Magnetic resonance imaging apparatus and body movement information processing method

Abstract

The invention provides a magnetic resonance imaging apparatus and a body movement information processing method capable of detecting and classifying body movement of a subject. A nuclear magnetic resonance imaging apparatus according to the present invention includes: an imaging unit that acquires an image of a subject by measuring a nuclear magnetic resonance signal generated by the subject; and a body movement information processing unit including a processor for processing movement information of a subject disposed in the imaging device, the body movement information processing unit including a body movement information calculation unit and a body movement information classification unit, the processor performing: receiving a signal from a measurement device for measuring the movement information of the subject, and calculating body movement information from the signal; and classifying the body movement information.

Description

Technical Field

[0001] The present invention relates to an apparatus for processing information related to the movement of a subject under examination. Background Art

[0002] In an examination using a medical imaging device such as a magnetic resonance imaging device (hereinafter referred to as an MRI (Magnetic Resonance Imaging) device), the subject (patient) is examined while lying on an examination table or the like or in a loosely fixed state. If the subject moves during the examination, artifacts will be generated in the image obtained by the medical imaging device, thereby hindering the diagnosis. In addition, in cases where the movement of the subject is large and sudden, the subject deviates significantly from the examination position, or even falls from the bed, the examination has to be interrupted. However, during the examination, the technician or doctor (referred to as the user) will often look at the monitor showing the image and sometimes fail to notice.

[0003] Conventionally, as a technology related to medical imaging apparatuses, a technology for reducing the influence of movement of a subject under examination on diagnostic images has been developed and proposed.

[0004] For example, Patent Document 1 proposes that, in an MRI apparatus, body motion is detected by a camera installed in or near an examination space, and scanning is stopped or restarted when body motion is detected.

[0005] Patent Document 1: Japanese Patent Application Laid-Open No. 2006-346235 Summary of the Invention

[0006] Movement can be either significant, which could affect safety, or minor, which could affect image quality. For example, if significant movement affects safety, the user must immediately go to the subject to provide support. Conversely, if minor movement affects image quality, the user can continue filming. In other words, even if movement occurs, the actions the user can take vary depending on the type of movement.

[0007] For example, if the motion is significant and safety-critical, notifying the user of the motion allows them to immediately assist the subject. If the motion is minor and image quality-critical, continuing to shoot may result in artifacts. However, if the motion information can be used to correct the image, the user does not need to reshoot.

[0008] Patent Document 1 does not describe a method for classifying body motion types. If a user is intended to be notified of significant body motion that could impact safety, detecting it as minor motion could result in missed notifications, potentially posing a safety concern. Furthermore, if minor body motion that could impact image quality, while still allowing the user to continue the examination, is detected as significant motion, this could hinder the technician's workflow.

[0009] When a large body motion is detected that theoretically makes it impossible to correct image artifacts, even if the user applies body motion correction, the task of outputting the corrected image becomes futile, potentially hindering the user's workflow.

[0010] As described above, it is important to appropriately classify body motions according to the intended use. However, it is currently difficult to appropriately classify the body motions of a subject.

[0011] The present invention has been made in view of such circumstances, and an object of the present invention is to provide a nuclear magnetic resonance imaging apparatus and a body motion information processing method capable of classifying the body motion of a subject.

[0012] The invention involved in the first embodiment is a nuclear magnetic resonance imaging device, which includes: a camera unit, which measures the nuclear magnetic resonance signal generated by the subject to obtain an image of the subject; and a body motion information processing unit, which has a processor for processing movement information of the subject arranged in the imaging device, and the body motion information processing unit has a body motion information calculation unit and a body motion information classification unit. The processor performs the following processing: receives a signal from a measuring device that measures the movement information of the subject, and calculates the body motion information based on the signal; and classifies the body motion information.

[0013] In the magnetic resonance imaging apparatus according to the second aspect, in the first aspect, the processor performs processing for classifying the body motion information based on at least two or more indices.

[0014] In a third aspect of the magnetic resonance imaging apparatus according to the second aspect, the at least two indicators include the magnitude of the body movement and the duration of the body movement.

[0015] In a fourth aspect of the magnetic resonance imaging apparatus according to the third aspect, the at least two indicators include the magnitude of the body movement, the duration of the body movement, and the envelope.

[0016] According to a fifth aspect of the nuclear magnetic resonance imaging apparatus, in the third aspect or the fourth aspect, the at least two or more indicators further include a spatial region where body motion occurs.

[0017] In a sixth aspect of the nuclear magnetic resonance imaging apparatus according to any one of the first to fifth aspects, the processor selects a partial region of the subject or a characteristic movement of the subject when calculating the body motion information.

[0018] According to a seventh aspect of the magnetic resonance imaging apparatus, in any one of the first to sixth aspects, the measuring device is a camera, the signal is an image captured by the camera, and the processor calculates the body motion information based on temporal changes in the image.

[0019] According to an eighth aspect of the magnetic resonance imaging apparatus, in any one of the first to sixth aspects, the measuring device is a camera, the signal is an image captured by the camera, and the processor calculates the body motion information based on temporal changes in a correlation coefficient of the image.

[0020] According to a ninth aspect of the magnetic resonance imaging apparatus, in any one of the first to sixth aspects, the measuring device is a stereo camera, the signal is a stereo image captured by the stereo camera, and the processor calculates body motion information including three-dimensional information based on the stereo image.

[0021] In the magnetic resonance imaging apparatus according to a tenth aspect, in any one of the second to fifth aspects, the processor sets threshold values for at least two or more indicators, and classifies the body motion information based on the threshold values.

[0022] In the nuclear magnetic resonance imaging apparatus according to an eleventh aspect, in the tenth aspect, the threshold value is a preset value.

[0023] In the nuclear magnetic resonance imaging apparatus according to the twelfth aspect, in the tenth aspect, the threshold value is a value determined by machine learning using body motion information collected in advance as correct answer data.

[0024] According to a 13th aspect of the nuclear magnetic resonance imaging apparatus, in the 12th aspect, an algorithm used in the machine learning includes a support vector machine or a decision tree.

[0025] In the nuclear magnetic resonance imaging apparatus according to a fourteenth aspect, in the twelfth or thirteenth aspect, the correct answer data includes extended correct answer data generated by data extension.

[0026] In a nuclear magnetic resonance imaging apparatus according to a fifteenth aspect, in any one of the first to fourteenth aspects, the processor applies a different calculation formula according to movement of the subject when calculating the body motion information.

[0027] In the magnetic resonance imaging apparatus according to a sixteenth aspect, in any one of the first to fifteenth aspects, the processor outputs the classification result from the body motion information processing unit and / or outputs a warning based on the classification result.

[0028] In the magnetic resonance imaging apparatus according to a seventeenth aspect, in any one of the first to sixteenth aspects, the processor determines whether to apply the body motion correction function based on the classification result from the body motion information processing unit.

[0029] In the MRI apparatus according to the eighteenth aspect, in any one of the first to seventeenth aspects, the processor applies a body motion correction function based on the classification result from the body motion information processing unit and outputs a body motion corrected image based on the body motion information.

[0030] In the nuclear magnetic resonance imaging apparatus according to a nineteenth aspect, in any one of the first to eighteenth aspects, the processor specifies a plurality of regions for measuring movement information of the subject.

[0031] The 20th embodiment of the body motion information processing method is a body motion information processing method in which a body motion information processing device having a processor processes movement information of a subject arranged in an imaging device, wherein the processor performs the following processing: receiving a signal from a measuring device that measures the movement information of the subject; calculating the body motion information based on the signal; and classifying the body motion information.

[0032] Effects of the Invention

[0033] According to the present invention, it is possible to classify the body motion information of the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a diagram showing an overview of a medical imaging apparatus.

[0035] Figure 2 This is a diagram showing the relationship between a camera and an MRI apparatus.

[0036] Figure 3 This is a diagram showing the flow of processing by the body motion information processing device.

[0037] Figure 4 This is a diagram showing an example of a medical imaging apparatus for determining a threshold value of an indicator.

[0038] Figure 5 This is a diagram for explaining the process of calculating body motion information and determining a threshold value.

[0039] Figure 6 Is used to illustrate the Figure 5 A diagram showing a method for extracting feature points from the extracted body motion information.

[0040] Figure 7 It is based on the description Figure 5 and Figure 6 A graph showing the extracted feature points of body motion information.

[0041] Figure 8 This is a diagram for explaining changes in threshold values of setting indicators.

[0042] Figure 9 It is a diagram for explaining changes in the method of calculating body motion.

[0043] Figure 10 This is a diagram showing an example of body motion calculated from a motion vector.

[0044] Figure 11 This is a diagram for explaining a method for calculating body motion information of a subject.

[0045] Figure 12 3 is a diagram showing the classification results of the subject's body motion information.

[0046] Figure 13 This is a diagram showing a display example of classification results and warnings displayed on the operation unit.

[0047] Figure 14 This is a flowchart for explaining the process of correcting body movement.

[0048] Figure 15 This is a diagram for explaining the body motion correction function.

[0049] Figure 16 This is a diagram showing the flow of processing by another body motion information processing device.

[0050] Figure 17 This is a diagram illustrating a mask.

[0051] Figure 18 This is a diagram showing the concept of a three-dimensional mask.

[0052] Figure 19 This is a graph used to illustrate other indicators.

[0053] Explanation of symbols

[0054] 1-medical imaging device, 10-body motion information processing device, 11-index setting unit, 13-body motion information calculation unit, 15-body motion information classification unit, 20-MRI device, 30-measuring device, 30A, 30B-cameras, 100, 100A-subject. DETAILED DESCRIPTION

[0055] Below, preferred embodiments of the present invention are described in detail with reference to the accompanying drawings. In the following description and drawings, identical components are denoted by identical reference numerals, and duplicate descriptions are omitted. Furthermore, when multiple components are listed in the following embodiments, they should be interpreted as including at least one of the multiple components. Below, embodiments of the magnetic resonance imaging apparatus and body motion information processing method of the present invention are described.

[0056] like Figure 1As shown, a medical imaging apparatus 1 includes an MRI apparatus 20 for acquiring diagnostic images of a subject; a measurement apparatus 30 for measuring movement information of the subject 100; and a body motion information processing apparatus 10 for classifying the subject's body motion information using the movement information from the measurement apparatus 30. The body motion information processing apparatus 10 is an example of a body motion information processing unit of the present invention. The medical imaging apparatus 1 is an example of a nuclear magnetic resonance imaging apparatus of the present invention.

[0057] in addition, Figure 1 The three-dimensional coordinate system described in the figure represents an example of the definition of the direction of the MRI device 20. The X-axis, Y-axis and Z-axis of the three-dimensional coordinate system are an example and are not limited to this. In the following description, for ease of understanding, the X-axis, Y-axis and Z-axis of the MRI device 20 are defined as the same direction in all figures. In addition, as an example of a three-dimensional coordinate system, the Z-axis direction is the static magnetic field direction and is the front-to-back direction of the long axis of the imaging space 140. The Y-axis direction is the up-down direction of the subject. The X-axis direction is the left-right direction of the subject. The MRI device 20 is an example of the imaging unit of the present invention.

[0058] The MRI apparatus 20 is installed in an examination room of an imaging diagnostic facility. In the examination room, a subject 100 is placed on a top plate 144 of an examination table 142 of a bed device 141 .

[0059] The MRI apparatus 20 includes a static magnetic field generating magnet 102, a gradient magnetic field coil 104, and a transmission coil 106. The subject 100 is transported toward the static magnetic field generating magnet 102 of the MRI apparatus 20 by moving the top plate 144.

[0060] The static magnetic field generating magnet 102 generates a uniform static magnetic field within the imaging space 140 in which the subject 100 is located. The gradient magnetic field coil 104 generates a gradient magnetic field within the imaging space 140. The transmitting coil 106 generates a high-frequency magnetic field within the imaging space 140 for causing the nuclei of atoms constituting the tissue of the subject 100 to generate nuclear magnetic resonance signals (NMR (Nuclear Magnetic Resonance) signals) (hereinafter referred to as NMR signals).

[0061] The MRI apparatus 20 includes a high-frequency transmitter 110 , a receiver 114 , a gradient magnetic field power supply 112 , a sequencer 108 , a signal processing unit 116 , and an operation unit 118 .

[0062] The sequencer 108 sends commands to the high-frequency transmitter 110 and the gradient magnetic field power supply 112 according to the imaging sequence (pulse sequence), and appropriately amplified signals are sent to the transmission coil 106 or the gradient magnetic field coil 104 .

[0063] The signal transmitted to the transmission coil 106 is applied to the subject 100 in the form of a pulsed high-frequency magnetic field (RF pulse) via the transmission coil 106. The NMR signal generated from the subject 100 is detected by the coil elements of the reception coil 150 and detected by the receiver 114.

[0064] The gradient magnetic field coil 104 is composed of gradient magnetic field coils in three directions: X, Y, and Z, and generates gradient magnetic fields in each direction based on signals from the gradient magnetic field power supply 112 .

[0065] In receiver 114, the nuclear magnetic resonance frequency (detection reference frequency f0), which serves as the reference for detection, is set by sequencer 108. Sequencer 108 controls each component so that it operates at pre-programmed timing and intensity. Programs that specifically describe the timing and intensity of RF pulses, gradient magnetic fields, and received signals are called pulse sequences.

[0066] Various pulse sequences are known depending on the purpose, but detailed description thereof will be omitted here.

[0067] The signal processing unit 116 controls the operation of the MRI apparatus 20 via the sequencer 108, receives the detected signal from the receiver 114, and performs various signal processing such as image reconstruction. Furthermore, the receiver 114 performs quadrature phase detection on the received signal (NMR signal) as an analog wave based on a set detection reference frequency f0, performs A / D (analog-to-digital) conversion, and transmits the result to the signal processing unit 116. This data is also referred to as a received signal or measurement data.

[0068] The signal processing unit 116 receives various command inputs from the operation unit 118 and centrally controls the various components of the MRI apparatus 20. Furthermore, the signal processing unit 116 generates MRI images by performing processes such as inverse Fourier transform on received signals in the spatial frequency domain (k-space) received via the sequencer 108 to convert them into real-space images.

[0069] Signal processing unit 116 is implemented by a general-purpose computer such as a personal computer or microcomputer. Signal processing unit 116 includes a processor (e.g., a CPU (Central Processing Unit)), ROM (Read Only Memory), RAM (Random Access Memory), and an input / output interface. The processor included in signal processing unit 116 is an example of the processor of the present invention.

[0070] In the signal processing unit 116, various programs such as the control program stored in the ROM are expanded into the RAM, and the CPU executes the expanded programs in the RAM. This realizes the functions of each unit of the MRI apparatus 20 and executes various calculation and control processes via the input / output interface.

[0071] The operating unit 118 includes a mouse, keyboard, display device, and gantry monitor. The operating unit 118 functions as part of a graphical user interface (GUI) that accepts user input. The display device of the operating unit 118 displays MRI images generated by the signal processing unit 116.

[0072] The MRI device 20 is an example of an imaging device according to the present invention. While the MRI device 20 is illustrated as an example, other imaging devices, such as a CT (Computed Tomography) device, an X-ray diagnostic device, and a PET (Positron Emission Tomography) device, may also be used. The type of imaging device is not particularly limited as long as it has an imaging space and can generate images of the subject.

[0073] The measuring device 30 is a device that non-contactly acquires in vivo information from the subject 100 being examined using the MRI apparatus 20. The measuring device 30 is comprised of, for example, a camera. When the measuring device 30 is a camera, dynamic image data (time-series image data) obtained by the camera capturing a predetermined range of the subject 100 is an example of a signal of movement information measured by the measuring device 30. Dynamic image data is an example of an image captured by the camera of the present invention.

[0074] Figure 2 FIG2 shows an example of a measuring device 30 in which two cameras 30A and 30B are provided in the imaging space 140 of the MRI device 20. A top plate 144 on which the subject 100 is placed is inserted into the imaging space 140 of the MRI device 20. The camera 30A is provided above the end portion of the imaging space 140 on the side of insertion. The camera 30B is provided above the side of the imaging space 140 opposite to the insertion side. The cameras 30A and 30B are provided at positions where the subject 100 is reflected from obliquely above. Figure 2 As shown, by installing cameras 30A and 30B on both sides of the long axis of the imaging space 140, dynamic image data suitable for detecting movement information of the subject 100 can be selected and used. Figure 2In the example shown, the camera 30A at the insertion end is close to the abdomen of the subject 100, and the camera 30B at the opposite end is close to the face of the subject 100. The cameras 30A and 30B can easily acquire signals of their respective movement information.

[0075] The following description uses the example of cameras 30A and 30B being monocular cameras. In the case of monocular cameras, the subject's body motion is calculated as a relative value. Alternatively, measurement device 30 may be a stereo camera. In the case of a stereo camera, three-dimensional information can be obtained from the stereo image. Therefore, the distance from the camera can be determined, and the body motion waveform can be obtained as an absolute value.

[0076] Return to Figure 1 The body motion information processing device 10 receives signals indicating movement information of the subject 100 from the measurement device 30, calculates body motion information based on the received signals, and classifies the calculated body motion information. The body motion information processing device 10 can present the classification results of the body motion information to the user and output them to the signal processing unit 116 of the MRI apparatus 20. By knowing the classification results of the body motion information, the user can determine whether to continue imaging, whether to perform body motion correction using the body motion information, and so on.

[0077] The body motion information processing device 10 can classify the body motion information based on at least two indicators. The body motion information processing device 10 can pre-set thresholds for the indicators and classify the body motion information. When calculating body motion information based on received signals, the body motion information processing device 10 can determine a spatial region to be calculated.

[0078] In order to perform these functions, the body motion information processing device 10 of the embodiment has an index setting unit 11, a body motion information calculation unit 13, a body motion information classification unit 15, etc. The body motion information processing device 10 can be composed of a computer having a memory and a processor (CPU: Central Processing Unit) and / or GPU (Graphic Processing Unit). The functions of each part of the body motion information processing device 10 can be realized by reading a program that realizes the function by the CPU, etc. In addition, part of the function can also be realized by a programmable IC such as ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array). These processors possessed by the body motion information processing device 10 are an example of the processor of the present invention.

[0079] In addition, Figure 1 , the body motion information processing device 10 is shown as a separate device from the MRI apparatus 20. However, the functions of the body motion information processing device 10 can also be incorporated into the signal processing unit 116 of the MRI apparatus 20, and this configuration is also included in the medical imaging apparatus of the present invention. Therefore, the signal processing unit 116 can also function as the processor of the body motion information processing device 10.

[0080] Hereinafter, an embodiment of the processing of the body motion information processing device 10 will be described by taking a case where the imaging device is the MRI device 20 as an example. Figure 3 This is a diagram showing the flow of processing by the body motion information processing device.

[0081] First, an index for classifying body motion information is determined (step S1). Specifically, the index setting unit 11 sets an index suitable for classifying body motion information. Furthermore, the index setting unit 11 may set a threshold value for each set index.

[0082] Here, the movement information of the subject 100 refers to information obtained from the actual movement of the subject 100. The signal is an electrical signal output from the measuring device 30 that measures the movement information of the subject 100, and includes dynamic image data, etc. The body motion waveform (sometimes simply referred to as body motion) is information calculated by the processor of the body motion information processing device 10 based on the signal output from the measuring device 30, and corresponds to the movement information. The body motion information is information used to classify body motion, and is information that includes at least two evaluation values corresponding to indicators. The body motion information can be calculated based on the body motion waveform.

[0083] Next, the processing in the indicator setting unit 11 will be described. As described later, the movement information of the subject 100 is calculated as body motion information. When classifying the body motion information based on an indicator, it is preferable that the indicator be sensed from the movement information of the subject 100. The sensed movement information of the subject 100 can be roughly divided into the size of the movement, the duration of the movement, and the spatial area where the movement occurs. As indicators for classifying the body motion information, it is preferable to set the size of the movement, the duration of the movement, and the spatial area where the movement occurs.

[0084] By classifying body motion information using at least two or more of these indicators, it is possible to determine not only whether the subject 100 has moved, but also the type of movement of the subject 100. For example, while imaging the subject 100, the user can determine whether the movement of the subject 100 is significant and may affect safety, or whether it is minor and may affect the quality of the captured image.

[0085] Next, the determination of threshold values for the two indicators, the magnitude and duration of the subject's body movements, will be described. The threshold values for each indicator determined are used for classification of body movement information described later.

[0086] Figure 4 2 is a diagram showing an example of a medical imaging apparatus 1 for determining a threshold value of an index. Figure 5 This is a diagram for explaining the calculation of body motion information and the setting of threshold values.

[0087] Figure 4 The MRI apparatus 20, cameras 30A and 30B, and body motion information processing apparatus 10 shown in FIG. Figure 1 and Figure 2 The body motion information processing device 10 is connected to an operating unit 12 including a display device 12A and an input device 12B. Furthermore, the operating unit 118 of the MRI device 20 can also serve as the operating unit 12. The threshold value is determined using, for example, the MRI device 20 used to examine the subject 100. By applying this threshold value, the accuracy of the body motion information classification, described later, can be ensured.

[0088] The subject 100A is a subject model used for collecting sample body motion information by the body motion information processing device 10. The subject 100A reproduces the possible movements of the subject 100 according to the instructions of the user U. The subject 100A reproduces, for example, movements with large changes and short duration (for example, coughing or sneezing, etc.), and reproduces movements with small changes and lasting for more than a certain time (for example, body peristalsis, etc.). In addition, the subject 100A can reproduce movements with large changes and lasting for more than a certain time (for example, movements involving safety), and reproduce movements with small changes and short duration that do not affect shooting.

[0089] First, upon receiving signals from cameras 30A and 30B, the body motion information calculation unit 13 calculates a motion vector using known computer vision techniques such as optical flow, thereby calculating body motion information. The calculation of body motion information by the body motion information calculation unit 13 will be described later. Here, the method for setting indicators and thresholds based on the calculated body motion information will be described.

[0090] Figure 5 5-1 is a graph showing a body motion waveform (also referred to as body motion) obtained from the motion vector results. This graph plots the calculated results with time (s) on the horizontal axis and magnitude (amplitude) on the vertical axis. The body motion waveform in 5-1 serves as a reference for determining the threshold value of the index. The body motion information calculation unit 13 calculates the body motion waveform and body motion information based on the index that is appropriate for the classification by the body motion information classification unit 15.

[0091] Since the body motion waveform according to 5-1 is calculated as the body motion information, for example, waveform data including body motions with large amplitude and body motions with small amplitude for 10 seconds (101 points) is used. Figure 5 As shown in the body motion waveform of 5-2, a frame (e.g., frame W1, frame W2, etc.) determined by the amplitude (10,000 points from 0.01 to 100) and duration (80 points from 0.1 to 8 seconds) is set for the waveform and extracted as body motion information. For example, Figure 5 The waveform graph shown is as follows Figure 4 The display device 12A of the operation unit 12 is shown.

[0092] Frames W1 and W2 set different amplitude sizes P1 and durations P2. For example, in a body movement waveform, if the amplitude exceeds the set amplitude, it is determined that body movement has occurred, and if the amplitude exceeds the set amplitude within the set duration, it is determined that body movement continues. In a body movement waveform, if the amplitude does not exceed the set amplitude within the set duration, it is determined that body movement has ended. When the conditions of frame W1 are met, body movement of one waveform is identified, and body movement information is extracted. Similarly, when the conditions of frame W2 are met, body movement of another waveform is identified, and body movement information is extracted. The body movement information has information on the amplitude and duration corresponding to the indicator.

[0093] Furthermore, when determining duration, the amplitude of the body motion waveform fluctuates, repeatedly exceeding and failing to exceed the set amplitude, making it difficult to determine duration. In such cases, a duration determination time interval P3 is set. For example, considering a case where the waveform has two peaks, the time from when the body motion waveform exceeds the set amplitude P1 once, then falls, until it exceeds the set amplitude P1 again is calculated. If this time is less than the duration determination time interval P3, the waveform with two peaks is determined to be a single continuous body motion. If it is greater than P3, the waveform with two peaks is determined to be a separate body motion waveform, not a single continuous body motion.

[0094] Figure 6 Is used to illustrate the Figure 5 A diagram showing a method of extracting feature points of body motion information from the extracted body motion information (body motion corresponding to one waveform). Figure 6 6-1 is the Figure 5 The extracted body motion information is mapped to a graph with the vertical axis being the magnitude (amplitude) and the horizontal axis being the time. Figure 6As shown in Figure 6-1, by performing mapping, body motion information can be represented by area AR1. As a result, the graph is divided into area AR1 and area AR2 outside area AR1. Specifically, area AR1 corresponds to situations where the amplitude or duration exceeds the set values, while area AR2 corresponds to situations where the amplitude or duration does not exceed the set values. The boundary between areas AR1 and AR2 provides information indicating the characteristics of the amplitude or duration of a particular body motion waveform.

[0095] Figure 6 6-2 is a graph showing boundary points on the boundary line between area AR1 and area AR2. Each boundary point can be represented by a value obtained by multiplying the magnitude (amplitude) by time.

[0096] Figure 6 6-3 is a graph showing only the boundary point where the product of size (amplitude) and time is the largest among the boundary points. Figure 6 6-4 is Figure 6 By extracting the boundary point with the largest product as a feature point for the body motion information, the body motion information can be defined as a single feature point with both magnitude (amplitude) and duration. This eliminates situations where the magnitude (amplitude) or duration is extremely small (possibly including a large amount of noise). Here, the boundary point with the largest product is defined as the feature point for the body motion information, but the feature points for body motion information are not limited to this, and the user can select as appropriate.

[0097] Figure 7 It will be based on Figure 5 and Figure 6 The characteristic points of each of the plurality of body motion information extracted are plotted on a graph (also called a classification graph) with magnitude (amplitude) on the vertical axis and duration (s) on the horizontal axis. The body motion information extracted by the body motion information calculation unit 13 is input to the index setting unit 11. The index setting unit 11 creates, for example, a classification graph based on each characteristic point of the plurality of body motion information. Figure 7 The classification curve diagram shown in 7-1.

[0098] Here, the · mark indicates a characteristic point (body motion information) corresponding to a large amplitude and long duration movement of the subject 100A. Furthermore, the × mark indicates a characteristic point (body motion information) corresponding to a large amplitude and short duration movement, a small amplitude and short duration movement, and a small amplitude and long duration movement of the subject 100A. The body motion information of the large movement (· mark) of the subject 100A includes, for example, movement of the subject in a range that affects safety assurance and makes it difficult to continue the examination. For example, Figure 7 The body movement shown in the body movement waveform of 7-2 has a large movement size (amplitude) and a long duration.

[0099] The body motion information of the small movement of the subject 100A (× mark) includes the movement of the subject within the range that the examination can continue and affects the image quality of the camera image but can be corrected, or the movement of the subject within the range that the examination can continue and does not affect the image quality of the camera image. Figure 7 The body movement shown in the body movement waveform of 7-3 has a small movement size (amplitude) and a short duration.

[0100] In addition, Figure 7 In 7-1, only Figure 6 The feature points extracted from 6-3 can also be depicted Figure 6 All boundary points shown in 6-2.

[0101] Then, according to Figure 8 The following describes how the threshold value of the index is set. The index setting unit 11 sets the threshold value of the index.

[0102] Figure 8 The classification curve diagram of 8-1 is a diagram showing the first change of the threshold setting method. Figure 8 The classification curve diagram of 8-2 is a diagram showing the second variation of the threshold setting method. Figure 8 The classification curve diagram of 8-3 is a diagram showing the third variation of the threshold setting method.

[0103] The method for setting the threshold value of the first change is a method for determining the threshold value based on a rule. As shown in 8-1, the body motion information can be divided into the first group of body motion information marked with a dotted line and the second group of body motion information marked with a solid line. It can be understood that the first group and the second group are classified by setting threshold values Th1 and Th2 for size (amplitude) and time (duration), respectively. That is, threshold value Th1 is determined for the size (amplitude) indicator, and threshold value Th2 is determined for the time (duration) indicator. The first change is determined by the user, so the content of the classification processing becomes clear.

[0104] Next, a method for setting a threshold using a machine learning method is described. In the machine learning method, a threshold is set using the extracted body motion information as correct answer data.

[0105] The method of setting the threshold value of the second variation is applicable to the case of a decision tree, which is one of the machine learning methods. A decision tree is one of the machine learning methods that uses a tree structure for classification, etc., and its purpose is to find the best conditions for segmenting the provided data. A decision tree has a tree structure, and the tree structure has leaves representing classifications and branches representing the feature quantities reaching the leaves. If the correct answer data is provided, the decision tree algorithm will find the best conditions for segmenting the data. Figure 8In the example of 8-2, a decision tree is used to find the conditions of y1 as the magnitude (amplitude) and x1 as the duration. Y1 is then determined as the threshold value Th1, and x1 is determined as the threshold value Th2. Compared to the threshold setting method of the first variation, the decision tree can improve classification accuracy and clarify the content of the classification process. Decision trees are well known technology, so a detailed description is omitted.

[0106] The method of setting the threshold value of the third variation is applicable to the case of Support Vector Machine (SVM), which is one of the machine learning methods. The purpose of SVM is to find the boundary line (hyperplane) that best classifies the provided data. In SVM, the support vector is determined to determine the dividing line. In SVM, nonlinear data can be processed by margin maximization and kernel method. Figure 8 In 8-3, straight lines are drawn near the support vectors marked with a circle and the support vectors marked with a circle. The body motion information marked with a circle and the body motion information marked with a circle are separated by the two straight lines. A straight line L1 represented by a dotted line is drawn between the two straight lines. The thresholds of the two indicators can be determined based on the dotted straight line L1, and the body motion information can be classified. A stepped line can also be drawn instead of the straight line L1. In SVM, the classification accuracy can be improved compared to the setting method of the threshold value of the first and second changes, and the content of the classification processing will become clear. In addition, SVM is a well-known technology, so the detailed description is omitted.

[0107] In the method for setting the threshold value of the second and third changes, when the body motion information of the correct answer data is insufficient, extended correct answer data of the body motion information can be generated by data expansion. For example, the data can be expanded along the direction of the size of the body motion within a desirable range, or the data can be expanded along the direction of the duration of the body motion within a desirable range. In this case, for example, data of 0.5 times, 0.6 times, ... 1.5 times the size of the body motion is generated, or data of 2 times or 3 times the duration is generated. When the body motion information is set as the correct answer data including the extended correct answer data, the classification accuracy is improved. In addition, since data expansion is a well-known technology, the description is omitted.

[0108] The threshold values determined in the first to third changes are stored in a memory or the like included in the body motion information processing device 10, thereby completing the setting of the threshold values. The threshold values are used for classifying body motion information.

[0109] While the embodiment describes a case where the threshold is determined using a machine learning function implemented in the indicator setting unit 11, this is not limiting. For example, the machine learning function may be implemented in another system and calculate the threshold when body motion information is input. In other words, the indicator setting unit 11 only needs to be able to select indicators and set thresholds based on user instructions or automatically. Furthermore, machine learning can be implemented before the product ships, with the threshold calculated using machine learning stored in the indicator setting unit 11.

[0110] After the index is set (step S1), the medical imaging apparatus 1 captures an image of the subject 100. If the index has been set, the subject 100 can be examined in subsequent examinations without setting the index (step S1).

[0111] like Figure 3 As shown, after setting the index (step S1), the motion information signal is received (step S2). Specifically, while the MRI image of the subject 100 is being captured, the body motion information calculation unit 13 receives the signal of the dynamic image data obtained by the cameras 30A and 30B capturing the subject 100.

[0112] After receiving the signal of the moving image data (step S2), the body motion information is calculated (step S3). Specifically, the body motion information calculation unit 13 calculates the movement based on the motion vector and the velocity vector to calculate the body motion.

[0113] After receiving the dynamic image data signal, the body motion information calculation unit 13 calculates the motion vector. This motion vector calculation can be performed using, for example, well-known computer vision techniques such as optical flow. Optical flow calculates a motion vector for each pixel in the image based on the temporal changes in the dynamic image data. Known optical flow algorithms include the Ferneback method and the Lucas-Kanade method, and any of these can be used.

[0114] The calculated result is the result of the change in pixel position between frames represented by the velocity vector for each pixel. Figure 9 As shown in FIG. 1 , when the vertical and horizontal directions of the image IM are defined as the x direction and the y direction, the speed in the x direction (Vx) and the speed in the y direction (Vy) are calculated.

[0115] The body motion is calculated using the velocity vectors Vx and Vy using the following formula. At this time, when the subject 100 is in a lying position on the examination table, the movement of the subject 100 due to breathing is mainly dominated by the movement perpendicular to the horizontal plane (desktop). On the other hand, the movement of the subject 100 other than the movement due to breathing moves in an irregular (or random) direction, so that the characteristics of the movement of the two are different. Calculations using different formulas can be performed according to the type of body motion. For example, the type of body motion can be determined based on the information of the examination site when the user sets the examination site at the beginning of the examination.

[0116] For sudden movement, Vmotion can be calculated using the following equation (1) or (2), for example.

[0117] [Formula 1]

[0118] V motion -(V x sinθ+V y cosθ) 2 (1)

[0119] [Formula 2]

[0120] v motion =(V x sinθ) 2 +(V y cosθ) 2 (2)

[0121] When it is desired to observe body motion (respiratory motion) in the moving direction of the chest with high sensitivity, Vabd_motion can be calculated by the following equation (3), or Vmotion can be calculated by equation (4) using equation (3).

[0122] [Formula 3]

[0123] V abd_motion =V x cosθ-V y sinθ (3)

[0124] [Formula 4]

[0125]

[0126] According to equation (4), integrating the number of data points tcount over time has the effect of improving sensitivity. In the equation, θ refers to the angle between the horizontal direction and the direction of respiratory movement (also called the abdomen-back direction or the A(anterior)-P(posterior) direction). In this example, optical flow is used to calculate the velocity component in the horizontal and vertical directions, and θ is set based on this information to improve the detection sensitivity of respiratory movement.

[0127] In addition, θ in the formula is the angle of the XY plane relative to the horizontal plane, which varies depending on the positional relationship between the cameras 30A and 30B or the installation angle of the cameras. In addition, θ can be determined after analyzing the direction of respiratory movement for each subject. It can be appropriately set to detect respiratory movement and movement or body movement with high sensitivity, or different values can be used for each position (pixel). In addition, the formula representing body movement can be any formula that represents the body movement to be detected, and is not limited to (1) to (4).

[0128] Furthermore, the body motion information calculation unit 13 can adjust the image size of the dynamic image data as needed, reducing the original image size. For example, the original camera image size can be reduced to approximately 1 / 2 to 1 / 10. By reducing the image size of the dynamic image data, the computational load of subsequent processing, such as motion vector calculation, can be reduced, thereby achieving high-speed computation (real-time rendering). Furthermore, in subsequent processing, to divide an image into multiple regions of the same pixel size, the resized image can be cropped to any zero-numbered image ends, so that both the vertical and horizontal dimensions are divisible by the number of divisions.

[0129] The resized image is divided into small areas, and the average velocity and motion vectors calculated for each pixel within each area are calculated for each of the small areas. For example, if the pixel size of the resized and decremented camera image is 720 x 540 pixels, then the size of each small area obtained by dividing it into 60 areas is 72 x 90 pixels. The average velocity and motion vectors are calculated for each small area of this size.

[0130] Furthermore, the method is not limited to optical flow and can also be used to calculate body motion information based on the temporal changes in the correlation coefficient of images captured by a camera. For example, first, the image data to be used is acquired. Next, the correlation coefficient between consecutive image data (frames) is calculated. Next, the correlation coefficient can be calculated in units of pixels. The correlation between corresponding pixels is evaluated. The temporal changes in the calculated correlation coefficient are analyzed and the changes are detected. The body motion waveform can be calculated based on the changes in the correlation coefficient.

[0131] Figure 10 1 and 2 are diagrams showing examples of body motion and body motion information calculated by the body motion information calculation unit 13 based on the motion vector. Figure 10 The body motion waveforms are graphs calculated for each small area, with magnitude (amplitude) on the vertical axis and time on the horizontal axis. Each body motion waveform is calculated by the body motion information calculation unit 13 based on dynamic image data obtained by cameras 30A and 30B capturing the entire subject 100. In this example, body motion is calculated for 60 small areas.

[0132] Next, refer to Figure 11 A method for calculating body movement information corresponding to an index will be described. Figure 11 11-1 shows an example of using two indicators (amplitude and duration) to calculate body motion information. Figure 11 11-2 shows an example of using three indicators (amplitude, time, and space) to calculate body motion information.

[0133] Figure 11 Reference numeral 11-1 represents a body motion waveform calculated by the body motion information calculation unit 13 for a specified region of the subject 100. Specifically, a portion of the region of the subject 100 is monitored. The body motion information calculation unit 13 calculates body motion information based on amplitude and duration as indicators. The calculated body motion information is classified by the body motion information classification unit 15. The body motion information calculation unit 13 calculates body motion information using the body motion waveform enclosed by the frame W as a single waveform.

[0134] exist Figure 11 In 11-2, the body motion information calculation unit 13 simultaneously calculates the body motion waveforms of multiple regions (spaces) 1, 2...N of the subject 100. That is, in 11-2, the body motion waveform is calculated using space as one of the indicators. When calculating the body motion waveforms of multiple regions at the same time, it is important to determine the body motion waveform of which region is used to calculate the body motion information. As a determination method, in 11-2, at each moment, the maximum value (maximum amplitude) of all regions is selected to calculate the body motion waveform. Region max becomes a body motion waveform that depicts the maximum amplitude over time. That is, region max becomes a body motion waveform that selects the characteristic movement of the subject 100. Therefore, region max becomes a body motion waveform that is a synthesis of the movements of multiple regions of the subject 100. As another determination method, a region with large body motion can also be selected based on the body motion waveform of a certain period of time.

[0135] Next, similar to step 11-1, the body motion information calculation unit 13 calculates body motion information from region max based on the amplitude and duration used as indices. The body motion information calculation unit 13 calculates body motion information using the body motion waveform enclosed by the frame W as one waveform. The calculated body motion information is classified by the body motion information classification unit 15.

[0136] As another determination method, any one area may be selected as a representative from a plurality of areas (spaces) 1, 2, ... N. The area to be monitored by the user may be appropriately set according to the part to be photographed.

[0137] After the body motion information is calculated (step S3), the body motion information is classified (step S4). Specifically, the body motion information classification unit 15 classifies the body motion information (including information related to amplitude and duration) according to the index. Figure 12The result (classification result) of the body motion information classified by the body motion information classification unit 15 is shown in FIG. Figure 12 In 12-1, body motion information classified regardless of the time of occurrence of body motion is plotted on a graph with size (amplitude) on the vertical axis and time on the horizontal axis. Furthermore, threshold values Th1 and Th2 set by the indicator setting unit 11 are displayed. Body motion information (● mark) in the upper right area A surrounded by threshold values Th1 and Th2 (amplitude greater than threshold value Th1 and duration greater than threshold value Th2) corresponds to large movements of the subject 100 as shown in 12-2. In addition, body motion information (× mark) in the area B outside area A (amplitude less than threshold value Th1 or duration less than threshold value Th2) corresponds to small movements of the subject 100 as shown in 12-3.

[0138] From the classification result, it can be understood that the body motion information of the subject 100 is classified based on the size (amplitude) of the body motion at the threshold value Th1 and the duration of the body motion at the threshold value Th2.

[0139] Next, after classifying the body motion information (step S4), the classification results are transmitted (step S5). Specifically, the body motion information processing device 10 transmits the calculated body motion waveform and the classification results to the signal processing unit 116 of the MRI apparatus 20. The body motion waveform includes the time and amplitude of the body motion.

[0140] Next, after transmitting the classification results (step S5), the classification results and / or warnings are output (step S6). Specifically, the signal processing unit 116 outputs the calculated body motion and the classification results of the body motion information to the display device (not shown) of the operation unit 118. In addition to the display device, the signal processing unit 116 can also output this information to a printer or storage device.

[0141] Figure 13 1 is a diagram showing an example of display of a body motion waveform 130, a classification result 131, and a warning 132 displayed on the display device 118A of the operation unit 118. Figure 13 As shown, display device 118A displays an image 133 captured by MRI apparatus 20. Furthermore, it displays a body motion waveform 130 and a classification result 131. Body motion waveform 130 and classification result 131 are associated with the time of occurrence. Therefore, the user can understand when the body motion information in classification result 131 was caused by the body motion.

[0142] The method for outputting warning 132 is not particularly limited. In this example, warning 132 is displayed as an icon and text below image 1.33. Warning 132 can be either an icon or text. Furthermore, the display of the icon and text (size, color, text content, etc.) can be changed according to the category. A warning sound or an audio-based warning can also be used. Furthermore, these can be appropriately combined.

[0143] By outputting warning 132 based on the classification, users who are focused on other tasks can be notified of abnormal movement. Furthermore, based on the classification, for example, if the subject sneezes or coughs temporarily, imaging can be continued for the safety of the subject, thereby avoiding unnecessary interruptions of imaging. On the other hand, in situations where imaging should be interrupted for the safety of the subject, the user can avoid dangers such as the subject falling.

[0144] Next, regarding the use of the classification results of body motion waveforms and body motion information, as a case other than warning, according to Figure 14 and Figure 15 Body motion correction will be described. Figure 14 This is a flowchart for explaining the process of correcting body movement. Figure 15 This figure is used to explain the body motion correction function. The description of the parts that are the same as the processing flow described above may be omitted. Here, body motion correction means reconstructing the image after reducing the artifacts of the generated MRI image.

[0145] like Figure 14 As shown, a motion information signal is received (step S11), body motion information is calculated (step S12), and the body motion information is classified (step S13). Specifically, the same processing as steps S1, S2, and S3 is performed.

[0146] The classification results are transmitted to the signal processing unit 116, which determines whether the movement is suitable for motion correction (step S14). The signal processing unit 116 can determine whether motion correction is suitable based on the classification results. This determination utilizes pre-set thresholds for the indicators. For example, the signal processing unit 116 comprehensively determines the amplitude and duration used as thresholds to determine whether motion correction is suitable.

[0147] If the amplitude is small and image quality is not affected even if the correction function is not executed, the correction function is not executed. Furthermore, if the amplitude is large and image quality is not improved even if the correction function is executed, the correction function is not executed. This is not limited to these situations; signal processing unit 116 may determine whether body motion correction can be executed.

[0148] If it is determined that body motion correction is not possible ("No" in step S14), the process ends without executing the body motion correction process. After the process ends, an MRI image without body motion correction is reconstructed, or an MRI image is not reconstructed.

[0149] On the other hand, if it is determined that body motion correction is possible ("YES" in step S14), a process of correcting body motion is executed.

[0150] like Figure 15 As shown in FIG15-1, in the MRI apparatus 20, an MRI image of the subject is generated by filling measurement lines (indicated by solid arrows) in a measurement data space (k-space) according to a certain measurement sequence (indicated by dotted arrows).

[0151] like Figure 15 As shown in FIG15 - 2 , an MRI image 1501 determined to be capable of body motion correction is input to the signal processing unit 116 .

[0152] Next, a body motion removal mask is made (step S15). Specifically, Figure 15 As shown in FIG15-2 , the signal processing unit 116 creates a body motion removal mask 1503 based on a body motion waveform 1502 calculated by the camera. Body motion waveform 1502 represents the body motion waveform during the MRI image capture period. Body motion removal mask 1503 extracts body motion that causes artifacts. Body motion removal mask 1503 is created by taking into account the amplitude and duration of body motion.

[0153] Next, the signal is removed in the k-space (step S16). Specifically, Figure 15 As shown in FIG15 - 2 , the k-space information is converted to generate k-space data 1504 excluding the measurement lines collected at the time when the body motion waveform is generated.

[0154] Next, the removed signal is interpolated (step S17). For example, the removed signal is interpolated by applying a known interpolation method such as successive reconstruction that repeatedly performs calculations while maintaining the inertia of the measurement data.

[0155] Finally, the image after body motion correction is output (step S18). Figure 15 As shown in FIG15-2 , an artifact-reduced MRI image 1505 is reconstructed and output. As a result, the image quality of the MRI image can be improved. When creating a motion removal mask, by referencing the classification results of the motion information, more accurate motion correction can be performed. The artifact-reduced MRI image 1505 is an example of a motion-corrected image according to the present invention.

[0156] Then, according to Figure 16The following describes a case where the medical imaging apparatus 1 utilizes body motion information for each ROI (Region of Interest) for movement information of the subject 100. Descriptions of portions identical to those in the previously described processing flow may be omitted.

[0157] like Figure 16 As shown in FIG. 1 , the dynamic images captured by the cameras 30A and 30B are input to the body motion information processing device 10 (step S21 ). In this example, the index and threshold are already set.

[0158] Next, the body motion information calculation unit 13 calculates optical flow from the dynamic image signal (step S22), calculates motion vectors (step S23), and calculates the average value of velocity vectors of each small area of the image based on the motion vectors (step S24).

[0159] Next, the body motion waveform and body motion information based on the maximum value within ROI1 are calculated (step S25), and the body motion waveform and body motion information based on the maximum value within ROI2 are calculated (step S28). Steps S25 and S28 can be performed simultaneously. ROI1 is an area used to monitor large body motions that are related to safety, such as the entire imaging interval or the entire subject. On the other hand, ROI2 is an area used to monitor small body motions that are related to image quality, such as the imaging area of the MRI device or the examination area of the subject (such as the head, abdomen, or joints).

[0160] Next, a threshold value of body motion information (subject movement) that may interrupt the examination is determined (step S26). The classification result of the body motion information classification unit 15 can be used. If the determination result exceeds the threshold, the classification result and a warning are output (step S27). By confirming this output by the user, the safety of the subject can be ensured.

[0161] In parallel with step S25, the threshold value of the body motion information around the inspection part is determined (step S29). As in step S26, the classification result based on the body motion information classification unit 15 can be used. When the result of the determination exceeds the threshold, the classification result and the warning are output (step S30). Since ROI2 is the area where the subject is photographed, the body motion waveform is output regardless of the determination result (step S31). Step S25 and step S29 are processed simultaneously, but step S25 monitors the area involving safety (ROI1). Therefore, compared with the warning of step S30, the output of the warning of step S27 is given priority.

[0162] While the calculation of body motion waveforms for each ROI has been described, masks can be used to extract and classify body motion information. The body motion information processing device 10 can pre-generate masks based on user instructions. Multiple regions for measuring movement information of the subject 100 can be specified. The user can instruct the body motion information processing device 10 to pre-generate masks via the signal processing unit 116 from the operation unit 118 of the MRI device 20. In this case, multiple regions for measuring movement information of the subject 100 can also be specified.

[0163] An example of a mask for extracting two regions is shown in Figure 17 In. Figure 17 , an example of applying two masks 122 and 123 to a region map 121 of body motion calculated for each small region of a camera image (after resizing) 120 is shown. The mask 122 extracts the imaging region and corresponds to ROI2. In the case of the MRI apparatus 20, a mask can be used that sets the region of the subject 100 wearing the receiving coil 150 to 1 and the rest of the region to 0. The position at which the receiving coil 150 is worn can be determined based on the images of the cameras 30A and 30B. In the MRI apparatus 20, the subject 100 is configured so that the center position of the receiving coil 150 is approximately near the center of the imaging space, and therefore can also be determined based on the center position of the imaging space and the size of the receiving coil 150.

[0164] On the other hand, the mask 123 extracts a region that includes the imaging region and is wider than the imaging region, and corresponds to ROI 1. The mask may be set to 1 for the region where the subject 100 is located and 0 for the other regions.

[0165] By monitoring body motion in the area extracted by mask 122, body motion that directly affects imaging can be detected, and this information can be fed back into the imaging. Furthermore, by monitoring body motion in the area extracted by mask 123, significant movement of the subject 100 being examined, even if it does not directly affect imaging, can be detected.

[0166] However, the generated mask is not limited to the above example. One or more (or more than two) regions may be set in consideration of the examination site or the mobility of the subject 100 (e.g., children). For example, in the case of a receiving coil composed of multiple connected small coils, the area where the receiving coils are worn may be divided into multiple regions, such as a head region including the receiving coils and a leg region including the receiving coils.

[0167] Furthermore, the mask can be a mask with a predetermined weighting, rather than a binary mask of 1 / 0, that takes into account the positional relationship with the cameras and the sensitivity distribution of the receiving coils. For example, the farther away from cameras 30A and 30B, the less body motion is captured, so the weight can be increased, while the closer the distance, the smaller the weight. Furthermore, since the sensitivity of receiving coil 150 is generally higher in the center, and the influence of body motion in this area is greater, the weight can be increased in the center and decreased in the periphery.

[0168] In the above description, the spatial mask is used as an example, but time elements and the magnitude of body movement can also be added. Time elements include whether the image is being taken or whether the image is being taken before and after the examination.

[0169] Figure 18 The concept of a three-dimensional mask 124 formed by combining the time element and the magnitude of body motion is shown in FIG. By using this three-dimensional mask, it is possible to monitor the appropriate area. Specifically, a three-dimensional mask can be used that combines a mask that selects the sensitivity distribution area of the receiving coil as a spatial mask, a mask that selects the imaging period as a temporal mask, and a mask that selects the first range as an amplitude mask. This can be used as a mask corresponding to ROI2. Alternatively, a three-dimensional mask can be used that combines a mask that selects the entire imaging space as a spatial mask, a mask that selects the examination period as a temporal mask, and a mask that selects a second range larger than the first range as an amplitude mask. This can be used as a mask corresponding to ROI1.

[0170] In this example, the body motion information is calculated from the body motion waveform using the size (amplitude) and duration as indicators. Figure 19 The case of using the envelope curve will be described. Figure 19 19-1 shows a body motion waveform 200, an envelope 201 and a straight line 202. Figure 19 19-2 shows a body motion waveform 203, an envelope 204, and a straight line 205 that are different from those in 19-1.

[0171] The body motion waveform 200 shows a waveform with a small rise. The envelope 201 is a line surrounding the outside of the body motion waveform 200 and shows a gentle peak shape. The straight line 202 is tangent to the envelope 201.

[0172] On the other hand, the body movement waveform 203 shows a waveform with a large rise. The envelope 204 is a line surrounding the outside of the body movement waveform 203 and has a shape with a steep slope. The straight line 205 is tangent to the envelope 204.

[0173] The slopes of the straight lines 202 and 205 may be calculated based on the envelope lines 201 and 204 , and the values thereof may be used for classification of body motion information.

[0174] Furthermore, the present invention is not limited to the above-described embodiment, and various modifications are possible, of course.

Claims

1. A magnetic resonance imaging apparatus comprising: an imaging unit for measuring magnetic resonance signals generated by a subject to acquire an image of the subject; and a processor for processing movement information of the subject disposed in the imaging device. The processor performs the following processing: receiving a signal from a measuring device for measuring movement information of the subject, and calculating body motion information based on the signal; and The body motion information is classified.

2. The magnetic resonance imaging apparatus according to claim 1, wherein The processor performs the following processing: The body motion information is classified according to at least two or more indicators.

3. The magnetic resonance imaging apparatus according to claim 2, wherein: The at least two indicators include the magnitude of the body movement and the duration of the body movement.

4. The magnetic resonance imaging apparatus according to claim 3, wherein: The at least two indicators include the size of the body movement, the duration and the envelope of the body movement.

5. The magnetic resonance imaging apparatus according to claim 3 or 4, wherein: The at least two indicators also include a spatial area where body movement occurs.

6. The magnetic resonance imaging apparatus according to claim 1 or 2, wherein: The processor selects a partial region of the subject or a characteristic movement of the subject when calculating the body motion information.

7. The magnetic resonance imaging apparatus according to claim 1 or 2, wherein: The measuring device is a camera, and the signal is an image captured by the camera. The processor calculates the body motion information based on temporal changes of the image.

8. The magnetic resonance imaging apparatus according to claim 1 or 2, wherein: The measuring device is a camera, and the signal is an image captured by the camera. The processor calculates the body motion information based on a temporal change in a correlation coefficient of the image.

9. The magnetic resonance imaging apparatus according to claim 1 or 2, wherein: The measuring device is a stereo camera, and the signal is a stereo image captured by the stereo camera. The processor calculates the body motion information including three-dimensional information based on the stereoscopic image.

10. The magnetic resonance imaging apparatus according to claim 2, wherein: The processor sets thresholds for the at least two indicators respectively, and classifies the body motion information according to the thresholds.

11. The magnetic resonance imaging apparatus according to claim 10, wherein: The threshold is a preset value.

12. The magnetic resonance imaging apparatus according to claim 10, wherein: The threshold value is a value determined by machine learning using pre-collected body movement information as correct answer data.

13. The magnetic resonance imaging apparatus according to claim 12, wherein: The algorithms used in the machine learning include support vector machines or decision trees.

14. The magnetic resonance imaging apparatus according to claim 12 or 13, wherein: The correct answer data includes extended correct answer data generated by data expansion.

15. The magnetic resonance imaging apparatus according to claim 1 or 2, wherein: The processor applies different calculation formulas according to the movement of the subject when calculating the body motion information.

16. The magnetic resonance imaging apparatus according to claim 1, wherein The processor outputs a classification result and / or outputs a warning according to the classification result.

17. The magnetic resonance imaging apparatus according to claim 1, wherein: The processor determines whether the body motion correction function is applicable according to the classification result.

18. The magnetic resonance imaging apparatus according to claim 1, wherein: The processor applies a body motion correction function according to the classification result and outputs a body motion correction image according to the body motion information.

19. The magnetic resonance imaging apparatus according to claim 1, wherein: The processor determines a plurality of regions for measuring movement information of the subject.

20. A body motion information processing method for processing movement information of a subject disposed in an imaging device equipped with a processor, wherein: The processor performs the following processing: receiving a signal from a measuring device that measures movement information of the subject; calculating body motion information based on the signal; and The body motion information is classified.

Images