CN111369465B - CT dynamic image enhancement method and device - Google Patents
CT dynamic image enhancement method and device Download PDFInfo
- Publication number
- CN111369465B CN111369465B CN202010144459.4A CN202010144459A CN111369465B CN 111369465 B CN111369465 B CN 111369465B CN 202010144459 A CN202010144459 A CN 202010144459A CN 111369465 B CN111369465 B CN 111369465B
- Authority
- CN
- China
- Prior art keywords
- image
- value
- pixel
- time domain
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000009466 transformation Effects 0.000 claims abstract description 7
- 238000000605 extraction Methods 0.000 claims description 13
- 230000002708 enhancing effect Effects 0.000 claims description 4
- 238000002591 computed tomography Methods 0.000 description 63
- 238000001914 filtration Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 230000002123 temporal effect Effects 0.000 description 4
- 210000004351 coronary vessel Anatomy 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001502 supplementing effect Effects 0.000 description 2
- 230000001131 transforming effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Classifications
-
- G06T5/90—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/10—Image enhancement or restoration by non-spatial domain filtering
-
- G06T5/70—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
Abstract
The embodiment of the invention provides a CT dynamic image enhancement method and device. According to the embodiment of the invention, the image to be processed is one of the K space image sequences corresponding to the CT dynamic image sequences, for each pixel point in the image to be processed, the time domain weighted average pixel value corresponding to the pixel point in the preset time period is determined according to the preset time domain weight value sequence, the target frequency domain weight value corresponding to the pixel point is searched out from the preset mask, the weight value corresponding to the first frequency band is smaller than the weight value corresponding to the second frequency band, the first frequency band is higher than the second frequency band, the target pixel value of the pixel point is obtained based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point, the frequency domain enhanced image of the image to be processed is subjected to Fourier inverse transformation, the time domain enhanced image corresponding to the image to be processed is obtained, the smear phenomenon is effectively weakened, and the image quality of the enhanced image is improved.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a CT dynamic image enhancement method and device.
Background
In applications, enhancement processing of medical images is often required. For example, an enhanced image obtained by enhancing a CT (Computed Tomography) dynamic image can assist a doctor in more accurate diagnosis and treatment. The CT dynamic image refers to a series of time-domain CT images obtained through conventional CT film scanning and reconstruction.
In the related art, a time domain filtering mode is adopted to enhance the CT dynamic image. However, temporal filtering introduces smear phenomena, resulting in poor quality of the enhanced image.
Disclosure of Invention
In order to overcome the problems in the related art, the invention provides a CT dynamic image enhancement method, a CT dynamic image enhancement device, a CT device and a CT system, and the image quality of a heart coronary vessel reconstruction image is improved.
According to a first aspect of an embodiment of the present invention, there is provided a CT moving image enhancement method, including:
acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to a CT dynamic image sequence;
for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence;
searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point;
and carrying out inverse Fourier transform on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed.
According to a second aspect of an embodiment of the present invention, there is provided a CT moving image enhancement apparatus including:
the image acquisition module is used for acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to the CT dynamic image sequence;
the determining module is used for determining a time domain weighted average pixel value corresponding to each pixel point in the image to be processed in a preset time duration according to a preset time domain weight value sequence;
the searching module is used for searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
the pixel value acquisition module is used for acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point;
the transformation module is used for carrying out inverse Fourier transformation on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, and the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
according to the embodiment of the invention, the image to be processed is one image in the K space image sequence corresponding to the CT dynamic image sequence, for each pixel point in the image to be processed, the time domain weighted average pixel value corresponding to the pixel point in the preset duration is determined according to the preset time domain weight value sequence, the target frequency domain weight value corresponding to the pixel point is found out from the preset mask, the weight value corresponding to the first frequency band in the mask is smaller than the weight value corresponding to the second frequency band, the first frequency band is higher than the second frequency band, the target pixel value of the pixel point is obtained based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point, and the frequency domain enhanced image of the image to be processed is inversely transformed to obtain the time domain enhanced image corresponding to the image to be processed, so that the image resolution can be kept better, noise can be well suppressed, the image with the drag and the image with the greatly improved quality can be greatly improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a flowchart illustrating a method for enhancing a CT moving image according to an embodiment of the present invention.
FIG. 2 is a time domain weight W t Normal distribution curve graph of (2).
FIG. 3 is a schematic diagram of a two-class mask.
FIG. 4 is a schematic diagram of a mask with normal distribution variation.
Fig. 5 is a functional block diagram of a CT moving image enhancement device according to an embodiment of the present invention.
Fig. 6 is a hardware configuration diagram of a CT apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of embodiments of the invention as detailed in the accompanying claims.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments of the invention only and is not intended to be limiting of embodiments of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in embodiments of the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present invention. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
To meet the needs of a physician for film reading, enhanced images are required to meet a range of requirements, such as image resolution, signal to noise ratio, and the like. The temporal filtering can effectively suppress noise and better maintain image resolution, but the single temporal filtering can introduce a smear phenomenon, which leads to a reduction in enhanced image quality.
The inventors found during the course of the study that the distribution in the high frequency region of the frequency domain (i.e. k-space) is similar between different frames of the CT dynamic image, while the distribution in the low frequency region is different. Based on the discovery, the inventor provides a novel CT dynamic image enhancement method based on the combination of time domain and frequency domain.
The CT dynamic image enhancement method will be described in detail by way of examples.
Fig. 1 is a flowchart illustrating a method for enhancing a CT moving image according to an embodiment of the present invention. As shown in fig. 1, in the present embodiment, the CT moving image enhancement method may include:
s101, acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to a CT dynamic image sequence.
S102, for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence.
S103, searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band.
S104, acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point.
S105, carrying out inverse Fourier transform on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed.
In this embodiment, the image to be processed belongs to a CT dynamic image of a frequency domain. In application, conventional CT film scanning is performed on a part to be scanned of a subject to obtain CT film scanning data, CT image sequences in a time domain are obtained by utilizing CT film scanning data reconstruction, and frequency domain image sequences corresponding to the CT image sequences, namely K space image sequences, can be obtained by performing Fourier transformation on the CT image sequences in the time domain.
Thus, in one exemplary implementation, acquiring an image to be processed may include:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
For example. By performing conventional CT cine scan and reconstruction of the subject's coronary artery, an n-layer CT image of the coronary artery, denoted as I, can be obtained 0 、I 1 、I 2 ……I n-1 。I 0 、I 1 、I 2 ……I n-1 Is a sequence of temporal images.
Respectively to I 0 、I 1 、I 2 ……I n-1 Fourier transforming to K-space image K 0 、K 1 、K 2 ……K n-1 。
Taking K 0 、K 1 、K 2 ……K n-1 Any one of the images K m (m is a natural number, and the value of m may be any one of 0, 1, and 2 … … n-1) as an image to be processed. The image K p The pixel value of the pixel point in (a) is equal to K i,j,t . Wherein i and j respectively represent the abscissa and the ordinate of the pixel point in the image, and t represents the acquisition time of the image.
In this embodiment, the preset extraction policy may be preset by a developer. For example, in one example, the preset extraction policy may be extraction in the acquisition time sequence of images. In another example, the preset extraction policy may also be extraction from the K-space image sequence according to a selection operation by the user. The present embodiment does not limit the preset extraction policy.
In this embodiment, the preset time domain weight value sequence may be evenly distributed or normally distributed (the current frame is the maximum value of the distribution, as shown in fig. 2).
Assume that the weight value in the time domain weight value sequence is W t ，K 0 、K 1 、K 2 ……K n-1 Corresponding time domain weight values are W respectively t0 、W t1 、W t2 ……W tn-1 . In the time domain weight value sequence of average distribution, W t0 ＝W t1 ＝W t2 ＝……＝W tn-1 =1/n. In the normally distributed time domain weight value sequence, the time domain weight value corresponding to the current frame is the largest. FIG. 2 is a time domain weight W t Normal distribution curve graph of (2). As shown in fig. 2, the whole curve is in normal form, and the time domain weight W of the current image frame t =1, weight W from current image frame to both sides t The value of (2) shows a decreasing trend.
In an exemplary implementation process, for each pixel point in the image to be processed, determining, according to a preset time domain weight value sequence, a time domain weighted average pixel value corresponding to the pixel point within a preset time duration may include:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
and obtaining the quotient of the sum of pixel value components corresponding to the reference pixel values and the weight sum, wherein the sum of the weight sum is the sum of the matched time domain weight values corresponding to the reference pixel values, and the quotient is used as the time domain weighted average pixel value of the pixel points in the preset time duration.
The preset time length can be set according to application requirements. Herein, s is referred to as a step size in the time series direction, and the preset time length=2s+1.
Let K be 0 、K 1 、K 2 ……K n-1 Current image frame K in m The time of the image to be processed is t, the target time point is t, and the target image is K 0 、K 1 、K 2 ……K n-1 Is in the time period [ t-s, t+s ]]An image of the inside.
In an application, the value of s can be set according to actual requirements. For example, if the noise in the picture is large, s may be set to a large value, and at this time, more image frames may be used for time-wise averaging, so as to obtain a better denoising effect. If the noise in the picture is smaller, the s can be set to be a smaller value, and at the moment, less image frames can be used for carrying out time-direction averaging, so that the calculated amount is reduced, and the processing speed is improved.
For K m Any pixel point in the set, wherein the time domain weighted average pixel value of the pixel point corresponding to the pixel point in the preset time length is equal to
For example, suppose K m For K 3 S=2, then K 3 Any pixel point K i,j Corresponding time-domain weighted average pixel value within preset duration is equal to W 1 *K i,j,1 、W 2 *K i,j,2 、W 3 *K i,j,3 、W 4 *K i,j,4 、W 5 *K i,j,5 Sum of (d) divided by W 1 、W 2 、W 3 、W 4 、W 5 And the resulting quotient. Wherein K is i,j,1 、K i,j,2 、K i,j,4 、K i,j,5 Respectively image K 1 、K 2 、K 4 、K 5 Intermediate and image K 3 Pixel point K of (2) i,j, Pixel value, W, of corresponding pixel point 1 、W 2 、W 3 、W 4 、W 5 Respectively K 1 、K 2 、K 3 、K 4 、K 5 Corresponding time domain weight values.
If the data in the front and back step sizes of the image to be processed are insufficient, the data can be supplemented in the time domain direction by using a mirror image method. In supplementing data, K 0 Supplementing data for the left and right symmetry of the world. For example, the image to be processed is K 1 T=t1, s=2, then W 1 The step length is 2 images K 2 、K 3 Sufficient data, K 1 Only one image K in the previous step 0 Insufficient data, in which case the image K can be supplemented before -1 Let image K -1 ＝K 1 。
In the CT dynamic image, there is a strong correlation between image frames, and in this embodiment, through step S102, weighted average is calculated in the time domain direction of the frequency domain, so that filtering can be performed by combining multiple frames of image data near the image frame to be processed, so as to effectively reduce the smear phenomenon, and facilitate improving the image quality.
In this embodiment, the first frequency band is a high frequency region of the frequency domain, and the second frequency band is a low frequency region of the frequency domain.
In step S103, the weight value corresponding to the first frequency band is smaller than the weight value corresponding to the second frequency band in the mask (mask), which means that the frequency domain weight value of the high frequency region is smaller than the frequency domain weight value of the low frequency region.
In this embodiment, the mask is preset. The mask may be represented by a matrix, which is referred to herein as a mask matrix for ease of description. The size of the mask matrix is the same as the size of the image to be processed. For example, the image to be processed is a 256×256 matrix, and the mask matrix is also a 256×256 matrix. Each element in the mask matrix is a frequency domain weight value corresponding to the pixel point with the same position in the image to be processed.
In an exemplary implementation, the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
For example, in one example, the masks of the high frequency region and the low frequency region of k-space may be set as classified masks, as shown in fig. 3, and fig. 3 is a schematic diagram of the classified masks. In the two classification masks of FIG. 3, the frequency domain weight value W of the middle low frequency region i,j A frequency domain weight value W of 1 in the surrounding high frequency region i,j Is 0.
For example, in another example, the mask of k-space may be set to a mask of normal distribution variation, as shown in fig. 4, fig. 4 is a schematic diagram of the mask of normal distribution variation. In the mask of fig. 4, which is changed in normal distribution, the frequency domain weight value of the center low frequency region is 1, and is extended from low frequency to high frequency, and the frequency domain weight value is changed with normal distribution.
Of course, other types of masks may be provided according to application requirements, and the specific form of the mask is not limited in this embodiment.
In the application, the frequency domain weight value W can be adjusted according to the actual requirement i,j So that high frequency regions of the sequence that are not strongly varying can be weighted averaged with more frames, while low frequency regions of greater variation can be weighted averaged with fewer frames.
In this embodiment, the pixel value of the enhanced image is calculated by combining the time domain (time domain weighted average pixel value) and the frequency domain (target frequency domain weight value), so that the characteristics of time domain filtering can be utilized to maintain better image resolution, better noise suppression, and the smear phenomenon cited by the time domain filtering can be effectively reduced by utilizing the frequency domain filtering.
In an exemplary implementation process, obtaining the target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value, and the current pixel value of the pixel point may include:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
For example, assume that the pixel value in the enhanced image is Ks i,j,t Ks is then i,j,t The value of (2) can be expressed by the following formula (1).
In the formula (1), K i,j,t For the pixel value of the pixel point in the image to be processed, W i,j The frequency domain weight value corresponding to the pixel point.
Since the frequency domain weight value of the high frequency region is smaller than that of the low frequency region, 1-W when the pixel belongs to the high frequency region i,j Greater than W i,j At this time, the ratio of the time-domain weighted average pixel value to the enhanced pixel value is higher than the original pixel value K i,j,t The duty ratio in the pixel value after enhancement, thereby the filtering degree of the high-frequency region is larger, and the noise of the high-frequency part is effectively removed. When the pixel point belongs to the low frequency region, 1-W i,j Less than W i,j At this time, the duty ratio of the time-domain weighted average pixel value in the enhanced pixel value is lower than the original pixel value K i,j,t The duty cycle in the pixel values after enhancement, thus making the degree of filtering in the low frequency region smaller. Therefore, the characteristics of more noise in a high-frequency region and less noise in a low-frequency region are utilized, different frequency parts of the image are properly denoised, and a better enhancement effect can be obtained.
According to the CT dynamic image enhancement method provided by the embodiment of the invention, the image to be processed is one image in the K space image sequence corresponding to the CT dynamic image sequence, for each pixel point in the image to be processed, the time domain weighted average pixel value corresponding to the pixel point in the preset duration is determined according to the preset time domain weight value sequence, the target frequency domain weight value corresponding to the pixel point is found out from the preset mask, the weight value corresponding to the first frequency band in the mask is smaller than the weight value corresponding to the second frequency band, the first frequency band is higher than the second frequency band, the target pixel value of the pixel point is obtained based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point, and the frequency domain enhancement image of the image to be processed is subjected to Fourier inverse transformation to obtain the time domain enhancement image corresponding to the image to be processed, so that the image resolution of the image to be processed can be better maintained, the noise can be well suppressed, and the image enhancement quality can be greatly improved.
Based on the method embodiment, the embodiment of the invention also provides a corresponding device, equipment and storage medium embodiment.
Fig. 5 is a functional block diagram of a CT moving image enhancement device according to an embodiment of the present invention. As shown in fig. 5, in the present embodiment, the CT moving image enhancement apparatus may include:
the image obtaining module 510 is configured to obtain an image to be processed, where the image to be processed is an image in a K-space image sequence corresponding to a CT dynamic image sequence;
a determining module 520, configured to determine, for each pixel point in the image to be processed, a time-domain weighted average pixel value corresponding to the pixel point within a preset duration according to a preset time-domain weight value sequence;
the searching module 530 is configured to search a target frequency domain weight value corresponding to the pixel point from a preset mask, where the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
a pixel value obtaining module 540, configured to obtain a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value, and a current pixel value of the pixel point;
the transforming module 550 is configured to perform inverse fourier transform on the frequency domain enhanced image of the image to be processed, so as to obtain a time domain enhanced image corresponding to the image to be processed, where a pixel value of each pixel point in the frequency domain enhanced image is equal to a target pixel value corresponding to a corresponding pixel point in the image to be processed.
In an exemplary implementation, the image acquisition module 510 may be specifically configured to:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
In an exemplary implementation, the determining module 520 may be specifically configured to:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
and obtaining the quotient of the sum of pixel value components corresponding to the reference pixel values and the weight sum, wherein the sum of the weight sum is the sum of the matched time domain weight values corresponding to the reference pixel values, and the quotient is used as the time domain weighted average pixel value of the pixel points in the preset time duration.
In an exemplary implementation, the pixel value obtaining module 540 may be specifically configured to:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
In an exemplary implementation, the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
The embodiment of the invention also provides CT equipment. Fig. 6 is a hardware configuration diagram of a CT apparatus according to an embodiment of the present invention. As shown in fig. 6, the CT apparatus includes: an internal bus 601, and a memory 602 connected by the internal bus, a processor 603 and an external interface 604, wherein the external interface is used for connecting a detector of the CT system, and the detector comprises a plurality of detector chambers and corresponding processing circuits;
the memory 602 is configured to store machine-readable instructions corresponding to CT dynamic image enhancement logic;
the processor 603 is configured to read machine readable instructions on the memory 602 and execute the instructions to implement the following operations:
acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to a CT dynamic image sequence;
for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence;
searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point;
and carrying out inverse Fourier transform on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed.
In one exemplary implementation, acquiring an image to be processed includes:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
In an exemplary implementation process, for each pixel point in the image to be processed, determining, according to a preset time domain weight value sequence, a time domain weighted average pixel value corresponding to the pixel point within a preset time duration, where the determining includes:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
and obtaining the quotient of the sum of pixel value components corresponding to the reference pixel values and the weight sum, wherein the sum of the weight sum is the sum of the matched time domain weight values corresponding to the reference pixel values, and the quotient is used as the time domain weighted average pixel value of the pixel points in the preset time duration.
In an exemplary implementation, obtaining the target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value, and the current pixel value of the pixel point includes:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
In an exemplary implementation, the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
The embodiment of the invention also provides a CT system, which comprises a detector, a scanning bed and CT equipment, wherein the detector comprises a plurality of detector chambers and corresponding processing circuits; wherein:
the detector chamber is used for detecting X-rays passing through a scanning object and converting the X-rays into electric signals in the scanning process of the CT system;
the processing circuit is used for converting the electric signal into a pulse signal and collecting energy information of the pulse signal;
the CT equipment is used for acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to a CT dynamic image sequence; for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence; searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band; acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point; and carrying out inverse Fourier transform on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed.
In one exemplary implementation, acquiring an image to be processed includes:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
In an exemplary implementation process, for each pixel point in the image to be processed, determining, according to a preset time domain weight value sequence, a time domain weighted average pixel value corresponding to the pixel point within a preset time duration, where the determining includes:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
and obtaining the quotient of the sum of pixel value components corresponding to the reference pixel values and the weight sum, wherein the sum of the weight sum is the sum of the matched time domain weight values corresponding to the reference pixel values, and the quotient is used as the time domain weighted average pixel value of the pixel points in the preset time duration.
In an exemplary implementation, obtaining the target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value, and the current pixel value of the pixel point includes:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
In an exemplary implementation, the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, wherein the program when executed by a processor realizes the following operations:
acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to a CT dynamic image sequence;
for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence;
searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point;
and carrying out inverse Fourier transform on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed.
In one exemplary implementation, acquiring an image to be processed includes:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
In an exemplary implementation process, for each pixel point in the image to be processed, determining, according to a preset time domain weight value sequence, a time domain weighted average pixel value corresponding to the pixel point within a preset time duration, where the determining includes:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
and obtaining the quotient of the sum of pixel value components corresponding to the reference pixel values and the weight sum, wherein the sum of the weight sum is the sum of the matched time domain weight values corresponding to the reference pixel values, and the quotient is used as the time domain weighted average pixel value of the pixel points in the preset time duration.
In an exemplary implementation, obtaining the target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value, and the current pixel value of the pixel point includes:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
In an exemplary implementation, the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
For the device and apparatus embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present description. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Other embodiments of the present description will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This specification is intended to cover any variations, uses, or adaptations of the specification following, in general, the principles of the specification and including such departures from the present disclosure as come within known or customary practice within the art to which the specification pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the specification being indicated by the following claims.
It is to be understood that the present description is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.
The foregoing description of the preferred embodiments is provided for the purpose of illustration only, and is not intended to limit the scope of the disclosure, since any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the disclosure are intended to be included within the scope of the disclosure.
Claims (6)
1. A method for enhancing a CT dynamic image, comprising:
acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to a CT dynamic image sequence;
for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence;
searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point;
performing inverse Fourier transform on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed;
for each pixel point in the image to be processed, determining a time domain weighted average pixel value corresponding to the pixel point in a preset time length according to a preset time domain weight value sequence, wherein the time domain weighted average pixel value comprises:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
obtaining the quotient of the sum of pixel value components corresponding to each reference pixel value and the weight sum, wherein the sum of the weight is the sum of the matched time domain weight values corresponding to each reference pixel value, and the quotient is used as the time domain weighted average pixel value of the pixel point in the preset time length;
acquiring the target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point, wherein the method comprises the following steps:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
2. The method of claim 1, wherein acquiring the image to be processed comprises:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
3. The method of claim 1, wherein the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
4. A CT dynamic image enhancement apparatus comprising:
the image acquisition module is used for acquiring an image to be processed, wherein the image to be processed is one image in a K space image sequence corresponding to the CT dynamic image sequence;
the determining module is used for determining a time domain weighted average pixel value corresponding to each pixel point in the image to be processed in a preset time duration according to a preset time domain weight value sequence;
the searching module is used for searching a target frequency domain weight value corresponding to the pixel point from a preset mask, wherein the weight value corresponding to a first frequency band in the mask is smaller than the weight value corresponding to a second frequency band, and the first frequency band is higher than the second frequency band;
the pixel value acquisition module is used for acquiring a target pixel value of the pixel point based on the target frequency domain weight value, the time domain weighted average pixel value and the current pixel value of the pixel point;
the transformation module is used for carrying out inverse Fourier transformation on the frequency domain enhanced image of the image to be processed to obtain a time domain enhanced image corresponding to the image to be processed, wherein the pixel value of each pixel point in the frequency domain enhanced image is equal to the target pixel value corresponding to the corresponding pixel point in the image to be processed;
the determining module is specifically configured to:
selecting target images in a preset duration centering on a target time point from the K space image sequence;
respectively obtaining pixel values of pixel points corresponding to the pixel points in each target image as reference pixel values;
obtaining a matching time domain weight value corresponding to each reference pixel value from a preset time domain weight value sequence, and determining the product of each reference pixel value and the corresponding matching time domain weight value as a pixel value component;
obtaining the quotient of the sum of pixel value components corresponding to each reference pixel value and the weight sum, wherein the sum of the weight is the sum of the matched time domain weight values corresponding to each reference pixel value, and the quotient is used as the time domain weighted average pixel value of the pixel point in the preset time length;
the pixel value acquisition module is specifically configured to:
obtaining the product of the time domain weighted average pixel value and a target difference value, wherein the target difference value is the difference value between 1 and the target frequency domain weight value;
obtaining the product of the current pixel value of the pixel point and the target frequency domain weight value as a second value;
and determining the sum of the first value and the second value as a target pixel value of the pixel point.
5. The apparatus of claim 4, wherein the image acquisition module is specifically configured to:
performing Fourier transform on the CT dynamic image sequence to obtain a K space image sequence corresponding to the CT dynamic image sequence;
and extracting an image from the K space image sequence according to a preset extraction strategy, and taking the image as an image to be processed.
6. The apparatus of claim 4, wherein the mask is any one of the following masks:
the weight values of all the pixel points of the first frequency band in the mask are equal to a first weight value, the weight values of all the pixel points of the second frequency band are equal to a second weight value, and the first weight value is smaller than the second weight value;
the weight values in the mask are normally distributed from low frequency to high frequency.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010144459.4A CN111369465B (en) | 2020-03-04 | 2020-03-04 | CT dynamic image enhancement method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010144459.4A CN111369465B (en) | 2020-03-04 | 2020-03-04 | CT dynamic image enhancement method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111369465A CN111369465A (en) | 2020-07-03 |
| CN111369465B true CN111369465B (en) | 2024-03-08 |
Family
ID=71211681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010144459.4A Active CN111369465B (en) | 2020-03-04 | 2020-03-04 | CT dynamic image enhancement method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111369465B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114062988B (en) * | 2020-07-31 | 2023-09-22 | 上海联影医疗科技股份有限公司 | Magnetic resonance spectrum imaging method, apparatus, computer device and storage medium |
| CN113781357A (en) * | 2021-09-24 | 2021-12-10 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| CN115690693B (en) * | 2022-12-13 | 2023-03-21 | 山东鲁旺机械设备有限公司 | Intelligent monitoring system and monitoring method for construction hanging basket |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103269412A (en) * | 2013-04-19 | 2013-08-28 | 华为技术有限公司 | Method and device for denoising video image |
| CN105074822A (en) * | 2013-03-26 | 2015-11-18 | 杜比实验室特许公司 | Device and method for audio classification and audio processing |
| CN105894460A (en) * | 2015-12-14 | 2016-08-24 | 乐视云计算有限公司 | Image filtering method and device |
| WO2017185772A1 (en) * | 2016-04-28 | 2017-11-02 | 深圳市中兴微电子技术有限公司 | Method and device for video image enhancement and computer storage medium |
| CN109743473A (en) * | 2019-01-11 | 2019-05-10 | 珠海全志科技股份有限公司 | Video image 3 D noise-reduction method, computer installation and computer readable storage medium |
| CN109873953A (en) * | 2019-03-06 | 2019-06-11 | 深圳市道通智能航空技术有限公司 | Image processing method, shooting at night method, picture processing chip and aerial camera |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104978717A (en) * | 2015-06-11 | 2015-10-14 | 沈阳东软医疗系统有限公司 | CT reconstruction image processing method, apparatus and device |
| EP3311117A1 (en) * | 2015-06-22 | 2018-04-25 | Omnisens S.A. | A method for reducing noise in measurements taken by a distributed sensor |
| US20170150014A1 (en) * | 2015-11-24 | 2017-05-25 | Le Holdings (Beijing) Co., Ltd. | Method and electronic device for video denoising and detail enhancement |
-
2020
- 2020-03-04 CN CN202010144459.4A patent/CN111369465B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105074822A (en) * | 2013-03-26 | 2015-11-18 | 杜比实验室特许公司 | Device and method for audio classification and audio processing |
| CN103269412A (en) * | 2013-04-19 | 2013-08-28 | 华为技术有限公司 | Method and device for denoising video image |
| CN105894460A (en) * | 2015-12-14 | 2016-08-24 | 乐视云计算有限公司 | Image filtering method and device |
| WO2017185772A1 (en) * | 2016-04-28 | 2017-11-02 | 深圳市中兴微电子技术有限公司 | Method and device for video image enhancement and computer storage medium |
| CN109743473A (en) * | 2019-01-11 | 2019-05-10 | 珠海全志科技股份有限公司 | Video image 3 D noise-reduction method, computer installation and computer readable storage medium |
| CN109873953A (en) * | 2019-03-06 | 2019-06-11 | 深圳市道通智能航空技术有限公司 | Image processing method, shooting at night method, picture processing chip and aerial camera |
Non-Patent Citations (2)
| Title |
|---|
| 郑佳 ; 张定华 ; 黄魁东 ; 李向欣 ; .一种基于反锐化掩模的锥束CT图像自适应增强方法.CT理论与应用研究.2013,(02),全文. * |
| 陈瑞瑞 ; .高光谱遥感影像增强算法探究.经纬天地.2019,(06),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111369465A (en) | 2020-07-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110461228B (en) | Improving quality of medical images using multi-contrast and deep learning | |
| CN111369465B (en) | CT dynamic image enhancement method and device | |
| Armanious et al. | Unsupervised medical image translation using cycle-MedGAN | |
| Lyu et al. | Cine cardiac MRI motion artifact reduction using a recurrent neural network | |
| Li et al. | Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution | |
| Mohsin et al. | Accelerated dynamic MRI using patch regularization for implicit motion compensation | |
| US20220114699A1 (en) | Spatiotemporal resolution enhancement of biomedical images | |
| CN111091517B (en) | Residual weighted imaging method and device | |
| CN116309524A (en) | Method and system for suppressing imaging artifacts of cardiac magnetic resonance movies based on deep learning | |
| Choi et al. | Study of the noise reduction algorithm with median modified Wiener filter for T2-weighted magnetic resonance brain images | |
| Gan et al. | Deformation-compensated learning for image reconstruction without ground truth | |
| US11308660B2 (en) | Motion compensated cardiac valve reconstruction | |
| Iwamoto et al. | Super-resolution of MR volumetric images using sparse representation and self-similarity | |
| CN111311531B (en) | Image enhancement method, device, console device and medical imaging system | |
| CN111340699B (en) | Magnetic resonance image denoising method and device based on non-local prior and sparse representation | |
| Mihai et al. | Non-rigid registration and KLT filter to improve SNR and CNR in GRE-EPI myocardial perfusion imaging | |
| Ahmed et al. | Dynamic imaging using deep bilinear unsupervised learning (deblur) | |
| Chandra et al. | Local contrast‐enhanced MR images via high dynamic range processing | |
| CN112801886B (en) | Dynamic PET image denoising method and system based on image wavelet transformation | |
| CN111932475B (en) | Filtering method, filtering device, CT equipment and CT system | |
| Hensel et al. | Noise reduction with edge preservation by multiscale analysis of medical x-ray image sequences | |
| CN110706338B (en) | Image reconstruction method, device, CT equipment and CT system | |
| CN111612719A (en) | CT image processing method and device, CT equipment and CT system | |
| Xu et al. | Noise Estimation-based Method for MRI Denoising with Discriminative Perceptual Architecture | |
| Thepade et al. | Mammographic Image Enhancement Using a Linear Weighted Fusion of Contrast Improvement Technique and CLAHE |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240202 Address after: 110167 No. 177-1 Innovation Road, Hunnan District, Shenyang City, Liaoning Province Applicant after: Shenyang Neusoft Medical Systems Co.,Ltd. Country or region after: China Address before: Room 336, 177-1, Chuangxin Road, Hunnan New District, Shenyang City, Liaoning Province Applicant before: Shenyang advanced medical equipment Technology Incubation Center Co.,Ltd. Country or region before: China |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |