Image processing system and intravascular imaging system

Abstract

The present disclosure describes an image processing system and an intravascular imaging system, the image processing system comprising an imaging instrument configured to acquire a plurality of continuous frames of acquired images in a blood vessel and a processor configured to collect a plurality of continuous data sets in chronological order, the data sets comprising a plurality of line data arranged in chronological order, the line data is a gray value set of pixel points in the collected image in the radial direction of the blood vessel, the processor judges the correlation between adjacent line data in the target data set, and if the correlation between the adjacent line data is larger than a first threshold value, at least one group of the two adjacent groups of line data is marked as abnormal; and if the ratio of the number of the line data marked as abnormal in the target data set to the number of all the line data is not greater than a second threshold value, correcting the line data marked as abnormal in the target data set based on the line data marked as normal in a corresponding region in at least one data set closest to the target data set in the multiple data sets.

Description

[0001] This application is a divisional application of a patent application with an application date of December 31, 2020, application number 2020116409983, and invention name “Image Processing Method”. Technical Field

[0002] The present disclosure generally relates to the technical field of image data processing, and more particularly to an image processing system and an intravascular imaging system. Background Art

[0003] Intravascular ultrasound (IVUS) system is an important medical device that uses an invasive ultrasound catheter to image blood vessels to assist doctors in diagnosing cardiovascular diseases. Specifically, the IVUS system usually includes an ultrasound catheter and a host. The ultrasound catheter can be introduced into the blood vessels and obtain ultrasound signals with vascular information in the blood vessels. The host can process the ultrasound signals to obtain images inside the blood vessels.

[0004] At present, a rotating ultrasound catheter is usually used to image blood vessels. The rotating ultrasound catheter usually includes an ultrasound probe, a motor, and a transmission shaft connecting the ultrasound probe and the motor. The motor actuates the ultrasound probe to rotate in the blood vessel through the transmission shaft. The ultrasound probe emits ultrasound pulses to the blood vessel wall while rotating to obtain ultrasound signals with blood vessel information.

[0005] However, in the above-mentioned rotary ultrasound catheter, when the motor drives the ultrasound probe to rotate in the blood vessel through the transmission shaft, friction between the transmission shaft and other locations may occur, resulting in non-uniform rotation of the ultrasound catheter, which may cause distortion of the image obtained in the blood vessel (Non-uniform Rotational Distortion, NURD). Summary of the invention

[0006] The present disclosure is proposed in view of the above-mentioned prior art conditions, and its purpose is to provide an image processing method that can effectively reduce image distortion caused by non-uniform rotation.

[0007] To this end, the present disclosure provides an image processing method, which includes using an imaging device to rotate in a blood vessel and transmit imaging signals to the blood vessel wall to obtain multiple frames of continuous acquisition images in the blood vessel, wherein the acquisition images are composed of line data, and the line data is a set of grayscale values ​​of pixel points along the radial direction of the blood vessel in the acquisition images. A frame of acquisition images is selected from the multiple frames of acquisition images as a target image. In the target image, it is determined whether the correlation between two adjacent line data is greater than a first threshold. If the correlation between two sets of adjacent line data is greater than the first threshold, at least one set of the two adjacent sets of line data is marked as abnormal, and the remaining line data is marked as normal. In the target image, if there are line data marked as abnormal and the ratio between the number of line data marked as abnormal and the number of all line data is not greater than a second threshold, the line data marked as abnormal in the target image is corrected based on the line data in the corresponding area of ​​at least one frame of acquisition images that is closest to the target image in the multiple frames of acquisition images and marked as normal.

[0008] In the image processing method involved in the present disclosure, by judging whether the correlation between adjacent line data in the target image is greater than a first threshold, abnormal line data in the target image is marked; by judging whether the ratio of the number of line data marked as abnormal to the number of all line data in the target image is not greater than a second threshold, it is determined whether to correct the line data marked as abnormal in the target image; and, in other acquired images, line data marked as normal in the corresponding area are selected to correct the line data marked as abnormal in the target image. In this case, by identifying abnormal line data and correcting the abnormal line data based on normal line data, image distortion caused by non-uniform rotation can be effectively reduced.

[0009] In addition, in the image processing method involved in the present disclosure, optionally, it also includes performing noise reduction processing on the target image.

[0010] In addition, in the image processing method involved in the present disclosure, optionally, the first threshold is 0.6 to 1.0.

[0011] In addition, in the image processing method involved in the present disclosure, optionally, in the target image, it is determined whether the correlation between two adjacent line data is greater than the first threshold value. If the correlation between two sets of adjacent line data is greater than the first threshold value, the earlier line data in the two adjacent sets of line data is marked as abnormal.

[0012] In addition, in the image processing method involved in the present disclosure, optionally, the multiple frames of acquired images are arranged in chronological order.

[0013] In addition, in the image processing method involved in the present disclosure, optionally, the at least one frame of acquired image precedes the target image.

[0014] In addition, in the image processing method involved in the present disclosure, optionally, in the target image, if there is line data marked as abnormal and the ratio between the number of line data marked as abnormal and the number of all line data is not greater than the second threshold, the target image is marked as valid; if there is line data marked as abnormal and the ratio between the number of line data marked as abnormal and the number of all line data is greater than the second threshold, the target image is marked as invalid.

[0015] In addition, in the image processing method involved in the present disclosure, optionally, if the target image is marked as invalid, the target image is replaced by the last frame of the multiple frames of acquired images that is closest to the target image and marked as valid.

[0016] In addition, in the image processing method of the present disclosure, optionally, the imaging device is an ultrasonic transducer, and the ultrasonic transducer continuously emits ultrasonic pulses to the blood vessel wall, thereby facilitating the acquisition of continuous multi-frame acquisition images.

[0017] In addition, the present disclosure provides an image processing system for intravascular imaging, which uses the image processing method involved in the present disclosure.

[0018] According to the present disclosure, image distortion caused by non-uniform rotation can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings, in which:

[0020] Figure 1 is a schematic diagram showing the application of the intravascular imaging system involved in the example of the present disclosure.

[0021] Figure 2 Schematic diagram showing an enlarged image of an imaging device placed in a blood vessel according to an example of the present disclosure.

[0022] Figure 3 is a schematic diagram showing the collected images involved in the examples of the present disclosure.

[0023] Figure 4A is a schematic diagram showing the collection line data involved in the example of the present disclosure, Figure 4B is a schematic diagram showing formation of frame data based on line data.

[0024] Figure 5 is a flowchart showing a method for identifying NURD based on line data involved in an example of the present disclosure.

[0025] Figure 6 It is a flowchart showing the method based on image recognition NURD involved in the example of the present disclosure.

[0026] Figure 7 is a flowchart illustrating a method for correcting NURD based on line data according to an example of the present disclosure.

[0027] Figure 8 It is a schematic diagram showing that multiple data sets involved in the examples of the present disclosure are arranged in chronological order.

[0028] Fig. 9 is a flowchart showing the method based on image correction NURD involved in the example of the present disclosure. DETAILED DESCRIPTION

[0029] Hereinafter, the preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same symbols are assigned to the same components, and repeated descriptions are omitted. In addition, the accompanying drawings are only schematic diagrams, and the ratio of the dimensions of the components or the shapes of the components may be different from the actual ones.

[0030] It should be noted that the terms "including" and "having" and any variations thereof in the present disclosure, such as a process, method, system, product or device that includes or has a series of steps or units, are not necessarily limited to those steps or units clearly listed, but may include or have other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] In addition, the subheadings and the like involved in the following description of the present disclosure are not intended to limit the content or scope of the present disclosure, but are merely used as a reminder for reading. Such subheadings should neither be understood as being used to divide the content of the article, nor should the content under the subheading be limited to the scope of the subheading.

[0032] Embodiments of the present disclosure provide an intravascular imaging system, which can acquire and process images collected within a blood vessel to effectively reduce image distortion (NURD) caused by non-uniform rotation.

[0033] An embodiment of the present disclosure provides an intravascular ultrasound system, which can acquire and process images collected within a blood vessel to effectively reduce image distortion (NURD) caused by non-uniform rotation.

[0034] An embodiment of the present disclosure provides an image processing system, which is an image processing system for rotational imaging, and can process an image acquired by rotation to effectively reduce image distortion (NURD) caused by non-uniform rotation.

[0035] An embodiment of the present disclosure provides an image processing method, which is an image processing method for rotational imaging, and can process an image acquired by rotation to effectively reduce image distortion (NURD) caused by non-uniform rotation.

[0036] In the following, the system and method involved in the present disclosure are described by taking an intravascular imaging system and an intravascular captured image as an example.

[0037] Figure 1 is a schematic diagram showing the application of the intravascular imaging system 1 involved in the example of the present disclosure. Figure 2 FIG. 1 is an enlarged schematic diagram showing an imaging device 10 placed in a blood vessel according to an example of the present disclosure.

[0038] In various embodiments, the intravascular imaging system 1 may include: an imaging device 10, which is placed in a blood vessel and transmits imaging signals to the blood vessel wall while rotating to obtain an acquisition image in the blood vessel; a transmission shaft 20, which is connected to the imaging device 10 and extends along the blood vessel to the outside of the body; an outer tube 30, which extends along the blood vessel to the outside of the body and has an inner cavity for accommodating the imaging device 10 and the transmission shaft 20, and the imaging device 10 can be rotated in the inner cavity of the outer tube 30; and a motor 40, which is located outside the body and can be connected to the transmission shaft 20, and the motor 40 can actuate the imaging device 10 via the transmission shaft 20 (see Figure 1 and Figure 2 ).

[0039] In various embodiments, the intravascular imaging system 1 further includes a processor 50 (see Figure 1 ), which is used to process the acquired images. In various embodiments, the intravascular imaging system 1 further includes a display 60 (see Figure 1 ), which is used to display the acquired image or the processed acquired image. The intravascular imaging system 1 as described in the present disclosure can acquire the acquired image in the blood vessel, process the acquired image, and display the acquired image or the processed acquired image.

[0040] In various embodiments, the image processing system may include: a processor 50, which is used to process the image acquired by the rotation. In various embodiments, the image processing system may include: a processor 50, which is used to process the acquired image in the blood vessel acquired by the imaging device 10. In various embodiments, the image processing system may also include: a display 60, which is used to display the acquired image or the processed acquired image.

[0041] In various embodiments, the imaging device 10 can be introduced into a blood vessel and can transmit imaging signals to the blood vessel wall, for example, transmitting imaging signals to the blood vessel wall substantially along the blood vessel radial direction D (see Figure 2). In some examples, the imaging device 10 may be an ultrasonic transducer, and the imaging signal may be an ultrasonic signal, such as an ultrasonic pulse. In some examples, the ultrasonic transducer may transmit an ultrasonic pulse to the vessel wall along the vessel radial direction D, and receive an ultrasonic echo with vessel information. In other embodiments, the imaging signal may also be an optical signal, an electromagnetic wave signal, etc.

[0042] In various embodiments, the motor 40 can actuate the imaging device 10 via the drive shaft 20, for example, within the lumen of the outer tube 30 (see Figure 2 In various embodiments, the motor 40 can drive the imaging device 10 to rotate via the transmission shaft 20, for example, along the R direction substantially orthogonal to the axial direction of the blood vessel (see Figure 2 In various embodiments, the motor 40 can actuate the imaging device 10 via the transmission shaft 20 to move along the extension direction of the blood vessel, for example, along the L direction toward the outside of the body (see Figure 2 ).

[0043] Figure 3 FIG. 4 is a schematic diagram showing frame data of a captured image formed based on line data according to an example of the present disclosure, wherein: Figure 4A is a schematic diagram showing the collection line data involved in the example of the present disclosure, Figure 4B is a schematic diagram showing formation of frame data based on line data.

[0044] In various embodiments, an image captured within the blood vessel may be acquired based on the imaging signal. In various embodiments, the image captured within the blood vessel acquired based on the imaging device 10 may be a cross-sectional image substantially along the radial direction D of the blood vessel (see Figure 3 In various embodiments, the imaging device 10 may continuously transmit imaging signals to the blood vessel wall to obtain continuous multi-frame acquisition images.

[0045] In some examples, ultrasound echoes based on ultrasound pulses can be used to acquire images within a blood vessel.

[0046] In some examples, a set of line data may be acquired based on a beam of ultrasound pulses. In various embodiments, the line data may be collected in a time sequence, and the line data may be arranged in a time sequence (see Figure 4A ). For example, in Figure 4A In the illustrated embodiment, the line data may include line data K1, line data K2, line data K3, line data K4, line data K5, line data K6, line data K7 and line data K8, and line data K1, line data K2, line data K3, line data K4, line data K5, line data K6, line data K7 and line data K8 may be arranged in chronological order.

[0047] In some examples, a set of frame data may be acquired based on the first predetermined number of line data (see Figure 4B In some examples, the first predetermined number of line data are arranged in time sequence in the R direction to obtain a set of frame data (see Figure 4B ). For example, in Figure 4B In the illustrated embodiment, line data K1, line data K2, line data K3, line data K4, line data K5, line data K6, line data K7, and line data K8 in the frame data are arranged in time sequence in the R direction. In some examples, a set of frame data can be acquired each time a first predetermined number of line data are collected. In some examples, a frame of captured image can be acquired based on a set of frame data.

[0048] It should be noted that in Figure 4A and Figure 4B In the illustrated embodiment, the first predetermined number is 8 for illustrative purposes only and does not limit the actual value of the first predetermined number. In some examples, the first predetermined number may be 128 to 1024, such as 128, 256, 512 or 1024.

[0049] In various embodiments, the line data may be a set of grayscale values ​​of pixels along the radial direction D of the blood vessel in the acquired image (see Figure 4A and Figure 4B ). For example, in Figure 4A and Figure 4B In the embodiment shown, the line data K1 may be a pixel point P 11 , pixel point P 12 , pixel point P 13 , pixel point P 14 and pixel P 15 The gray value set of line data K2 can be pixel point P 21 , pixel point P 22 , pixel point P 23 , pixel point P 24 and pixel P 25 A set of grayscale values. It should be noted that Figure 4A and Figure 4BIn the embodiment shown, the second predetermined number is 5 only for schematic illustration, and it does not limit the actual value of the second predetermined number. In various embodiments, a set of line data may include the grayscale values ​​of the second predetermined number of pixels. In some examples, the second predetermined number may be 1000 to 4000, such as 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900 or 4000.

[0050] Figure 5 is a flowchart showing a method for identifying NURD based on line data involved in an example of the present disclosure.

[0051] Various embodiments of the present disclosure provide a method for identifying NURD based on line data, which includes: collecting continuous line data, wherein the line data is arranged in time sequence (see Figure 4A ); determining the correlation between adjacent line data; if the correlation between adjacent line data is greater than a first threshold, marking at least one of the two adjacent sets of line data as abnormal. In various embodiments, the method further includes marking the remaining line data in the collected line data as normal. In various embodiments, the method further includes denoising the line data.

[0052] In some examples, the amount of line data collected is a first predetermined amount. In some examples, the line data collected is line data collected in real time from the imaging device 10. In some examples, the line data is collected in real time and the NURD is identified in real time. The above method can be used to identify NURD in data in rotational data acquisition.

[0053] In some examples, the correlation of adjacent line data can be calculated by the following formula:

[0054]

[0055] In this formula, x and y represent the grayscale value sets of two adjacent sets of line data (i.e., line data x and line data y), μ x Represents the average value of each gray value in the line data x, μ y represents the average value of each gray value in the line data y, and E represents (x-μ x ) and (y-μ y ), σ x Represents the standard deviation of each grayscale value in the line data x, σ yRepresents the standard deviation of each grayscale value in the line data y.

[0056] by Figure 4A The embodiment shown in the figure is used to illustrate the above formula by taking line data K1 as line data x and line data K2 as line data y.

[0057] x={P 11 , P 12 , P 13 , P 14 , P 15},

[0058]

[0059] y={P 21 , P 22 , P 23 , P 24 , P 25},

[0060]

[0061] In this case, based on the grayscale values ​​of all pixels in two adjacent sets of line data, the correlation between the two sets of line data can be accurately calculated.

[0062] In some examples, the first threshold may be between 0.60 and 1.0, such as 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, or 1.0.

[0063] In some examples, if the correlation between two adjacent line data is greater than a first threshold, the two adjacent sets of line data are marked as abnormal. Figure 4A In the illustrated embodiment, if the correlation between line data K1 and line data K2 is greater than a first threshold, line data K1 and line data K2 are marked as abnormal. Specifically, if the correlation between adjacent line data is greater than the first threshold, the imaging signals corresponding to the two sets of line data may be emitted to a close position. In rotational imaging, non-uniform rotation, such as slow rotation, may cause this situation, resulting in that the image acquired based on the two sets of line data cannot accurately or completely reflect the information of the blood vessel.

[0064] In other examples, if the correlation between two adjacent sets of line data is greater than a first threshold, the earlier line data in the two adjacent sets of line data is marked as abnormal. Figure 4A In the illustrated embodiment, if the correlation between the line data K1 and the line data K2 is greater than a first threshold, the line data K1 is marked as abnormal.

[0065] In other examples, if the correlation between two adjacent sets of line data is greater than a first threshold, the subsequent line data in the two adjacent sets of line data is marked as abnormal. Figure 4A In the illustrated embodiment, if the correlation between the line data K1 and the line data K2 is greater than a first threshold, the line data K2 is marked as abnormal.

[0066] Figure 6 It is a flowchart showing the method based on image recognition NURD involved in the example of the present disclosure.

[0067] Various embodiments of the present disclosure provide a method for recognizing NURD based on an image, which includes: obtaining an image (see Figure 3 ); obtain the frame data of the image, wherein the frame data is formed by arranging the line data in time sequence (see Figure 4B ); determining the correlation between adjacent line data; if the correlation between adjacent line data is greater than a first threshold, marking at least one of the two adjacent sets of line data as abnormal. In various embodiments, the method further includes marking the remaining line data as normal. In various embodiments, the method further includes denoising the image. The above method can be used to identify NURD in an image in rotational imaging.

[0068] In some examples, if the correlation between two adjacent line data is greater than a first threshold, the two adjacent sets of line data are marked as abnormal. Figure 4B In the illustrated embodiment, if the correlation between the line data K1 and the line data K2 is greater than a first threshold, the line data K1 and the line data K2 are marked as abnormal.

[0069] In other examples, if the correlation between two adjacent sets of line data is greater than a first threshold, the earlier line data in the two adjacent sets of line data is marked as abnormal. Figure 4B In the illustrated embodiment, if the correlation between the line data K1 and the line data K2 is greater than a first threshold, the line data K1 is marked as abnormal.

[0070] In other examples, if the correlation between two adjacent sets of line data is greater than a first threshold, the subsequent line data in the two adjacent sets of line data is marked as abnormal. Figure 4B In the illustrated embodiment, if the correlation between the line data K1 and the line data K2 is greater than a first threshold, the line data K2 is marked as abnormal.

[0071] Figure 7 is a flowchart illustrating a method for correcting NURD based on line data according to an example of the present disclosure. Figure 8 It is a schematic diagram showing that multiple data sets involved in the examples of the present disclosure are arranged in chronological order.

[0072] Various embodiments of the present disclosure provide a method for correcting NURD based on line data, which includes: collecting a plurality of continuous data sets in time sequence, wherein each data set includes a first predetermined number of continuous line data arranged in time sequence (see Figure 4A ); select a data set as a target data set from multiple data sets; use the method described in this article to determine whether there is line data marked as abnormal in the target data set; if there is line data marked as abnormal in the target data set, determine whether the ratio of the number of line data marked as abnormal in the target data set to the number of all line data is not greater than a second threshold; if the ratio of the number of line data marked as abnormal in the target data set to the number of all line data is not greater than the second threshold, then correct the line data marked as abnormal in the target data set based on the line data marked as normal in the corresponding area of ​​at least one data set that is closest to the target data set among the multiple data sets. The above method can be used to correct NURD in data during rotational data acquisition.

[0073] In various embodiments, the method for correcting NURD based on line data may also include: if the ratio of the number of line data marked as abnormal to the number of all line data in the target data set is not greater than a second threshold, marking the target data set as valid, and generating a corrected image corresponding to the target data set based on the normal line data in the target data set and the corrected abnormal line data.

[0074] In various embodiments, the method for correcting NURD based on line data may also include: if the ratio of the number of line data marked as abnormal in the target data set to the number of all line data in the target data set is greater than a second threshold, marking the target data set as invalid, and generating a corrected image corresponding to the target data set based on the last, two or three and / or next, two or three data sets marked as valid that are closest to the target data set among multiple data sets, for example, replacing the target data set with the last data set marked as valid that is closest to the target data set among multiple data sets and generating a corresponding image.

[0075] In some examples, the second threshold may be 0.1 to 0.5, such as 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, or 0.5.

[0076] In some examples, based on line data in a corresponding area in a data set closest to the target data set in multiple data sets and marked as normal and a linear interpolation algorithm, line data marked as abnormal in the target data set are corrected. For example, each grayscale value in the line data marked as abnormal is corrected.

[0077] In such Figure 8In the illustrated embodiment, data sets C1, data set C2, data set C3 and data set C4 are continuous data sets. It is assumed that data set C3 is the target data set and line data N3 in data set C3 is abnormal line data. If line data N2 in the corresponding area of ​​data set C2 is normal and line data N4 in the corresponding area of ​​data set C4 is normal, line data N3 is corrected based on a linear interpolation algorithm and using line data N2 and line data N4.

[0078] In some examples, N3=f×N2+(1-f)×N4, the value range of f is 0 to 1 and f can be selected based on the distance between two sets of line data (in this example, the distance between N3 and N2). 31 =f×N 21 +(1-f)×N 41 , N 32 =f×N 22 +(1-f)×N 42 , N 33 =f×N 23 +(1-f)×N 43 , N 34 =f×N 24 +(1-f)×N 44 , and N 35 =f×N 25 +(1-f)×N 45 .

[0079] In some other examples, N3=f×N2, the value range of f is 0 to 1 and f can be selected based on the distance between two sets of line data (in this example, the distance between N3 and N2). 31 =f×N 21 , N 32 =f×N 22 , N 33 =f×N 23 , N 34 =f×N 24 , and N 35 =f×N 25 .

[0080] In such Figure 8 In the illustrated embodiment, if the line data N2 of the corresponding area in the data set C2 is abnormal and the line data N1 of the corresponding area in the data set C1 is normal, the line data N3 is corrected based on the linear interpolation algorithm and using the line data N1 and the line data N4. In some examples, N3 = f × N1 + (1-f) × N4. In other examples, N3 = f × N1. In other examples, N3 = f × N4.

[0081] In various embodiments, the method for correcting NURD based on line data is as follows: Figure 8 As explained by way of example, if the ratio of the number of line data marked as abnormal in the target data set to the number of all line data is not greater than a second threshold, the line data marked as abnormal in the target data set is corrected based on the line data in the corresponding area in at least one data set among the multiple data sets that is closest to the target data set and is marked as normal. For example, the at least one data set may be one, two or three prior data sets and / or one, two or three subsequent data sets, and the line data in the corresponding area in the at least one data set is marked as normal.

[0082] Fig. 9 is a flowchart showing the method based on image correction NURD involved in the example of the present disclosure.

[0083] Various embodiments of the present disclosure provide a method based on image correction NURD, which includes: acquiring a plurality of consecutive frames of acquired images in time sequence, wherein the plurality of frames of acquired images are arranged in time sequence; acquiring frame data of the acquired images, wherein the frame data of the acquired images is formed by consecutive line data arranged in time sequence (see Figure 4B ); select a frame of acquired image from multiple frames of acquired images as the target image; use the method described in this article to determine whether there is line data marked as abnormal in the target image; if there is line data marked as abnormal in the target image, determine whether the ratio of the number of line data marked as abnormal in the target image to the number of all line data is not greater than a second threshold; if the ratio of the number of line data marked as abnormal in the target image to the number of all line data is not greater than the second threshold, then correct the line data marked as abnormal in the target image based on the line data marked as normal in the corresponding area of ​​at least one acquired image that is closest to the target image in the multiple frames of acquired images. The above method can be used to correct the NURD of an image in rotational imaging.

[0084] In the method based on image correction NURD involved in various embodiments, the method may also include: if the ratio of the number of line data marked as abnormal to the number of all line data in the target image is not greater than a second threshold, the target image is marked as valid, and a corrected image corresponding to the target image is generated based on the normal line data in the target image and the corrected abnormal line data.

[0085] In the method based on image correction NURD involved in various embodiments, the method may also include: if the ratio of the number of line data marked as abnormal to the number of all line data in the target image is greater than a second threshold, the target image is marked as invalid, and a corrected image corresponding to the target image is generated based on the previous frame, two frames or three frames and / or the next frame, two frames or three frames of acquired images marked as valid that are closest to the target image in multiple frames of acquired images, for example, the target image is replaced with the previous frame of acquired images marked as valid that is closest to the target image in multiple frames of acquired images.

[0086] Various embodiments of the present disclosure provide a data processing method, which includes: using the method for identifying NURD based on line data described herein to identify NURD; using the method for correcting NURD based on line data described herein to correct NURD. The above method can be used to identify and correct NURD in data during rotational data acquisition.

[0087] Various embodiments of the present disclosure provide an image processing method, which includes: using the method for identifying NURD based on image as described herein to identify NURD; using the method for correcting NURD based on image as described herein to correct NURD. The above method can be used to identify and correct NURD in an image in rotational imaging.

[0088] The method described herein can acquire the data or image acquired by the imaging device 10 in real time, can perform NURD recognition and NURD correction on the data or image in real time, and can acquire the corrected data or image in real time.

[0089] In various embodiments, the processor 50 may use the methods described herein to identify and correct NURD in data or images. In various embodiments, the processor 50 may acquire line data or captured images acquired by the imaging device 10 in real time, and perform NURD identification and NURD correction on the line data acquired by the imaging device 10 in real time. In various embodiments, the processor 50 may transmit the corrected image to the display 60 in real time for display.

[0090] In various embodiments, the processor 50 may obtain the probability of NURD occurrence, such as the ratio of the number of images with abnormalities to the number of all images within a predetermined time. In various embodiments, the processor 50 may obtain the severity of NURD, such as the ratio of the number of line data marked as abnormal in a frame of acquired image to the number of all line data. In various embodiments, the processor 50 may generate a feedback parameter based on the probability of NURD occurrence and / or the severity of NURD, and the motor 40 may dynamically adjust the voltage and / or speed based on the feedback parameter to reduce the occurrence of NURD.

[0091] According to the present disclosure, image distortion caused by non-uniform rotation can be effectively reduced.

[0092] Although the present disclosure is specifically described above in conjunction with the accompanying drawings and examples, it is to be understood that the above description does not limit the present disclosure in any form. Those skilled in the art may modify and change the present disclosure as needed without departing from the essential spirit and scope of the present disclosure, and these modifications and changes all fall within the scope of the present disclosure.

Claims

1. An image processing system, characterized in that: The invention comprises an imaging device and a processor, wherein the imaging device is configured to obtain a continuous multi-frame acquisition image in a blood vessel. The processor is configured to collect a plurality of continuous data sets in time sequence, wherein each of the data sets includes a first predetermined number of line data arranged in time sequence, and the line data is a set of grayscale values ​​of pixel points along the radial direction of the blood vessel in the acquired image, The processor is also configured to select a data set from the multiple data sets as a target data set and determine the correlation between adjacent line data in the target data set. If the correlation between adjacent line data in the target data set is greater than a first threshold, at least one of the two adjacent groups of line data is marked as abnormal, and the remaining line data is marked as normal. If the ratio of the number of line data marked as abnormal to the number of all line data in the target data set is not greater than a second threshold, the line data marked as abnormal in the target data set is corrected based on the line data in the corresponding area of ​​at least one data set among the multiple data sets that is closest to the target data set and is marked as normal.

2. The image processing system according to claim 1, characterized in that The processor is also configured to mark the target data set as valid if the ratio of the number of line data marked as abnormal to the number of all line data in the target data set is not greater than the second threshold, and generate a corrected image corresponding to the target data set based on the normal line data in the target data set and the corrected abnormal line data.

3. The image processing system according to claim 1, characterized in that: The processor is also configured to mark the target dataset as invalid if the ratio of the number of line data marked as abnormal to the number of all line data in the target dataset is greater than the second threshold, and use the last dataset marked as valid that is closest to the target dataset among the multiple datasets to replace the target dataset and generate a corresponding image.

4. The image processing system according to claim 1, characterized in that: The processor is further configured to correct the line data marked as abnormal in the target data set based on the line data in the corresponding area of ​​the data set closest to the target data set in the multiple data sets and marked as normal and a linear interpolation algorithm.

5. The image processing system according to claim 1, characterized in that: The processor is further configured to perform noise reduction processing on the line data.

6. The image processing system according to claim 1, characterized in that: The imaging device is an ultrasound transducer configured to continuously transmit ultrasound pulses to the blood vessel wall.

7. The image processing system according to claim 1, characterized in that: The at least one group is the preceding line data, the succeeding line data or the two adjacent groups of line data.

8. The image processing system according to claim 1, characterized in that: The second threshold is between 0.1 and 0.

5.

9. An intravascular imaging system, characterized in that: include: A transmission shaft, an outer tube, a motor, and an image processing system as described in any one of claims 1 to 8, wherein the transmission shaft is connected to an imaging device and extends outside the body along a blood vessel, the outer tube extends outside the body along a blood vessel and has an inner cavity for accommodating the imaging device and the transmission shaft, the imaging device rotates in the inner cavity of the outer tube, the motor is located outside the body and connected to the transmission shaft, and the motor actuates the imaging device to rotate via the transmission shaft.

10. The intravascular imaging system according to claim 9, characterized in that: Also included is a display configured to display the acquired image or the processed acquired image.

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