Imaging support system and magnetic resonance imaging system

Abstract

To enable users to understand whether the current settings can achieve the desired image quality for the imaging conditions. [Solution] The imaging support device according to the embodiment comprises a storage unit, an acquisition unit, an estimation unit, and an output unit. The storage unit stores the first imaging conditions, first setting information, and evaluation results regarding the image quality of the first magnetic resonance image used in the first imaging of the first magnetic resonance image in association with each other. The acquisition unit acquires the second imaging conditions and second setting information to be used in the second imaging of the second magnetic resonance image. The estimation unit estimates the evaluation of the image quality of the second magnetic resonance image to be acquired when the second imaging is performed, based on the second imaging conditions and second setting information, the first imaging conditions, the first setting information, and the evaluation results. The output unit outputs the estimation results from the estimation unit.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to an imaging support device and a magnetic resonance imaging device.

Background Art

[0002] Conventionally, when imaging a patient with a magnetic resonance imaging (MRI) device, a technician or the like performs setting work to adjust the posture of the patient to be imaged and the mounting state of the coil on the patient. The way the imaged region is depicted in the magnetic resonance image varies depending on the settings during imaging. Therefore, even when imaging is performed under correct coil and imaging conditions, there are cases where a magnetic resonance image with the expected image quality cannot be obtained depending on the settings.

[0003] However, since the acceptable level of image quality varies depending on the imaging conditions, it has been difficult to uniformly define desirable settings.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] [[ID=X]] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to enable the user to grasp whether the current settings can obtain high image quality according to the imaging conditions. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the respective effects of each configuration shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0006] The imaging support device according to the embodiment comprises a storage unit, an acquisition unit, an estimation unit, and an output unit. The storage unit stores, in association with the first imaging conditions used in the first imaging for the first magnetic resonance image, first setting information indicating the setting state of the subject and coil in the first imaging, and the evaluation result regarding the image quality of the first magnetic resonance image acquired in the first imaging. The acquisition unit acquires second imaging conditions to be used in the second imaging for the second magnetic resonance image, and second setting information indicating the setting state of the subject and coil in the second imaging. The estimation unit estimates the evaluation regarding the image quality of the second magnetic resonance image to be acquired when the second imaging is performed, based on the second imaging conditions and second setting information, the first imaging conditions, the first setting information, and the evaluation result. The output unit outputs the estimation result by the estimation unit. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 shows an example of the configuration of a system according to an embodiment. [Figure 2] Figure 2 shows an example of the correspondence between a typical imaging purpose and the image quality required for magnetic resonance imaging. [Figure 3] Figure 3 is a block diagram showing an example of the configuration of an MRI apparatus according to this embodiment. [Figure 4] Figure 4 is a block diagram showing an example of the configuration of a determination device according to an embodiment. [Figure 5] Figure 5 shows an example of the configuration of the imaging condition database according to this embodiment. [Figure 6] Figure 6 shows an example of the configuration of a setting information database according to the embodiment. [Figure 7] Figure 7 shows an example of the configuration of the evaluation results database according to the embodiment. [Figure 8] Figure 8 shows an example of image quality evaluation in the case of research purposes in the embodiment. [Figure 9]Figure 9 shows an example of image quality evaluation in an emergency outpatient setting according to the embodiment. [Figure 10] Figure 10 shows an example of a comparison result between "current imaging" and "past imaging" under the same imaging conditions as in the embodiment. [Figure 11] Figure 11 is a flowchart showing an example of the image quality estimation process performed by the judgment device in the embodiment. [Modes for carrying out the invention]

[0008] The embodiments of the imaging support device and the magnetic resonance imaging device will be described in detail below with reference to the drawings.

[0009] (Embodiment) Figure 1 shows an example of the configuration of system S according to this embodiment. System S comprises an MRI (Magnetic Resonance Imaging) device (magnetic resonance imaging device) 100 and a judgment device 200. The MRI device 100 and the judgment device 200 are connected to each other so as to be able to communicate with each other, for example, via an in-hospital network.

[0010] The determination device 200 estimates the image quality of the magnetic resonance image to be acquired based on the imaging conditions and subject settings for the imaging to be performed by the MRI device 100, and outputs the estimation result to support the setting work by technicians, etc. The determination device 200 is an example of an imaging support device in this embodiment. The subject is, for example, a patient who is the target of imaging by the MRI device 100.

[0011] The determination device 200 estimates an evaluation of the image quality of the magnetic resonance images to be acquired based on information obtained from the MRI device 100 and information on past magnetic resonance imaging stored in the memory circuit 222. More specifically, as shown in Figure 1, the determination device 200 estimates an evaluation of the image quality of the magnetic resonance images to be acquired based on the imaging conditions, setting information, and image quality evaluation results of past magnetic resonance imaging stored in the memory circuit 222, and the imaging conditions and setting information for the magnetic resonance imaging to be performed. Past imaging is an example of the first imaging in this embodiment. Also, the magnetic resonance images acquired in the past are an example of the first magnetic resonance images in this embodiment. Also, the magnetic resonance imaging to be performed is an example of the second imaging in this embodiment. The magnetic resonance images acquired when the imaging to be performed is performed are an example of the second magnetic resonance images in this embodiment. When comparing the imaging to be performed with past imaging, it is also called the current imaging.

[0012] Generally, when imaging a subject with the MRI system 100, a technician or other professional performs the setup work. The setup work involves adjusting the posture of the subject to be imaged and the state in which the coils are attached to the subject. In the setup work, there are certain established methods depending on the area to be imaged and the imaging sequence, but fine-tuning of the position and adjustments is performed based on the technician's experience. The setup of the MRI system 100 has a significant impact on image quality, and even if imaging is performed with the correct coils and imaging conditions, the magnetic resonance image may not be of the expected quality depending on the setup. For this reason, the importance of setup in the MRI system 100 is greater than in modalities that do not use magnetic resonance technology, such as X-ray CT (Computed Tomography) systems and X-ray systems.

[0013] As a specific example of how the setting state affects image quality, when the coil width is narrow, if the mounting position of the coil shifts in the H (magnetic field) direction or the F (transmission two-section) direction, the sensitivity range may become insufficient. Also, in imaging of the knee joint, shoulder joint, ankle joint, etc., when cord-like structures such as ligaments are positioned at a specific angle with respect to the static magnetic field direction, a phenomenon called the "Magic Angle effect" may occur, where artifacts are generated due to a specific increase in the signal intensity depending on the molecular arrangement direction inside the subject. Generally, when imaging a joint, it is a theory to set it in a slightly bent state, but in such a setting, since the coil does not flex in the Z-axis direction, the adhesion to the body is impaired, and sensitivity unevenness and a decrease in the S / N ratio are likely to occur.

[0014] Also, when the imaging target is a configuration where two objects such as the knee and breast are arranged side by side, especially when the MRI apparatus 100 is a 3T apparatus, it is necessary to consider the unevenness of the B1 distribution due to the setting. In such cases, it is necessary to take measures such as changing the height of the subject's knee or separating the feet in the setting.

[0015] Also, if the fixation of the subject is not enhanced, motion artifacts are likely to occur, but it may be difficult to enhance the fixation depending on the characteristics of the subject. For example, when the subject has a physical handicap or when the subject is a child, etc., it may be necessary to adjust the setting according to the characteristics.

[0016] Generally, since the magnetic field center has the best B0 and B1 uniformity, it is desirable for the subject to be positioned at the magnetic field center. However, depending on the imaging site, there are cases where the imaging site is set at the magnetic field center by deliberately offsetting the position of the subject slightly in the RL (left - right) direction.

[0017] In addition, when a flex coil used for winding around a subject's foot or arm is used as a local RF coil, a shape of the flex coil attached to the subject closer to a perfect circle is more suitable for reducing a decrease in the S / N ratio and maintaining B0 and B1 uniformity.

[0018] Thus, by performing appropriate settings according to the imaging purpose and imaging site, the possibility of obtaining a magnetic resonance image with good image quality is increased.

[0019] However, since the acceptable image quality varies depending on the imaging purpose, it is difficult to uniformly determine a desirable setting. For example, generally, in order to obtain a magnetic resonance image with high image quality, the imaging time including the setting tends to be long. Therefore, in cases where urgency is required, even a relatively low image quality is acceptable and used for diagnosis.

[0020] FIG. 2 is a diagram showing an example of the correspondence relationship between a general imaging purpose, the image quality required for a magnetic resonance image, and the imaging time. For example, as shown in FIG. 2, in imaging for research purposes, the image quality required by a radiologist is high and the acceptable imaging time is long. Also, in precise examinations such as neurosurgery and brain surgery, although not as high as in research, the required image quality is high and the acceptable imaging time is long. Also, for imaging purposes with high urgency such as examinations in an emergency department, the image quality required by a radiologist is low and the acceptable imaging time is short. Also, in general examinations and health checkups, the requirement for image quality is at an intermediate level between that of an emergency department and a precise examination.

[0021] Note that in the present embodiment, the imaging purpose is included in the imaging conditions. For this reason, the determination device 200 determines whether the current setting state is a state in which an appropriate image quality can be imaged according to the imaging conditions.

[0022] Here, the configuration of the MRI device 100 according to the present embodiment will be described.

[0023] Figure 3 is a block diagram showing an example of the configuration of the MRI apparatus 100 according to this embodiment. The MRI apparatus 100 is connected to the camera 30 in a communicative manner, as shown in Figure 3.

[0024] Camera 30 is an optical camera, such as a ceiling camera installed on the ceiling of the examination room, and it images the subject P placed on the examination table 104. Camera 30 may be included as part of the MRI apparatus 100 or it may be an external device of the MRI apparatus 100. Camera 30 is not limited to a ceiling camera and may be installed at the end of the examination table 104. Camera 30 only needs to be able to image the entire body of the subject P placed on the examination table 104 before it is inserted into the bore of the gantry 150. Camera 30 may also be able to image the subject P when the examination table 104 is inserted into the bore of the gantry 150.

[0025] Furthermore, although not shown in Figure 3, the MRI device 100 may be equipped with various sensors to measure the contact state between the subject P and the coil or bed 104. For example, the MRI device 100 may be equipped with one or all of the following: an optical sensor, a load sensor, and a thermal camera. The optical sensor and load sensor measure the contact state between the subject P and the coil or bed 104. The thermal camera, for example, captures the posture of the subject P as a thermographic image. Generally, the room temperature in the examination room where the MRI device 100 is installed may be low, so a blanket or other warming device may be placed over the subject P's body. In such cases, the optical image captured by the camera 30 may not adequately determine the posture of the subject P, so determining the posture of the subject P using a thermographic image is useful. These sensors may be included as part of the MRI device 100's configuration or may be external devices of the MRI device 100. Hereinafter, the optical sensor, load sensor, and thermal camera will be collectively referred to as "various sensors."

[0026] The camera 30 and various sensors are devices for imaging or measuring the setting status of the subject P and the coil. The images captured by the camera 30 and the measurement results from the various sensors are examples of setting information that shows the setting status of the subject P and the coil.

[0027] The MRI apparatus 100 comprises a static magnetic field magnet 101, a gradient magnetic field coil 102, a gradient magnetic field power supply 103, a patient table 104, a patient table control circuit 105, a transmitting coil 106, a transmitting circuit 107, a receiving coil 108, a receiving circuit 109, a sequence control circuit 110, a computer system 120, a gantry 150, and a local RF coil 130. Note that the MRI apparatus 100 does not include a subject P. Subject P is, for example, a patient being imaged by the MRI apparatus 100.

[0028] The static magnetic field magnet 101 is a magnet formed in the shape of a hollow cylinder (including those in which the cross-section perpendicular to the axis of the cylinder is elliptical), and generates a uniform static magnetic field in the space inside.

[0029] The gradient magnetic field coil 102 is a coil formed in the shape of a hollow cylinder (including one in which the cross-section perpendicular to the axis of the cylinder is elliptical), and generates a gradient magnetic field. The gradient magnetic field coil 102 is formed by combining three coils corresponding to the mutually orthogonal X, Y, and Z axes, and these three coils receive current individually from the gradient magnetic field power supply 103 to generate a gradient magnetic field in which the magnetic field strength changes along the X, Y, and Z axes.

[0030] The gradient power supply 103 supplies current to the gradient coil 102. For example, the gradient power supply 103 supplies current individually to each of the three coils that make up the gradient coil 102.

[0031] The bed 104 is equipped with a top plate 104a on which the subject P is placed, and under the control of the bed control circuit 105, the top plate 104a is inserted into the cavity (imaging port, bore) of the gradient magnetic field coil 102 with the subject P placed on it. The bed control circuit 105 is a processor that drives the bed 104 to move the top plate 104a in the longitudinal and vertical directions under the control of the computer system 120.

[0032] The transmitting coil 106 is positioned inside the gradient coil 102 and receives an RF (Radio Frequency) signal from the transmitting circuit 107 to apply a high-frequency magnetic field to the subject P. The subject P is excited by the application of the high-frequency magnetic field by the transmitting coil 106. The region to which the high-frequency magnetic field is applied by the transmitting coil 106 is, for example, the imaging region (Field of View: FOV). The imaging region is an example of the excitation region in this embodiment.

[0033] The transmitting circuit 107, under the control of the sequence control circuit 110, supplies the transmitting coil 106 with RF pulses corresponding to the Larmor frequency, which is determined by the type of atomic nucleus being targeted and the strength of the magnetic field.

[0034] The receiving coil 108 is positioned inside the gradient magnetic field coil 102 and receives the magnetic resonance signal (hereinafter referred to as the MR signal) emitted from the subject P due to the influence of the high-frequency magnetic field. When the receiving coil 108 receives the MR signal, it outputs the received MR signal to the receiving circuit 109. In Figure 3, the receiving coil 108 is shown as being provided separately from the transmitting coil 106, but this is just one example and the system is not limited to this configuration. For example, a configuration in which the receiving coil 108 also serves as the transmitting coil 106 may be adopted.

[0035] The receiving circuit 109 converts the analog MR signal output from the receiving coil 108 from analog to digital to generate MR data. The receiving circuit 109 then transmits the generated MR data to the sequence control circuit 110. Note that the analog-to-digital conversion may also be performed within the receiving coil 108. In addition to analog-to-digital conversion, the receiving circuit 109 is capable of performing any other signal processing.

[0036] The local RF coil 130 is attached to the imaging site of the subject P by a technician or other person and receives the MR signal generated from the subject P. The local RF coil 130 outputs the received MR signal to the receiving circuit 109.

[0037] Local RF coils 130 come in various types depending on the imaging site, such as head coils, knee coils, ankle coils, and elbow coils. In the example shown in Figure 3, the local RF coil 130 is a knee coil, but it is not limited to this. The local RF coil 130 may also have the function of a transmitting coil that applies RF pulses to the subject P. Hereinafter, the local RF coil 130 will be referred to as the coil attached to the subject P.

[0038] The sequence control circuit 110 performs imaging of the subject P by controlling the gradient power supply 103, the transmitting circuit 107, and the receiving circuit 109 based on sequence information transmitted from the computer system 120. The sequence control circuit 110 also receives MR data from the receiving circuit 109. The sequence control circuit 110 then transfers the received MR data to the computer system 120.

[0039] The sequence control circuit 110 may be implemented by a processor, for example, or by a combination of software and hardware.

[0040] Sequence information is information that defines the procedure for performing imaging. Sequence information is generated by the computer system 120 based on conditions specified by the operator, such as the selected excitation position, TR (Repetition Time), TE (Echo Time), slice cross-section position, slice thickness, slice cross-section inclination, FOV (Field of View), and other imaging parameters.

[0041] The computer system 120 performs overall control of the MRI device 100, as well as data acquisition and image reconstruction. The computer system 120 includes a network interface 121, a memory circuit 122, a processing circuit 123, an input interface 124, and a display 125. The computer system 120 is also called a console device.

[0042] The network interface 121 transmits sequence information to the sequence control circuit 110 and receives MR data from the sequence control circuit 110. The MR data received by the network interface 121 is stored in the memory circuit 122.

[0043] Furthermore, the network interface 121 acquires information about the subject P's disease or case. For example, the network interface 121 acquires patient information about subject P from an HIS (Hospital Information System) or RIS (Radiology Information System) outside the MRI device 100 via the hospital network or the like.

[0044] The network interface 121 sends the acquired patient information to the processing circuit 123. Alternatively, the network interface 121 may store the acquired patient information in the memory circuit 122.

[0045] Furthermore, the network interface 121 acquires images of the subject P placed on the top plate 104a from the camera 30. The network interface 121 sends the acquired images to the processing circuit 123. The network interface 121 may also store the acquired images in the storage circuit 122.

[0046] Furthermore, the network interface 121 may transmit to the determination device 200 that an imaging start operation has been performed when an imaging start operation, such as pressing the imaging start button by the operator, is performed on the MRI device 100.

[0047] Furthermore, the network interface 121 acquires measurement results from the optical sensor, load sensor, and thermal camera regarding the contact state between the subject P and the coil or bed 104. The network interface 121 sends the acquired measurement results to the processing circuit 123. The network interface 121 may also store the acquired measurement results in the storage circuit 122.

[0048] Furthermore, under the control of the processing circuit 123, the network interface 121 transmits imaging conditions and setting information for the imaging to be performed to the determination device 200. The network interface 121 also receives the estimated evaluation results regarding the image quality of the magnetic resonance image to be acquired, and setting correction suggestion information for improving the settings, output from the determination device 200. The network interface 121 sends the acquired evaluation estimate results and setting correction suggestion information to the processing circuit 123. The network interface 121 may also store the acquired evaluation estimate results and setting correction suggestion information in the storage circuit 122.

[0049] The memory circuit 122 stores various programs and various data used in the processing of the MRI device 100. The memory circuit 122 can be implemented using semiconductor memory elements such as ROM (Read Only Memory), RAM (Random Access Memory), flash memory, hard disk, optical disc, etc. The memory circuit 122 can also be used as a non-transient storage medium provided by hardware.

[0050] The input interface 124 accepts various instructions and information inputs from operators such as physicians and radiologic technologists. For example, the input interface 124 accepts inputs from the operator such as the purpose of imaging, imaging site, various imaging parameters for determining the imaging sequence, the ID of the physician interpreting the image, whether or not the subject P has a physical disability, whether or not the subject P has claustrophobia, whether or not contrast is used in imaging, whether or not the subject P is conscious, and whether or not the subject P can communicate or interact with the technologist (operator). This information may also be obtained from an external device via the network interface 121.

[0051] The input interface 124 can be implemented by, for example, a trackball, switch buttons, a mouse, a keyboard, or the like.

[0052] In this embodiment, the input interface 124 is not limited to those equipped with physical operating components such as a mouse or keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the MRI device 100 and outputs this electrical signal to the processing circuit 123 is also included as an example of the input interface 124.

[0053] The input interface 124 is connected to the processing circuit 123 and converts various input operations received from the operator into electrical signals, which are then output to the processing circuit 123.

[0054] The display 125 displays various GUIs (Graphical User Interfaces), MR (Magnetic Resonance) images, or various images generated by the processing circuit 123, under the control of the processing circuit 123. The display 125 is also called the display unit.

[0055] The processing circuit 123 is a processor that controls the entire MRI device 100 by, for example, reading programs that can be executed by a computer stored in the memory circuit 122 from the memory circuit 122 and executing each of the read programs. The processing circuit 123 controls each part of the MRI device 100 to perform magnetic resonance imaging.

[0056] Furthermore, the processing circuit 123 transmits the imaging conditions and setting information for the imaging to be performed to the determination device 200. For example, the processing circuit 123 may transmit various information input by the operator via the input interface 124, and patient information acquired from the outside via the network interface 121, to the determination device 200 as imaging conditions. Details of the information content included in the imaging conditions will be described later. In addition, the processing circuit 123 may transmit the camera image captured by the camera 30, the measurement results of the optical sensor and load sensor attached to the MRI device 100, and the thermographic image captured by the thermal camera to the determination device 200 as setting information.

[0057] Furthermore, the processing circuit 123 may display on the display 125 the estimated evaluation results regarding the image quality of the magnetic resonance image to be captured, which are output from the determination device 200, as well as suggested setting corrections to improve the settings.

[0058] Next, the configuration of the determination device 200 will be described.

[0059] Figure 4 is a block diagram showing an example of the configuration of the determination device 200 according to the embodiment. The determination device 200 is, for example, a PC (Personal Computer) or a server device.

[0060] The determination device 200 includes a network interface 221, a memory circuit 222, a processing circuit 223, an input interface 224, and a display 225.

[0061] The network interface 221 acquires imaging conditions and setting information for the upcoming imaging session, which is output from the MRI device 100. The network interface 221 then sends the acquired imaging conditions and setting information to the processing circuit 223.

[0062] Furthermore, the network interface 221 may receive a signal from the MRI device 100 indicating that the operation to start imaging has been performed. Also, the network interface 221 may receive a signal from the door switch of the examination room where the MRI device 100 is installed indicating that the door to the examination room has been closed.

[0063] Furthermore, the network interface 221 transmits to the MRI device 100 the estimated results of the evaluation of the image quality of the magnetic resonance images to be acquired, which are generated by the processing circuit 223, as well as suggested setting modifications to improve the settings.

[0064] The memory circuit 222 stores various programs and various data used in the processing of the determination device 200. The memory circuit 122 is implemented by, for example, semiconductor memory elements such as ROM, RAM, and flash memory, as well as hard disks, optical discs, etc. The memory circuit 222 can also be used as a non-transient storage medium provided by hardware. The memory circuit 222 is an example of a storage unit.

[0065] The memory circuit 222 stores, for example, the imaging conditions, setting information indicating the setting status of the subject and coil, and evaluation results regarding image quality for magnetic resonance images previously taken of the subject, in an associated manner.

[0066] In the example shown in Figure 4, the memory circuit 222 stores an imaging condition database (DB) 222a containing the imaging conditions of previously captured magnetic resonance images, a setting information database 222b containing setting information indicating the setting status of the subject and coil in previously captured magnetic resonance images, and an evaluation result database 222c containing evaluation results regarding the image quality of previously captured magnetic resonance images. The past imaging conditions registered in the imaging condition database 222a are an example of the first imaging conditions used in the first imaging of magnetic resonance images. The past setting information registered in the setting information database 222b is an example of the first setting information.

[0067] The imaging condition database 222a, the setting information database 222b, and the evaluation result database 222c may be configured as a single database. The memory circuit 222 of the determination device 200 does not need to store the previously captured magnetic resonance images themselves. This is because the previously captured magnetic resonance images themselves are not used in the image quality estimation process described later.

[0068] Furthermore, the imaging condition database 222a, the setting information database 222b, and the evaluation result database 222c may only register information about magnetic resonance images previously acquired by the MRI device 100 that is the subject of the image quality estimation process described later, or they may also register information about magnetic resonance images previously acquired by other MRI devices of the same type as the MRI device 100.

[0069] Figure 5 shows an example of the configuration of the imaging condition database 222a according to this embodiment. As shown in Figure 5, the imaging condition database 222a registers information such as the purpose of imaging, imaging site, imaging sequence, imaging parameters related to image quality or speed, patient information, the ID of the radiologist interpreting the image, whether the subject has a physical disability, whether the subject has claustrophobia, whether contrast is used during imaging, whether the subject is conscious, and whether the subject can communicate or interact with the technician (operator).

[0070] A magnetic resonance image ID is an identifier that can identify a magnetic resonance image that has been captured in the past.

[0071] The imaging purpose is information indicating the intended use of previously acquired magnetic resonance images or the areas the radiologist wishes to examine. Examples of imaging purposes include research, detailed examinations, general examinations / screenings, and examinations in emergency departments, as illustrated in Figure 2 above. General examinations / screenings may include health checkups and comprehensive medical examinations. Furthermore, more detailed imaging purposes may be registered for each target of detailed examination. For example, if the imaging site is the head and neck, the targets of detailed examination may include cerebral hemorrhage, brain function, epilepsy, pharynx, and plaque evaluation.

[0072] The imaging area is the area previously targeted for magnetic resonance imaging. The imaging area may be a general area such as the whole body, head, neck, chest, or abdomen, or a more detailed area may be registered. For example, for the head, more specific imaging areas such as the entire head, pituitary gland, cerebellopontine angle / inner ear, orbit, paranasal sinuses, or temporomandibular joint may be registered. For the neck, examples of specific imaging areas include the parotid gland, oral cavity / tongue, pharynx, thyroid gland, and cervical blood vessels / blood flow. For the chest, examples of specific imaging areas include the heart, mammary glands, and thoracic blood vessels / blood flow. For the abdomen, examples of specific imaging areas include the liver, pancreas / pancreatic duct / bile duct, adrenal gland, kidney, and abdominal blood vessels / blood flow.

[0073] While the registered information for items other than the imaging area can be somewhat general (for example, "head"), the more detailed the registered information, the more accurately past imaging conditions can be identified for comparison in the image quality estimation process described later.

[0074] The imaging sequence is the imaging sequence used in previous magnetic resonance imaging. Furthermore, the imaging parameters related to image quality or speed are the values ​​of the imaging parameters entered into the MRI device 100 by the operator during previous magnetic resonance imaging.

[0075] Patient information refers to information about the patient P who was the subject of past magnetic resonance imaging (MRI) scans. Patient information includes, for example, gender, age, implant information, and whether the patient is pregnant or breastfeeding. Implant information indicates whether the patient has implants and the location of any implants.

[0076] The radiologist's ID is identification information that identifies the radiologist who interpreted previously acquired magnetic resonance images. The desired image quality may vary depending on the radiologist. The radiologist's ID is just one example of identification information for a radiologist.

[0077] In the example shown in Figure 5, the imaging conditions database 222a also includes other information such as whether the subject P has a physical disability, whether the subject P has claustrophobia, whether contrast is used during imaging, whether the subject is conscious, and whether the subject can communicate or interact with the technician (operator). This information can affect the subject P's settings and image quality during imaging. For example, if the subject P has a physical disability, there may be limitations on their posture during imaging. The imaging conditions database 222a does not necessarily have to include all the items shown in Figure 5. In this embodiment, the imaging conditions registered in the imaging conditions database 222a include at least one of the following: imaging purpose, imaging site, imaging sequence, patient information, and identification information of the attending physician. The imaging conditions database 222a may also include other information in addition to the example shown in Figure 5.

[0078] Figure 6 shows an example of the configuration of the setting information database 222b according to this embodiment. The setting information database 222b stores setting information from past magnetic resonance imaging. For example, as shown in Figure 6, the setting information database 222b stores camera images captured by the camera 30, measurement results from optical sensors and load sensors attached to the MRI device 100, and thermographic images captured by a thermal camera, all of which are associated with the magnetic resonance image ID.

[0079] The setting information database 222b does not have to include all of the information shown in Figure 6, and may also include other information. In this embodiment, the setting information includes at least one of the following: a camera image of the subject P placed on the bed of the MRI device 100, and measurement results regarding the contact state between the subject P and the coil or bed 104, measured by various sensors provided on the coil or bed of the MRI device 100.

[0080] Figure 7 shows an example of the configuration of the and evaluation result database 222c according to this embodiment. For example, as shown in Figure 7, the evaluation result database 222c registers the magnetic resonance image ID of a previously captured magnetic resonance image and the evaluation result of the image quality for the magnetic resonance image indicated by that magnetic resonance image ID in association with each other.

[0081] The evaluation of the image quality of previously acquired magnetic resonance imaging (MMRI) images is, for example, a judgment made by the radiologist who interpreted the previously acquired MMRI images, either "good image quality" or "poor image quality." The criteria for judging "good image quality" or "poor image quality" vary depending on the purpose of the imaging or the radiologist who interprets the images.

[0082] For example, Figures 8 and 9 illustrate the differences in image quality evaluation for different imaging purposes. Figure 8 shows an example of image quality evaluation in the case of research purposes in this embodiment. Figure 9 shows an example of image quality evaluation in the case of an emergency outpatient department in this embodiment.

[0083] As shown in Figures 8 and 9, the user 50, such as a radiologist, makes a judgment on the acquired magnetic resonance images 90a to 90d as "good image quality" or "poor image quality," and attaches the judgment result to the magnetic resonance images 90a to 90d as an evaluation of image quality.

[0084] As shown in Figure 9, for imaging purposes with short allowable imaging times, user 50 will rate magnetic resonance imaging 90c as "good quality" if it determines that the image is still diagnostically viable for emergency use, even if the image quality is generally poor. Conversely, if the image quality is so low that it cannot even be used for diagnosis, even in an emergency setting, user 50 will rate magnetic resonance imaging 90d as "poor quality." Furthermore, as shown in Figure 8, for imaging purposes with longer allowable imaging times, such as research, user 50 evaluates image quality using stricter criteria than in the emergency room case shown in Figure 9. In other words, magnetic resonance imaging 90c, which is rated as "good quality" when the imaging purpose is an examination in an emergency room, may be rated as "poor quality" if the imaging purpose is research.

[0085] Furthermore, the evaluation of the image quality of previously acquired magnetic resonance images may be determined by criteria other than evaluation by the radiologist interpreting the images. For example, magnetic resonance images acquired by MRI device 100 are transferred from MRI device 100 to PACS or a workstation for interpretation. At that time, the technician who performed the imaging may check the magnetic resonance images before the transfer and exclude those with poor image quality from the transfer. For this reason, magnetic resonance images transferred from MRI device 100 to PACS or a workstation may be associated with an evaluation of "good image quality." However, in the case of detailed examinations or research purposes, the required image quality is high, so even transferred magnetic resonance images may be judged as "poor image quality" by the radiologist interpreting them. For this reason, the automatic assignment of evaluations based on whether or not an image is transferred may be applied only when the purpose of imaging is other than detailed examinations or research, and the application of automatic evaluations may be distinguished according to the purpose of imaging, such as not assigning an automatic judgment when the purpose of imaging is detailed examination or research.

[0086] Another example of an evaluation criterion is whether or not a retake was performed. A retake occurs when the settings remain the same, but the imaging conditions have been changed. For example, artifacts may occur in the magnetic resonance image or the image may be unclear due to inappropriate imaging parameters or imaging sequences. In such cases, the technician corrects the imaging parameters or imaging sequence and performs a retake. In such cases, the magnetic resonance image before the retake may be evaluated as having "poor image quality."

[0087] Another example of an evaluation criterion is the setting of thresholds for B1 and B0. Generally, image unevenness related to the uniformity of B1 and B0 is one of the causes of artifacts. For this reason, regarding the values ​​of B1 and B0, those below the thresholds set for each imaging purpose may be evaluated as "poor image quality."

[0088] The evaluation result database 222c may be generated by the judgment device 200. For example, the processing circuit 223 of the judgment device 200 may include a registration function that registers the user 50's evaluation of the image quality of the magnetic resonance images 90a to 90d, or an evaluation based on the above-mentioned evaluation criteria, in association with the magnetic resonance images 90a to 90d in the evaluation result database 222c. The registration function is an example of a registration unit. Alternatively, the evaluation result database 222c may be generated on a device such as a PC different from the judgment device 200 and stored in the judgment device 200 via the network interface 221.

[0089] Returning to Figure 4, the processing circuit 223 is a processor that controls the entire determination device 200. The processing circuit 223 includes an acquisition function 223a, an estimation function 223b, a generation function 223c, and an output function 223d. The acquisition function 223a is an example of the acquisition unit. The estimation function 223b is an example of the estimation unit. The generation function 223c is an example of the generation unit. The output function 223d is an example of the output unit.

[0090] Here, for example, the acquisition function 223a, estimation function 223b, generation function 223c, and output function 223d, which are components of the processing circuit 123, are stored in the memory circuit 122 in the form of programs that can be executed by a computer. The processing circuit 123 reads each program from the memory circuit 122 and executes each program that has been read, thereby realizing the function corresponding to each program. In other words, the processing circuit 123 in the state in which each program has been read has the functions shown in the processing circuit 223 of Figure 4. In Figure 4, the processing functions of acquisition function 223a, estimation function 223b, generation function 223c, and output function 223d are realized by a single processing circuit 223, but it is also possible to configure the processing circuit 223 by combining multiple independent processors, and each processor realizes each processing function by executing each program.

[0091] In the above description of the MRI device 100 and the judgment device 200, the term "processor" refers to circuits such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), or a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). Alternatively, instead of storing the program in the memory circuit 122, the program may be directly embedded within the processor circuit. In this case, the processor functions by reading and executing the program embedded within the circuit.

[0092] The acquisition function 223a acquires, via the network interface 221, the imaging conditions for the planned magnetic resonance imaging and setting information indicating the current setting status of the subject P and coil from the MRI device 100. The imaging conditions for the planned magnetic resonance imaging are an example of the second imaging conditions in this embodiment. The setting information indicating the current setting status of the subject P and coil is an example of the second setting information in this embodiment.

[0093] The imaging conditions for the planned magnetic resonance imaging will include information such as the magnetic resonance image ID, imaging purpose, imaging site, imaging sequence, imaging parameters related to image quality or speed, patient information, the ID of the radiologist interpreting the image, whether the subject has any physical disabilities, whether the subject has claustrophobia, whether or not contrast is used during imaging, whether the subject is conscious, and whether or not the subject can communicate with the technician (operator).

[0094] Setting information indicating the current setting status of the subject and coil includes, for example, at least one of the following: camera images captured by camera 30, measurement results from optical sensors and load sensors attached to the MRI device 100, and thermographic images captured by a thermal camera.

[0095] The estimation function 223b estimates the image quality of the magnetic resonance image that would be obtained if the planned imaging were performed, based on the imaging conditions and current setting information for the planned imaging acquired by the acquisition function 223a, and the imaging conditions, setting information, and evaluation results for past imaging stored in the memory circuit 222.

[0096] More specifically, the estimation function 223b compares the current setting information with past setting information stored in the memory circuit 222 that corresponds to past imaging conditions that match the imaging conditions of the planned imaging, and calculates the similarity. Based on the image quality evaluation results corresponding to past imaging conditions that match the imaging conditions of the planned imaging, and the calculated similarity, the estimation function 223b estimates the image quality evaluation of the magnetic resonance image obtained from the planned imaging.

[0097] Figure 10 shows an example of the comparison results of "past imaging" under the same imaging conditions as "current imaging" in this embodiment. The estimation function 223b searches the imaging condition database 222a (in other words, the imaging conditions of "past imaging") stored in the memory circuit 222 for imaging conditions that are the same as the imaging conditions of the planned magnetic resonance image acquisition (in other words, "current imaging") acquired from the MRI device 100. The estimation function 223b then obtains the image quality evaluation and setting information associated with the retrieved magnetic resonance image ID (i.e., the magnetic resonance image ID of the magnetic resonance image acquired under the same imaging conditions as "current imaging") from the evaluation result database 222c and the setting information database 222b, respectively. In the example shown in Figure 10, four past imagings were acquired under the same imaging conditions as "current imaging".

[0098] The estimation function 223b compares the current setting information with past setting information and calculates the similarity. The estimation function 223b may, for example, determine the similarity by comparing the current camera image with past camera images from the setting information using image processing. Alternatively, the estimation function 223b may determine the similarity by comparing the current measurement results of various sensors with the measurement results of various sensors from past imaging.

[0099] Furthermore, the estimation function 223b may not only compare current and past camera images, but may also recognize the position and shape of the coil, the distance between the subject P and the magnetic field center, the joint angles, etc., from the current and past camera images, and then compare the recognition results.

[0100] In the example shown in Figure 10, the evaluation of the first past image was "good image quality," and the similarity between the current setting information and the setting information from the first past image is 60%. The evaluation of the second past image was "poor image quality," and the similarity between the current setting information and the setting information from the first past image is 85%. The evaluation of the third past image was "good image quality," and the similarity between the current setting information and the setting information from the first past image is 50%. The evaluation of the fourth past image was "poor image quality," and the similarity between the current setting information and the setting information from the first past image is 70%.

[0101] In this case, the similarity between the current setting information and the setting information of the second and fourth past imaging instances, which are associated with an evaluation of "poor image quality," is higher than the similarity between the current setting information and the setting information of the first and third past imaging instances, which are associated with an evaluation of "good image quality." In other words, the current setting state is closer to the setting state when a magnetic resonance image judged to be "poor quality" was acquired than to the setting state when a magnetic resonance image judged to be "good quality" was acquired in the past. For this reason, the estimation function 223b estimates that the magnetic resonance image obtained by performing imaging with the current setting state will be of "poor quality."

[0102] The similarity between the current setting information shown in Figure 10 and the setting information of the first and third past imaging instances, which are associated with the evaluation "good image quality," is an example of the first similarity in this embodiment. Furthermore, the similarity between the current setting information shown in Figure 10 and the setting information of the second and fourth past imaging instances, which are associated with the evaluation "poor image quality," is an example of the second similarity in this embodiment.

[0103] Furthermore, it is desirable that the imaging condition database 222a, the setting information database 222b, and the evaluation result database 222c register past information regarding both magnetic resonance images that received a "good image quality" evaluation and magnetic resonance images that received a "poor image quality" evaluation. This is because the setting information is not similar to that of past imaging that received a "good image quality" evaluation, which does not necessarily mean that the acquired magnetic resonance image will have poor image quality. For this reason, as shown in Figure 10, the estimation function 223b compares the current setting information with the setting information of past imaging that received a "good image quality" evaluation and the setting information of past imaging that received a "poor image quality" evaluation.

[0104] Furthermore, the imaging conditions of "past imaging" that match the imaging conditions of "current imaging" may include not only cases where the imaging conditions are exactly the same, but also cases where only some of the imaging conditions are the same, or where the content of the imaging condition items is similar. For example, the estimation function 223b may use past setting information and evaluation results associated with past imaging conditions where only the "imaging purpose" and "imaging area" match the imaging conditions of "current imaging" to estimate image quality. Alternatively, the estimation function 223b may use past setting information and evaluation results associated with other "imaging purposes" that require a similar level of accuracy as the "imaging purpose" of the imaging conditions of "current imaging" to estimate image quality. Alternatively, the estimation function 223b may use past setting information and evaluation results where the "ID of the radiologist" and "imaging area" are the same as the imaging conditions of "current imaging" to estimate image quality.

[0105] Returning to Figure 4, the generation function 223c generates setting correction suggestion information when the estimation function 223b estimates that the magnetic resonance image obtained by imaging with the current settings is "poor quality". The setting correction suggestion information is an example of a suggestion for improving the settings of the subject P and the coil.

[0106] For example, the generation function 223c compares the setting information from "past imaging" that was evaluated as having "good image quality" under the same imaging conditions as "current imaging," with the current setting information that is estimated to produce a magnetic resonance image with "poor image quality," and extracts the points of dissimilarity. For example, in the example shown in Figure 10, the first and third past imaging were evaluated as having "good image quality." In this case, the generation function 223c extracts the differences between the current setting information and the setting information from the first and third past imaging, and generates setting correction suggestion information to resolve the extracted differences. For example, if the position of the subject P on the top plate 104a as depicted in the camera image in the current setting information is shifted from the center of the top plate 104a than the position of the subject P on the top plate 104a as depicted in the camera image in the first and third past setting information, the generation function 223c generates setting correction suggestion information indicating that the position of the subject P should be moved to the center of the top plate 104a, the direction of movement, and the amount of movement.

[0107] Furthermore, the generation function 223c may not only compare current and past camera images, but also recognize the position and shape of the coil, the distance between the subject P and the magnetic field center, the joint angles, etc., from the current and past camera images, and compare the recognition results. In addition, the generation function 223c also compares the measurement results from various sensors included in the setting information of "past imaging" which was evaluated as having "good image quality" under the same imaging conditions as "current imaging," extracts the differences by comparing them with the measurement results included in the current setting information, and generates setting correction suggestion information to resolve the differences.

[0108] The setting modification suggestion information is, for example, text data indicating the details of setting modifications for the technician operating the MRI device 100. Alternatively, the setting modification suggestion information may be text data or audio data instructing the subject P to change their posture.

[0109] The output function 223d outputs the estimated result regarding the image quality of the magnetic resonance image obtained with the current settings, as determined by the estimation function 223b. More specifically, the output function 223d transmits the estimated result of the evaluation of the image quality of the magnetic resonance image to be acquired to the MRI device 100 via the network interface 221.

[0110] Furthermore, if the estimation function 223b estimates that the magnetic resonance image obtained by imaging with the current settings is "poor quality," the output function 223d transmits the setting correction suggestion information generated by the generation function 223c to the MRI device 100 via the network interface 221.

[0111] Furthermore, the method of outputting the estimation results and setting correction suggestion information is not limited to outputting to the MRI device 100. The output function 223d may also display the estimation results and setting correction suggestion information on the display 225. In addition, if the judgment device 200 is equipped with a speaker, the output function 223d may output the estimation results and setting correction suggestion information as audio from the speaker.

[0112] The input interface 124 accepts various instructions and information inputs from operators such as physicians and radiologic technologists. For example, the input interface 124 accepts inputs from the operator such as the purpose of imaging, imaging site, various imaging parameters for determining the imaging sequence, the ID of the physician interpreting the image, whether or not the subject P has a physical disability, whether or not the subject P has claustrophobia, whether or not contrast is used in imaging, whether or not the subject P is conscious, and whether or not the subject P can communicate or interact with the technologist (operator). This information may also be obtained from an external device via the network interface 121.

[0113] The input interface 224 can be implemented by, for example, a trackball, switch buttons, a mouse, a keyboard, etc. However, in this embodiment, the input interface 224 is not limited to those equipped with physical operating components such as a mouse or keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the determination device 200 and outputs this electrical signal to the processing circuit 223 is also included as an example of the input interface 224.

[0114] The input interface 224 is connected to the processing circuit 223 and converts various input operations received from the operator into electrical signals, which are then output to the processing circuit 123.

[0115] The display 225 displays various GUIs, etc., under the control of the processing circuit 223. Note that if the estimation results and setting correction suggestion information are displayed on the display 125 of the MRI device 100, the judgment device 200 does not need to have a display 225.

[0116] Next, we will explain the processing flow for estimating image quality performed by the judgment device 200 configured as described above.

[0117] Figure 11 is a flowchart illustrating an example of the image quality estimation process performed by the determination device 200 in this embodiment. The process shown in Figure 11 is initiated, for example, when the determination device 200 receives a signal from the MRI device 100 indicating that an imaging start operation has been performed, or when it receives a signal from the examination room door switch indicating that the door has been closed. However, the triggers for initiating the image quality estimation process are not limited to these. For example, an operator may manually input the execution of the image quality estimation process in the determination device 200 or the MRI device 100.

[0118] First, the acquisition function 223a of the determination device 200 acquires the imaging conditions for the imaging to be performed and the current setting information from the MRI device 100 (S1).

[0119] Then, the estimation function 223b of the determination device 200 estimates the evaluation of the image quality of the magnetic resonance image obtained when imaging is performed with the current settings (S2). Specifically, the estimation function 223b searches the imaging condition database 222a stored in the memory circuit 222 for the same imaging conditions as those acquired in S1. The estimation function 223b then acquires the image quality evaluation and setting information associated with the retrieved magnetic resonance image ID from the evaluation result database 222c and the setting information database 222b, respectively. The estimation function 223b calculates the similarity between the setting information acquired in S1 and past setting information associated with the retrieved magnetic resonance image ID. Based on the calculated similarity and the evaluation of the magnetic resonance image corresponding to the past setting information, the estimation function 223b estimates whether the evaluation of the magnetic resonance image obtained when imaging is performed with the current settings is likely to be "good image quality" or "poor image quality".

[0120] If the estimation result is "poor image quality" (S3 "Yes"), the generation function 223c of the judgment device 200 compares the current setting information with the setting information corresponding to past magnetic resonance images that were evaluated as "good image quality" and extracts the difference (S4).

[0121] The generation function 223c generates setting correction suggestion information based on the extracted differences (S5).

[0122] The output function 223d of the determination device 200 outputs the estimation result obtained in processing S2 and the setting correction suggestion information obtained in processing S5 to the MRI device 100 (S6). The estimation result and the setting correction suggestion information are displayed, for example, on the display 125 of the MRI device 100.

[0123] If the estimation result indicates "poor image quality," the operator of the MRI device 100 may correct the settings and then restart the process from S1. Alternatively, the MRI device 100 may allow the operator to proceed with imaging regardless of the estimation result. In this embodiment, the decision of whether or not to correct the settings based on the estimation result and the suggested setting correction information is left to the operator of the MRI device 100.

[0124] Furthermore, if the estimation result is "good image quality" (S3 "No"), the output function 223d of the judgment device 200 outputs a notification to the MRI device 100 indicating that there are no problems with the current settings (S7). The notification indicating that there are no problems with the current settings is, for example, the estimation result obtained in the processing of S2. Note that if the estimation result is "good image quality", the output function 223d may be configured not to send any notification. In this case, imaging is performed on the MRI device 100 with the current settings. At this point, the processing of this flowchart ends.

[0125] As described above, the determination device 200 of this embodiment includes a storage circuit 222 that stores, in association with the imaging conditions of previously captured magnetic resonance images, setting information indicating the setting status of the subject and coil, and evaluation results regarding image quality. Based on the imaging conditions and current setting information for the planned magnetic resonance image acquisition and the imaging conditions, setting information, and evaluation results of past acquisitions stored in the storage circuit 222, the device estimates the evaluation of the image quality of the magnetic resonance image to be obtained from the planned acquisition and outputs the estimation result. Therefore, with the determination device 200 of this embodiment, the user can understand whether a high level of image quality corresponding to the imaging conditions can be obtained with the current settings.

[0126] Furthermore, the setting information of this embodiment includes at least one of the following: a camera image of a subject P placed on the bed 104 of the MRI device 100, and measurement results regarding the contact state between the subject P and the coil or bed 104, as measured by a sensor provided on the coil or bed 104 of the MRI device 100. Therefore, the determination device 200 of this embodiment can identify the current setting state of the subject P and the coil, which affects the image quality of the magnetic resonance image.

[0127] Furthermore, the imaging conditions of this embodiment include at least one of the following: imaging purpose, imaging target area, imaging sequence, patient information, and the ID of the radiologist. Therefore, the determination device 200 of this embodiment can accurately estimate the required image quality level, which varies depending on the imaging purpose, imaging target area, imaging sequence, patient information, and the ID of the radiologist, and the appropriate setting state, to evaluate the image quality of the magnetic resonance image obtained with the current setting state.

[0128] Furthermore, the imaging conditions of this embodiment include at least the ID of the radiologist, and the determination device 200 of this embodiment estimates the evaluation of the image quality of the magnetic resonance image obtained from the planned imaging based on the setting information and evaluation results associated with the same radiologist ID as the radiologist ID of the planned imaging, from among the past setting information and evaluation results stored in the memory circuit 222. Therefore, according to the determination device 200 of this embodiment, the criteria for evaluating image quality by individual radiologists can be reflected in the estimation of the evaluation of the image quality of the magnetic resonance image.

[0129] Furthermore, the determination device 200 of this embodiment compares the current setting information with past setting information stored in the memory circuit 222 that matches the imaging conditions for the planned imaging, and calculates the similarity. Based on the evaluation results of past magnetic resonance images that match the imaging conditions for the planned imaging and the calculated similarity, the device estimates the evaluation. Since the required image quality and appropriate setting state differ depending on the imaging conditions, the determination device 200 of this embodiment can accurately estimate the evaluation of the image quality of magnetic resonance images obtained with the current setting state.

[0130] Furthermore, if the determination device 200 of this embodiment indicates that the estimated image quality of the magnetic resonance image obtained with the current settings is "poor quality," it outputs setting correction suggestion information to improve the settings of the subject P and the coil. Therefore, according to the determination device 200 of this embodiment, it is possible to suggest improvements to the settings to the user based on the estimated image quality of the magnetic resonance image obtained with the current settings.

[0131] (Variation 1) In the above-described embodiment, the determination device 200 was connected to the MRI device 100 on a one-to-one basis, but the determination device 200 may be connected to multiple MRI devices 100. In this case, the determination device 200 may store the imaging conditions, setting information, and image quality evaluation results of magnetic resonance images previously acquired by multiple MRI devices 100.

[0132] (Modification 2) In the above-described embodiment, the determination device 200 was used as an example of an imaging support device, but the MRI device 100 may also be equipped with each of the functions of the determination device 200 described in the above-described embodiment. In this case, the MRI device 100 becomes an example of an imaging support device.

[0133] (Variation 3) In the above-described embodiment, the estimation function 223b of the determination device 200 estimated the image quality obtained with the current setting state by comparing the current setting state with past setting states stored in the setting information database 222b and determining the degree of similarity. However, the image quality estimation process is not limited to this.

[0134] For example, a pre-trained model that has been trained by machine learning using the information registered in the above-mentioned imaging condition database 222a, setting information database 222b, and evaluation result database 222c may be used. In this case, the pre-trained model receives input of the imaging conditions for the planned magnetic resonance image acquisition and setting information indicating the current setting state, and outputs an estimation result of "good image quality" or "good image quality". Alternatively, setting correction suggestion information may be generated by a pre-trained model that has been trained by machine learning using the information registered in the above-mentioned imaging condition database 222a, setting information database 222b, and evaluation result database 222c.

[0135] (Modification 4) In the above-described embodiment, the estimation function 223b of the determination device 200 may use a past image where the current image, the ID of the radiologist interpreting the image, and the imaged area match. However, it may also specify a radiologist ID different from the radiologist ID in the current image and use it to estimate image quality.

[0136] For example, if there is a physician who is particularly skilled in image interpretation, performing image quality estimation processing based on past images interpreted by that physician increases the likelihood of obtaining magnetic resonance images with image quality close to that of past images that the physician deemed to be "good quality."

[0137] (Variation 5) In the embodiments described above, it was stated that it is desirable for the imaging condition database 222a, the setting information database 222b, and the evaluation result database 222c to register past information regarding both magnetic resonance images that received an evaluation of "good image quality" and magnetic resonance images that received an evaluation of "poor image quality." However, it is not necessarily required that information on past imaging for both "good image quality" and "poor image quality" be stored. The memory circuit 222 of the determination device 200 shall store at least information regarding magnetic resonance images that have received an evaluation of "good image quality" in the past.

[0138] (Experimental variation 6) In the embodiments described above, the setting correction suggestion information is output from the display 125 or speaker of the MRI device 100, but the output method is not limited to these. For example, the MRI device 100 may be equipped with a projector that can project the setting correction suggestion information onto the patient table 104. The projector may, for example, display as an image on the patient table 104 the posture that the subject P should take and the position of the coil.

[0139] (Example 7) In the above-described embodiment, the estimation function 223b of the determination device 200 uses information from past imaging with the same imaging conditions as the current imaging to estimate image quality. However, if there is no imaging condition database 222a stored in the memory circuit 222 that matches the imaging conditions of the current imaging, the estimation function 223b may use information from past imaging taken under similar imaging conditions to the current imaging conditions to estimate image quality. In this case, the estimation function 223b may weight the reliability of the estimation result based on the degree of similarity between the current imaging conditions and the past imaging conditions used for estimation. The output function 223d may also output the reliability along with the estimation result.

[0140] (Variation 8) Furthermore, the estimation function 223b of the determination device 200 may weight the estimation result according to the differences between the patient information and the various patient-related information when the imaging condition database 222a stored in the memory circuit 222 differs from the patient information of the current imaging conditions and the various patient-related information included in "Other" shown in Figure 5. For example, the older the patient, the more likely it is that the image quality will be worse in imaging where breath-holding is required during imaging. Whether or not breath-holding is required during imaging can be determined, for example, from the imaging site or imaging sequence. In this case, the estimation function 223b may weight the image quality estimation result towards the side where the patient's age is likely to be worse.

[0141] Furthermore, if subject P has physical disabilities that prevent them from lying on their back or straightening their back, it can be difficult to achieve an ideal setting, which tends to result in poorer image quality. In addition, subject P may be unconscious, or too young or too old, and may not be able to understand the technician's instructions. In such cases, the estimation function 223b may weight the image quality estimation result towards the side that worsens, taking into account the patient's physical disabilities, age, consciousness status, etc.

[0142] Alternatively, since factors such as age, physical disability, and consciousness level can sometimes be difficult to improve, the estimation function 223b may weight the image quality to tolerate poor image quality. The user may be able to set whether the weighting for each item of the imaging condition is applied in the direction of "good image quality" or "poor image quality."

[0143] According to at least one embodiment described above, the user can determine whether the current settings can achieve the desired image quality according to the imaging conditions.

[0144] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be implemented in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments are possible without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0145] 30 Cameras 50 users 90a, 90b, 90c, 90d Magnetic Resonance Images 100 MRI machine 101 Static Magnetic Field Magnet 102 Gradient field coil 103 Gradient magnetic field power supply 104 berths 104a Top plate 105 Bed control circuit 106 Transmitter coil 107 Transmitter Circuit 108 Receiving coil 109 Receiving Circuit 110 Sequence control circuit 120 Computer Systems 121,221 Network Interfaces 122,222 Memory circuit 123,223 Processing Circuits 124,224 Input Interfaces 125,225 displays 130 Local RF Coils 150 Gantry 200 Judgment device 222a Imaging Conditions Database 222b Setting Information Database 222c Evaluation Results Database 223a Acquisition function 223b Estimation function 223c generation function 223d Output function P Subject S System

Claims

1. A storage unit that stores, in association with, first imaging conditions used in the first imaging of the first magnetic resonance image, first setting information indicating the setting state of the subject and coil in the first imaging, and evaluation results regarding the image quality of the first magnetic resonance image acquired in the first imaging, An acquisition unit that acquires second imaging conditions used for second imaging related to a second magnetic resonance image, and second setting information indicating the setting state of the subject and coil in the second imaging, An estimation unit estimates an evaluation of the image quality of the second magnetic resonance image obtained when the second imaging is performed, based on the second imaging conditions and second setting information, the first imaging conditions, the first setting information, and the evaluation result. An output unit that outputs the estimation result from the estimation unit, An imaging support device equipped with the following features.

2. The first setting information and the second setting information include at least one of the following: a camera image of the subject placed on the bed of the magnetic resonance imaging apparatus, and measurement results regarding the contact state between the subject and the coil or the bed, measured by the coil or a sensor provided on the bed of the magnetic resonance imaging apparatus. The imaging support device according to claim 1.

3. The first imaging conditions and the second imaging conditions include at least one of the following: imaging purpose, imaging target area, imaging sequence, patient information, and identification information of the attending physician. The imaging support device according to claim 1.

4. The estimation unit compares the first setting information stored in the storage unit, specifically the first setting information associated with the first imaging conditions that match the second imaging conditions, with the second setting information to determine the similarity, and estimates the evaluation regarding the image quality of the second magnetic resonance image obtained when the second imaging is performed, based on the determined similarity and the evaluation result associated with the first imaging conditions that match the second imaging conditions. The imaging support device according to claim 3.

5. The first imaging conditions and the second imaging conditions include at least the identification information of the physician in charge. The estimation unit estimates the evaluation of the image quality of the second magnetic resonance image based on the first setting information and evaluation results stored in the storage unit, specifically those to which the same identification information of the attending physician included in the first imaging conditions is associated. The imaging support device according to claim 3.

6. The evaluation result indicates the user's evaluation of the first magnetic resonance image as either "good image quality" or "poor image quality." The estimation unit determines a first similarity between the second setting information and the first setting information stored in the storage unit that is associated with the evaluation result "good image quality," and a second similarity between the first setting information stored in the storage unit and the first setting information that is associated with the evaluation result "poor image quality," and estimates the evaluation result corresponding to the higher of the first similarity and the second similarity as the evaluation regarding the image quality of the second magnetic resonance image. The imaging support device according to claim 1.

7. If the estimation result from the estimation unit indicates that the image quality of the second magnetic resonance image is poor, the output unit outputs suggestions for improving the settings of the subject and the coil. The imaging support device according to any one of claims 1 to 6.

8. A storage unit that stores, in association with, first imaging conditions used in the first imaging of the first magnetic resonance image, first setting information indicating the setting state of the subject and coil in the first imaging, and evaluation results regarding the image quality of the first magnetic resonance image acquired in the first imaging, An acquisition unit that acquires second imaging conditions used for second imaging related to a second magnetic resonance image, and second setting information indicating the setting state of the subject and coil in the second imaging, An estimation unit estimates an evaluation of the image quality of the second magnetic resonance image obtained when the second imaging is performed, based on the second imaging conditions and second setting information, the first imaging conditions, the first setting information, and the evaluation result. An output unit that outputs the estimation result from the estimation unit, A magnetic resonance imaging system equipped with the following features.

Images