Stepwise reconstruction of planning images for cardiac magnetic resonance imaging.

Abstract

Disclosed herein is a medical system 100, 300, 700 including a magnetic resonance imaging system 102 configured to acquire lines 144 of k-space data from a chest region 122 of a subject 118. Execution of the machine executable instructions 140 causes a computing system 132 to repeatedly acquire (200) the lines of k-space data by controlling the magnetic resonance imaging system with pulse sequence commands, repeatedly compile (202) motion resolved k-space data 146 from the lines of k-space data using at least one cardiac phase and one respiratory phase of the subject as the k-space data is acquired, retrieve (204) at least a portion 148 of the motion resolved k-space data during acquisition of the k-space data, and construct (206) a preliminary three-dimensional cardiac image 150 using at least a portion of the motion resolved k-space data before acquisition of the lines of k-space data is terminated. The pulse sequence commands follow a three-dimensional free-running cardiac magnetic resonance imaging protocol.

Description

Technical Field

[0001] The present invention relates to magnetic resonance imaging, and more particularly to cardiac magnetic resonance imaging.

Background Art

[0002] Various tomographic medical imaging techniques such as magnetic resonance imaging (MRI), computed tomography, positron emission tomography, and single photon emission computed tomography enable detailed visualization of the anatomical structure of a subject. A common feature of all these imaging modalities is that it takes a significant amount of time to acquire the medical imaging data necessary to reconstruct a medical image. During the acquisition of medical imaging data, the subject may move spontaneously or unconsciously, which results in image degradation or artifacts. This is particularly true in cardiac imaging where the subject's heart is beating. The subject may also breathe during cardiac imaging.

[0003] The journal article by Kustner, Thomas et al., "Fully self-gated free-running 3D Cartesian cardiac CINE with isotropic whole-heart coverage in less than 2 min.", NMR in Biomedicine 34.1 (2021):e4409 discloses a free-breathing 3D Cartesian cardiac cine scan with water-selective balanced steady-state free precession motion, and describes spiral profile ordering, sampling from outside to inside, and continuous (non-ECG synchronized) variable density Cartesian sampling with alternating increments of a micro golden angle and a golden angle adapted for acquisition between spiral arms. The data is retrospectively binned based on the respiratory and cardiac navigator signals. The translational images corrected for respiratory motion and resolved for cardiac motion are reconstructed using multi-bin patch-based low-rank reconstruction (MB-PROST) within about 15 minutes. The respiratory motion resolution technique is also investigated. The proposed 3D Cartesian cardiac cine has a resolution of 1.9 mm 3The images are acquired in sagittal orientation for 1 minute 50 seconds against isotropic WH coverage. Left ventricular (LV) functional parameters and image quality derived from blinded readings of the proposed 3D cine framework are compared with conventional multislice 2D cine imaging in 10 healthy subjects and 10 subjects suspected of having cardiovascular disease. [Overview of the project]

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

[0005] There are several obstacles to performing cardiac magnetic resonance imaging (MRI). One major obstacle is that operators of the MRI system need detailed training to properly perform cardiac imaging. Another difficulty is that performing cardiac imaging protocols can be very time-consuming. The operator first places the patient in the MRI system, performs a planning scan, and then precisely positions the region of interest before beginning the diagnostic imaging.

[0006] The embodiment provides a method for reducing the training burden on the operator, and in some cases, a means for shortening the overall procedure time. To achieve this, the subject is placed in a magnetic resonance imaging system and begins to repeatedly acquire lines of k-space data (continuously in some examples). While acquiring lines of k-space data, these are aggregated into motion-resolved k-space data. A portion of the motion-resolved k-space data is read out while further acquisition of lines of k-space data is still occurring. This portion of the motion-resolved k-space data is used to aggregate preliminary 3D cardiac images (possibly using a compressed-sensor-reconstruction algorithm) before the acquisition of lines of k-space data is completed. This has the advantage of providing the operator with images that can be used for further planning while the acquisition of lines of k-space data is still occurring. This greatly accelerates the overall workflow.

[0007] In one embodiment, the present invention provides a medical system comprising a magnetic resonance imaging system configured to acquire k-space data from a subject's chest region. The chest region is considered to be a part of the subject's chest, including the subject's heart. The medical system further comprises a memory for storing machine-executable instructions and pulse sequence commands. A pulse sequence command is either a command used to control the magnetic resonance imaging system according to a magnetic resonance imaging protocol, or data that is converted into such a command. For example, a pulse sequence command could be a timing diagram for controlling different components of the magnetic resonance imaging system.

[0008] The pulse sequence command is configured to repeatedly acquire lines of k-space data according to a three-dimensional self-propelled cardiac magnetic resonance imaging protocol. In some examples, the same line of k-space data is repeatedly acquired. In other examples, lines of k-space data are modified to reduce the likelihood that they will be acquired for the same location. For example, the acquired k-space data may be a rotating line or spoke of k-space data. The three-dimensional self-propelled cardiac magnetic resonance imaging protocol used herein encompasses a magnetic resonance imaging protocol that is three-dimensional. This protocol also encompasses a magnetic resonance imaging protocol configured to image the heart or cardiac region. Note here that the subject's chest region includes the subject's heart. The term “self-propelled” in the self-propelled cardiac magnetic resonance imaging protocol indicates that the acquisition of k-space data is performed either sequentially or on a repetitive basis.

[0009] The pulse sequence command is configured to repeatedly acquire lines of k-space data for a given region of interest. The medical system further includes a computing system. The computing system is intended to represent one or more processing or computing cores located in one or more locations. Execution of machine-executable instructions causes the computing system to repeatedly acquire lines of k-space data by controlling the magnetic resonance imaging system with pulse sequence commands. This self-propulsion is expressed in the repeated acquisition of k-space data, as this is a self-propelled cardiac magnetic resonance imaging protocol. Execution of machine-executable instructions further causes the computing system to repeatedly aggregate the k-space data, which has been moved and decomposed from the lines of k-space data, using at least one cardiac phase and one respiratory phase of the subject as the k-space data is acquired.

[0010] Aggregation of motion-decomposed k-space data can be performed in various ways. For example, motion-decomposed k-space data is aggregated by binning or sorting the k-space data. Self-navigation within the k-space data, for instance, could be one way to sort it. Another approach involves using external sensors such as respiration, a VCG monitor, or an ECG monitor. Another interpretation of aggregating motion-decomposed k-space data is so-called soft gating, where weighting factors are assigned to k-space data for specific motion phases, which contribute to this particular phase.

[0011] The motion-decomposed k-space data is repeatedly aggregated. This means the data is aggregated at least twice. Once sufficient k-space data is obtained for a particular motion phase, a line of k-space data may be taken using at least one cardiac phase and one respiratory phase.

[0012] Another possibility is that, after one or more lines of k-space data have been acquired, motion-decomposed k-space data is further aggregated using this further acquired k-space data, so that as k-space data acquisition for the self-propelled cardiac magnetic resonance imaging protocol progresses, k-space data using at least one cardiac phase and one respiratory phase is more completely sampled.

[0013] The motion-decomposed k-space data is then aggregated after the k-space data has been fully acquired, i.e., after all iterations of acquiring lines of k-space data have been acquired. In some embodiments, this is then used to generate a cine image sequence.

[0014] In another embodiment, the portion of the motion-decomposed k-space data is undersampled.

[0015] In another embodiment, a compressed detection (CS) reconstruction algorithm is used to reconstruct a preliminary 3D cardiac image using at least a portion of the motion-decomposed k-space data.

[0016] In another embodiment, the execution of a machine-executable instruction further causes the computing system to read out at least a portion of the motion-resolved k-space data during k-space data acquisition. In other words, as pulse sequence commands are executed and lines of k-space data are acquired sequentially, portions of the motion-resolved k-space data are read out. The execution of the machine-executable instruction then further causes the computing system to construct a preliminary 3D cardiac image using at least a portion of the motion-resolved k-space data before k-space data acquisition is completed. That is, while the system is still acquiring lines of k-space data, the computing system constructs a preliminary 3D cardiac image. This is beneficial because the main cause of delay in cine or cardiac 3D imaging is the time it takes to reconstruct and align the image.

[0017] This embodiment has the advantage of accelerating the process of further performing magnetic resonance imaging protocols because preliminary 3D cardiac images are already available before the acquisition of k-space data lines is completed. This can be used to plan further magnetic resonance imaging protocols or to provide preliminary data that enables a physician or other operator to make decisions regarding further areas of examination of the subject.

[0018] Motion-decomposed k-space data is data that contains or references motion data. For example, as mentioned above, there may be k-space data divided into different bins, where the labels or metadata for the bins indicate specific cardiac and / or respiratory phases. Similarly, individual parts of k-space data may have data that provides weights for different cardiac and / or respiratory phases. This is called soft gating. When a particular phase is reconstructed, k-space data is taken, and weights are applied to see how much this k-space data contributes to that particular motion phase or state.

[0019] A preliminary 3D cardiac image may be constructed from a single motion phase, such as the cardiac or respiratory phase, or from k-space data representing a range of several adjacent motion phases. For example, a preliminary 3D cardiac image may be constructed before all of the k-space data is acquired. In this case, it is beneficial to construct the image from several phases, such as adjacent phases, to enable a better fit, for example, by creating a deformable model.

[0020] It should be noted that cardiac phase can be determined using different methods, for example. ECG systems, VCG systems, or camera-based patient monitoring systems that measure the movement of the subject's chest wall, or the use of self-propulsion in k-space are all effective examples. Pulse sequence commands can be, for example, orthogonal coordinate system sampling patterns in k-space. In some examples, the same location in k-space is acquired repeatedly. If there are a sufficiently large number of phases, such as numerous respiratory and cardiac phases, it is unlikely that the k-space data will be resampled many times.

[0021] Another option is a pulse sequence command that samples k-space data using radial acquisition of four or more dimensions with isotropic voxel sizes. This embodiment is beneficial because the blade or line of k-space data may rotate in three-dimensional space or two-dimensionally. This means that when data is acquired for the same motion state or phase, it contributes to providing a better sample of k-space rather than resampling the same location again. In both the case of Cartesian coordinate systems and radial four-dimensional acquisition, additional acquisitions may be made using compressed sensing. This is particularly beneficial as it provides a way to greatly accelerate the acquisition of complete k-space data.

[0022] In the case of radial acquisition beyond four dimensions, additional dimensions may include various contrasts, diffuse weightings, and so on.

[0023] In another embodiment, the memory further includes a deformable heart model. The deformable heart model used herein encompasses a heart model in which curves or surfaces with incorporated elasticity are defined internally. This ensures that the surface of the model remains smooth when deformed. Typically, anatomical landmarks identified in the image conform to the anatomical landmarks of the deformable heart model. The deformable heart model is three-dimensional. The deformable heart model also further defines a set of planes. As the deformable heart model is deformed, the location of the planes also changes. The planes are defined, for example, by specific points or anatomical landmarks contained within the deformable heart model. Therefore, when the deformable heart model is adjusted, for example, to conform to a preliminary three-dimensional heart image, the set of planes is also automatically positioned.

[0024] The execution of machine-executable instructions further causes the computing system to construct a fitted heart model by fitting a deformable heart model to a preliminary 3D heart image. Fitting the deformable heart model to the preliminary 3D heart image adjusts the location of a set of planes in the fitted heart model. The set of planes includes at least one heart observation plane. The execution of machine-executable instructions further causes the computing system to provide at least one heart observation plane. This may be, for example, simply to provide the location of the heart observation plane, or to render a cross-sectional image of the preliminary 3D heart image from that particular heart observation plane. This embodiment is useful because it automatically provides observation planes that can be used to view a preliminary 3D heart image or other image that will be later reconstructed from repeated lines of k-space data aggregated into motion-decomposed k-space data.

[0025] In another embodiment, the three-dimensional electrocardiographic magnetic resonance imaging protocol is a functional cardiac imaging protocol configured to reconstruct a cine image sequence. The medical system further comprises a user interface. Execution of the machine-executable instructions further causes the computing system to reconstruct a cine image sequence from the motion-decomposed k-space data after acquisition of the k-space data is complete. The k-space data is repeatedly acquired and then aggregated into motion-decomposed k-space data. This is the final aggregation of the motion-decomposed k-space data used to reconstruct the cine image sequence. The fitting of the deformable cardiac model to the preliminary three-dimensional cardiac image begins before or simultaneously with the reconstruction of the cine image sequence. This is beneficial in order to reduce the overall time required for the protocol or the use of the medical system.

[0026] Execution of the machine-executable instructions further causes the computing system to render at least a portion of the cine image sequence from at least one cardiac observation plane. This embodiment is beneficial for several reasons. First, at least one cardiac observation plane is automatically provided and the operator did not do this manually. An additional and greater benefit is that this is executed before or simultaneously with the reconstruction of the cine image sequence. This means that the delay until the operator is able to view the cine image sequence is reduced. This results in a more effective and cost-effective use of the medical system.

[0027] In another embodiment, execution of the machine-executable instructions further causes the computing system to detect cardiac abnormalities using the cine image sequence and / or the fitted cardiac model. For example, various criteria may be made or measurements taken for the cine image sequence and / or the fitted cardiac model, and specific cardiac problems or abnormalities may be detected using these measurements. Execution of the machine-executable instructions further causes the computing system to receive a workflow for the recommended image flow by creating a workflow database for the cardiac abnormality. [[ID=--9]]

[0028] The workflow database includes a plurality of magnetic resonance imaging workflows, each of which refers to at least one cardiac abnormality identifier. The workflow database is configured to return a recommended imaging workflow by matching the cardiac abnormality identifier of the recommended imaging workflow. For example, a medical system may be configured to perform various measurements on a cine image sequence and / or a fitted heart model, and if these measurements are outside a predetermined range, specific or potential cardiac abnormalities can be identified. This can then be used to query or retrieve the recommended imaging workflow from the workflow database. Execution of the machine-executable instructions further causes the computing system to display the recommended imaging workflow on a user interface.

[0029] This embodiment is beneficial because the operator does not have to manually retrieve a specific imaging workflow. The identified cardiac abnormalities are used to query the database, and these instructions are automatically provided to the operator. In some examples, the operator may have the option to ignore or retrieve other workflows. In other embodiments, the recommended imaging workflow may also include instructions or pulse sequence commands used to automatically configure the magnetic resonance imaging system for additional measurements.

[0030] In another embodiment, the recommended imaging workflow includes further pulse sequence commands. Execution of machine-executable instructions further causes the computing system to construct the geometric orientation of the further pulse sequence commands using a set of planes from a fitted cardiac model. As previously mentioned, a deformable cardiac model may have planes defined by specific points within the model. As the model is deformed, this also changes the location of the planes. Therefore, for example, specific planes used to configure magnetic resonance imaging acquisition can then be automatically positioned. This has the benefit that the operator does not need to have the skill to manually position these planes. Additionally, even if the operator does not know how the planes should be positioned, these may be done automatically, and the operator may have the option, for example, to simply proceed or to make fine manual adjustments to the plane positions. In either case, this greatly accelerates the use of the medical system.

[0031] In another embodiment, detecting cardiac anomalies using a cine image sequence and / or a fitted cardiac model includes reconstructing a static three-dimensional cardiac image from at least a portion of the cine image sequence. The detection of cardiac anomalies further includes identifying the anomaly as a thickened right ventricle in the static three-dimensional cardiac image using a right ventricular wall thickness measurement algorithm. For example, an automated algorithm may be used or configured to measure the thickness of the right ventricle. If the right ventricular thickness exceeds a predetermined thickness threshold, this may be identified as a potential cardiac anomaly.

[0032] Detecting cardiac abnormalities using cine image sequences and / or fitted cardiac models further includes identifying the abnormality as a thickened left ventricle in a static 3D cardiac image using a left ventricular wall thickness measurement algorithm. In either of these cases, if the abnormality is a thickened right ventricle or a thickened left ventricle, the recommended imaging workflow is quantitative hemodynamic analysis and / or cardiac motion pattern analysis.

[0033] The algorithm for measuring left or right ventricular wall thickness may be, for example, a machine learning algorithm or a segmentation algorithm. Quantitative blood flow analysis includes ejection fraction, stroke volume, end-diastolic volume, end-systolic volume, and / or other quantitative measures of cardiac function. Quantification of these measures is advantageous because it can be used to guide clinical decision-making.

[0034] In another embodiment, detection of cardiac anomalies using cine image sequences and / or a fitted cardiac model includes inputting the cine image sequences into an abnormal cardiac wall thickness motion detection algorithm to identify the cardiac anomaly as abnormal cardiac wall motion. For example, cine images are fed into this algorithm, which can monitor the location of the cardiac wall. If it is outside a predetermined range of motion, this may be triggered as a potential cardiac anomaly. In this case, a recommended imaging workflow includes a delayed-enhancement magnetic resonance imaging protocol for detecting cardiac scar tissue.

[0035] In another embodiment, if no abnormal cardiac wall motion is detected, the recommended imaging workflow includes cardiac stress testing and / or magnetic resonance perfusion testing. If contrast agent injection is recommended, the workflow is carefully adjusted to include a quick assessment of first-pass perfusion, possibly including stress medication, and sufficient time, 10–15 minutes, until delayed contrast enhancement is established.

[0036] In another embodiment, detection of cardiac abnormalities using cine image sequences and / or a fitted cardiac model includes identifying cardiac abnormalities as potential myocarditis if no abnormalities are detected. Recommended imaging workflows include a T2-weighted imaging protocol for identifying cardiac information and / or a delayed-enhancement magnetic resonance imaging protocol for identifying diffuse fibrosis. In these and other recommended imaging workflows, positioning of regions of interest may be performed automatically using a set of planes.

[0037] The recommended imaging workflow may, in some cases, include a T2-weighted imaging protocol for identifying cardiac information and / or a delayed-enhancement magnetic resonance imaging protocol for identifying diffuse fibrosis. Delayed enhancement is a technique used in cardiac magnetic resonance imaging for cardiac tissue characterization, particularly for the assessment of localized scarring and myocardial fibrosis.

[0038] In another embodiment, a preliminary 3D cardiac image is motion-resolved. For example, to provide a respiratory motion-resolved preliminary 3D cardiac image, portions of the motion-resolved k-space data may be read from multiple cardiac phases and multiple respiratory phases. For each combination of cardiac and respiratory phases, a sub-image may be reconstructed from the preliminary data representing that combination of cardiac and respiratory phases. If the motion-resolved k-space data is aggregated by binning different cardiac and respiratory phases, a sub-image is reconstructed for each bin. A deformable cardiac model is fitted to the sub-images from each bin, and as a result, the deformable cardiac model and the fitted cardiac model are motion-resolved. This embodiment is advantageous because at least one cardiac observation plane defined within the model is also known as a function of the respiratory phase. This is useful for further accelerating magnetic resonance imaging.

[0039] In another embodiment, the execution of machine-executable instructions further causes the computing system to determine a set of field deformations between different cardiac and respiratory phases of the adapted cardiac model. This can be done, for example, by focusing on the deformations between models for different cardiac and respiratory phases.

[0040] The execution of machine-executable instructions further causes the computing system to reconstruct stationary magnetic resonance images from motion-resolved k-space data according to motion-compensated magnetic resonance imaging reconstruction, which uses a set of field deformations between different breathing phases to perform motion correction. For example, the deformation fields may be included in the transformation matrix during iterative reconstruction before data consistency operation. This embodiment is beneficial because motion correction does not require additional reconstruction time after the acquisition of lines of k-space data is complete.

[0041] An exemplary method for performing motion correction is based on a preliminary image deformation vector field between different estimated respiratory motion states. These are then included in the reconstruction by minimizing the function ||E T_r x - y_r||_2^2, where T_r is the deformation that distorts the image by 4d times (3d + cardiac phase) from a reference respiratory state, e.g., exhalation to respiratory state r. E is the usual coding operator describing the measurements, and y_r is the acquired portion of the k-space for state r. In the procedure, the "^" operator means that the following character or variable is superscript, and the "_" operator means that the following character or variable is subscript.

[0042] In another embodiment, the execution of machine-executable instructions further causes the k-space data to be additionally motion-decomposed according to the subject's respiratory phase. This is beneficial because the cine data can be decomposed according to both, for example, cardiac and respiratory phases.

[0043] In another embodiment, the medical system may include a respiratory phase measurement system. This may be, for example, a respiratory belt attached to the subject's chest. In another example, it may be a measuring device on the subject's breathing tube. In yet another embodiment, this may be done using a camera or imaging system to measure the subject's chest movement. For example, respiratory phase is acquired using a respiratory flow meter or spirometer.

[0044] In another embodiment, at least a portion of the motion-resolved k-space data is read out once during k-space data acquisition, either after a predetermined acquisition duration or after a predetermined number of k-space data acquisitions. In this embodiment, the k-space data used to reconstruct a preliminary 3D cardiac image is read out only once.

[0045] In another embodiment, at least a portion of the motion-resolved k-space data is repeatedly read during k-space data acquisition. A preliminary 3D cardiac image is reconstructed from the motion-resolved k-space data in iterative steps. For example, if the motion-resolved k-space data is undersampled, a compression-sensing algorithm may be used to reconstruct the preliminary 3D cardiac image. Compression-sensing reconstruction uses iterative reconstruction of the image. The advantage of continuously updating the k-space data is that as the algorithm progresses, the k-space data contains more and more information, and the degree of undersampling decreases. This can result in a better quality reconstruction of the preliminary 3D cardiac image.

[0046] In another embodiment, a predetermined region of interest is 750 cm². 3 It has a larger volume than the heart. In this embodiment, a given region of interest has a relatively large volume compared to the heart. This has the advantage that, for example, the subject can be moved into the magnet after being aligned using a positioning device such as a projection light image or laser to identify the approximate location of the subject's heart, and the procedure can be started without the use of scout images. This can reduce the burden on operators with respect to training in performing cardiac imaging.

[0047] In another embodiment, the machine-executable instruction is configured to disable the adjustment of a predetermined region of interest. In this embodiment, the predetermined region of interest is actually set and fixed for this particular magnetic resonance imaging protocol or procedure. This is then set up to operate more in automated mode. In this example, the operator may also pre-align the rough position of the subject's heart, move it into the magnet, and then start the procedure.

[0048] In another embodiment, the present invention provides a computer program for execution by a computing system that controls a magnetic resonance imaging system configured to acquire k-space data from the chest region of a subject. Execution of the machine-executable instructions causes the computing system to repeatedly acquire lines of k-space data by controlling the magnetic resonance imaging system with pulse sequence commands. The pulse sequence commands are configured to repeatedly acquire lines of k-space data according to a three-dimensional self-propelled cardiac magnetic resonance imaging protocol. The pulse sequence commands are configured to repeatedly acquire lines of k-space data for a predetermined region of interest. For example, the predetermined region of interest may be fixed. The predetermined region of interest may have a volume larger than the volume of the subject's heart or a typical heart.

[0049] The execution of the machine-executable instructions further causes the computing system to repeatedly aggregate the motion-decomposed k-space data from the k-space data line using at least one cardiac phase and one respiratory phase of the subject as the k-space data is acquired. The execution of the machine-executable instructions further causes the computing system to read out at least a portion of the motion-decomposed k-space data during the acquisition of the k-space data. The execution of the machine-executable instructions further causes the computing system to construct a preliminary 3D cardiac image using at least a portion of the motion-decomposed k-space data before the acquisition of the k-space data is completed.

[0050] In another embodiment, the present invention provides a method for operating a magnetic resonance imaging system. The magnetic resonance imaging system is configured to acquire k-space data from a subject's chest region. The method includes the step of repeatedly acquiring lines of k-space data by controlling the magnetic resonance imaging system with pulse sequence commands. The pulse sequence commands are configured to repeatedly acquire lines of k-space data according to a three-dimensional self-propelled cardiac magnetic resonance imaging protocol. The pulse sequence commands are configured to repeatedly acquire lines of k-space data for a given region of interest.

[0051] The method further includes the step of repeatedly aggregating motion-decomposed k-space data from the k-space data line using at least one cardiac phase and one respiratory phase of the subject when the k-space data is acquired. The method further includes the step of reading out at least a portion of the motion-decomposed k-space data during the acquisition of the k-space data. The method further includes the step of constructing a preliminary 3D cardiac image using at least a portion of the motion-decomposed k-space data before the acquisition of the k-space data is completed.

[0052] In another embodiment, the method further includes the step of positioning the subject's chest region within a predetermined region of interest. There are various ways to achieve this. For example, there may be reference marks on the subject support, and the approximate location of the heart may be recorded relative to these marks. The operator may, for example, move an optical pointing device over the subject to mark where the heart is located, and then this heart region may be moved within the predetermined region of interest. In another example, a camera may take an overhead view of the subject lying on the subject support, and a rough anatomical model may be fitted to the image or photograph of the subject. This also provides a rough or approximate location of the heart, so that the heart can be moved within the predetermined region of interest in the magnet.

[0053] It should be understood that one or more of the above-described embodiments of the present invention can be combined, provided that the combined embodiments are not mutually exclusive.

[0054] As will be understood by those skilled in the art, aspects of the present invention may be embodied as apparatus, methods, or computer program products. Accordingly, 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 embodiments, all of which may be generally referred to herein as “circuits,” “modules,” or “systems.” Furthermore, aspects of the present invention may take the form of computer program products embodied in one or more computer-readable media having computer executable code embodied on a computer-readable medium.

[0055] Any combination of one or more computer-readable media may be used. The computer-readable media may be computer-readable signal media or computer-readable storage media. As used herein, “computer-readable storage media” encompasses any tangible storage media capable of storing instructions executable by the processor or computing system of a computing device. Computer-readable storage media may also be referred to as computer-readable non-temporary storage media. Computer-readable storage media may also be referred to as tangible computer-readable media. In some embodiments, computer-readable storage media may also be capable of storing data that can be accessed by the computing system of a computing device. Examples of computer-readable storage media include, but are not limited to, floppy disks, magnetic hard disk drives, semiconductor hard disks, flash memory, USB thumb drives, random access memory (RAM), read-only memory (ROM), optical disks, magneto-optical disks, and computer system register files. Examples of optical disks include, for example, compact disks (CDs) and digital-purpose disks (DVDs), such as CD-ROMs, CD-RWs, CD-Rs, DVD-ROMs, DVD-RWs, or DVD-R discs. The term "computer-readable storage medium" also refers to various types of recording media that can be accessed by computer devices via a network or communication link. For example, data may be read by a modem, the Internet, or a local area network. Computer executable code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination thereof.

[0056] A computer-readable signal medium may include a propagating data signal comprising computer-executable code embodied internally, for example, in the baseband or as part of a carrier wave. Such a propagating signal may take any of various forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium may not be a computer-readable storage medium and may be any computer-readable medium capable of communicating, propagating, or transporting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0057] "Computer memory" or "memory" is an example of a computer-readable storage medium. Computer memory is any memory that is directly accessible to the computing system. "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.

[0058] As used herein, “computational system” includes electronic components capable of executing programs, machine-executable instructions, or computer-executable code. References to computational systems, including examples of “computational systems,” should be interpreted as including, where applicable, two or more computational systems or processing cores. A computational system is, for example, a multi-core processor. A computational system also refers to a collection of computational systems within a single computer system or distributed across multiple computer systems. The term computational system should be understood to refer to a collection or network of computing devices, each having a processor or computational system. Machine-executable code or instructions are executed by multiple computational systems or processors within the same computing device or distributed across multiple computing devices.

[0059] A machine-executable instruction or computer-executable code comprises instructions or programs that cause a processor or other computing system to perform one aspect of the present invention. Computer-executable code for performing operations according to an aspect of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java®, Smalltalk, or C++, and conventional procedural programming languages ​​such as C or similar programming languages, and may be compiled into machine-executable instructions. In some cases, the computer-executable code may be in the form of a high-level language or a pre-compiled form and may be used with an interpreter that generates machine-executable instructions on a case-by-case basis. In another example, a machine-executable instruction or computer-executable code is a form of programming for a programmable logic gate array.

[0060] Computer executable code can run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or this connection may be made to an external computer (for example, through the internet using an Internet service provider).

[0061] Aspects of the present invention will be described with reference to flowcharts, diagrams, and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block or part of a set of blocks in a flowchart, diagram, and / or block diagram can be implemented by computer program instructions in the form of computer executable code, where applicable. It will be further understood that combinations of blocks in different flowcharts, diagrams, and / or block diagrams may be combined, provided they are not mutually exclusive. These computer program instructions may be provided to a general-purpose computer, a special-purpose computer, or a computer system of other programmable data processing equipment to create a machine such that instructions executed via the computer system or other programmable data processing equipment will produce means for performing one or more functions / actions specified in the flowchart and / or block diagrams.

[0062] These machine-executable instructions or computer program instructions may also be stored on computer-readable media that can instruct a computer, other programmable data processing device, or other device to function in a particular way, so as to create a product that includes instructions that perform a function / action specified in one or more blocks of a flowchart and / or block diagram.

[0063] Machine-executable instructions or computer program instructions may also be loaded onto a computer, other programmable data processing device, or other device to cause a computer execution process to occur, by causing a series of operational steps to be performed on the computer, other programmable device, or other device, such that the instructions to be executed on the computer or other programmable device provide a process for performing a function / action specified in one or more blocks of a flowchart and / or block diagram.

[0064] As used herein, “user interface” is an interface that enables a user or operator to interact with a computer or computer system. “User interface” may also be referred to as “human interface device.” A user interface can provide information or data to an operator and / or receive information or data from an operator. A user interface may enable input from an operator to be received by the computer and may provide output from the computer to the user. In other words, a user interface may enable an operator to control or operate a computer, and the interface may enable the computer to display the results of the operator's control or operation. Displaying data or information on a display or graphical user interface is one example of providing information to an operator. Receiving data via a keyboard, mouse, trackball, touchpad, pointer, graphic tablet, joystick, gamepad, webcom, headset, pedal, wired glove, remote control, and accelerometer are all examples of user interface elements that enable the reception of information or data from an operator.

[0065] As used herein, “hardware interface” encompasses interfaces that enable a computing system of a computer system to interact with and / or control external computing devices and / or equipment. A hardware interface may also enable a computing system to send control signals or commands to external computing devices and / or equipment. A hardware interface may also enable a computing system to exchange data with external computing devices and / or equipment. Examples of hardware interfaces include, but are not limited to, universal serial buses, IEEE 1394 ports, parallel ports, IEEE 1284 ports, serial ports, RS-232 ports, IEEE 488 ports, Bluetooth® connections, wireless LAN connections, TCP / IP connections, Ethernet® connections, control voltage interfaces, MIDI interfaces, analog input interfaces, and digital input interfaces.

[0066] As used herein, “display” or “display device” encompasses output devices or user interfaces configured to display 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, touchscreens, tactile electronic displays, braille screens, cathode ray tubes (CRTs), storage tubes, bistable displays, electronic paper, vector displays, flat panel displays, vacuum fluorescent displays (VFs), light-emitting diode (LED) displays, electroluminescent displays (ELDs), plasma display panels (PDPs), liquid crystal displays (LCDs), organic light-emitting diode (OLED) displays, projectors, and head-mounted displays.

[0067] In this specification, k-space data is defined as the recorded measurement result of radio frequency signals emitted by atomic spins by the antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan. Magnetic resonance data is an example of tomographic medical image data. Motion-resolved k-space data is k-space data that has been binned, sorted, or soft-gated according to one or more motion phases of a subject. For example, motion-resolved k-space data may be sorted, binned, or soft-gated according to the subject's respiratory and / or cardiac phases.

[0068] Magnetic resonance imaging (MRI) images, or MR images, are defined herein as reconstructed two-dimensional or three-dimensional visualizations of anatomical data contained within magnetic resonance imaging data. This visualization can be performed using a computer.

[0069] In the following, preferred embodiments of the present invention will be described with reference to the following drawings, merely as examples. [Brief explanation of the drawing]

[0070] [Figure 1] This is a diagram illustrating an example of a healthcare system. [Figure 2] Figure 1 is a flowchart showing how to use the medical system. [Figure 3] This figure shows further examples of healthcare systems. [Figure 4] Figure 3 is a flowchart showing how to use the medical system. [Figure 5] This figure shows a method for continuously updating k-space data during compressed detection and reconstruction of magnetic resonance images. [Figure 6] This diagram shows further methods for operating the medical system. [Figure 7] This figure shows further examples of healthcare systems. [Figure 8] Figure 7 is a further diagram of the medical system. [Modes for carrying out the invention]

[0071] Elements with similar reference numbers in the diagram are either equivalent elements or perform the same function. Elements considered earlier are not necessarily considered in later diagrams if their functions are equivalent.

[0072] Figure 1 shows an example of a medical system 100. The medical system 100 is shown to include a magnetic resonance imaging system 102 and a computer 130. The magnetic resonance imaging system 102 includes a magnet 104. The magnet 104 is a superconducting cylindrical magnet with a bore 106 passing through it. Different types of magnets can also be used; for example, segmented cylindrical magnets and so-called open magnets are both available. A segmented cylindrical magnet is similar to a standard cylindrical magnet except that the cryostat is divided into two sections to allow access to the isogonal planes of the magnet, and such magnets are 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 to accommodate a patient between them. The arrangement of the two section areas is similar to that of a Helmholtz coil. Open magnets are common because they reduce the degree to which the patient is confined. Inside the cryostat of a cylindrical magnet is a group of superconducting coils.

[0073] Within the bore 106 of the cylindrical magnet 104, there is an imaging zone 108 where the magnetic field is strong and uniform enough to perform magnetic resonance imaging. A predetermined region of interest 109 is shown within the imaging zone 108. The acquired magnetic resonance data is typically acquired for the region of interest. The subject 118 is shown supported by the subject support 120 so that at least a portion of the subject 118 is within the imaging zone 108 and the predetermined region of interest 109.

[0074] Within the magnet's bore 106 is also a set of field gradient coils 110 used for acquiring preliminary magnetic resonance data to spatially encode magnetic spins within the imaging zone 108 of the magnet 104. The field gradient coils 110 are connected to a field gradient coil power supply 112. The field gradient coils 110 are intended to be representative. Generally, the field gradient coils 110 include three separate sets of coils for spatial encoding in three orthogonal spatial directions. The field gradient power supply provides current to the field gradient coils. The current supplied to the field gradient coils 110 is controlled as a function of time and is ramped or pulsed.

[0075] Adjacent to imaging zone 108 is a radio frequency coil 114 for manipulating the orientation of magnetic spins within imaging zone 108 and for receiving radio transmissions from spins also within imaging zone 108. The radio frequency antenna includes multiple coil elements. The radio frequency antenna is also called a channel or antenna. The radio frequency coil 114 is connected to a radio frequency transceiver 116. The radio frequency coil 114 and radio frequency transceiver 116 can be replaced with separate transmit and receive coils and separate transmitters and receivers. It should be understood that the radio frequency coil 114 and 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 also represents a separate transmitter and receiver. The radio frequency coil 114 may also have multiple receive / transmit coil elements, and the radio frequency transceiver 116 may have multiple receive / transmit channels. For example, when a parallel imaging technique such as SENSE is performed, the radio frequency coil 114 has multiple coil elements.

[0076] The transceiver 116 and the gradient controller 112 are shown as being connected to the hardware interface 106 of the computer system 102.

[0077] The subject 118 is positioned within the magnet's bore 106 such that the subject 118's chest region 122 is positioned within a predetermined region of interest 109. The subject's heart 124 is located within the predetermined region of interest 109. When the k-space data line 144 is acquired, the heart 124 is automatically imaged.

[0078] The medical system 100 is further shown to include a computer 130. The computer 130 is intended to represent one or more computing or calculating devices located in one or more locations. The computer 130 is shown to include a computing system 132. The computing system 132 is intended to represent one or more computing systems, which may be, for example, one or more processing cores located in one or more locations. Various combinations of the computing system 132 and / or the computer 130 may be connected using a network and function together in a collaborative manner. The computing system 132 is shown to communicate with a hardware interface 134, a user interface 136, and 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 a 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. Memory 138 is intended to represent various types of memory communicating with the computing system 132.

[0079] Memory 138 is shown as containing machine-executable instructions 140. Machine-executable instructions 140 are instructions that enable the computing system 132 to perform various processes and tasks such as image processing, numerical calculations, and control of the magnetic resonance imaging system 102. Memory 138 is further shown as containing pulse sequence commands 142. As used herein, pulse sequence commands are commands or data converted into commands that enable the computing system 132 to control the magnetic resonance imaging system 102 to acquire k-space data, such as line 144 of k-space data.

[0080] In this example, the pulse sequence command 142 follows a three-dimensional self-propelled cardiac magnetic resonance imaging protocol. The pulse sequence command is configured to repeatedly acquire lines of k-space data for a given region of interest 109. Because the lines of k-space data are acquired repeatedly or continuously, the self-propelled nature of the pulse sequence command 142 means that the lines of k-space data 144 are acquired for various motion states or phases of the subject 118.

[0081] Although not shown in this figure, there may be one or more systems for measuring respiration and / or cardiac phases in relation to the movement of subject 118. Memory 138 is further shown to contain motion-decomposed k-space data 146. Motion-decomposed k-space data 146 is lines 144 of k-space data sorted or grouped using soft gating so that the k-space data has a reference to a specific motion state of subject 118. For example, motion-decomposed k-space data 146 contains data such that it is binned or soft-gated for a range of cardiac and respiratory phases of subject 118. Memory 138 is further shown to contain a portion 148 of motion-decomposed k-space data. This is a portion of motion-decomposed k-space data 146 for a specific respiratory phase and at least one cardiac phase of subject 118. Memory 138 is further shown to contain a preliminary 3D cardiac image 150 reconstructed from the portion 148 of motion-decomposed k-space data. This may be reconstructed, for example, using a compressed sensing algorithm.

[0082] Figure 2 shows a flowchart illustrating how to operate the medical system 100 of Figure 1. First, in step 200, lines 144 of k-space data are acquired repeatedly or sequentially. Next, in step 202, lines 144 of k-space data are aggregated into motion-decomposed k-space data 146. The motion-decomposed k-space data 146 may be aggregated using sensor data indicating the cardiac and / or respiratory phase of the subject 118, or lines 144 of k-space data themselves may be used. For example, the autopilot of lines 144 of k-space data may be used. It should be noted that step 202 may occur concurrently with step 200 so that as additional lines 144 of k-space data are acquired, they are sequentially aggregated into motion-decomposed k-space data, thereby ensuring that the quality of the motion-decomposed k-space data 146 continuously improves as the acquisition of lines 144 of k-space data progresses. In step 204, the method shows that a portion 148 of the motion-decomposed k-space data is read from the aggregated motion-decomposed k-space data 202.

[0083] This is, for example, for a single respiratory phase and several cardiac phases. For example, at least one cardiac phase, or possibly several cardiac phases, are adjacent to this one, i.e., selected cardiac phase. The method proceeds to step 206, in which a preliminary 3D cardiac image 150 is reconstructed from a portion 148 of the motion-resolved k-space data. Since the portion 148 of the motion-resolved k-space data may be undersampled, the reconstruction of the 3D cardiac image 150 may be achieved, for example, using a compression sensing algorithm. The method is shown to proceed to step 208 after step 202, in which lines 144 of the k-space data are acquired sequentially or continuously. The intention in Figure 2 is to show that the preliminary 3D cardiac image 150 is constructed before the acquisition of the k-space data lines is completed. This has the advantage that the preliminary 3D cardiac image is available as soon as the acquisition of the k-space data lines is completed. This greatly accelerates the procedure for imaging the subject 118.

[0084] Figure 3 shows a further example of the medical system 300. The medical system 300 shown in Figure 3 is similar to the medical system 100 shown in Figure 1. The medical system 300 in Figure 3 includes additional data via the system 100 shown in Figure 1. Memory 138 is further shown to include a deformable heart model 340. The deformable heart model 340 is a three-dimensional model of the heart that includes, for example, anatomical landmarks, as well as a set of planes 146 defined therein. The deformable heart model 340 includes specifications of the elastic relationships between the various anatomical components of the heart so that it remains smooth and realistic when deformed or stretched to fit the subject's heart 124. Memory 138 further includes a fitted heart model 342. The fitted heart model 342 is constructed by fitting the deformable heart model 340 to a preliminary three-dimensional heart image 150. This automatically positions the set of planes 346. The set of planes 346 is useful, for example, for locating additional magnetic resonance imaging locations, as well as providing a set of planes from which data can be viewed.

[0085] Memory 138 is further shown to include at least one cardiac observation plane 348 defined by a set of planes 346 and precisely positioned by a fitted cardiac model 342. Memory 138 is further shown to include a cine image sequence 350 reconstructed from the motion-decomposed k-space data 146 after lines 144 of k-space data have been fully acquired and used to constitute the motion-decomposed k-space data 146. Memory 138 is further shown to include a rendering of the cine image sequence 352 as seen from at least one cardiac observation plane 348. The rendering 352 may be displayed, for example, using a user interface 136. Various algorithms are used to detect cardiac anomalies 354 in either the cine image sequence 350 or the fitted cardiac model 342. Once a cardiac anomaly 354 is detected, a workflow database 356 is shown to be located within Memory 138, but may be located remotely via a network. The workflow database 356 includes several workflows referenced or keyed by the type of cardiac anomaly 354. By querying the workflow database 356 with the identified cardiac anomaly 354, a recommended imaging workflow 358 is retrieved or provided to the computing system 132. The recommended imaging workflow 358 is then, naturally, displayed using the user interface 136. The recommended imaging workflow 358 includes, for example, instructions 360 to the operator and further pulse sequence commands 362. The further pulse sequence commands 362 may be placed as templates, and a set of planes 346 from a fitted cardiac model 342 may be used to automatically configure these further pulse sequence commands 362.

[0086] Figure 4 shows a flowchart illustrating how to operate the medical system 300 shown in Figure 3. The method shown in Figure 4 is similar to the method shown in Figure 2. The method shown in Figure 4 performs steps 200, 202, 204, 206, and 208 as shown in Figure 2.

[0087] After step 208 is performed, the method is shown to proceed to step 400, which marks the completion of acquiring line 144 of the k-space data. A dashed horizontal line 401 indicates the point in time when the acquisition of k-space data 144 is completed. It is clearly visible that step 206, in which a preliminary 3D cardiac image is constructed, is performed before time 401. After step 206 is performed, a portion of the preliminary 3D cardiac image is used to construct a fitted cardiac model 342 from the deformable cardiac model 340. After the fitted cardiac model is provided in step 402, step 404 is performed. In step 404, at least one cardiac observation surface 348 is provided. In this figure, steps 402 and 404 are shown to be performed after time 401. However, both steps 402 and 404 may be performed before time 401.

[0088] Returning to step 400, after the acquisition of the k-space data lines is complete, the method proceeds to step 406. In step 406, if the motion-decomposed k-space data 146 is not fully aggregated, fully aggregated motion-decomposed k-space data is provided in step 406. Next, in step 408, the reconstructed cine image sequence is reconstructed from the motion-decomposed k-space data 146. It can be seen that the method essentially has two simultaneous branches. This results in significant time savings and accelerates the overall time of the method presented in Figure 4. After both steps 408 and 404 are completed, the method then proceeds to a sequence in which step 410 is performed first. In step 410, the cine image sequence 350 is rendered so that it can be viewed from at least one cardiac observation plane. This ensures that the operator of the medical system 300 can view the cine image sequence as soon as it is reconstructed and precisely positioned. The operator does not need to position the observation planes, or may only need to fine-tune them. This saves a lot of time.

[0089] Next, in step 412, a cardiac anomaly is detected using the cine image sequence 350 or the fitted cardiac model 342. For example, cardiac output or cardiac diameter, as defined by the fitted cardiac model 342 or measured in the cine image sequence 350, may indicate that the subject has a cardiac anomaly. This cardiac anomaly may be used to query the workflow database 356. Next, in step 414, the cardiac anomaly 354 is used solely for this purpose, and the workflow database 356 is used to query the workflow database 356, which returns a recommended imaging workflow 358. Then, in step 416, the recommended imaging workflow 358 is displayed using the user interface 136. This may be provided in various ways in various examples. For example, the system may be set up automatically, and the operator may simply accept the recommendation of the recommended imaging workflow 358, which includes automatically adjusting the position of the region of interest for further pulse sequence commands 362 using a set of planes 346 from the fitted cardiac model 342. In other examples, instruction 360 may simply provide instructions for a trained operator to perform; in one example, it may provide an automated action, and in another example, it may provide a recommendation or guidance to a trained operator.

[0090] In Figures 2 and 4, steps 200, 202, 204, and 206 are represented in a linear manner. That is, it appears that a portion 148 of the motion-decomposed k-space data is read only once from the motion-decomposed k-space data 146. This is one way of doing it, but an alternative example is presented in Figure 5. The steps presented in Figure 5 can be used to modify the methods shown in both Figures 2 and 4. In Figure 5, two main steps are shown: sequential acquisition 500 of lines 144 of the k-space data and compressed-sensor reconstruction 502 of a preliminary 3D cardiac image 150. The sequential acquisition portion includes step 200, which is sequential acquisition of a profile or line of the k-space data.

[0091] The aggregation 202 of motion-decomposed k-space data is described, for example, as the selection of profiles for exhalation, diastole, and resolution in step 504, which are accumulated 506 to provide incremental k-space data. Step 204 is the updating of k-space data 148. This indicates that the k-space data is updated periodically in step 508. In contrast to a single readout of a portion of the motion-decomposed k-space data, as shown in Figures 2 and 4, this is performed, for example, after each acquisition of k-space data or repeatedly after a predetermined number of lines of k-space data. The portion 148 of the motion-decomposed k-space data is then used in the compressed detection image reconstruction 510.

[0092] In compression detection, the image is reconstructed using a sparse transform that is typically filtered for noise, and then the k-space data 148 is corrected and used for data consistency before being used for further iterations. This then yields a 3D monophase image, i.e., a preliminary 3D cardiac image 150. The method presented in step 5 differs from typical compression detection algorithms. The data used for image reconstruction is k-space data that is sampled only when performing compression detection, then provided, and then used throughout the entire image reconstruction. In this case, the pool of k-space data used for compression detection image reconstruction is updated continuously or periodically.

[0093] Cardiac MRI (CMR) is a clinical procedure for evaluating cardiac motion, myocardial perfusion, and dormant tissue scarring via delayed enhancement (LGE) after contrast agent injection, among other things, within the anatomical structure of the heart. In many of these applications, quantification plays a role in obtaining functional parameters such as ejection fraction. Despite the high value of the diagnostic information, CMR techniques are used only in specialized imaging centers. Major obstacles include the complex procedure, long overall acquisition time, and high burden on the patient (breath-holding). This invention describes a workflow that combines known, advanced CMR techniques, image reconstruction, image processing, and decision support techniques in a preferred manner, enabling the resolution of the described problems and obstacles, facilitating patient readmission to CMR examinations within 10–30 minutes, and improving the reproducibility of results across imaging centers with different levels of expertise. This is based on highly accelerated, unplanned 4D / 5D cardiac acquisition combined with high-speed image reconstruction at various stages, providing all data in a timely manner, as well as automated planning / MPR and decision support modules for simplified adaptation to various disease representations.

[0094] In a typical CMR workflow, numerous manual planning steps and individual breath-holds are beneficial. Typically, up to six minutes of total examination time is used to identify the essential cardiac cross-sectional orientation, such as short-axis (SA), four-chamber (4-ch), three-chamber (3-ch), or two-chamber (2-ch) views, and to interactively plan their geometry. This procedure requires highly trained personnel and is one inherent obstacle to the widespread adoption of CMR. Another aspect of using expert support during CMR acquisition is decision-making regarding contrast agent injection for perfusion / LGE, which relies on an initial assessment of the disease using information on the cardiac anatomy and function provided by the initial MR scan.

[0095] Furthermore, most contrast and functional imaging types are acquired slice by slice (package), each using a separate breath-hold for the patient. Breath-holding is a significant burden for patients with cardiac conditions scheduled for CMR, often resulting in shortness of breath or chest pain, and frequently leading to early discontinuation of the MR procedure. Contrast injection is typically used to assess myocardial perfusion and scar tissue in delayed enhancement (LGE).

[0096] Geometric planning may be fully automated, decision support may be provided to facilitate / simplify decision-making regarding contrast agent injection, and MRI scans should be performed while the patient is breathing freely. All of these should be combined using specialized techniques within a suitable workflow, resulting in minimized examination time and broad accessibility, including an environment where radiological diagnostics are routinely available.

[0097] While 4D / 5D unangled ("unplanned") free-breath whole-heart CMR techniques are known, they suffer from long reconstruction times. Therefore, information on the anatomical structure and function of the heart cannot be used in a timely manner during the workflow to enable flexible decision-making regarding further contrast and to provide automated planning of angled cardiac cross-sectional images for image contrast that cannot (yet) be performed in an unangled manner.

[0098] The example describes a workflow that combines known advanced CMR techniques, image reconstruction, image processing, and decision support techniques in a preferred manner, enabling the resolution of the described problems and obstacles.

[0099] The exemplary workflow is based on highly accelerated, unplanned, non-contrast 4D cardiac acquisition, acquired in standard fixed geometric shapes combined with high-speed image reconstruction, which provides all the data for (i) automated anatomical planning of whole cardiac cross-sections, (ii) diagnostic information on the anatomical structure of the heart, (iii) diagnostic information on cardiac function (e.g., ejection fraction / wall thickness), and (iv) improvement of disease classification to support further image contrast / contrast injection selection.

[0100] This 4D cardiac acquisition provides an isotropic 3D representation covering the entire chest, divided into multiple phases (e.g., at least 5, 8, 10, or 12 phases) over the heart cycle, representing one selected respiratory state (e.g., end of exhalation). Therefore, it does not require prior geometric planning or angulation.

[0101] In an exemplary workflow, information regarding (i) is reconstructed via a fast (e.g., GPU-based) algorithm from a subset of the 4D data, starting long before the 4D acquisition run is complete, so that further automated planning steps can be executed earlier and the diagnostic data (ii) and (iii) are ready to be reconstructed.

[0102] By using the initial disease classification at the time of CMR prescription in conjunction with the information available from (i), (ii), and (iii), comprehensive information becomes available regarding appropriate decision support algorithms (known in the art) that suggest further execution of image acquisition steps for operator selection, or regarding the termination of the CMR examination (when the functional and anatomical structures for diagnosis in this case are sufficient).

[0103] Figure 6 below shows a detailed visualization of an exemplary workflow. The CMR image contrasts described are illustrative and may include any known CMR techniques performed after the initial automated scan, processing, and planning phase. Figure 6 shows a flowchart representing this workflow, which provides an alternative explanation of the method presented in Figure 4. Figure 6 shows how the entire procedure can be performed within 30 minutes. Step 200 performs a 4D unplanned cine magnetic resonance imaging protocol, which corresponds to the iterative acquisition of lines of k-space data in step 200. Dashed lines 204 indicate that a portion of the motion-resolved k-space data is read out, which is used to construct a preliminary 3D cardiac image 206.

[0104] When the unplanned cine MRI acquisition 200 is completed at time 401, the data becomes available for automated anatomical planning. The method is then shown to proceed as proceeding to two different branches. Block 408 represents the reconstruction of the cine image sequence. Using current technology, the reconstructions in steps 206 and 408 can be performed, for example, using a graphical processing unit. While step 408 is being performed, step 404 has already been completed or is being performed concurrently with the automated planning. This corresponds to fitting the cardiac model to the preliminary 3D cardiac image 150. Once these are completed, the method proceeds to step 412, which in this example is a guided decision-making process. Once this is completed, the method reaches step 414, which is to receive a recommended imaging flow by querying the workflow database for cardiac abnormalities.

[0105] The user may then receive one or more different recommendations. The first recommendation is to complete the examination (600). Another recommendation is to perform additional imaging without contrast agent injection (602). For example, an automated plan using angled 2D or 3D scans may be performed using an established magnetic resonance imaging protocol. A third option is that the recommendation is to perform perfusion imaging 604. This may involve various steps. This may involve, for example, giving the subject a breath-hold instruction 608 after contrast agent injection 606. During the breath-hold instruction 608, a breath-hold scan 610 is performed. Subsequently, there may be an automated plan using angled 2D and 3D scans 612, and then, finally, delayed contrast imaging 614 may be performed. These steps are described in more detail below.

[0106] The cardiac 4D cine MR scan 200 (which repeatedly acquires lines of k-space data) is performed immediately after positioning the subject 118 on the subject support 120 without prior geometric planning or angulation ("unplanned"). This is accelerated, for example, via compression detection in the spatial and temporal, or cardiac phase or respiratory dimensions, or via deep learning methods (subsampling for AI reconstruction), using isotropic voxel size (e.g., 1.5 × 1.5 × 1.5 mm). 3 This is an orthogonal coordinate system or radial 4D acquisition. Reconstruction 408 (e.g., "fast GPU reconstruction") is selected to ensure that diagnostic quality image information is available within minutes after the completion of 4D acquisition 200.

[0107] This proposed workflow also allows for the use of 5D acquisition and reconstruction for 200 (decomposing the respiratory dimension in addition to the cardiac phase time dimension).

[0108] Long before the 4D scan 200 is completed, a subset of the acquired data (motion-resolved k-space data 148) is sent to the high-speed reconfiguration module 206. This reconstructs an image dataset (e.g., 3D, one cardiac phase, possibly low resolution) suitable for detecting the anatomical location and orientation of the heart for automated orientation and planning ("Automated Planning / MPR") of cardiac cross-sectional images by the processing module 404.

[0109] After completing MR acquisition 401, (i) both modules 404 and 408 can be run in parallel because data for automated anatomical planning is already available. Automated anatomical planning is performed, for example, by adjusting a cardiac model, which also provides information on anatomical malformations used in 412 (see below). After completing 408, the detected scan orientation (at least one cardiac observation plane 348) can be used to quickly display anatomical images and cine data (cine image sequence 350) in the orientation (e.g., short-axis view) via MPR (multiplanar reconstruction).

[0110] Using this available data (cine image sequence 350), the decision support module 412 can be initiated in parallel with the operator's (brief) image review. Based on all available information (initial disease classification, anatomical information, e.g., cardiac malformations, functional defects, etc. detected by model fitting (cardiac anomalies 354)), a suggestion (recommended imaging workflow 358) is made, and further steps in the CMR examination are advised. The actual decision will be made by the operator (or in consultation with a radiologist / cardiologist who may be provided with the same information (including remote operation)).

[0111] If the current patient is fully diagnostically possible based on anatomical and functional information, option 600 will terminate the examination. This allows for a complete CMR examination within 10 minutes. If further diagnostic information is desired but contrast injection is not indicated, a non-contrast scan is added in the subsequent option 602. For example, if contrast injection is indicated for perfusion or delayed enhancement, option 604 in the workflow may be followed. Planning for subsequent scans for options 602 or 604 may be angled 2D / multislice scans, angled 3D scans, or non-angled 3D scans, and may be performed fully automatically based on the information provided by module 404.

[0112] In case 604, where contrast agent injection 606 is proposed, the workflow is carefully adjusted to allow for a quick assessment of first-pass perfusion (including stress medication if applicable) and to include sufficient time (10-15 minutes) for LGE contrast to be established.

[0113] Here, it is proposed that contrast agent be injected first 606, and a breath-hold instruction 608 be given for a multi-slice 2D or 3D perfusion scan 610. In the future, if a suitable technique becomes available, 608 and 610 may be replaced by a free-breathing perfusion scan. The waiting time until the LGE becomes measurable may be suitably filled with other 2D / multi-slice / 3D acquisitions 612 (e.g., resting perfusion, quantitative CMR such as T1, T2, T2* mapping, MR fingerprinting, ...). Finally, for option 604, a free-breathing LGE scan 614 may be performed (e.g., angled 3D acquisition).

[0114] Figure 7 shows a further example of the medical system 700. The medical system 700 shown in Figure 7 is similar to the medical system shown in Figure 1, except that it further includes a camera system 704 configured to image the chest region 122 of a subject 118 when the subject is outside the bore 106 of the magnet 104, and an actuator 702 configured to move a subject support 120 so that the heart 124 is moved into a predetermined region of interest 109.

[0115] The features of medical system 700 shown in Figure 7 may be combined with the features of medical system 100 in Figure 1 and medical system 300 in Figure 3.

[0116] Figure 7 may show that a predetermined region of interest 109 is pre-placed before the subject 118 is moved into the bore 106 of the magnet 104. The predetermined region of interest 109 is also significantly larger than the subject's heart 124. This makes it easier for a minimally trained operator to properly perform an example of the method shown herein. There are various ways in which the operator can position the heart 124 within the predetermined region of interest 109. For example, the operator makes a rough estimate and marks this position on a table or laser pointer so that it can be identified. In this particular example, there is a camera 704 positioned above the subject 118 to image the subject 118.

[0117] The operator may then manually provide a likely cardiac location 734, for example, by using a mouse to point to a likely cardiac location. Memory 138 is shown as containing a subject image 730 of subject 118. An alternative to the operator manually identifying a likely cardiac location 734 is to perform image segmentation 732. This may be done, for example, using an image segmentation algorithm or neural network trained to estimate the locations of joints and / or organs of subject 118. Memory 138 is shown as containing a representative image segmentation 732 representing one of these possibilities. A likely cardiac location 734 is obtained from this image segmentation 732 or from manual identification. The likely cardiac location 734 is then used to control the actuator 702 to move the subject support 120 and subject 118 so that the cardiac location 124 is positioned within a predetermined region of interest 109.

[0118] Figure 8 shows another diagram of the medical system 700 after the subject has been positioned so that the heart 124 is within a predetermined region of interest 109. Here, it can be seen that the medical system 700 has the same configuration as shown in Figure 1. The pulse sequence command 142 may then be used to perform the method shown in Figure 2 or also shown in Figure 4.

[0119] Although the present invention has been illustrated and described in detail in the drawings and the foregoing description, such illustrations and descriptions should be considered descriptive or illustrative and not limiting. In other words, the present invention is not limited to the disclosed embodiments.

[0120] Other variations of the disclosed embodiments may be understood and implemented by those skilled in the art to practice the claimed invention, based on a consideration of the drawings, this disclosure, and the appended claims. In the claims, the words “includes” and “equip” do not exclude other elements or steps, and singular elements do not exclude plural elements. A single processor or other unit may perform the functions of several items described in the claims. The mere fact that certain means are enumerated in mutually different dependent claims does not imply that combinations of these means cannot be used advantageously. Computer programs may be stored / distributed on suitable media such as optical storage media or solid-state media supplied together with or as part of other hardware, but may also be distributed in other forms such as via the Internet or other wired or wireless telecommunications systems. Any reference numerals in the claims should not be construed as limiting the scope of the invention. [Explanation of Symbols]

[0121] 100 Healthcare Systems 102 Magnetic Resonance Imaging System 104 Magnets 106 Magnet Bore 108 Imaging Zones 109 A predetermined area of ​​interest 110 Magnetic field gradient coil 112 Magnetic field gradient coil power supply 114 Radio frequency coil 116 Transceiver 118 Target Persons 120 Target Supporter 122 Chest area 124 Heart 130 Computers 132 Computing Systems 134 Hardware Interfaces 136 User Interface 138 memory 140 Machine Executable Instructions 142 Pulse Sequence Command 144k spatial data lines 146 Motion-decomposed k-space data 148 The portion of the k-space data that has been decomposed into motion 150 Preliminary 3D cardiac images By controlling the magnetic resonance imaging system with 200 pulse sequence commands, lines of k-space data are repeatedly acquired. When 202 k-spatial data is acquired, the k-spatial data is repeatedly aggregated from the k-spatial data line, using at least one cardiac phase and one respiratory phase of the subject. 204 Read out at least a portion of the motion-decomposed k-space data while acquiring k-space data. Before the acquisition of 206 k-space data lines is completed, a preliminary 3D cardiac image is constructed using at least a portion of the motion-decomposed k-space data. Continuously acquire lines of 208k spatial data. 300 healthcare systems 340 Deformable Heart Models 342 Suitable Heart Models Set of 346 planes 348 At least one cardiac observation surface 350 Cine Image Sequence 352 Rendering of cine image sequences from at least one cardiac observation plane 354 Heart abnormality 356 Workflow Database 358 Recommended Imaging Workflows 360 instructions 362 Further pulse sequence commands Ending the acquisition of lines for 400k spatial data. Time when line acquisition of 401k spatial data is completed. 402 A fitted cardiac model is constructed by fitting a deformable cardiac model to a preliminary 3D cardiac image. 404 Provides at least one cardiac observation surface. 406 Final aggregation of motion-decomposed k-space data After acquiring 408 k-space data, the cine image sequence is reconstructed from the motion-decomposed k-space data. 410 Render at least a portion of the cine image sequence from at least one cardiac observation plane. 412 Detecting cardiac abnormalities using cine image sequences and / or adapted cardiac models 414 Receive recommended imaging workflows by querying the workflow database due to cardiac abnormalities. 416 Display recommended imaging workflows in the user interface. Continuous acquisition of 500k spatial data 502 Iterative Compression and Detective Reconstruction of Preliminary 3D Cardiac Images 504 Select Profile 506 Accumulate incremental k-space data The 508k spatial data is updated periodically. 510 Iterative compression detection and reconstruction 600 Workflow Options: End Inspection 602 Workflow Option: No contrast agent injection 604 Workflow Option: Perfusion Imaging 606 Contrast agent injection 608 Breath-holding instruction 610 Breath-hold scan 612 Automated planning used for angled 2D / 3D scanning 614 Delayed contrast imaging 700 healthcare systems 702 Actuator 704 Camera System 730 Subject images 732 Image Segmentation 734 Most likely cardiac locations

Claims

1. A magnetic resonance imaging system that acquires k-space data lines from the chest region of the subject, A memory for storing machine-executable instructions and pulse sequence commands, wherein the pulse sequence command repeatedly acquires the lines of k-space data according to a three-dimensional self-propelled cardiac magnetic resonance imaging protocol, the pulse sequence command repeatedly acquires the lines of k-space data for a predetermined region of interest, and the memory further includes a deformable cardiac model, the deformable cardiac model is three-dimensional, and the deformable cardiac model further defines a set of planes. A computing system wherein the execution of the machine executable instruction is performed by the computing system, By controlling the magnetic resonance imaging system with the aforementioned pulse sequence command, the lines of k-space data are repeatedly acquired. When the k-space data is acquired, the k-space data that has been decomposed from the line of the k-space data is repeatedly aggregated using at least one cardiac phase and one respiratory phase of the subject, During the acquisition of the k-space data, at least a portion of the motion-decomposed k-space data is read out. Before the acquisition of the aforementioned lines of k-space data is completed, a preliminary three-dimensional cardiac image is constructed using at least a portion of the motion-decomposed k-space data, A fitted cardiac model is constructed by fitting the deformable cardiac model to the preliminary three-dimensional cardiac image, wherein the fitting of the deformable cardiac model to the preliminary three-dimensional cardiac image adjusts the location of the set of planes in the fitted cardiac model, and the set of planes includes at least one cardiac observation plane. To provide at least one cardiac observation surface A computing system that performs this task A medical system equipped with, The three-dimensional self-propelled cardiac magnetic resonance imaging protocol is a functional cardiac imaging protocol for reconstructing cine image sequences, and the medical system further includes a user interface, and the execution of the machine executable instructions is further performed by the computing system. Reconstructing the cine image sequence from the motion-decomposed k-space data after the acquisition of the k-space data is completed, wherein the fitting of the deformable heart model to the preliminary three-dimensional heart image begins before or simultaneously with the reconstruction of the cine image sequence. Rendering at least a portion of the cine image sequence as seen from at least one cardiac observation plane. A medical system that enables this to happen.

2. The execution of the aforementioned machine-executable instruction further involves the computing system, Detecting cardiac abnormalities using the aforementioned cine image sequence and / or the adapted cardiac model, The process involves querying a workflow database based on the cardiac anomaly to receive a recommended imaging workflow, wherein the workflow database includes a plurality of magnetic resonance imaging workflows, each referencing at least one cardiac anomaly identifier, and the workflow database returns the recommended imaging workflow by matching the cardiac anomaly to the cardiac anomaly identifier of the recommended imaging workflow. The recommended imaging workflow is displayed in the user interface. A medical system according to claim 1, which causes the following to be performed.

3. The medical system according to claim 2, wherein the recommended imaging workflow includes further pulse sequence commands, and the execution of the machine-executable instructions further causes the set of planes from the adapted cardiac model to constitute the geometric orientation of the further pulse sequence commands.

4. The detection of cardiac abnormalities using the cine image sequence and / or the adapted cardiac model, Reconstructing a static three-dimensional cardiac image from at least a portion of the aforementioned cine image sequence, The cardiac abnormality is identified as a thickened right ventricle in the static three-dimensional cardiac image using a right ventricular wall thickness measurement algorithm, The cardiac abnormality is identified as a thickened left ventricle in the static 3D cardiac image using a left ventricular wall thickness measurement algorithm. Includes, The medical system according to claim 3, wherein if the cardiac abnormality is a thickened right ventricle or a thickened left ventricle, the recommended imaging workflow is quantitative blood flow analysis and / or cardiac motion pattern analysis.

5. The medical system according to claim 3 or 4, wherein the detection of the cardiac abnormality using the cine image sequence and / or the adapted cardiac model includes identifying the cardiac abnormality as abnormal cardiac wall motion by inputting the cine image sequence into an abnormal cardiac wall motion detection algorithm, and the recommended imaging workflow includes a delayed contrast-enhanced magnetic resonance imaging protocol for detecting cardiac scar tissue.

6. If the abnormal cardiac wall movement is not detected, the medical system according to claim 5, wherein the recommended imaging workflow includes a cardiac stress test and / or magnetic resonance perfusion test.

7. The medical system according to any one of claims 3 to 6, wherein the detection of cardiac abnormalities using the cine image sequence and / or the adapted cardiac model includes identifying the cardiac abnormality as potential myocarditis if no cardiac abnormality is detected, and the recommended imaging workflow includes a T2-weighted imaging protocol for identifying cardiac inflammation and / or a delayed-enhancement magnetic resonance imaging protocol for identifying diffuse fibrosis.

8. The medical system according to any one of claims 1 to 7, wherein the preliminary three-dimensional cardiac image is motion-decomposed, the deformable cardiac model is motion-decomposed, and the adapted cardiac model is motion-decomposed.

9. The execution of the aforementioned machine-executable instruction further involves the computing system, Determining a set of field deformations between different cardiac and / or respiratory phases of the adapted cardiac model, To perform motion correction, the magnetic resonance image is reconstructed from the motion-resolved k-space data according to motion-compensated magnetic resonance imaging reconstruction using the set of field deformations between different cardiac and / or respiratory phases. A medical system according to claim 8, which causes the following to be performed.

10. A magnetic resonance imaging system that acquires k-space data lines from the chest region of a subject, A memory for storing machine-executable instructions and pulse sequence commands, wherein the pulse sequence command repeatedly acquires the lines of k-space data according to a three-dimensional self-propelled cardiac magnetic resonance imaging protocol, the pulse sequence command repeatedly acquires the lines of k-space data for a predetermined region of interest, and the memory further includes a deformable cardiac model, the deformable cardiac model is three-dimensional, and the deformable cardiac model further defines a set of planes. A computing system wherein the execution of the machine executable instruction is performed by the computing system, By controlling the magnetic resonance imaging system with the aforementioned pulse sequence command, the lines of k-space data are repeatedly acquired. When the k-space data is acquired, the k-space data that has been decomposed from the line of the k-space data is repeatedly aggregated using at least one cardiac phase and one respiratory phase of the subject, During the acquisition of the k-space data, at least a portion of the motion-decomposed k-space data is read out. Before the acquisition of the aforementioned lines of k-space data is completed, a preliminary three-dimensional cardiac image is constructed using at least a portion of the motion-decomposed k-space data, A fitted cardiac model is constructed by fitting the deformable cardiac model to the preliminary three-dimensional cardiac image, wherein the fitting of the deformable cardiac model to the preliminary three-dimensional cardiac image adjusts the location of the set of planes in the fitted cardiac model, and the set of planes includes at least one cardiac observation plane. To provide at least one cardiac observation surface A computing system that performs this task Equipped with, At least a portion of the motion-decomposed k-space data is read out once during the acquisition of the k-space data, either after a predetermined acquisition duration or after a predetermined number of k-space data acquisitions, and The motion-decomposed k-space data is repeatedly read during the acquisition of the k-space data, and the preliminary three-dimensional cardiac image is reconstructed from the motion-decomposed k-space data in an iterative step. A healthcare system to which one of the following applies.

11. The aforementioned predetermined region of interest has a volume greater than 750 cubic centimeters. The machine-executable instruction disables the adjustment of the predetermined region of interest, and These combinations A medical system according to any one of claims 1 to 10, wherein any one of the following is applied.

12. A computer program comprising machine-executable instructions for execution by a computing system that controls a magnetic resonance imaging system that acquires k-space data lines from the chest region of a subject, The execution of the machine executable instruction in the computing system The process involves repeatedly acquiring the lines of k-space data by controlling the magnetic resonance imaging system with pulse sequence commands, wherein the pulse sequence commands repeatedly acquire the lines of k-space data in accordance with a three-dimensional self-propelled cardiac magnetic resonance imaging protocol, and the pulse sequence commands repeatedly acquire the lines of k-space data for a predetermined region of interest. When the k-space data is acquired, the k-space data that has been decomposed from the line of the k-space data is repeatedly aggregated using at least one cardiac phase and one respiratory phase of the subject, During the acquisition of the k-space data, at least a portion of the motion-decomposed k-space data is read out. Before the acquisition of the aforementioned lines of k-space data is completed, a preliminary three-dimensional cardiac image is constructed using at least a portion of the motion-decomposed k-space data, A fitted cardiac model is constructed by fitting a three-dimensional deformable cardiac model to the preliminary three-dimensional cardiac image, wherein the deformable cardiac model further defines a set of planes, and the fitting of the deformable cardiac model to the preliminary three-dimensional cardiac image adjusts the location of the set of planes within the fitted cardiac model, wherein the set of planes includes at least one cardiac observation plane. To provide at least one cardiac observation surface Have them do it, The three-dimensional self-propelled cardiac magnetic resonance imaging protocol is a functional cardiac imaging protocol for reconstructing a cine image sequence, and the execution of the machine-executable instructions is further performed on the computing system. Reconstructing the cine image sequence from the motion-decomposed k-space data after the acquisition of the k-space data is completed, wherein the fitting of the deformable heart model to the preliminary three-dimensional heart image begins before or simultaneously with the reconstruction of the cine image sequence. Rendering at least a portion of the cine image sequence as seen from at least one cardiac observation plane. A computer program that performs an action.

13. A method for operating a magnetic resonance imaging system, wherein the magnetic resonance imaging system acquires k-space data lines from the chest region of a subject, and the method A step of repeatedly acquiring the lines of k-space data by controlling the magnetic resonance imaging system with a pulse sequence command, wherein the pulse sequence command repeatedly acquires the lines of k-space data in accordance with a three-dimensional self-propelled cardiac magnetic resonance imaging protocol, and the pulse sequence command repeatedly acquires the lines of k-space data for a predetermined region of interest. The steps include repeatedly aggregating the k-space data that has been decomposed from the line of the k-space data using at least one cardiac phase and one respiratory phase of the subject when the k-space data is acquired, The steps include reading out at least a portion of the motion-decomposed k-space data while acquiring the k-space data, The steps include constructing a preliminary three-dimensional cardiac image using at least a portion of the motion-decomposed k-space data before the acquisition of the lines of the k-space data is completed, A step of configuring a fitted cardiac model by fitting a three-dimensional deformable cardiac model to the preliminary three-dimensional cardiac image, wherein the deformable cardiac model further defines a set of planes, and the fitting of the deformable cardiac model to the preliminary three-dimensional cardiac image adjusts the location of the set of planes in the fitted cardiac model, and the set of planes includes at least one cardiac observation plane. The steps of providing at least one cardiac observation surface and It has, The aforementioned three-dimensional self-propelled cardiac magnetic resonance imaging protocol is a functional cardiac imaging protocol for reconstructing cine image sequences, and the method further comprises After the acquisition of the k-space data is completed, a step of reconstructing the cine image sequence from the motion-decomposed k-space data, wherein the fitting of the deformable heart model to the preliminary three-dimensional heart image begins before or simultaneously with the reconstruction of the cine image sequence; A method comprising the step of rendering at least a portion of the cine image sequence as viewed from at least one cardiac observation plane.

14. The method according to claim 13, further comprising the step of positioning the chest region of the subject within the predetermined region of interest.

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