Anatomical measurements using magnetic resonance imaging

Abstract

Medical systems 100, 300, 500 are disclosed. Execution of machine-executable instructions 112 causes computing system 104 to receive (200) baseline anatomical measurements 114 that describe a clinical magnetic resonance image of subject 318, receive (202) scan metadata 116 that describes the clinical magnetic resonance image of the subject, transmit (204) scan parameters to low-field magnetic resonance imaging system 301 via network connection 350, receive (206) subsequent k-space data 122 from the low-field magnetic resonance imaging system via the network connection in response to the transmission of the scan parameters, reconstruct (208) a subsequent magnetic resonance image 124 from the subsequent k-space data, determine (210) subsequent anatomical measurements 128 in response to the input of the subsequent magnetic resonance image to a segmentation module, and provide (212) a warning signal 132 when the subsequent anatomical measurements differ from the baseline anatomical measurements by more than a predetermined amount.

Description

Technical Field

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

Background Art

[0002] Magnetic resonance imaging (MRI) scanners use a large static magnetic field to align the nuclear spins of atoms as part of the procedure for generating images of a patient's body. The main magnetic field used to generate the large static magnetic field is called the B0 magnetic field or the main magnetic field. Various quantities or characteristics of a subject can be spatially measured and imaged using MRI.

Summary of the Invention

Problems to be Solved by the Invention

[0003] One of the difficulties of MRI is that the equipment is expensive. The installation of an MRI scanner is a significant expense for a hospital. Using a main magnet that generates a low B0 magnetic field can reduce costs, but this may reduce the resolution and / or signal-to-noise ratio of the MRI image. Typically, these low-field MRI images are of poor quality and undesirable.

[0004] The journal article "Minimum Field Strength Simulator for Proton Density Weighted MRI" by Wu Z, Chen W, Nayak KS (PLoS One. 2016; 11(5)e0154711.doi:10.1371 / journal.pone.0154711.PMID:27136334;PMCID:PMC4852924) discloses a framework for simulating MRI acquisitions weighted with low-field proton density based on high-field acquisitions.

[0005] U.S. Patent Application No. 2018 / 0025466 is related to accurately collecting follow-up magnetic resonance data with the same geometry as the previous baseline image.

Means for Solving the Problem

[0006] The present invention provides a medical system, a computer program product, and a method in the independent claims. Embodiments are described in the dependent claims.

[0007] Embodiments provide means for performing a follow-up of a subsequent magnetic resonance imaging scan using a so-called low-field magnetic resonance imaging system. To do this, baseline anatomical measurements obtained from clinical magnetic resonance images and scan metadata describing the baseline anatomical measurements are provided. Scan parameters are provided using the scan metadata and the baseline anatomical measurements. The scan parameters are transmitted to the low-field magnetic resonance imaging system via a network connection. In response to the scan parameters, subsequent k-space data is received from the low-field magnetic resonance imaging system and used to reconstruct a subsequent magnetic resonance image. The scan metadata describes the clinical magnetic resonance image as having a first resolution and a first signal-to-noise ratio. The subsequent magnetic resonance image has a second resolution and a second signal-to-noise ratio. The first signal-to-noise ratio is higher than the second signal-to-noise ratio and / or the first resolution is higher than the second resolution. Thereafter, subsequent anatomical measurements are determined by inputting the subsequent magnetic resonance image into a segmentation module. Thereafter, the subsequent anatomical measurements are compared with the baseline anatomical measurements. The low-field magnetic resonance imaging system does not have a main magnetic field strong enough to generate clinically quality images but can be used to provide anatomical information.

[0008] In one aspect, the present invention provides a medical system including a memory storing machine-executable instructions. The medical system further includes a computing system. Execution of the machine-executable instructions causes the computing system to receive baseline anatomical measurements that describe a clinical magnetic resonance image of a subject. Execution of the machine-executable instructions further causes the computing system to receive scan metadata that describes a clinical magnetic resonance image of the subject. The scan metadata includes scan coordinates referenced to a predetermined anatomical landmark of the subject. The scan metadata describes a clinical magnetic resonance image having a first resolution and a first signal-to-noise ratio.

[0009] The baseline anatomical measurements take different forms in different examples. For example, the baseline anatomical measurements are relative distances referenced to a predetermined anatomical landmark. In another example, the baseline anatomical measurements are sizes, volumes, or other physical quantities that describe anatomical structures such as organs and / or tumors. In some examples, the baseline anatomical measurements are numerical values. In other examples, the baseline anatomical measurements are lines or segmentations within the clinical magnetic resonance image. In the example, the clinical magnetic resonance image need not actually be present. This is because the baseline anatomical measurements are provided with scan metadata that provides scan coordinates, a first resolution, a first signal-to-noise ratio, and the like.

[0010] The execution of the machine-executable instructions further causes the computing system to transmit scan parameters to a low-field magnetic resonance imaging system via a network connection. As used herein, a low-field magnetic resonance imaging system includes a magnetic resonance imaging system having a lower magnetic field than that typically used for clinical magnetic resonance imaging. For example, in a low-field magnetic resonance imaging system, the main magnetic field strength may be 0.6T or less, and further may be 0.2T or less. Scan parameters may be provided from several different sources. Of course, the scan parameters may be standard parameters used in a low-field magnetic resonance imaging system or scan parameters optimized for measuring specific baseline anatomical measurements in subsequent images.

[0011] The execution of the machine-executable instructions further causes the computing system to receive subsequent k-space data from the low-field magnetic resonance imaging system via a network connection in response to the transmission of the scan parameters. The subsequent k-space data is collected using the scan parameters transmitted to the low-field magnetic resonance imaging system. The execution of the machine-executable instructions further causes the computing system to reconstruct a subsequent magnetic resonance image from the subsequent k-space data. The execution of the machine-executable instructions further causes the computing system to determine subsequent anatomical measurements in response to the input of the subsequent magnetic resonance imaging data to the segmentation module.

[0012] The segmentation module can perform several different tasks. The segmentation module can be used, for example, to perform subsequent segmentation or measurement of magnetic resonance images. The subsequent magnetic resonance images have a second resolution lower than the first resolution and / or a signal-to-noise ratio lower than the first signal-to-noise ratio. This is a result of using low-field magnetic resonance images. The execution of machine-executable instructions further causes the computing system to provide a warning signal when subsequent anatomical measurements differ from the baseline anatomical measurements by more than a predetermined amount. For example, if the baseline anatomical measurement is a distance, a warning signal is triggered when the distance changes by more than a certain amount. If the anatomical measurement for comparison is, for example, the size of a tumor or other volume measurement, an increase in the size or volume of the measurement object can also be used to trigger a warning signal. This embodiment is beneficial because it provides a means for more inexpensively monitoring the anatomical measurements of a subject. For example, when a physician diagnoses or examines a subject, a clinical magnetic resonance imaging system with a magnetic field strength greater than 0.6, and even greater than 1T, is used. These systems are more expensive than low-field magnetic resonance imaging systems. In one example, a medical system can provide image reconstruction and other services to various low-field magnetic resonance imaging systems, for example, as a cloud service or a remote server.

[0013] Warning signals take different forms in different examples. In one example, the warning signal is an email or, further, a signal provided to a physician via a cellular telecommunications system. In other examples, the warning signal is a message or instruction transmitted to a low-field magnetic resonance imaging system for the subject to receive. This is an instruction for performing various health tasks, such as instructions for a particular change in behavior to the subject, such as by a healthcare provider or for adjusting food consumption or performing other actions. In other examples, the warning signal is used to transmit additional scan parameters to a low-field magnetic resonance imaging system to repeat the subsequent k-space data collection. For example, if anatomical measurements indicate that there may be a problem or health issue with the subject, the provision of a warning signal can be used to automatically re-perform the measurements to confirm that subsequent anatomical measurements are correct.

[0014] The present invention relates to generating subsequent k-space data after a clinical magnetic resonance image. The subsequent k-space data is collected at a lower magnetic field strength than that associated with the clinical magnetic resonance image. Thereby, after a lesion is identified in the (original) clinical magnetic resonance image, a follow-up examination of the patient under examination can be performed. For that purpose, baseline anatomical measurements are received from the clinical magnetic resonance image. This is related to, for example, the size and shape of the lesion. Also received is scan metadata associated with the clinical magnetic resonance image and representing the (first) spatial resolution and signal-to-noise ratio of the clinical diagnostic image. The scan parameters of the subsequent k-space data are available for a low-field magnetic resonance imaging system. These scan parameters are associated with the (second) spatial resolution and signal-to-noise ratio of the subsequent magnetic resonance image reconstructed from the subsequent k-space data. The segmentation module returns subsequent anatomical measurements of the reconstructed subsequent magnetic resonance image. This corresponds to the baseline anatomical measurements in that these measurements are related to the same anatomical lesion. The segmentation module can be implemented as a conceptual algorithm, an expert system, a simple but extensive lookup table, or based on artificial intelligence (e.g., in the form of a trained neural network). Finally, from the comparison between the baseline anatomical measurements and the subsequent anatomical measurements, a warning signal is issued to warn the user if the change exceeds a predetermined threshold value.

[0015] Subsequent k-space data is collected by a low-field magnetic resonance imaging system. The low-field magnetic resonance imaging system has a main magnetic field strength that is significantly lower than the main magnetic field strength associated with the clinical magnetic resonance image from which the baseline anatomical measurements were obtained. In the context of the present invention, "significantly" means that the magnetic field strength is low to such an extent that the appearance of the contrast in the subsequent magnetic resonance image is substantially different from that of the clinical magnetic resonance image. The subsequent magnetic resonance image and the clinical diagnostic image have a low similarity and have a more significant difference than the differences that occur between magnetic resonance images of the same subject collected by various magnetic resonance imaging systems having the same nominal main magnetic field strength. These differences are due to the dependence of the relaxation parameters (1 / T1, 1 / T2, 1 / (T2 * )) on the main magnetic field strength and on the signal-to-noise ratio of the reconstructed image. For example, a significantly low magnetic field strength is associated with the relaxation time (Ti(B0)), which differs by more than 10% from the value of the main magnetic field strength associated with the clinical magnetic resonance image. For example, the relaxation time usually differs by about 25% between main magnetic field strengths of 1.5 T and 3.0 T and is twice as different between main magnetic field strengths of 0.3 T and 3.0 T.

[0016] The segmentation module functions to perform segmentation and / or measurement on the subsequent magnetic resonance image, and the subsequent anatomical measurements correspond to the baseline anatomical measurements. Further, the segmentation module performs identification of a predetermined anatomical landmark within the subsequent magnetic resonance image that corresponds to a predetermined anatomical landmark associated with the baseline anatomical measurements. When implemented with artificial intelligence, the trained neural network returns the subsequent anatomical measurements in response to the baseline anatomical measurements and the subsequent magnetic resonance image.

[0017] In another aspect, the medical system further includes a low-resolution magnetic resonance imaging system. The low-resolution magnetic resonance imaging system includes a local memory and a controller. The local memory stores a survey scan pulse sequence command, a measurement pulse sequence command, and further stores a controller command. Execution of the controller command causes the controller to receive scan parameters via a network connection. Execution of the controller command further causes the controller to collect survey scan k-space data by controlling the low-resolution magnetic resonance imaging system with the survey scan pulse sequence command. Execution of the controller command further causes the controller to reconstruct a survey scan image from the survey scan k-space data. Execution of the controller command further causes the controller to detect the position of a predetermined anatomical landmark of the subject within the survey scan image.

[0018] For example, this position can be provided using a segmentation module that identifies a predetermined anatomical landmark of the subject. Other things such as a segmentation module, such as a deformable shape model, can also be used to determine the position of the predetermined anatomical landmark. Execution of the controller command further causes the controller to adjust the acquisition pulse sequence command using the position of the predetermined anatomical landmark of the subject within the survey scan image, a second resolution, and scan coordinates based on the predetermined anatomical landmark of the subject. Execution of the controller command further causes the controller to collect subsequent k-space data by controlling the low-resolution magnetic resonance imaging system with the acquisition pulse sequence command. Execution of the controller command further causes the controller to transmit the subsequent k-space data to a computing system via a network connection. This embodiment is beneficial as it provides a means for automatically remotely collecting magnetic resonance images.

[0019] In another embodiment, the low-resolution magnetic resonance imaging system includes a main magnet that generates a main magnetic field. The main magnetic field has an intensity of 0.6T or less. In another embodiment, the main magnetic field preferably has an intensity of 0.2T or less. Using a main magnet with a magnetic field intensity of 0.6T has the advantage that assembly and operation are much less expensive. Thus, a magnet with a magnetic field intensity of 0.2T or less has an even lower cost for assembly and maintenance. The disadvantage of using a magnet with such a low magnetic field intensity is that the signal-to-noise is low and it becomes more difficult to measure at the same resolution.

[0020] In another embodiment, the controller performs subsequent k-space data collection and automatically transmits the subsequent k-space data to the calculation system upon receiving scan parameters. This embodiment is advantageous because it does not require an operator trained in low-field magnetic resonance imaging systems.

[0021] In another embodiment, the memory further stores a scan parameter configuration module. The scan parameter configuration module outputs scan parameters in response to receiving a second resolution and / or a second signal-to-noise ratio, scan coordinates, and baseline anatomical measurements. Execution of the machine-executable instructions further causes the calculation system to receive scan parameters in response to inputting a second resolution and / or a second signal-to-noise ratio, scan coordinates, and baseline anatomical measurements into the scan parameter configuration module. This embodiment is beneficial because, although the low-field magnetic resonance imaging system has a lower magnetic field and possibly lower quality than the clinical magnetic resonance imaging system used to obtain baseline anatomical measurements, scan parameters that can accurately measure the anatomical measurements can be automatically generated using the scan parameter configuration module.

[0022] In another embodiment, the scan parameter configuration module is implemented at least in part as a look-up table, a neural network, or an expert system. Over time, as the system is used for more examinations, data regarding appropriate scan parameters for specific configurations and baseline anatomical measurements can be stored and used later. In other examples, a neural network can be trained for this purpose. Similarly, data collected through repeated use can be encoded in an expert system that can be used to automatically provide scan parameters.

[0023] In another embodiment, the scan parameters include a selection of pulse sequence type. The baseline anatomical measurements can be obtained from clinical magnetic resonance images having a specific weighting or image type. This image type is stored, for example, in the scan metadata and is then used to select the pulse sequence type for collecting subsequent k-space data in a low-field magnetic resonance imaging system.

[0024] In another embodiment, the computing system is implemented as a cloud computing system. This is beneficial as it allows the computing system to support a large number of low-field magnetic resonance imaging systems. For example, in the case where low-field magnetic resonance imaging systems are distributed in small clinics or stores. The use of a cloud computing system enables a single or very few medical systems to provide subsequent reconstruction and determination of anatomical measurements.

[0025] In another embodiment, the segmentation module is implemented as a neural network. The neural network is, for example, a U-net or a ResNet. The training of the neural network is special in some embodiments. The baseline anatomical measurements are obtained from clinical magnetic resonance images having a first resolution and a first signal-to-noise ratio. Since subsequent anatomical measurements are determined using subsequent magnetic resonance images, the data is likely obtained from a magnetic resonance imaging system with a lower magnetic field and / or image quality. To properly train the neural network, training images are acquired and the images are segmented either automatically or manually to provide baseline anatomical measurements. These anatomical measurements for training can be used as part of the training data. Then, the training magnetic resonance images are resampled to simulate low resolution and low signal-to-noise to generate images that can be input into the neural network during training. The low-resolution images can be input into the neural network, and the segmentation or anatomical measurements obtained from the original images can be used as ground truth data. In this way, the neural network can be trained to generate anatomical measurements as if they were taken from high-resolution images.

[0026] In another embodiment, the neural network is trained by repeatedly receiving training clinical magnetic resonance images or training clinical k-space data. The training clinical magnetic resonance images have a first resolution and / or a first signal-to-noise ratio. The neural network is further trained by repeatedly receiving training anatomical measurements that describe baseline anatomical measurements for the training clinical magnetic resonance images. As described above, this can be performed, for example, either manually or by a segmentation module. The neural network is further trained by repeatedly calculating subsequent clinical magnetic resonance images simulated at a second resolution and / or a second signal-to-noise ratio. This can be either the training clinical magnetic resonance images or the training clinical k-space data. The neural network is further trained by repeatedly constructing training data from pairs of the simulated subsequent magnetic resonance images and the training anatomical measurements. The neural network is further trained by using the training data to repeatedly train the neural network. This is performed, for example, using a deep learning algorithm.

[0027] In another embodiment, the medical system further includes a clinical magnetic resonance imaging system. The clinical magnetic resonance imaging system collects clinical magnetic resonance images of a subject and determines baseline anatomical measurements from the clinical magnetic resonance images. The baseline anatomical measurements are provided automatically by an algorithm or a neural network, or are provided by data received by a physician or operator of the clinical magnetic resonance imaging system. The clinical magnetic resonance imaging system further provides the baseline anatomical measurements to the calculation system. For example, this data can be provided via a network or other connection.

[0028] In another aspect, the present invention provides a medical imaging method. The method includes receiving baseline anatomical measurements that describe a clinical magnetic resonance image of a subject. The method further includes receiving scan metadata that describes the clinical magnetic resonance image of the subject. The scan metadata includes scan coordinates referenced to a predetermined anatomical landmark of the subject. The scan metadata describes a clinical magnetic resonance image having a first resolution and / or a first signal-to-noise ratio. The method further includes transmitting scan parameters to a low-field magnetic resonance imaging system via a network connection. The scan parameters include a second resolution and / or a second signal-to-noise ratio. The scan parameters further include scan coordinates referenced to a predetermined anatomical landmark of the subject. The first resolution is higher than the second resolution. The first signal-to-noise ratio is higher than the second signal-to-noise ratio. The method further includes receiving subsequent k-space data from the low-field magnetic resonance imaging system via the network connection in response to the transmission of the scan parameters.

[0029] The method further includes reconstructing a subsequent magnetic resonance image from the subsequent k-space data. The method further includes determining subsequent anatomical measurements in response to the input of the subsequent magnetic resonance image to a segmentation module. The method further includes providing a warning signal if the subsequent anatomical measurements differ from the baseline anatomical measurements by more than a predetermined amount.

[0030] In another embodiment, the medical system further includes a low-resolution magnetic resonance imaging system. The method further includes receiving scan parameters via a network connection. The method further includes collecting survey scan k-space data by controlling the low-resolution magnetic resonance imaging system with a survey scan pulse sequence command. The method further includes reconstructing a survey scan image from the survey scan k-space data. The method further includes detecting the position of a predetermined anatomical landmark of the subject within the survey scan image. This can be done, for example, using a segmentation algorithm. The method further includes adjusting an acquisition pulse sequence command using the position of a predetermined anatomical landmark of the subject within the survey scan image, a second resolution, and scan coordinates referenced to the predetermined anatomical landmark of the subject. The method further includes collecting subsequent k-space data by controlling the low-resolution magnetic resonance imaging system with the acquisition pulse sequence command. The method further includes transmitting the subsequent k-space data to a computing system via a network connection.

[0031] In another aspect, the present invention provides a computer program product including machine-executable instructions for execution by a computing system that controls a medical system. Execution of the machine-executable instructions causes the computing system to receive baseline anatomical measurements that describe a clinical magnetic resonance image of a subject. Execution of the machine-executable instructions further causes the computing system to receive scan metadata that describes a clinical magnetic resonance image of the subject. The scan metadata includes scan coordinates referenced to a predetermined anatomical landmark of the subject. The scan metadata describes a clinical magnetic resonance image having a first resolution and / or a first signal-to-noise ratio.

[0032] The execution of the machine-executable instructions further causes the computing system to transmit scan parameters to the low-field magnetic resonance imaging system via a network connection. The scan parameters include a second resolution and / or a second signal-to-noise ratio. The scan parameters further include scan coordinates that are based on predetermined anatomical landmarks of the subject. The first resolution is higher than the second resolution and / or the first signal-to-noise ratio is higher than the second signal-to-noise ratio. The execution of the machine-executable instructions further causes the computing system to receive subsequent k-space data from the low-field magnetic resonance imaging system via the network connection in response to the transmission of the scan parameters. The execution of the machine-executable instructions further causes the computing system to reconstruct a subsequent magnetic resonance image from the subsequent k-space data. The execution of the machine-executable instructions further causes the computing system to determine subsequent anatomical measurements in response to the input of the subsequent magnetic resonance image to the segmentation module. The execution of the machine-executable instructions further causes the computing system to provide a warning signal if the subsequent anatomical measurements differ from the baseline anatomical measurements by more than a predetermined amount.

[0033] It is understood that one or more of the above-described embodiments of the present invention can be combined as long as the combined embodiments are not mutually exclusive.

[0034] As will be recognized by those of skill in the art, aspects of the present invention may be embodied as an apparatus, method, or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which may generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer-readable media having computer-executable code embodied thereon.

[0035] Any combination of one or more computer-readable media can be utilized. The computer-readable media is either a computer-readable signal medium or a computer-readable storage medium. As used herein, "computer-readable storage medium" includes any tangible storage medium that can store instructions executable by a processor of a computing device or a computing system. A computer-readable storage medium is also referred to as a computer-readable non-transitory storage medium. A computer-readable storage medium is also referred to as a tangible computer-readable medium. In some embodiments, a computer-readable storage medium can also store data accessible by a computing system of a computing device. Examples of computer-readable storage media include, but are not limited to, floppy (registered trademark) disks, magnetic hard disk drives, solid state hard disks, flash memory, USB thumb drives, random access memory (RAM), read-only memory (ROM), optical disks, magneto-optical disks, and register files of a computing system. Examples of optical disks include compact disks (CDs) and digital versatile disks (DVDs), such as CD-ROM, CD-RW, CD-R, DVD-ROM, DVD-RW, or DVD-R disks. The term computer-readable storage medium also refers to various types of recording media accessible by a computer device via a network or a communication link. For example, data is obtained via a modem, the Internet, or a local area network. The computer-executable code embodied on a computer-readable medium can be transmitted using any medium including, but not limited to, wireless, wireline, fiber optic cable, RF, or any suitable combination thereof.

[0036] A computer-readable signal medium may include a propagated data signal in which computer-executable code is embodied, such as in a baseband or as part of a carrier wave. Such a propagated signal can take various forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is any computer-readable medium that can communicate, propagate, or transport a program for use in or in connection with an instruction execution system, apparatus, or device, and is not a computer-readable storage medium.

[0037] "Computer memory" or "memory" is an example of a computer-readable storage medium. Computer memory is any memory directly accessible from a computing system. "Computer storage" or "storage" is an 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, and vice versa.

[0038] As used herein, a "computing system" includes electronic components capable of executing a program, machine-executable instructions, or computer-executable code. References to a computing system that include examples of a computing system should be construed as potentially including multiple computing systems or processing cores. A computing system may be, for example, a multi-core processor. A computing system may also refer to an aggregate of computing systems within a single computer system, or an aggregate of computing systems distributed across multiple computer systems. The term computing system should also be construed as potentially referring to an aggregate or network of computing devices each including a processor or computing system. Machine-executable code or instructions may be executed by multiple computing systems or processors within the same computing device, or may even be executed by multiple computing systems or processors distributed across multiple computing devices.

[0039] Machine-executable instructions or computer-executable code may include instructions or programs that cause a processor or other computing system to perform aspects of the present invention. The computer-executable code for performing the operations of aspects of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java (registered trademark), Smalltalk (registered trademark), C++, and conventional procedural programming languages such as the "C" programming language or similar programming languages, and may be compiled into machine-executable instructions. In some cases, the computer-executable code may be in a high-level language or a compiled form and may be used in conjunction with an interpreter that generates machine-executable instructions on the fly. In other cases, the machine-executable instructions or computer-executable code may be in a programmable logic gate array programming format.

[0040] The computer-executable code may be executed entirely on the user's computer, partially on the user's computer, as stand-alone software, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN). Alternatively, a connection to an external computer (e.g., via the Internet using an Internet service provider) can also be established.

[0041] Aspects of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block or part of a block in the flowchart illustrations, diagrams, and / or block diagrams can be implemented in computer-executable code form by corresponding computer program instructions, where appropriate. Further, where not mutually exclusive, combinations of blocks in different flowchart illustrations, diagrams, and / or block diagrams can be combined. These computer program instructions are provided to a computing system of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions are executed via the computing system of the computer or other programmable data processing apparatus to create a machine for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0042] These machine-executable instructions or computer program instructions can also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, whereby the instructions stored in the computer-readable medium produce a product including instructions for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0043] Machine-executable instructions or computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to generate a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide a process for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0044] As used herein, a "user interface" is an interface that enables a user or operator to interact with a computer or computer system. A "user interface" is also referred to as a "human interface device". A user interface can provide information or data to an operator or receive information or data from an operator. Using a user interface, an input from an operator can be received by a computer and an output from the computer can be provided to the user. That is, using a user interface, an operator can be enabled to control or operate a computer, or using the interface, a computer can be enabled to indicate an effect of control or operation by the operator. Displaying data or information on a display or a graphical user interface is an example of providing information to an operator. Receiving data via a keyboard, a mouse, a trackball, a touch pad, a pointing stick, a graphics tablet, a joystick, a game pad, a web camera, a headset, a pedal, a wired glove, a remote control, and an accelerometer are all examples of user interface components that enable reception of information or data from an operator.

[0045] As used herein, the "hardware interface" includes an interface that enables the computing system of a computer system to interact with and / or control external computing devices and / or apparatuses. Using the hardware interface, the computing system can send control signals and instructions to external computing devices and / or apparatuses. Also, using the hardware interface, the computing system can exchange data with external computing devices and / or apparatuses. Examples of the hardware interface include, but are not limited to, Universal Serial Bus, IEEE 1394 port, parallel port, IEEE 1284 port, serial port, RS-232 port, IEEE-488 port, Bluetooth (registered trademark) connection, wireless local area network connection, TCP / IP connection, Ethernet (registered trademark) connection, control voltage interface, MIDI interface, analog input interface, and digital input interface.

[0046] As used herein, the "display" or "display device" includes an output device or user interface adapted for displaying images or data. The display can output visual, audio, or tactile data. Examples of the display include, but are not limited to, computer monitors, television screens, touchscreens, tactile electronic displays, braille screens, cathode ray tubes (CRT), storage tubes, bistable displays, electronic paper, vector displays, flat panel displays, vacuum fluorescent displays (VF), light emitting diode (LED) displays, electroluminescent displays (ELD), plasma display panels (PDP), liquid crystal displays (LCD), organic light emitting diode displays (OLED), projectors, and head mounted displays.

[0047] k-space data is defined herein as the measured values of radio frequency signals emitted from atomic spins, recorded using the antenna of a magnetic resonance apparatus during a magnetic resonance imaging scan. Magnetic resonance data is an example of tomographic medical image data.

[0048] A magnetic resonance imaging (MRI) image, or MR image, is defined herein as a reconstructed two-dimensional or three-dimensional visualization of anatomical data included in magnetic resonance imaging data. This visualization is performed using a computer.

Brief Description of the Drawings

[0049] Preferred embodiments of the present invention will be described below by way of example only and with reference to the drawings.

[0050]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Modes for Carrying Out the Invention

[0051] Elements with the same numbers in these figures are equivalent elements or perform the same function. For elements that have already been described, if the functions are equivalent, they are not necessarily described in later figures.

[0052] FIG. 1 shows an example of a medical system 100. For example, the medical system 100 is a cloud-based or virtual-based system. The medical system 100 can also be incorporated into a magnetic resonance imaging system. The medical system 100 is shown as including a computer 102. The computer 102 is intended to represent one or more computers in one or more locations. The computer 102 is shown as including a computing system 104. The computing system 104 is intended to represent one or more computing systems or processing cores within one or more computer systems. The computing system 104 is shown as communicating with a network interface 106 and an optional user interface 108 and a memory 110. The memory 110 is intended to represent various types of memory accessible to the computing system 104.

[0053] The memory 110 is shown as including machine-executable instructions 112. The memory 110 is further shown as including baseline anatomical measurements 114 and scan metadata 116 that describe clinical magnetic resonance images. The memory 110 is further shown as including an optional scan parameter configuration module 118. The scan parameter configuration module can receive, for example, the scan metadata 116 and / or the baseline anatomical measurements 114 as inputs and output scan parameters 120. The memory 110 is further shown as including the scan parameters 120. The memory 110 is further shown as including subsequent k-space data 122 obtained from a low-field magnetic resonance imaging system. The memory 110 is further shown as including subsequent magnetic resonance images 124 reconstructed from the subsequent k-space data 122.

[0054] Memory 110 is further shown to include a segmentation module 126 that receives the subsequent magnetic resonance image 124 as input and outputs subsequent anatomical measurements 128 in response thereto. Memory 110 is shown to include subsequent anatomical measurements 128 obtained by inputting the subsequent magnetic resonance image 124 to the segmentation module 126. Memory 110 is further shown to include a predetermined amount 130 used as a threshold when comparing the subsequent anatomical measurements 128 with the baseline anatomical measurements 114. Memory 110 is further shown to include a warning signal 132 provided when the subsequent anatomical measurements 128 and the baseline anatomical measurements 114 differ by more than a predetermined amount 130. The warning signal 132 is, for example, a warning or data presented to the operator. In another example, the warning signal 132 is a command executed by a remote server or computer, or even a low-field magnetic resonance imaging system.

[0055] Figure 2 shows a flowchart illustrating a method of operating the medical system 100 of FIG. 1. First, at step 200, baseline anatomical measurements 114 are received. The baseline anatomical measurements 114 describe the clinical magnetic resonance image of the subject. Next, at step 202, scan metadata 116 that describes the clinical magnetic resonance image is received. Next, at step 204, scan parameters 120 are transmitted to the low-field magnetic resonance imaging system via the network connection. Next, at step 206, in response to the transmission of the scan parameters 120, subsequent k-space data 122 is received from the low-field magnetic resonance imaging system via the network connection. Next, at step 208, a subsequent magnetic resonance image 124 is reconstructed from the subsequent k-space data 122. Next, at step 210, subsequent anatomical measurements are determined in response to the input of the subsequent magnetic resonance image 124 to the segmentation module 126. Finally, at step 212, a warning signal 132 is provided when the subsequent anatomical measurements 128 differ from the baseline anatomical measurements 114 by more than a predetermined amount 130.

[0056] Figure 3 shows a further example of a medical system 300. The medical system 300 is similar to that shown in FIG. 1, except that it additionally includes a low-field magnetic resonance imaging system 301 and an optional clinical magnetic resonance imaging system 302.

[0057] The low-field magnetic resonance imaging system 301 includes a main magnet 304. The main magnet 304 is a superconducting cylindrical magnet having a bore 306 therein. The use of different types of magnets is also possible, for example, both a split cylindrical magnet and a so-called open magnet can be used. The split cylindrical magnet is similar to a standard cylindrical magnet, except that the cryostat is split into two sections to allow access to the isoplanes of the magnet, and such a magnet can be used, for example, in combination with charged particle beam therapy. The open magnet has two magnet sections, one section on top of the other, with sufficient space between them to receive a subject. The arrangement of the two sections is similar to that of a Helmholtz coil. The open magnet is popular because the subject is not overly confined. Inside the cryostat of the cylindrical magnet is an assembly of superconducting coils.

[0058] Inside the bore 306 of the cylindrical magnet 304 is an imaging zone 308 where the magnetic field is sufficiently strong and uniform to perform magnetic resonance imaging. A field of view 309 is shown within the imaging zone 308. Usually, the k-space data collected is for the field of view 309. The region of interest can be the same as the field of view 309 or a sub-volume of the field of view 309. The subject 318 is shown as being supported by a subject support 320 such that at least a portion of the subject 318 is within the imaging zone 308 and the field of view 309.

[0059] Within the bore 306 of the main magnet 304, there is also a set of magnetic field gradient coils 310 used for collecting preliminary k-space data to spatially encode the magnetic spins within the imaging zone 308 of the magnet 304. The magnetic field gradient coils 310 are connected to a magnetic field gradient coil power supply 312. The magnetic field gradient coils 310 are intended to be representative. Typically, the magnetic field gradient coils 310 include three separate coil sets for spatially encoding in three orthogonal spatial directions. The magnetic field gradient power supply supplies current to the magnetic field gradient coils. The current supplied to the magnetic field gradient coils 310 is controlled as a function of time and is ramped or pulsed.

[0060] Adjacent to the imaging zone 308, there is a radio frequency coil 314 for manipulating the orientation of the magnetic spins within the imaging zone 308 or receiving wireless transmissions from the spins within the imaging zone 308. The radio frequency antenna may include a plurality of coil elements. The radio frequency antenna is also referred to as a channel or an antenna. The radio frequency coil 314 is connected to a radio frequency transceiver 316. The radio frequency coil 314 and the radio frequency transceiver 316 may be replaced by separate transmit and receive coils, as well as separate transmitters and receivers. The radio frequency coil 314 and the radio frequency transceiver 316 are understood to be representative. The radio frequency coil 314 is also intended to represent a dedicated transmit antenna and a dedicated receive antenna. Similarly, the transceiver 316 may represent separate transmitters and receivers. The radio frequency coil 314 may have a plurality of transmit / receive elements, and the radio frequency transceiver 316 may have a plurality of receive / transmit channels.

[0061] The low-field magnetic resonance imaging system 301 includes a computer 102' having a controller 104' connected to a hardware interface 303. The hardware interface 303 enables the controller 104' to send commands to and receive data in response from the low-field magnetic resonance imaging system 300. The controller 104' is further shown as being connected to a network interface 106' and a local memory 110'. The local memory 110' is intended to represent a type of memory accessible by the controller 104'.

[0062] The local memory 110' is shown as including a copy of the scan parameters 120 and subsequent k-space data 122. The memory 110 is further shown as including controller commands 330. The controller commands 330 are commands that enable the controller 104' to perform various tasks such as controlling the low-field magnetic resonance imaging system 300 and executing data and image analysis. The local memory 110' is further shown as storing a survey scan pulse sequence command 332. The pulse sequence command is a command that enables the controller 104' to control the low-field magnetic resonance imaging system 300 to collect k-space data. Thus, the survey scan pulse sequence command 332 is a command that enables the execution of a survey scan in the low-field magnetic resonance imaging system 300.

[0063] The local memory 110’ is further shown to include survey scan k-space data 334 collected by controlling the magnetic resonance imaging system 300 with a survey scan pulse sequence command 332. The memory 110’ is further shown to include a survey scan image 336 which is a magnetic resonance image reconstructed from the survey scan k-space data 334. The memory 110’ is further shown to include the position 338 of a predetermined anatomical landmark within the survey scan image 336. This position 338 within the survey scan image 336 can be identified using various segmentation and landmark recognition techniques. For example, template images and various segmentation algorithms can be used. The memory 110’ is further shown to include a collection pulse sequence command 340. Using this collection pulse sequence command 340, the low-field magnetic resonance imaging system 300 can be controlled to collect subsequent k-space data 122. The memory 110’ is further shown to include a modified collection pulse sequence command 342 modified by the scan parameters 120. This is used, for example, for the accurate positioning of the field of view 309 during the collection of k-space data.

[0064] The low-field magnetic resonance imaging system 300, the computer 102, and the optional clinical magnetic resonance imaging system 302 are shown to be connected via a network connection 350. The baseline anatomical measurements 114 and the scan metadata 116 are received from the clinical magnetic resonance imaging system 302 via the network connection 350.

[0065] Figure 4 shows a flowchart illustrating a method of operating the medical system 300 of FIG. 3. The method shown in FIG. 4 is similar to the method shown in FIG. 2, with additional steps being performed. First, as shown in FIG. 2, steps 200, 202, and 204 are performed. After step 204, the method proceeds to step 400. In step 400, a low-field magnetic resonance imaging system receives scan parameters 120 via network connection 350. Next, in step 402, the low-resolution magnetic resonance imaging system 300 is controlled with a survey scan pulse sequence command 332 to collect survey scan k-space data 334. Next, in step 404, a survey scan image 336 is reconstructed from the survey scan k-space data 334. Next, in step 406, the position 338 of a predetermined anatomical landmark within the survey scan image 336 is detected. Next, in step 408, the acquisition pulse sequence command 340 is adjusted using the position 338 of the predetermined anatomical landmark and / or the scan parameters 120.

[0066] This includes a second resolution and scan coordinates based on a predetermined anatomical landmark of the subject, etc. Next, in step 410, subsequent k-space data 122 is collected by controlling the low-resolution magnetic resonance imaging system using the modified acquisition pulse sequence command 342. Next, in step 412, the subsequent k-space data 122 is transmitted from the low-field magnetic resonance imaging system 300 to the computing system 104 via network connection 350. After performing step 412, as shown in FIG. 2, steps 206, 208, 210, and 212 are performed.

[0067] FIG. 5 shows a further example of a medical system 500 in which the computer 102 functions as a cloud-based system connected to a clinical magnetic resonance imaging system 302 and a plurality of low-field magnetic resonance imaging 300. Each low-field magnetic resonance imaging system 300 has a controller 104' connected to the computer 102 via a network connection 350. The system shown in FIG. 5 has the advantage that the clinical magnetic resonance imaging system 302 is not required to repeatedly collect baseline anatomical measurements 114. A subject can enter any of the low-field magnetic resonance imaging systems 300 and subsequent anatomical measurements 128 can be taken.

[0068] Although the present invention has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description should be considered exemplary or exemplary and not restrictive. The present invention is not limited to the disclosed embodiments.

[0069] Other variations of the disclosed embodiments will be understood and practicable by those skilled in the art in implementing the invention according to the claims, from a study of the drawings, the disclosure, and the appended claims. In the claims, the term "comprising" does not exclude other elements or steps, and a singular element does not exclude a plurality. A single processor or other unit may perform the functions of several items recited in the claims. The mere fact that certain means are recited in mutually different dependent claims does not mean that these means cannot be used advantageously in combination. A computer program can be stored / distributed on any suitable medium, such as an optical storage medium or a solid state medium, supplied together with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless communication systems. Any reference signs in the claims should not be construed as limiting the scope.

Description of the Reference Numerals

[0070] 100 Medical system 102 Computer 104 Computing system 104’ Controller 106 Network interface 108 User interface 110 Memory 110’ Local memory 112 Machine-executable instructions 114 Baseline anatomical measurements 116 Scan metadata 118 Scan parameter configuration module 120 Scan parameters 122 Subsequent k-space data 124 Subsequent magnetic resonance images 126 Segmentation module 128 Subsequent anatomical measurements 130 Predetermined amount 132 Warning signal 200 Receive baseline anatomical measurements that describe a subject's clinical magnetic resonance image 202 Receive scan metadata that describes a subject's clinical magnetic resonance image 204 Transmit scan parameters to a low-field magnetic resonance imaging system via a network connection 206 In response to the transmission of the scan parameters, receive subsequent k-space data from the low-field magnetic resonance imaging system via a network connection 208 Reconstruct subsequent magnetic resonance images from the subsequent k-space data 210 In response to the input of the subsequent magnetic resonance image to the segmentation module, determine subsequent anatomical measurements 212 Provide a warning signal if the subsequent anatomical measurements differ from the baseline anatomical measurements by more than a predetermined amount 300 Medical system 301 Low-field magnetic resonance imaging system 302 Clinical magnetic resonance imaging system 303 Hardware interface 304 Magnet 306 Bore of Magnet 308 Imaging Zone 309 Field of View 310 Magnetic Gradient Coil 312 Power Supply for Magnetic Gradient Coil 314 Radio Frequency Coil 316 Transceiver 318 Subject 320 Subject Support 330 Controller Command 332 Survey Scan Pulse Sequence Command 334 Survey Scan k-Space Data 336 Survey Scan Image 338 Position of Predetermined Anatomical Landmark 340 Measurement Pulse Sequence Command 342 Modified Measurement Pulse Sequence Command 400 Receive Scan Parameters via Network Connection 402 Control a Low-Resolution Magnetic Resonance Imaging System with a Survey Scan Pulse Sequence Command to Collect Survey Scan k-Space Data 404 Reconstruct a Survey Scan Image from Survey Scan k-Space Data 406 Detect the Position of a Predetermined Anatomical Landmark of the Subject in the Survey Scan Image 408 Adjust the Acquisition Pulse Sequence Command Using the Position of a Predetermined Anatomical Landmark of the Subject in the Survey Scan Image, a Second Resolution, and Scan Coordinates Referenced to the Predetermined Anatomical Landmark of the Subject 410 Control a Low-Resolution Magnetic Resonance Imaging System with the Acquisition Pulse Sequence Command to Collect Subsequent k-Space Data 412 Transmit Subsequent k-Space Data to a Computing System via a Network Connection 350 Network Connection 500 Medical System

Claims

1. A medical system including a memory and a computing system for storing machine-executable instructions, The execution of the aforementioned machine-executable instruction is performed by the computing system. Receiving baseline anatomical measurements that describe the subject's clinical magnetic resonance imaging associated with the principal magnetic field strength, Receiving scan metadata describing the clinical magnetic resonance image of the subject, wherein the scan metadata includes scan coordinates relative to a predetermined anatomical landmark of the subject, and the scan metadata describes the clinical magnetic resonance image having a first resolution and a first signal-to-noise ratio. Transmitting scan parameters to a low-field magnetic resonance imaging system via a network connection, wherein the scan parameters are associated with a second resolution and a second signal-to-noise ratio, the first resolution being higher than the second resolution, and / or the first signal-to-noise ratio being higher than the second signal-to-noise ratio, In response to the transmission of the scan parameters, the system receives subsequent k-space data of the subject from the low-field magnetic resonance imaging system via the network connection, wherein the subsequent k-space data is acquired by the low-field magnetic resonance imaging system, and the low-field magnetic resonance imaging system has a principal magnetic field strength significantly lower than the principal magnetic field strength associated with the clinical magnetic resonance image from which the baseline anatomical measurements were derived. Reconstructing subsequent magnetic resonance images from the aforementioned subsequent k-space data, In response to the input of the subsequent magnetic resonance image to the segmentation module, subsequent anatomical measurements are determined, A warning signal is provided when the subsequent anatomical measurement differs from the baseline anatomical measurement by more than a predetermined amount. A medical system that enables the execution of [the procedure].

2. The low-field magnetic resonance imaging system further includes a local memory and a controller, the local memory stores survey scan pulse sequence commands and measurement pulse sequence commands, the local memory further stores controller commands, and the execution of the controller commands is performed by the controller. Receiving the scan parameters via the aforementioned network connection, The survey scan k-space data is collected by controlling the low-field magnetic resonance imaging system with the aforementioned survey scan pulse sequence command. Reconstructing a survey scan image from the aforementioned survey scan k-space data, To detect the location of the predetermined anatomical landmark of the subject in the survey scan image, Adjusting the acquisition pulse sequence command using the position of the predetermined anatomical landmark of the subject in the survey scan image, the second resolution, and the scan coordinates relative to the predetermined anatomical landmark of the subject, The subsequent k-space data is collected by controlling the low-resolution magnetic resonance imaging system with the modified acquisition pulse sequence command, Transmitting the subsequent k-space data to the computing system via the aforementioned network connection. A medical system according to claim 1, which causes the following to be performed.

3. The medical system according to claim 2, wherein the low-resolution magnetic resonance imaging system includes a main magnet for generating a main magnetic field, the main magnetic field having an intensity of 0.6 Tesla or less, and preferably having an intensity of 0.2 Tesla or less.

4. The medical system according to claim 2 or 3, wherein the controller performs the collection of the subsequent k-space data and automatically transmits the subsequent k-space data to the computing system.

5. The medical system according to any one of claims 1 to 3, wherein the memory further stores a scan parameter configuration module, the scan parameter configuration module outputs the scan parameters in response to the reception of the second resolution, the scan coordinates, and the baseline anatomical measurement, and the execution of the machine executable instruction further causes the computing system to receive the scan parameters in response to the input of the second resolution, the scan coordinates, and the baseline anatomical measurement to the scan parameter configuration module.

6. The medical system according to claim 5, wherein the scan parameter configuration module is implemented at least partially as a lookup table, a neural network, or an expert system.

7. The medical system according to claim 5, wherein the scan parameters include the selection of a pulse sequence type.

8. The medical system according to any one of claims 1 to 3, wherein the calculation system is implemented as a cloud computing system.

9. The medical system according to any one of claims 1 to 3, wherein the segmentation module is implemented as a neural network.

10. Receiving training clinical magnetic resonance images or training clinical k-space data having the first resolution, Receiving training anatomical measurements that describe the baseline anatomical measurements for the training clinical magnetic resonance images, The process involves calculating a subsequent magnetic resonance image simulated at the second resolution from either the training clinical magnetic resonance image or the training clinical k-space data, The process involves constructing training data from the pair of simulated subsequent magnetic resonance images and the training anatomical measurements, The neural network is trained using the aforementioned training data. The medical system according to claim 9, wherein the neural network is trained by repeatedly performing the procedure.

11. The medical system according to any one of claims 1 to 3, further comprising a clinical magnetic resonance imaging system, wherein the clinical magnetic resonance imaging system collects the clinical magnetic resonance images of the subject, determines the baseline anatomical measurements from the clinical magnetic resonance images, and provides the baseline geometric measurements to the calculation system from the clinical magnetic resonance images.

12. The steps of receiving baseline anatomical measurements that describe the clinical magnetic resonance image of the subject associated with the principal magnetic field strength, A step of receiving scan metadata describing the clinical magnetic resonance image of the subject, wherein the scan metadata includes scan coordinates relative to a predetermined anatomical landmark of the subject, and the scan metadata describes the clinical magnetic resonance image having a first resolution, A step of transmitting scan parameters to a low-field magnetic resonance imaging system via a network connection, wherein the scan parameters are associated with a second resolution and the scan coordinates relative to a predetermined anatomical landmark of the subject, and the first resolution is higher than the second resolution, Steps of receiving subsequent k-space data of the subject from the low-field magnetic resonance imaging system via the network connection in response to the transmission of the scan parameters, wherein the subsequent k-space data is acquired by the low-field magnetic resonance imaging system, and the low-field magnetic resonance imaging system has a principal magnetic field strength significantly lower than the principal magnetic field strength associated with the clinical magnetic resonance image from which the baseline anatomical measurements were derived. The steps include: reconstructing a subsequent magnetic resonance image from the subsequent k-space data; A step of determining subsequent anatomical measurements in response to the input of the subsequent magnetic resonance image to the segmentation module, The steps include providing a warning signal if the subsequent anatomical measurement differs from the baseline anatomical measurement by more than a predetermined amount, Medical imaging methods, including [specific examples].

13. The medical system further includes the low-field magnetic resonance imaging system, and the method further includes The steps include receiving the scan parameters via the aforementioned network connection, The steps include: collecting survey scan k-space data by controlling the low-field magnetic resonance imaging system with the survey scan pulse sequence command; The steps include: reconstructing a survey scan image from the survey scan k spatial data; The steps include detecting the location of the predetermined anatomical landmark of the subject within the survey scan image, The steps include adjusting the acquisition pulse sequence command using the position of the predetermined anatomical landmark of the subject in the survey scan image, the second resolution, and the scan coordinates relative to the predetermined anatomical landmark of the subject, The steps include: collecting subsequent k-space data by controlling the low-resolution magnetic resonance imaging system with the aforementioned acquisition pulse sequence command; The steps include transmitting the subsequent k-space data to the computing system via the network connection, A medical imaging method according to claim 12, including the following:

14. A computer program including machine-executable instructions for execution by a computing system that controls a medical system, wherein the execution of the machine-executable instructions is performed by the computing system. Receiving baseline anatomical measurements that describe the subject's clinical magnetic resonance imaging associated with the principal magnetic field strength, Receiving scan metadata describing the clinical magnetic resonance image of the subject, wherein the scan metadata includes scan coordinates relative to a predetermined anatomical landmark of the subject, and the scan metadata describes the clinical magnetic resonance image having a first resolution. Transmitting scan parameters to a low-field magnetic resonance imaging system via a network connection, wherein the scan parameters are associated with a second resolution and the scan coordinates relative to a predetermined anatomical landmark of the subject, and the first resolution is higher than the second resolution, and transmitting the scan parameters. In response to the transmission of the scan parameters, the system receives subsequent k-space data of the subject from the low-field magnetic resonance imaging system via the network connection, wherein the subsequent k-space data is acquired by the low-field magnetic resonance imaging system, and the low-field magnetic resonance imaging system has a principal magnetic field strength significantly lower than the principal magnetic field strength associated with the clinical magnetic resonance image from which the baseline anatomical measurements were derived. Reconstructing subsequent magnetic resonance images from the aforementioned subsequent k-space data, In response to the input of the subsequent magnetic resonance image to the segmentation module, subsequent anatomical measurements are determined, A warning signal is provided when the subsequent anatomical measurement differs from the baseline anatomical measurement by more than a predetermined amount. A computer program that executes something.

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