is maximally weighted and the outer box 1308 is minimally weighted. As one moves further away from the inner region indicated by the inner box 1304, the weighting of the KWIC filter decreases.

[0226] Fig.14 The k-space 1404 and the leaves of the leaf composition 1406 relative to each other for the standard Propeller k-space sampling mode are shown. Fig.13 For k-space and leaf components, standard Propeller without contrast weighting is shown in 1400 and 1402. Propeller reconstruction can be combined with joint leaf reconstruction and compressed sensing with k-space weighting applied.

[0227] 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 only and not restrictive; the invention is not limited to the disclosed embodiments.

[0228] By studying the drawings, the specification and the appended claims, those skilled in the art can understand and implement other variations of the disclosed embodiments when practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the word "one" or "an" does not exclude multiple. A single processor or other unit can implement the functions of several items recorded in the claims. Although specific measures are recorded in different dependent claims, this does not indicate that the combination of these measures cannot be used to advantage. The computer program can be stored / distributed on a suitable medium, such as an optical storage medium or solid-state medium provided together with other hardware or as part of other hardware, but the computer program can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. Any figure mark in the claims should not be interpreted as limiting the scope. In the claims, the words "respiratory state resolution" can be interchanged with "respiratory resolution", "time resolution" and "temporal resolution".

Claims

1. A medical system (100, 400), comprising: a magnetic resonance imaging system (102) configured to acquire k-space data for a field of view (109) of a subject (118), wherein the magnetic resonance imaging system is configured to provide a respiration signal (146), wherein the respiration signal describes a respiration position of the subject, wherein the respiration signal also describes inhalation and exhalation of the subject; a memory (138) storing machine-executable instructions (140) and pulse sequence commands (142), wherein the pulse sequence commands are configured to control the magnetic resonance imaging system to acquire the k-space data as a plurality of acquisitions of individually acquired k-space data (144) according to a multidimensional k-space sampling pattern (1102, 1104), wherein the pulse sequence commands are configured such that the multidimensional k-space sampling pattern is changed between each individual k-space data acquisition in the plurality of acquisitions of k-space data; - a computing system (132), wherein execution of the machine-executable instructions causes the computing system to repeatedly perform the following operations: - performing (200) said individual acquisitions of k-space data by controlling said magnetic resonance imaging system using said pulse sequence commands; - receiving (202) the respiration signal when the individual acquires k-space data by the magnetic resonance imaging system; - binning (204) the individual's acquired k-space data into one of a predetermined number of k-space bins (148) using the respiratory signal and the subject's inhalation or exhalation; wherein execution of the machine-executable instructions further causes the computing system to reconstruct (208) a magnetic resonance image for each of the predetermined number of k-space bins to provide a respiratory state-resolved magnetic resonance image (150) of the field of view; and wherein execution of the machine-executable instructions causes the computing system to repeatedly bin (300) k-space data acquired from the individual into a respiratory position histogram (152), wherein execution of the machine-executable instructions further causes the computing system to use the respiratory position histogram to identify (302) respiratory outliers (154), and wherein the respiratory state-resolved magnetic resonance image is corrected for the respiratory outliers.

2. The medical system according to claim 1, wherein Execution of the machine executable instructions further causes the computing system to calculate extreme respiratory state resolved image data by calculating a modulus average or a complex average of the extreme inspiratory and expiratory respiratory states in the respiratory state resolved magnetic resonance image, wherein the extreme inspiratory and expiratory respiratory states can be end-inspiratory or end-expiratory states, but can also be automatic selection of extreme states by gradient entropy comparison and / or selection of a preselected gating window setting.

3. The medical system according to claim 1 or 2, wherein: The multidimensional k-space sampling pattern is configured to oversample a central k-space region.

4. The medical system according to any one of the preceding claims, wherein The multidimensional k-space sampling mode is any one of the following: 3D star stack sampling mode, 3D variable density star stack sampling mode, 3D variable density spiral Cartesian sampling mode, Cartesian sampling mode, variable density sampling mode, 2D variable density Cartesian sampling mode, 2DPropeller sampling mode, KOOSH ball sampling mode, Floret sampling mode and spiral sampling mode including golden angle sampling strategy.

5. The medical system according to any one of the preceding claims, wherein Execution of the machine-executable instructions causes the computing system to apply a k-space weighting filter to each of the individual acquisitions of k-space data in the predetermined number of k-space bins before reconstructing the magnetic resonance image for each of the k-space bins, wherein the k-space filter is configured to apply a higher weighting to the central k-space region than outside the central k-space region.

6. The medical system according to claim 5, wherein: Weights are assigned by the k-space weighting filter to the individual acquisitions of k-space data as a function of a position within or near a k-space bin determined by the respiratory signal.

7. The medical system according to claim 5 or 6, wherein: The k-space weighting filter is a k-space weighted image contrast filter, and / or wherein, The k-space weighting filter is configured to perform phase correction to correct for motion and / or increase image sharpness according to a gradient entropy metric.

8. The medical system according to any one of the preceding claims, wherein The memory also stores a segmentation algorithm (420) configured to output a respiratory state-resolved segmentation in response to receiving the respiratory state-resolved magnetic resonance image, wherein execution of the machine-executable instructions also causes the computing system to provide (500) the respiratory state-resolved segmentation of the respiratory state-resolved magnetic resonance image by inputting the respiratory state-resolved magnetic resonance image into the segmentation algorithm.

9. The medical system according to claim 8, wherein: The memory also stores a radiation therapy system control module (424) configured to output control commands for a radiation therapy system (402) in response to receiving the respiratory state-resolved segmentation and the radiation therapy plan as input, wherein execution of the machine-executable instructions further causes the computing system to provide (502) the radiation therapy control commands in response to inputting the respiratory state-resolved segmentation and the radiation therapy plan into the radiation therapy system control module.

10. The medical system according to claim 9, wherein The medical system also includes the radiation therapy system, wherein execution of the machine-executable instructions further causes the computing system to control (504) the radiation therapy system using the radiation therapy control commands to irradiate the subject, wherein the respiration signal is used to at least partially control, gate, track and / or follow the irradiation of the subject.

11. The medical system according to any one of the preceding claims, wherein The magnetic resonance image for each of the predetermined number of k-space bins is reconstructed using compressed sensing or artificial intelligence based reconstruction.

12. The medical system according to any one of the preceding claims, wherein The respiratory state resolved magnetic resonance image is corrected for respiratory outliers by deleting outliers from the k-space bins or by assigning a predetermined weighting value to the respiratory outliers during reconstruction of the magnetic resonance image for each of the predetermined number of k-space bins.

13. A computer program product comprising machine executable instructions (140) for execution by a computing system (132) for controlling a medical system (100, 400), wherein: The medical system includes a magnetic resonance imaging system (102) configured to acquire k-space data for a field of view (109) of a subject (118), wherein the magnetic resonance imaging system is configured to provide a respiration signal (146), wherein the respiration signal describes a respiratory position of the subject, wherein the respiration signal further describes inhalation and exhalation of the subject, wherein execution of the machine-executable instructions causes the computing system to repeatedly perform the following operations: - performing (200) an individual acquisition of k-space data by controlling the magnetic resonance imaging system using pulse sequence commands (142), wherein the pulse sequence commands are configured to perform the acquisition of the k-space data as a plurality of acquisitions of individually acquired k-space data (144) according to a multidimensional k-space sampling pattern (1102, 1104), wherein the pulse sequence commands are configured such that the multidimensional k-space sampling pattern is changed between each individual k-space data acquisition in the plurality of acquisitions of k-space data; - receiving (202) the respiratory signal during acquisition of k-space data acquired for the individual; and - binning (204) the individual's acquired k-space data into one of a predetermined number of k-space bins (148) using the respiratory signal and the subject's inhalation or exhalation; wherein execution of the machine-executable instructions further causes the computing system to reconstruct (208) a magnetic resonance image for each of the k-space bins to provide a respiratory state-resolved magnetic resonance image (150) of the field of view; and wherein execution of the machine-executable instructions causes the computing system to repeatedly bin (300) k-space data acquired from the individual into a respiratory position histogram (152), wherein execution of the machine-executable instructions causes the computing system to use the respiratory position histogram to identify (302) respiratory outliers (154), and wherein the respiratory state-resolved magnetic resonance image is corrected for the respiratory outliers.

14. A method of operating a medical system (100, 400), wherein: The medical system comprises a magnetic resonance imaging system configured to acquire k-space data for a field of view (109) of a subject (118), wherein the magnetic resonance imaging system is configured to provide a respiration signal (146), wherein the respiration signal describes a respiration position of the subject, wherein the respiration signal further describes inhalation and exhalation of the subject; The method comprises repeatedly performing the following operations: - performing (200) an individual acquisition of k-space data by controlling the magnetic resonance imaging system using pulse sequence commands (142), wherein the pulse sequence commands are configured to perform the acquisition of the k-space data as a plurality of acquisitions of individually acquired k-space data (144) according to a multidimensional k-space sampling pattern, wherein the pulse sequence commands are configured such that the multidimensional k-space sampling pattern is changed between each individual k-space data acquisition in the plurality of acquisitions of k-space data; - receiving (202) the respiratory signal during acquisition of k-space data acquired for the individual; and - binning (204) the individual's acquired k-space data into one of a predetermined number of k-space bins (148) using the respiratory signal and the subject's inhalation or exhalation; wherein the method further comprises reconstructing (208) a magnetic resonance image for each of the k-space bins to provide a respiratory state-resolved magnetic resonance image of the field of view, and wherein the method comprises repeatedly binning (300) the k-space data acquired for the individual into a respiratory position histogram (152), using the respiratory position histogram to identify (302) respiratory outliers (154), and correcting the respiratory state-resolved magnetic resonance image for the respiratory outliers.

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