Medical imaging device and motion data processing method

Abstract

In a medical imaging device equipped with a motion processing function, it is possible to set a threshold for determining body motion, taking into account the different motion conditions depending on the subject and the area being examined, thereby effectively reducing the impact of body motion in medical images in accordance with the subject. [Solution] The medical imaging device of the present invention is equipped with means for setting a threshold for detecting body movement for each subject. Optical images of the subject acquired by an optical imaging device separate from the medical imaging device are used not only to detect the subject's body movement but also to determine the threshold for detecting body movement, and this threshold is used to determine body movement.

Description

Technical Field

[0001] The present invention relates to a medical imaging device that performs imaging by arranging a subject to be examined in an imaging space, such as a magnetic resonance imaging device (hereinafter referred to as an MRI device), a CT device, a PET device, etc. In particular, in a medical imaging device, the present invention relates to a technique for reducing the influence of measurement data affected by the body movement of the subject during imaging on the image.

Background Art

[0002] Medical imaging devices can non-invasively grasp the internal tissues of the subject to be examined and are widely used in the medical field. One of the problems for obtaining good images with medical imaging devices is the body movement of the subject during imaging. The body movement of the subject includes movements associated with breathing and heartbeat, as well as other unintentional movements, such as movements associated with physiological reactions such as spasms, sneezing, or coughing, and other sudden movements. Such body movement generates artifacts in the image, and the resulting image quality degradation hinders image diagnosis.

[0003] Among body movements, respiratory movement and pulsation are easy to predict due to their periodicity, and various countermeasures have been taken, such as performing imaging at the time when the movement is least, correcting measurement data acquired based on the periodicity (for example, Patent Document 1). In the technique described in Patent Document 1, an apparatus that optically photographs a subject, such as an optical camera, is used separately from the medical imaging device, and it is disclosed that information on the subject's breath-holding may be obtained based on the moving image captured by the optical camera. Patent Document 1 also describes that distortion caused by the wide-angle imaging lens should be corrected for the camera image obtained using the wide-angle imaging lens.

[0004] On the other hand, for motions other than periodic motion, methods have been proposed in MRI devices to detect the subject's movement and use the obtained motion information to determine whether remeasurement is necessary, or to reconstruct the image by excluding measurement data affected by body movement (motion-corrected reconstruction) (Patent Document 2). Another proposed method involves placing a pressure sensor on the bed where the subject lies, determining body movement from the output of the pressure sensor, and, if body movement is detected during imaging, switching the data acquisition method to a motion-correctable data acquisition method to obtain MR images with suppressed artifacts caused by body movement (Patent Document 3). [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2023-027608 [Patent Document 2] Japanese Patent Publication No. 2023-022669 [Patent Document 3] Japanese Patent Publication No. 2010-162332 [Overview of the project] [Problems that the invention aims to solve]

[0006] The level (magnitude), frequency, and duration of body movement vary greatly depending on the subject (patient) and the area being examined. For example, some patients may find it difficult to remain still for extended periods during an examination due to age or medical condition. Furthermore, even with the same patient, the impact of body movement on the image can differ significantly depending on whether the area where the movement occurs is the same as, close to, or far from the area being examined (the target of imaging).

[0007] Conventional technologies use thresholds set by the user based on experience, for example, to determine body movement, and do not take into account individual differences in body movement. Furthermore, while the technology described in Patent Document 3 discloses that the threshold for determining body movement is made different for each imaging site when switching imaging methods, the threshold is a value arbitrarily set by the user and may not necessarily be an appropriate threshold for all patients.

[0008] Furthermore, when body movement is detected by an optical imaging device such as a camera, the resulting image is a two-dimensional image that includes distortion due to the lens and internal parameters of the optical imaging device. Depending on the position of the imaged area in the optical image, this may result in under-detection or over-detection of body movement.

[0009] Patent Document 1 mentions correcting distortion of optically captured images when determining the breath-holding period, but simply correcting distortion of a two-dimensional image does not solve the problem of under-detection or over-detection of body movement for each patient.

[0010] The present invention aims to provide a technology that enables the setting of thresholds that take into account the different body movement conditions depending on the subject and the area being examined, thereby enabling more appropriate body movement processing. [Means for solving the problem]

[0011] To solve the above problems, the medical imaging device of the present invention is equipped with means for setting a threshold for determining body movement for each subject. The present invention utilizes optical images of a subject acquired by an optical imaging device separate from the medical imaging device not only for detecting the subject's body movement but also for determining a threshold for determining body movement, and uses this threshold to determine body movement.

[0012] In other words, the medical imaging device of the present invention comprises a measurement unit that collects measurement data for generating an image of a subject during examination, and a processor that receives an optical image from an optical imaging device that optically photographs a region of the subject including the area to be examined, and analyzes the subject's body movement based on the optical image. The processor sets a threshold for determining body movement for the subject based on the optical image of the subject, and identifies body movement effect data affected by the subject's body movement from the measurement data collected by the measurement unit based on the set threshold.

[0013] The motion data processing method monitors the body movements of a subject during imaging using a medical imaging device and identifies measurement data affected by the subject's body movements as motion-affected data. The method receives an optical image from an optical imaging device that optically photographs a region including the subject's area to be examined, sets a threshold for determining body movements for the subject based on the optical image, and identifies motion-affected data from the measurement data collected by the medical imaging device that is affected by the subject's body movements based on the set threshold. [Effects of the Invention]

[0014] According to the present invention, by using the optically captured image of the subject being imaged not only for detecting body movement but also for determining the threshold for determining body movement, an appropriate threshold can be set for each subject being imaged, and appropriate body movement processing, such as selecting a body movement correction method or switching imaging methods, can be performed. Furthermore, it is possible to reduce the deletion of excessive measurement data or the retention of measurement data that should have been deleted, and obtain an image with the effects of body movement suppressed. [Brief explanation of the drawing]

[0015] [Figure 1] A block diagram of a medical imaging device to which the present invention is applied. [Figure 2] A diagram showing an MRI machine as an example of a medical imaging device. [Figure 3] Functional block diagram of the processor (motion processing unit). [Figure 4] A diagram illustrating the process of Embodiment 1. [Figure 5] A flowchart showing an example of body movement correction processing by a processor. [Figure 6] A diagram for explaining the processing of Modification 2 of Embodiment 1. [Figure 7] A flowchart showing an example of the processing of Embodiment 2.

Mode for Carrying Out the Invention

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings. A configuration example of a medical imaging device and an optical imaging device used in the medical imaging device of the present invention is shown in FIG. 1. As shown in the figure, the medical imaging device 1 includes a bed device 40 and a gantry that provide an examination space for imaging a subject 50, a measurement unit (hereinafter also referred to as an imaging unit) 10, and a processor 20. Further, the medical imaging device 1 of the present embodiment includes an optical imaging device 80 for monitoring the subject 50 arranged in the examination space, for example, one or more monitoring cameras are arranged in or near the examination space. Although not shown in the figure, there may be a device for measuring biological signals such as the heartbeat and respiration of the subject 50, such as an electrocardiograph or a pressure gauge.

[0017] The configuration of the imaging unit 10 varies depending on the modality. For example, in the case of a CT device, it includes a scanner equipped with an X-ray source and an X-ray detector, a high-voltage device for supplying a high voltage to the X-ray source, a drive source for driving the scanner, and the like. In the case of an MRI device, it includes a magnet that generates a static magnetic field in the examination space, a gradient magnetic field coil that generates a gradient magnetic field and its power supply, a high-frequency generation device that generates a high-frequency magnetic field, a high-frequency transmission coil, a high-frequency reception coil that receives nuclear magnetic resonance signals, and the like.

[0018] The processor 20 functions, for example, as a control unit for controlling the operation of the imaging unit 10 and as an arithmetic unit for processing the measurement data collected by the imaging unit 10 to generate or correct an image. Further, the processor 20 of the present embodiment processes an optical imaging image from the optical imaging device 80 and a biological signal from a biological signal measurement device as necessary, and performs processing of the body movement of the subject 50.

[0019] The processor 20 can be composed of known processing devices such as, for example, a computer equipped with a CPU and memory, a programmable IC, or a combination thereof. In the case of a computer, for example, the processing by the processor is realized by the CPU loading a program that achieves each function of control and calculation. Although it is possible to realize all the functions of the processor with a single processor, it is also possible to realize each function by combining one or more processors. In FIG. 1, one processor 20 is shown representing one or more processors, and the individual functions realized by the one or more processors 20 are shown. Further, a display device for displaying images and GUIs and an input device (collectively referred to as the UI unit 30) for the user to input commands, necessary numerical values, etc. are connected to the processor 20. Although not shown, it can also be provided with an interface function for communicating or being wired-connected to an external cloud, database, storage device, or other image processing devices, etc.

[0020] Among the functions of the processor 20, operations such as device control and image generation are the same as those of general medical imaging devices except for the functions related to body movement processing, and the description thereof is omitted in this specification. The content of body movement processing will be described in detail in the embodiments described later. Typically, the body movement of the subject 50 being imaged by the medical imaging device 1 is acquired from the optical capture image during imaging, and detection such as the time point of occurrence of the body movement and the magnitude of the body movement, identification of measurement data (body movement influence data) affected by the body movement, and processing determination according to the body movement influence data are performed. The processing determination is, for example, to determine whether it is necessary to retake the body movement influence data, whether it is possible to delete the body movement influence data and generate an image, etc.

[0021] In this embodiment of body motion processing, before identifying body motion effect data, an appropriate threshold for determining body motion in a subject during examination (while imaging is being performed by a medical imaging device) is set using optically captured images, and this threshold is used to determine body motion and identify body motion effect data. In other words, a threshold for determining body motion is set for each subject, and even for each part of the subject. This makes it possible to determine body motion appropriately according to the various forms of body motion of each subject, preventing over-detection or under-detection of body motion, and enabling efficient and accurate body motion correction.

[0022] The following describes the processing embodiments of the processor in the medical imaging device of the present invention, using the case where the medical imaging device is an MRI device as an example. First, the configuration and processing flow of the MRI device common to each embodiment will be described.

[0023] As shown in Figure 2, the configuration of the imaging unit 10 is similar to that of a known MRI device, and includes a static magnetic field magnet 101 that generates a uniform magnetic field (static magnetic field) in the examination space where the subject 50 is placed, a gradient magnetic field coil 102 that applies a gradient magnetic field to the static magnetic field, an RF transmitting coil 103 that applies a high-frequency magnetic field to excite the atomic nuclei of atoms constituting the subject's tissue, and an RF receiving coil 104 that receives NMR signals generated by the subject. The gradient magnetic field coil 102, RF transmitting coil 103, and RF receiving coil 104 are connected to a gradient magnetic field power supply 105, a transmitter 106, and a receiver 107, respectively.

[0024] The static magnetic field magnet 101, gradient magnetic field coil 102, and RF transmitting coil 103 are housed inside the gantry. The subject 50 has the RF receiving coil 104 attached to the area to be examined and is positioned in the examination space inside the gantry while lying on the examination table 40.

[0025] The sequencer 108 operates under the control of the processor 20 and determines the pulse sequence for each scan using the pulse sequence type and imaging conditions such as imaging parameters that are set in advance or by the user. According to the determined pulse sequence, it operates the gradient magnetic field power supply 105, the transmitter 106, and the receiver 107 to control the imaging unit 10 so that it collects the echo signal (k-space data) necessary for image reconstruction.

[0026] The processor 20 controls the imaging unit 10 via the sequencer 108, performs calculations such as image reconstruction using k-space data, controls the display of the UI unit 30, and processes information entered by the user. The functions and operations of each part when the imaging unit 10 collects k-space data, and the related processing of the processor 20, are the same as those of a typical MRI device, so a detailed explanation is omitted here.

[0027] The optical imaging device 80 is positioned at one end of the gantry (the entrance where the subject is inserted) and at one or more locations inside the gantry. It photographs the subject before imaging begins and sends the optically captured images to the processor 20. Hereinafter, the optical imaging device 80 may be a surveillance camera equipped with one or more cameras, a surveillance camera equipped with a wide-angle lens or a fisheye lens, an infrared camera, or any other device that can continuously capture images by optical means and output a time-series frame image (video). In the following description, the optical imaging device 80 will also be referred to as a surveillance camera, and the optically captured images will also be referred to as camera images or frame images.

[0028] The processor 20 receives frame images from the surveillance camera 80, detects body movement between frames, compares the timestamp of the frame image with the acquisition time of each echo data that constitutes the k-space data, and processes the body movement by associating the time elapsed since the collection of the k-space data with the body movement information.

[0029] Figure 3 shows a block diagram illustrating an example of the functions of the processor 20 involved in motion processing. As shown in the figure, the motion processing unit 230 includes a motion determination unit 231 that determines the magnitude and duration of motion, a threshold setting unit 232 that sets a threshold for motion determination to be applied to the subject using an optically captured image of the subject, and a processing determination unit 233 that determines subsequent processing based on the determination of the motion determination unit 231. It may also include a distortion correction unit 234 that calculates distortion of the camera image and a second threshold setting unit 235 that sets a threshold (second threshold) related to the persistence of motion, as shown by the dotted block.

[0030] The processing of each part of the motion processing unit 230 may be performed by a single processor, or it may be performed by multiple processors (including computers, programmable ICs such as ASICs and FPGAs).

[0031] The overview of the motion processing of the processor 20 with the above configuration will be explained with reference to Figure 4. The motion processing unit 230 analyzes the frame images received from the surveillance camera 80, and the motion detection unit determines whether the motion affects the image (motion detection unit 231). The frame image analysis involves, for example, setting an ROI in the image portion containing the area of ​​the subject to be monitored, tracking the change in that ROI between frames using methods such as optical flow, obtaining a motion vector, and detecting the motion. In addition, to determine whether the motion affects the image, a threshold for motion detection is set for each subject (threshold setting unit 232), and the determination is made using this threshold. The threshold is set based on prior information or information identified from the frame images, such as the subject's tendency to move frequently, whether the subject is likely to experience motion accompanied by large convulsions, or whether specific body parts such as the limbs are prone to movement.

[0032] Meanwhile, the motion processing unit 230 acquires information on the imaging sequence being executed by the imaging unit 10 (Spin Echo (SE) sequence is an example in Figure 4), and identifies motion-affect data in the k-space data collected by the imaging sequence based on the detected motion and the ongoing imaging sequence. Furthermore, the motion processing unit 230 decides whether to recapture the motion-affect data or perform image reconstruction with motion-affect data correction (motion-corrected reconstruction) based on the position of the motion-affect data in k-space, the number of motion-affect data, and the proportion of the k-space occupied by the motion-affect data (processing determination unit 233). For this processing determination, the determination criteria and thresholds for the number of motion-affect data or the proportion of the k-space occupied by the motion-affect data are set in advance. For example, the determination criteria include recapturing if there is motion-affect data in the low-frequency range of k-space, recapturing if the number or proportion of motion-affect data in k-space exceeds a predetermined threshold, and performing motion-corrected reconstruction if it is below the threshold.

[0033] According to this embodiment, in motion processing that determines body movement and determines subsequent processing according to the k-space position and number of body movement effect data, setting a threshold for body movement detection for each subject or body part makes it possible to perform processing that matches the body movement characteristics of the subject. This prevents under- or over-detection of body movement, prevents insufficient motion correction or extension of imaging time due to unnecessary re-capture of data, and makes it possible to obtain images with suppressed body movement effects efficiently.

[0034] The following describes specific embodiments of methods for detecting body movement and setting its threshold.

[0035] <Embodiment 1> This embodiment is characterized in that the processor 20 has a function (distortion correction unit 234) to correct the distortion of frame images acquired from the optical imaging device 80, and uses the distortion-corrected frame images to set thresholds and detect body motion.

[0036] The process of this embodiment will be described below with reference to Figure 5. In the following description, "imaging" includes not only imaging to obtain the target diagnostic image (main imaging), but also pre-measurement and other preparatory imaging to determine the conditions for main imaging.

[0037] At the start of imaging, once the subject is positioned in the imaging space, the surveillance camera 80 is activated and captures frame images from the surveillance camera 80 (S1). Next, the distortion correction unit 234 calculates the distortion (distortion parameter) of the camera image and corrects the distortion of the frame image using the distortion parameter (S2).

[0038] Generally, the main distortions that occur in camera images are (1) radial distortion and (2) tangential distortion. Radial distortion is an image distortion that increases as you move away from the center of the image, and can be expressed by the following equation (Equation 1).

[0039]

number

[0040] Tangent distortion is a distortion that occurs when the lens and the image plane are not perfectly parallel. This distortion causes certain areas to appear closer than they actually are. This tangent distortion can be expressed by equation (Equation 2).

[0041]

number

[0042]

number

[0043] Furthermore, in addition to the distortion parameters mentioned above, there are also internal parameters (equation 4) called the camera matrix, and external parameters (equation 5) which are matrices that transform world coordinates to camera coordinates, which can cause distortion in camera images. Internal parameters are camera-specific values ​​(known).

[0044]

number

[0045]

number

[0046] The distortion of camera images, or the parameters that determine the distortion, varies depending on the type of surveillance camera and its installation location. For example, in the case of a fisheye lens camera installed on the ceiling of a gantry, the center of the image is enlarged and distorted little, but the distortion increases and the image becomes smaller towards the periphery. With a wide-angle lens camera, the distortion also increases towards the outside, but in that image, the outside is enlarged more than the center.

[0047] The distortion correction unit 234 estimates the distortion parameters and external parameters mentioned above by photographing a test chart, for example, one composed of a pattern whose appearance without distortion is known, such as a chessboard. That is, it obtains the amount of displacement of the test pattern from its original position from the camera image obtained by photographing the test chart, and estimates the distortion parameters based on this amount of displacement.

[0048] Image acquisition for distortion parameters can be performed during the development of the MRI device or when it is installed in a facility. For example, during device development, a wide-angle camera can be installed on the ceiling directly above the MRI table in an experimental MRI room. To calculate distortion parameters with even greater accuracy, service technicians can perform image acquisition for distortion parameters using the test chart described later, at the time of MRI installation or during regular maintenance at each hospital where the MRI is installed. This eliminates various variations such as the actual camera installation height, installation position, and variations in camera lenses.

[0049] As mentioned above, various known methods have been proposed for calculating estimated distortion parameters, algorithms for distortion correction, or simple distortion correction algorithms that do not use distortion parameters (for example, camera calibration for distortion correction of wide-angle lens images, MathWorks camera calibration, and for wide-angle lens cameras, Japanese Patent Publication No. 2013-258679, Japanese Patent Publication No. 2019-29978, etc., and for fisheye lens cameras, Japanese Patent Publication No. 2008-048443, Japanese Patent Publication No. 2008-061260, etc.), and correction can be performed using these known methods.

[0050] Distortion correction can improve errors caused by distortion in the amount of motion in the image center and at the edges. In other words, it becomes possible to detect the amount of motion in the image center and at the edges using almost the same scale, and the threshold for motion detection can be unified between the center and the edges.

[0051] The distortion correction unit 234 applies this distortion parameter to the frame image used for subsequent body motion detection to perform distortion correction. If an ROI (Region of Interest) is set in the frame image to identify an area where body motion should be monitored (such as the inspection area or its vicinity), distortion correction may be performed targeting this ROI.

[0052] Next, the threshold setting unit 232 sets the motion threshold based on the frame image after distortion correction (S3). The motion threshold can be determined, for example, as follows: First, based on multiple frame images from a resting period in which no sudden motions other than periodic motions associated with vital activities such as respiratory and heartbeats occur, various quantities (statistics) such as the motion level of the subject's examination area, the range of motion volume over a predetermined period, the maximum value, minimum value, average value, and deviation are calculated. Based on these quantities, the motion detection threshold is determined.

[0053] For example, the threshold can be the maximum or average value, the deviation (σ), or a value obtained by multiplying these quantities by a predetermined coefficient, or a value calculated using a function that utilizes these quantities or coefficients (e.g., average value + nσ or average value - nσ). Here, even at rest, the amount of body movement differs depending on whether the examination site is the head, chest, abdomen, or limbs, due to the effects of respiration and heartbeat. This should be taken into consideration when determining the threshold or predetermined coefficient. For example, in the head, where the effect of periodic motion is relatively small, the threshold value should be set low, and in areas where the effect of periodic motion is large, the threshold value should be set high. In this case, the effect of periodic motion can be corrected by other methods. The predetermined coefficient may be set by the user or set by the device, depending on whether the effect of body movement should be strictly or loosely considered depending on the desired image quality. Furthermore, the threshold may be adjusted by taking into account prior information obtained about the subject's body movement tendencies.

[0054] In this way, different coefficients are applied to the statistical data of body movement obtained at rest, depending on the body part which exhibits different movement tendencies, and thresholds are set for each body part. This allows for accurate body movement assessment using these thresholds.

[0055] Next, the motion detection unit 231 uses a set threshold to detect and determine motion in the distortion-corrected frame image, that is, to determine whether the motion has a significant impact on the image (S4). In other words, if the level of motion exceeds the set threshold, it is determined that there is motion. If thresholds are set for each body part, the determination is made for each part, and motion is determined for each part. This determination may be made before the start of imaging. Then, when the imaging unit 10 starts imaging according to the imaging sequence, as shown in Figure 4, the progress of the imaging sequence (e.g., the main imaging) is linked to the time when motion was determined to be present, and the measurement data (motion impact data) collected at that time is labeled (identified).

[0056] The processing after identifying motion-affect data is the same as the processing described in, for example, Patent Document 2, and is determined according to the number of motion-affect data and their position in k-space (S5). For example, if motion-affect data is present in the low region of k-space, the data is recaptured (remeasured) under the control of the imaging control unit 210. If motion-affect data is present only in the high region of k-space, the decision of whether or not to remeasure is made based on the number and position of the motion-affect data. For example, in a sequential ordering sequence that scans sequentially from one end to the other along the phase encoding direction of k-space, if k-space data from 0 encoding to one end has already been collected and there is motion when moving toward the other end, motion correction reconstruction is performed using a data estimation method, etc., without recapture. When scanning from one end of k-space toward the center, if the number of identified motion-affect data is large or the range of motion-affect data is large, the processing is determined by criteria such as whether to recapture.

[0057] Once the collection of k-space data that can be reconstructed into an image is complete, the image generation unit 220 performs image reconstruction (including motion correction reconstruction) to generate an image. The generated image, along with its associated information such as subject information and imaging conditions, is displayed as a display image on the display device of the UI unit 30 by the display control unit 250 (S6).

[0058] In addition to the final image of the subject, the display device may also display, for example, the threshold for the subject (a numerical value or a qualitative display indicating whether it is a strict or lenient value for image quality), which is the condition for motion correction, and / or, as shown in Figure 4, the motion detection status, k-space data in which the motion impact data has been identified, etc. These can also be done during the progress of the steps described above.

[0059] According to this embodiment, by analyzing frame images from a surveillance camera (optical imaging device) 80 to determine a threshold for body motion detection and correcting distortion of the frame images caused by the configuration of the surveillance camera 80 as a prerequisite for performing body motion detection, the threshold setting and the body motion detection using it can be optimized. Furthermore, by setting the threshold using camera images of the subject at rest, accurate body motion detection can be performed for frame images of various subject states acquired thereafter.

[0060] <Modification 1 of Embodiment 1> In Embodiment 1, the distortion of the frame image was corrected using the distortion correction amount calculated by the distortion correction unit 234. However, instead of correcting the distortion, it is also possible to set a threshold while taking the distortion into consideration. In this modified example, the processing of the distortion correction unit 234 is replaced with processing that only calculates the distortion.

[0061] In this modified example, for instance, the distortion correction unit 234 calculates parameters such as the distortion parameters mentioned above for a surveillance camera mounted on a medical imaging device. The method for calculating the parameters is the same as in Embodiment 1.

[0062] Next, the threshold setting unit 232 uses only the central region of multiple frame images to calculate various quantities such as the body movement level of the subject's examination area, the range of body movement over a predetermined period, the maximum value, minimum value, average value, and deviation. The threshold setting unit then sets a threshold using the various quantities of body movement information calculated for the central region of the image. Here, the inter-frame movement vector (amount of movement due to body movement) values ​​obtained for camera images without distortion correction differ between the central and peripheral regions of the image. Therefore, for regions other than the central region of the image, the threshold is adjusted using the distortion parameters calculated by the distortion calculation unit. For example, the threshold is adjusted so that it increases as you move towards the peripheral regions of the image where the distortion increases. In other words, different thresholds are set depending on the region of the image.

[0063] This means the threshold itself reflects the distortion, making it possible to perform motion detection that takes distortion into account.

[0064] <Modification 2 of Embodiment 1> In Embodiment 1, a threshold was set for the motion vector (amount of body movement) on the image using a frame image, which is a planar image, and body movement was determined. However, in this modified example, a 3D position is detected using multiple camera images, and body movement is determined based on the change in the 3D position.

[0065] The following describes an embodiment in which three-dimensional position correction is performed using camera images 810 and 820 acquired by two surveillance cameras 81 and 82, with reference to Figure 6.

[0066] As shown in Figure 6, in camera images taken by two surveillance cameras 81 and 82 of a predetermined area or feature point 51 of the subject 50, if the image plane is the xy plane, then fluctuations within the plane, i.e., changes in the position of the feature point 51 in the x and y directions, can be seen. However, even if, for example, the position of feature point 51 changes to that of feature point 51a, it is not possible to grasp the position change in the z direction (depth direction). On the other hand, the positions of the feature point 51 on the two camera images 810 and 820 differ due to the difference in camera positions. If the two surveillance cameras 81 and 82 are spaced apart in the y direction, the feature point 51 on the image is shifted in the y direction, and if the two surveillance cameras 81 and 82 are spaced apart in the x direction, the feature point 51 on the image is shifted in the x direction. If the amount of shift in the position (two coordinates) of the feature point 51 in the images from the two surveillance cameras 81 and 82 is known, the position of the feature point 51 in the z direction can be calculated using this information. In other words, if S is the displacement of the feature point 51, f is the focal length of the camera, and B is the distance between the focal points of the two cameras, then the distance D (distance in the z direction) from the focal point to feature point 51a can be calculated using the following formula. D = B × f / S

[0067] The amount of displacement S in the camera images of the two surveillance cameras 81 and 82 can be calculated using methods such as block matching. For example, one image can be used as a reference, divided into multiple regions, and regions with high correlation in the other image can be determined for each region. The displacement S between the images can then be calculated frame by frame. This allows us to obtain the variation in distance D for each region.

[0068] In this way, the position and focal length of the two surveillance cameras, as well as the amount of shift in the camera images, allow us to obtain the variation in the 3D position of a predetermined region (ROI) or feature point. This variation, i.e., body motion, is then determined using a body motion threshold. In this case, the threshold is also determined based on the variation in 3D position. It should be noted that there are other known methods for detecting 3D positions from multiple camera images besides the method described above (for example, Japanese Patent Publication No. 2022-094744), and the method is not limited to the one described above; other known methods can also be used.

[0069] The method for setting the threshold after motion detection and determining motion is the same as in Embodiment 1, and redundant explanations will be omitted. In this modified example as well, depending on the lens characteristics of the surveillance camera, necessary distortion correction may be applied or the threshold may be set considering distortion, which will enable more accurate motion detection.

[0070] <Embodiment 2> This embodiment is characterized in that motion detection in Embodiment 1 is performed during pre-scan (pre-measurement), and the results of the pre-scan are reflected in determining the imaging method or changing the imaging conditions for the main imaging. Pre-scan refers to measurements or imaging performed by placing the subject in the imaging space, such as pre-measurement for static magnetic field non-uniformity correction performed before the main imaging to obtain a diagnostic image of the subject in the case of an MRI device, or imaging to position the subject at a desired imaging position or determine the imaging cross-section, and any of these may be used.

[0071] The processing flow of this embodiment will be explained with reference to the flowchart in Figure 7. In Figure 7, processes that are the same as those in Figure 5 are indicated by the same reference numerals, and redundant explanations are omitted.

[0072] Prior to the main imaging, a pre-scan is initiated (S10). Before and after the start of the pre-scan, a camera image from a surveillance camera is acquired (S1), a threshold for a predetermined area is set, or a threshold is set for each area (S3), preferably the distortion of the camera image is corrected (S2), and body movement is determined using the threshold (S4). This is the same as in Embodiment 1.

[0073] Meanwhile, the motion processing unit 230 calculates the percentage of the time during which motion is detected by the motion detection unit 231 relative to the total prescan execution time, and determines whether this percentage is within or above a predetermined threshold (threshold for motion time). The predetermined threshold may be set in advance as a value such as 30% or 50%, or it may be set by the user depending on the subject or the purpose of imaging. For example, some subjects may have difficulty remaining still due to a disease. For such patients, a somewhat higher threshold, such as 50%, may be set. The threshold can also be set higher if the pre-set data acquisition method is less susceptible to the effects of motion. To distinguish between the two thresholds, the threshold for the motion level set in process S3 is called the first threshold, and the threshold for the duration of motion is called the second threshold.

[0074] The motion processing unit 230 uses these first and second thresholds to determine whether or not to change the imaging method for this imaging. The following explanation will use the case of changing the data acquisition method as an example. For example, if the duration of motion exceeding the first threshold (or the total duration if multiple motions occur) exceeds the second threshold at the end of the prescan, the data acquisition method for this imaging is changed to a data acquisition method that is less affected by motion (S50).

[0075] Examples of data acquisition methods that are less susceptible to motion include EPI (Echo Planar Imaging), which can acquire k-space data (phase-encoded data) after a single excitation; radial scanning, which acquires data radially around the k-space origin; and a method (called Propeller Scan) that acquires data while rotating parallel data blocks (Blades) containing phase encoding radially. The choice of which of these motion-robust data acquisition methods to use can be pre-configured, or a changeable data acquisition method can be presented to the user via the UI unit 30, allowing the user to select.

[0076] If the body movement during prescan is below the second threshold, the pre-set method for data acquisition during the main imaging is used as is, and the main imaging is performed in the same manner as in Embodiment 1 (S22, S23). That is, the frame images sent from the surveillance camera during imaging are monitored, body movement is detected, body movement effect data is identified, and processing decisions are made, such as whether to measure unmeasured data including body movement effect data or to perform body movement correction, depending on the position and number of data points of the identified body movement effect data in k-space (S23).

[0077] Whether the data collection method is changed or not, if k-space data that can ultimately be reconstructed into an image has been collected, image reconstruction (including motion-corrected reconstruction) is performed, and the image is displayed or the image data is saved (S6).

[0078] If the pre-set data acquisition method is a pulse sequence robust to body movement, the imaging may be performed using the pre-set data acquisition method. Alternatively, for example, parameters that can further shorten the time among the set imaging parameters, such as the PI (parallel imaging) speed ratio, repetition time TR, slice thickness, and number of additions, may be changed. Such changes are also included in the "change of imaging method" in this embodiment. Regarding such changes to imaging parameters, the user may be presented with options via the UI unit 30 and the user may specify them, or the device may set them according to a predetermined priority order and present the results to the user.

[0079] According to this embodiment, in a pre-scan performed prior to the main imaging, motion information including the tendency of body movement is acquired, and the imaging method for the main imaging is set based on that motion information. This makes it possible to acquire images that suppress the effects of body movement while minimizing the time required for the main imaging.

[0080] Although embodiments of the present invention have been described above using an MRI device as an example, the present invention is applicable to any medical imaging device that has the function of acquiring information from an optical imaging device such as a surveillance camera. Furthermore, the present invention is not limited to the embodiments or modifications described above, and can also be combined as appropriate, configurations or processes that are not essential for carrying out the invention can be omitted, and known configurations or processes can be added. [Explanation of Symbols]

[0081] 10: Imaging unit, 20: Processor, 30: UI unit, 40: Patient bed device, 50: Subject, 80, 81, 82: Surveillance camera (optical imaging device), 230: Motion processing unit

Claims

1. A measurement unit that collects measurement data to generate an image of the subject during the examination, A medical imaging device comprising: an optical imaging device that optically captures an optical image of a region including the area to be examined of the subject, and a processor that analyzes the subject's body movement based on the optical image; The medical imaging apparatus is characterized in that the processor sets a threshold for determining body movement for the subject based on an optically captured image of the subject, and identifies body movement effect data affected by the subject's body movement from the measurement data collected by the measurement unit based on the set threshold.

2. A medical imaging apparatus according to claim 1, The medical imaging apparatus is characterized in that the processor corrects the distortion of the optically captured image, determines the magnitude of body movement using the corrected optically captured image, and identifies the body movement effect data.

3. A medical imaging apparatus according to claim 1, The medical imaging apparatus is characterized in that the processor adjusts a threshold used to identify the motion effect data based on the distortion of the optically captured image.

4. A medical imaging apparatus according to claim 1, The medical imaging apparatus is characterized in that the processor selects an optical image obtained when the change in body movement is least significant from among a plurality of optical images obtained in time series by the optical imaging apparatus as a still image, and calculates the threshold using the still image.

5. A medical imaging apparatus according to claim 1, The medical imaging apparatus is characterized in that the processor divides the optically captured image into multiple regions and sets a threshold used to identify the motion effect data for each region.

6. A medical imaging apparatus according to claim 1, The measurement performed by the measurement unit includes the main measurement for generating an image of the subject and a preliminary measurement preceding the main measurement. The medical imaging device is characterized in that the processor analyzes the body movements of the subject during pre-measurement and determines the imaging method for the main measurement based on the results of the analysis.

7. A medical imaging apparatus according to claim 6, The measurement unit includes an imaging unit that operates according to predetermined imaging conditions and collects nuclear magnetic resonance signals generated from the subject, The medical imaging apparatus is characterized in that the processor changes the imaging conditions for the main measurement when the proportion or frequency of motion effect data among the measurement data collected during the pre-measurement is above a predetermined level.

8. A medical imaging apparatus according to claim 7, The medical imaging apparatus is characterized in that the imaging conditions include a pulse sequence and imaging parameters used for imaging.

9. A medical imaging apparatus according to claim 1, The medical imaging device is characterized in that the processor acquires optical images from a plurality of optical imaging devices arranged at at least two different locations, and detects body movement using the parallax of the plurality of optical images.

10. A method for processing body motion data, which monitors the body movements of a subject during imaging using a medical imaging device and identifies measurement data affected by the body movements of the subject as body motion effect data, The optical imaging device receives an optical image of the area of ​​the subject to be examined, and based on the optical image, sets a threshold for determining body movement for the subject. A method for processing body motion data, characterized by identifying body motion effect data affected by the body movement of the subject from among the measurement data collected by the medical imaging device, based on a set threshold.

11. A method for processing motion data according to claim 10, The distortion of the aforementioned optically captured image is corrected, A motion data processing method characterized by detecting the body movement of a subject during imaging using a corrected optically captured image and identifying motion-affect data.

12. A method for processing motion data according to claim 10, The distortion contained in the optically captured image received from the aforementioned optical imaging device is calculated. A method for processing motion data, characterized by determining a threshold for motion used when identifying motion-affected data based on the said distortion.

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