A method for imaging non-contact gating signal acquisition for a magnetic resonance system

Abstract

The application relates to an imaging non-contact gating signal acquisition method for a magnetic resonance system, and belongs to the field of physiological signal detection. The application collects pulse and breathing signals of a patient detected by a camera through a magnetic resonance instrument, acquires patient face information through a mirror in a cavity of the magnetic resonance instrument, efficiently selects a region of interest on the patient face by using a living skin detection technology, combines an IPPG technology to process video images to acquire a curve of image gray along a time sequence, traces chest cavity micro-movement by using a motion detection algorithm, and extracts a stable and accurate breathing signal by combining frequency selection. By analyzing and quantifying a pulse wave signal and a delay of a breathing signal and an electrocardiosignal, the purpose of triggering by using pulse wave and breathing signal characteristics instead of an electrocardiosignal R peak gating trigger is achieved. The application aims at the problems that traditional ECG and other contact gating technologies are easily affected by a strong static magnetic field, an alternating gradient magnetic field and a radio frequency pulse electromagnetic field in a magnetic resonance imaging system, and that motion artifacts are generated by fluid movement of human blood and the like, and by breathing and heart contraction and the like, and proposes an imaging non-contact gating technology, so that the quality of a magnetic resonance imaging image is improved.

Description

TECHNICAL FIELD

[0001] The application relates to an imaging non-contact gating signal acquisition method for a magnetic resonance system, in particular to a magnetic resonance imaging gating signal acquisition method and hardware device based on image photoplenthysmography (IPPG) and an optical flow method, and belongs to the field of physiological signal detection. BACKGROUND

[0002] Cardiac magnetic resonance imaging has become a main means for clinicians to diagnose and treat heart diseases due to its non-invasiveness and advantages suitable for soft tissues. Since the heart movement will affect the magnetic resonance imaging, resulting in image blurring and affecting the imaging quality, traditional contact gating technologies such as electrocardiogram and pulse are easily affected by the strong static magnetic field, alternating gradient magnetic field and radio frequency pulse electromagnetic field in the magnetic resonance imaging system. These factors will cause distortion of the gating signal, destruction of the synchronization, cause magnetic resonance image artifacts, low scanning efficiency and complex operation and other problems. Therefore, developing a non-contact gating technology has become the key to improving the imaging quality and efficiency, so as to more accurately reflect the heart structure and function. The image photoplenthysmography (IPPG) technology adopts an imaging sensor to acquire a video of the skin color change caused by the reflected light intensity after the human blood and tissue absorption, and then extracts a pulse wave signal from a single frame of the video through image processing technology. During the magnetic resonance imaging, the breath-holding is usually needed to exclude the artifacts caused by the respiratory movement. Therefore, the IPPG technology is combined with the optical flow method to obtain the human respiratory signal after filtering and denoising, the pulse signal and the respiratory signal are positioned in the respective gating feature regions according to the characteristics of the electrocardiogram R peak, and the composite gating signal is constructed by combining the multi-signal fusion. Since the IPPG technology is used to obtain the image gray scale curve along the time sequence through the video image processing, that is, the blood volume change caused by the heart contraction is embodied in the periodic change of the image light intensity. At the same time, the electrocardiogram (ECG) used in the conventional electrocardiogram gating reflects the heart contraction and diastole. The IPPG technology is used to replace the electrocardiogram (ECG) for gating triggering, which can ensure the same triggering characteristics reflecting the heart diastole during the magnetic resonance imaging, so as to ensure the high-quality imaging results in various physiological states and improve the overall robustness and accuracy of the imaging system. SUMMARY

[0003] The purpose of the present application is to solve the problems of motion artifacts and electromagnetic interference generated during traditional magnetic resonance gating imaging, which affect the imaging quality of patients. The present application proposes an imaging non-contact gating signal acquisition method for a magnetic resonance system. When a patient is subjected to magnetic resonance detection imaging, the device uses a non-contact camera to collect physiological signals, and suppresses the artifacts caused by the patient's own heart and respiratory motion, further improving the quality of magnetic resonance imaging. The present application is applicable to the control of gating imaging of magnetic resonance equipment for detecting pathological changes in the human body, but is not limited to magnetic resonance imaging technology. Other imaging technologies can also use the gating technology of the present application to achieve the purpose of artifact suppression when acquiring physiological information of patients. The present application avoids image blurring caused by motion artifacts when a patient is subjected to magnetic resonance imaging of pathological changes, so as to better assist doctors in analyzing and diagnosing the cause and subsequent treatment plan of the patient.

[0004] The purpose of the present application is achieved by the following technical solutions.

[0005] An imaging non-contact gating signal acquisition method for a magnetic resonance system, characterized by comprising the following steps:

[0006] Step 1, collecting a subject lying in the cavity of a magnetic resonance device, using a plastic mechanical arm to control a convex mirror, so that the face of the subject is reflected by the convex mirror, and using a camera to collect video of the skin tissue of the face of the subject;

[0007] Step 2, filtering and other image processing operations are performed on the video collected in step 1 to obtain an IPPG signal.

[0008] Step 2-1, using the superpixel segmentation of IPPG to detect the living skin of the subject video to obtain the skin pixels of the video and determine the region of interest (ROI) of the face of the subject, wherein the size of the region of interest is smaller than the image size of the CMOS camera;

[0009] Step 2-2, the region of interest is obtained in the manner of step 2-1 for each frame of the video;

[0010] Step 2-3, calculating the pixel mean values of the R, G and B channels of the region of interest of each frame of image, and obtaining the original IPPG signal by changing the pixel mean values of multiple frames of images;

[0011] Step 3, using amplitude threshold method and window sliding method to determine the pulse gating feature position and region based on the R peak feature of electrocardiogram for the original IPPG signal;

[0012] Step 4, using a CMOS camera to record the video of the posture of the subject;

[0013] Step 4-1, the gray of adjacent frames of the video is calculated, and a light flow signal is obtained by combining a light flow algorithm;

[0014] Step 4-2, the signal extracted from the light flow field of the ROI region is filtered and denoised, so that the breathing signal of the subject is obtained;

[0015] Step 5, the breathing signal obtained in step 4-1 is subjected to periodic feature analysis, and a frequency domain analysis method and an amplitude change method are used to position a respiratory gating feature region;

[0016] Step 6, the physiological gating signals obtained in steps 3 and 5 are combined to construct a composite signal, and the magnetic resonance imaging is triggered according to the gating feature region of the composite signal, so that the purpose of removing the respiratory and motion artifacts is achieved.

[0017] Advantages

[0018] 1. The imaging type non-contact gating signal acquisition method for a magnetic resonance system is suitable for extracting human robust pulse waves and breathing signals, and can effectively improve the signal-to-noise ratio of the signals.

[0019] 2. The imaging type non-contact gating signal acquisition method for a magnetic resonance system is suitable for the fusion of multiple signal features in gated imaging, eliminates the influence of respiratory motion on single pulse signals, and improves the quality and precision of gated imaging.

[0020] 3. The imaging type non-contact gating signal acquisition method for a magnetic resonance system uses IPPG technology and a light flow method to extract physiological information, realizes a non-contact, low-interference, and high-robustness human magnetic resonance imaging process, and provides more accurate image information for doctors' diagnosis. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A video acquisition schematic diagram of the imaging type non-contact gating signal acquisition method for a magnetic resonance system is provided for the embodiment.

[0022] Figure 2 A general flowchart of the imaging type non-contact gating signal acquisition method for a magnetic resonance system is provided for the embodiment. DETAILED DESCRIPTION

[0023] In order to make the purposes, advantages and features of the present application clearer, a kind of imaging non-contact gating signal acquisition method for magnetic resonance system is further described in detail below in conjunction with the drawings and specific embodiments.It should be noted that: all the drawings are very simplified and all use non-precise scale, only to facilitate, clearly assist the purpose of the embodiment of the present application, the structure shown in the drawing is part of the actual structure;The gating technology included in the present application is not limited to magnetic resonance imaging, other imaging techniques that need to overcome cardiac motion and respiratory artifacts are also applicable.The present application is not limited to the face of human face IPPG signal acquisition site, other parts that can be detected by living skin are also applicable, only for the convenience of magnetic resonance imaging, select the face as the IPPG signal extraction site.

[0024] A kind of imaging non-contact gating signal acquisition method for magnetic resonance system, its face video acquisition schematic diagram as shown in Figure 1 As shown in Figure 2 .

[0025] Step 1, use plastic mechanical arm to remotely control convex lens to the inside of magnetic resonance cavity, moves to the position that can record face information;

[0026] Step 1-1, visible light source and near-infrared light source are used to irradiate facial skin tissue, and camera is used to collect the video of the corresponding irradiated skin tissue area;

[0027] Step 1-2, start light source and camera:

[0028] The subject lies in the cavity of magnetic resonance instrument, uses visible light source and near-infrared light source to uniformly irradiate mirror face skin tissue, and opens COMS camera.At the front of visible light source, a polarizer is placed, and a polarizer is placed in front of CMOS camera to remove the influence of mirror reflection light.The human skin tissue area is not limited to the face area, and the human body parts such as arm and finger that can extract pulse wave are applicable.The implementation is designed for the human face video as the part of extracting IPPG signal.

[0029] Step 1-3, video acquisition:

[0030] The imaging video of the face area containing pulse information corresponding to the mirror reflection is collected by CMOS camera.The frame rate of the camera for collecting face video is 30fps or more, and the wave band is 4 wave bands.The subject remains in a stationary state during shooting.

[0031] Step 2, the video collected in step 1 is subjected to living skin detection, and the part of interest containing pulse wave is segmented, and IPPG signal is obtained after image processing operation:

[0032] Step 2-1, for the collected video of the face of the subject, real-time live skin detection is performed on the face by using the trained superpixel segmentation SPASD algorithm, the skin pixels of the video are obtained, and the region of interest (ROI) of the face of the subject is determined, wherein the size of the region of interest is smaller than the image size of the CMOS camera;

[0033] Step 2-2, the region of interest is obtained in the manner of step 2-1 for each frame in the video;

[0034] Step 2-3, the pixel mean value of the region of interest of each frame image is calculated, and the change of the pixel mean value of multiple frame images obtains the original IPPG signal:

[0035] Each pixel value in the matrix [a1, b1] region in the IPPG imaging device can be calculated by formula (1):

[0036] C(x, y) = I x (p s (t) + p d (t)) + V n (1)

[0037] Wherein, C(x, y) represents the light intensity value corresponding to the pixel with coordinates (x, y); I represents the light intensity of the light source; p s (t) and p d (t) represent the specular reflection coefficient and the diffuse reflection coefficient, respectively; V n represents the quantization noise of the image sensor.

[0038] The quantization noise V n of the image sensor is removed by formula (2), that is, all pixel averaging processing is performed on each frame image.

[0039]

[0040] Wherein, represents the average light intensity of all pixels on a frame image. The polarizer and the polarizer remove the non-physiological parameter light intensity information related to the mirror surface reflection, and all sets at time sequence t constitute the IPPG signal according to formula (3).

[0041]

[0042] Step 2-4, peak detection is performed on the obtained pulse wave signal, the peak value of the pulse wave corresponding to the R peak of the electrocardio monitoring is found, and thus the region or position of the diastolic period gating feature of the pulse signal is determined:

[0043] Step 2-5, a delay time prediction model is established with the R peak of the electrocardio, and the prediction relationship is calculated by using the least square method.

[0044] d=t Rpeak -t PPG (4)

[0045] Step 3, video record the human body posture of the subject by using CMOS camera;

[0046] Step 3-1, locate the chest and abdomen region efficiently and in real time according to the human body posture detection framework, and then perform image grayscale processing and use optical flow algorithm to solve the ROI region optical flow field.

[0047] Step 3-2, select a band-pass Butterworth filter with a cutoff frequency of 0.1-0.8 Hz to filter and obtain the subject's breathing signal.

[0048] Step 3-3, perform cycle analysis on the obtained breathing signal, and specifically divide the breathing signal according to the phase and amplitude to locate the respiratory signal gating feature position;

[0049] Step 4, combine the physiological gating signals obtained in steps 3 and 4 to perform feature fusion and construct a composite signal, obtain the composite signal through signal weighting, and set the threshold value of the composite signal to trigger imaging when reaching a certain threshold value:

[0050] S f =ω1·S ippg +ω2·S breath (5)

[0051] Wherein, S f is a composite signal, and ω1 and ω2 are the respective weights of the pulse signal and the breathing signal.

[0052] The above examples are only used to illustrate but not to limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the present application can still be modified or replaced equivalently without departing from the spirit and scope of the present application. Any modification or partial replacement should be covered in the scope of the claims of the present application.

Claims

1. An imaging-gated signal acquisition method for a magnetic resonance system, characterized by, The method comprises the following steps: Step 1: Collecting a subject lying in the cavity of a magnetic resonance device, using a non-magnetic mechanical arm to control the convex mirror, so that the face of the subject is reflected by the convex mirror, and using a camera to collect a video of the subject having a pulsating part; Step 2: performing image processing operation on the video collected in step 1 to obtain an IPPG signal, and specifically, step 2 comprises Step 2-1, for the collected face video of the subject, real-time living skin detection is performed on the face by using a trained superpixel segmentation SPASD algorithm to obtain skin pixels of the video and determine a region of interest (ROI) of the face of the subject, wherein the size of the region of interest is smaller than the image size of the CMOS camera; Step 2-2, the region of interest is obtained in the manner of step 2-1 for each frame in the video; Step 2-3, calculating the pixel mean value of the region of interest of each frame image, and obtaining an IPPG signal by the change of pixel mean values of multiple frames of pictures: Each pixel value in the matrix [a1, b1] region in the IPPG imaging device can be calculated by formula (1): C(x, y) = I x (p s (t) + p d (t)) + V n (1) where C(x, y) represents the light intensity value corresponding to the pixel with coordinates (x, y); I represents the light intensity of the light source; p s (t) and p d (t) represent the specular reflection coefficient and the diffuse reflection coefficient, respectively; V n represents the quantization noise of the image sensor; V = V - Vq (2) removing the quantization noise V of the image sensor from V n i.e. all pixel averaging is performed on each frame of image; wherein, represents the average light intensity of all pixels on a frame of image, the polarizer and the analyzer remove the non-physiological parameter light intensity information related to the specular reflection, and all set constitutes an IPPG signal; Step 3: determining the pulse gating feature position and region based on the R-peak characteristics of electrocardiogram by using the amplitude threshold method and the window sliding method; Step 4: recording the video of the human posture of the subject by using the CMOS camera, calculating the gray scale of adjacent frames of the video, and obtaining an optical flow signal by combining the optical flow algorithm; Step 5: performing cycle feature analysis on the respiratory signal obtained in step 4, and positioning the respiratory gating feature region by using the frequency domain analysis method and the amplitude change method; Step 6: combining the physiological gating signals obtained in steps 3 and 5 to perform feature fusion to construct a composite signal, and triggering the magnetic resonance imaging according to the gating feature region of the composite signal.

2. The method of claim 1, efficiently and real-timely positioning the chest and abdominal regions according to the human posture detection framework, performing image gray scale processing, and then using an optical flow algorithm to solve the ROI region optical flow field.

3. The method of claim 2, comprising filtering and denoising the signal extracted from the ROI region optical flow field, so as to obtain the respiratory signal of the subject.

4. The method of claim 1, wherein step 6 comprises combining the physiological gating signals obtained in steps 3 and 5 to perform feature fusion to construct a composite signal, and obtaining the composite signal by signal weighting.

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