CN111260771A - Image reconstruction method and device - Google Patents
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- CN111260771A CN111260771A CN202010032753.6A CN202010032753A CN111260771A CN 111260771 A CN111260771 A CN 111260771A CN 202010032753 A CN202010032753 A CN 202010032753A CN 111260771 A CN111260771 A CN 111260771A
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Abstract
The present specification provides an image reconstruction method and apparatus, the method comprising: determining the boundary of a scanning object according to two-dimensional projection data of the scanning object; fitting a truncated curve of the scanned object according to the boundary; performing truncation correction on the two-dimensional projection data according to the truncation curve; and performing three-dimensional reconstruction based on the two-dimensional projection data after truncation correction to obtain a reconstructed image of the scanning object. So as to eliminate truncation artifacts in the reconstructed image and improve the quality of the reconstructed image.
Description
Technical Field
The present disclosure relates to the field of medical imaging, and in particular, to an image reconstruction method and apparatus.
Background
X-ray imaging devices are widely used in medical fields, such as fluoroscopy, vascular machine, CT (computed tomography), and the like. The cone beam CT is a new stage of CT development and becomes a research hotspot, the quality requirement on the reconstruction of cone beam CT images is higher and higher, and the quality of the reconstructed images directly relates to the accuracy of the judgment of detection results.
In many scanning situations, the scanned object is too large, has a yaw bias, or has a limited detector width, etc., which may cause a portion of the scanned object to be out of view, resulting in truncated projection data being generated. The truncated projection data is used for image reconstruction, truncation artifacts are generated, the quality of a reconstructed image is deteriorated, and diagnosis of a doctor is influenced.
One solution is to determine a data compensation range for each line of data by means of an extrapolation algorithm based on truncated projection data and perform data compensation, thereby compensating for missing projection data, and then perform image reconstruction using the compensated projection data. However, this method often corrects the lost projection data too much or too little, so that the truncation artifacts in the reconstructed image cannot be eliminated, resulting in poor quality of the reconstructed image.
Disclosure of Invention
At least one embodiment of the present specification provides an image reconstruction method to eliminate truncation artifacts in a reconstructed image and improve the quality of the reconstructed image.
In a first aspect, an image reconstruction method is provided, the method including:
determining the boundary of a scanning object according to two-dimensional projection data of the scanning object;
fitting a truncated curve of the scanned object according to the boundary;
performing truncation correction on the two-dimensional projection data according to the truncation curve;
and performing three-dimensional reconstruction based on the two-dimensional projection data after truncation correction to obtain a reconstructed image of the scanning object.
In a second aspect, there is provided an image reconstruction apparatus, the apparatus comprising:
the boundary determining module is used for determining the boundary of the scanning object according to the two-dimensional projection data of the scanning object;
the truncated curve fitting module is used for fitting a truncated curve of the scanned object according to the boundary;
the truncation correction module is used for performing truncation correction on the two-dimensional projection data according to the truncation curve;
and the image reconstruction module is used for performing three-dimensional reconstruction on the basis of the two-dimensional projection data after truncation correction to obtain a reconstructed image of the scanning object.
In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the processor implements the image reconstruction method according to any embodiment of the present specification.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor, performs the steps of the image reconstruction method according to any one of the embodiments of the present description.
According to the technical scheme, in at least one embodiment of the specification, a missing truncation curve is fitted through the boundary of a scanned object, and truncation correction is performed on two-dimensional projection data according to the truncation curve, so that the two-dimensional projection data are accurately corrected. And then, three-dimensional reconstruction is carried out by using the corrected two-dimensional projection data, so that truncation artifacts in the reconstructed image are eliminated, and the quality of the reconstructed image is 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 specification.
Drawings
FIG. 1 is a flow chart illustrating a method of image reconstruction according to an exemplary embodiment;
FIG. 2 is a schematic diagram of an image reconstruction device according to an exemplary embodiment;
FIG. 3 is a schematic diagram of a reconstructed image slice according to an exemplary embodiment;
FIG. 4 is a schematic illustration of a medical imaging device according to an exemplary embodiment;
FIG. 5 is a schematic diagram illustrating a two-dimensional projection of data from a head, according to an exemplary embodiment;
FIG. 6 is a diagram illustrating a corrected two-dimensional projection data in accordance with an exemplary embodiment;
FIG. 7 is a schematic diagram illustrating a reconstructed image slice according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the specification, as detailed in the appended claims.
The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. As used in this specification 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 and 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 herein 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 the present specification. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
During scanning, the X-ray imaging device may cause a part of a scanned object to be out of a scanning range due to the fact that the scanned object is too large, the scanned object is deflected or the width of a scanning area is limited, that is: the two-dimensional projection data resulting from the scan is truncated. One approach is to estimate the missing projection data using an extrapolation algorithm. However, this method often corrects the missing projection data too much or not enough, and cannot accurately compensate the missing projection data.
If three-dimensional reconstruction is performed by using the two-dimensional projection data, the obtained reconstructed image can not eliminate truncation artifacts, so that the reconstructed image is degraded and diagnosis of doctors is influenced. For example, when the truncated two-dimensional projection data is insufficiently corrected, a bright ring still exists at the edge of the reconstructed image; when the correction is excessive, the bright ring in the reconstructed image may become a dark ring, and even the edge of the original image in the field of view may be damaged, which may be clinically mistaken for bleeding. As shown in fig. 3, when the truncated two-dimensional projection data is corrected too much, dark rings exist at the edges of the reconstructed image, and the image may be clinically mistaken for bleeding, thereby affecting the diagnosis of the doctor.
The present specification provides an image reconstruction method: determining the boundary of a scanned object from the two-dimensional projection data, and fitting a truncation curve missing from the scanned object by using the boundary to obtain a profile curve of the scanned object; then, the fitted truncation curve is used for carrying out truncation correction on the two-dimensional projection data, and missing projection data in the two-dimensional projection data are accurately supplemented; and then, performing three-dimensional reconstruction by using the corrected two-dimensional projection data to obtain a reconstructed image. The method can accurately correct the truncated two-dimensional projection data and supplement the missing projection data, thereby eliminating the truncation artifact in the reconstructed image and improving the quality of the reconstructed image.
In order to make the image reconstruction method provided by the present specification clearer, the following describes in detail the implementation procedure of the scheme provided by the present specification with reference to the drawings and specific embodiments.
Referring to fig. 1, fig. 1 is a flowchart illustrating an image reconstruction method according to an embodiment provided in the present specification. It is understood that the image reconstruction method provided in the present specification is applicable to any process of reconstructing an image from two-dimensional projection data in the field of medical imaging, and the present embodiment does not set any limit to this. The scanning object in the image reconstruction method provided by the present specification may be any object diagnosed by a doctor. For example, the head, chest, waist, etc., of a human body, various human body parts; for example, an animal body diagnosed by a doctor, or in short, a "subject" to be subjected to medical imaging in the diagnosis of the doctor may be a subject to be scanned in the present specification. The present embodiment exemplifies a case where the head of a patient is a scan target.
Specifically, as shown in fig. 1, the process includes:
Before image reconstruction, the head needs to be scanned in a three-dimensional rotation mode by means of an imaging device to obtain a series of two-dimensional projection data. For example, a three-dimensional rotational scan of the patient's head may be performed with a digital subtraction angiography as shown in fig. 4, resulting in a series of two-dimensional projection data of the patient's head. The digital subtraction angiography machine includes an X-ray acquisition device 103(103 includes an X-ray transmitting device 101 and an X-ray receiving device 102), a control and data processing device 104, an image display device 105, a patient support device 106, and the like.
A fixed scanning area is formed between the X-ray emitting device 101 and the X-ray receiving device 102, and if the head of the patient is not in the scanning area during the scanning process, the two-dimensional projection data obtained by the corresponding scanning is truncated. As shown in fig. 5, a truncated region is clearly present in the two-dimensional projection data of the head.
In this step, the boundary of the head in the two-dimensional projection data is determined by using a certain algorithm according to the two-dimensional projection data obtained by scanning the head. Taking fig. 5 as an example, that is: and determining the boundaries of the scanning object head such as the boundary curve IE, the boundary curve BC and the like.
In one example, the determining the boundary of the scanned object from the two-dimensional projection data of the scanned object includes: determining at least one boundary pixel block according to the two-dimensional projection data, wherein the gray value of the boundary pixel block is equal to a preset gray threshold value; and fitting according to the at least one boundary pixel block to obtain the boundary of the scanning object.
In the above example, a fixed pixel gray level threshold value, or a fixed range of pixel gray level threshold values, may be set in advance. For example, the preset gray threshold may be determined by integrating the attenuation coefficient of the X-ray in the human body, the imaging quality, the characteristics of the imaging device itself, the image processing experience, and the like. It is understood that the preset gray level threshold may be a fixed value or range, or may be a floating range, which is not limited in this specification. All the values that can be used as the basis for judging the pixels on the boundary of the scanning object in the two-dimensional projection data can be regarded as the pixel values.
And identifying pixel blocks with gray values identical to a preset gray threshold value in the two-dimensional projection data, and recording the positions of the pixels in the two-dimensional projection data. And fitting the dispersed points of each pixel to the boundary of the head in the two-dimensional projection data by using a curve fitting method. The two-dimensional projection data shown in fig. 5 is taken as an example for explanation: assume that the gray value of the pixel on the head boundary in the figure is 2, i.e. the preset gray threshold value is 2. Specifically, the pixels on the line KH are sequentially identified in the gray value of each pixel from K to H, and the position of the pixel with the first gray value equal to or close to the preset gray threshold is recorded, that is, the position of the point F is recorded; similarly, the location of the pixel on the other side boundary can be recorded by identifying the pixel grey value from the other direction. Finally, the positions of a plurality of pixel points on the boundary of the scanning object are recorded through the identification of each line of pixels in the image, and the curve of the head boundary is fitted by utilizing each dispersed point on each pixel position. For example, by fitting the pixel points E, F and I on the boundary, a boundary curve IE of the front side of the head is obtained; and obtaining a boundary curve JA of the back side of the head by fitting the pixel points J and A on the boundary.
And step 102, fitting a truncated curve of the scanned object according to the boundary.
Continuing with the example of fig. 5, when the boundary curve of the head in the two-dimensional projection data is determined, that is, the boundary curve JA and the boundary curve BC are determined, an interpolation algorithm is used to fit the missing truncated curve AB. By combining the boundary curve JA, the boundary curve BC and the fitted truncated curve AB, a complete profile curve JC on the back side of the head is obtained in the two-dimensional projection data. Based on the same method, a truncated curve DE can be fitted through the determined head boundary curve IE and the boundary curve SD, thereby obtaining a front complete contour curve SI in front of the two-dimensional projection data.
In one example, the fitting a truncated curve of the scanned object according to the boundary includes: and fitting a truncated curve of the scanning object by utilizing a spline interpolation method according to the boundary.
And 103, performing truncation correction on the two-dimensional projection data according to the truncation curve.
In fig. 5, the truncated curve AB and the straight line AB form a closed truncated region, which is the missing projection data portion, and the truncation correction in this step is to complete the pixels of this region. The gray value of each pixel contained in the truncated region in fig. 5 can be estimated by a reasonable algorithm according to the pixels of which the gray values have been determined in the two-dimensional projection data. The truncation-corrected two-dimensional projection data is shown in fig. 6.
For example, the gray value of the pixel on the truncation curve AB is estimated and fitted by an interpolation algorithm based on the gray value variation of the pixel on the boundary curve JA and the gray value variation of the pixel on the boundary curve BC. And determining an adjacent internal curve on the inner side of the profile curve JC, identifying the existing gray values of the pixels on the internal curve, and determining the gray values of the pixels on the internal curve in the truncated area according to the change of the existing gray values. Let it be assumed that point J, A, B, C corresponds to points j, a, b, and c on the inner curve of the adjacent inner side. The gray value of the pixel on the internal curve ab can be determined according to an interpolation algorithm by using the gray value of the pixel on the internal curve ja and the gray value of the pixel on the internal curve bc. And determining more internal curves by analogy, and determining the gray value of the pixel on the internal curve in the truncated region by utilizing an interpolation algorithm according to the gray value of the pixel in the two-dimensional projection data on the internal curve, thereby determining the gray value of each pixel in the truncated region and completing the supplement of the projection pixel of the truncated region.
In one example, the truncating correcting the two-dimensional projection data according to the truncating curve includes: for each row of pixels within the truncated curve: determining the supplement length of a line of pixels according to the truncation curve; and determining the gray value of the pixel in the supplement length according to the gray value of the pixel in the same row in the two-dimensional projection data and the supplement length.
In the above example, the length of the pixels to be supplemented in each line is determined by using the pixels in each line where the truncated curve is located as the processing object, and the gray value of each pixel to be supplemented is determined by using an interpolation algorithm according to the gray value change of the pixels with known gray values in the line. Taking fig. 5 as an example, it is assumed that the straight line KH in the figure is composed of a row of pixels. The length of the pixel needing to be supplemented in the line can be determined through the truncated straight line AB and the fitted truncated curve AB, namely the length of the pixel on the straight line GH is determined. Here, the straight line FG is in the two-dimensional projection data, so the gradation value of each pixel on the straight line FG is known. From these known gray values of the pixels, the gray value of each pixel on the straight line GH is estimated by means of an interpolation algorithm. Thereby, the two-dimensional projection data of the row of GH is supplemented. And by analogy, intercepting more straight lines in the contour, and estimating the gray value of the pixel on the straight line needing to be supplemented by utilizing an interpolation algorithm according to the change of the known gray value of the pixel on the straight line. The supplemental lines of pixels are accumulated to eventually supplement the projection data in the complete "truncated region".
In the above example, the determining the gray scale value of the pixel in the supplemental length includes: and determining the gray value of the pixel in the complementary length by using a binomial interpolation method. For example, taking the gray value of the pixel on the straight line FG as the known data, the binomial difference is performed by:
(1) approximately 100 pixel points are selected on a straight line FG, a curve is established by utilizing the gray values of the pixel points, and the slope of the curve is solved.
(2) The coefficients of the binomial equation are calculated. Assuming that the equation is y ═ ax x + b x + c, the values of a, b, and c are obtained.
(3) The projection data is extrapolated.
And 104, performing three-dimensional reconstruction based on the two-dimensional projection data after truncation correction to obtain a reconstructed image of the scanning object.
And performing truncation correction on the truncated two-dimensional projection data. And finally, integrating the truncated and corrected two-dimensional projection data and the original complete two-dimensional projection data to perform three-dimensional reconstruction. With the image reconstruction method provided by the embodiment, a slice image of a reconstructed image is obtained, as shown in fig. 7. Compared with the slice image of the reconstructed image shown in fig. 3, obviously, the quality of the image reconstructed by using the image reconstruction method provided by the embodiment is better, and the influence of the truncation artifact on the reconstructed image can be better eliminated.
The image reconstruction method of the embodiment determines the boundary of the scanned object from the two-dimensional projection data, and fits a truncation curve missing from the scanned object by using the boundary to obtain a profile curve of the scanned object; then, the fitted truncation curve is used for carrying out truncation correction on the two-dimensional projection data, and missing projection data in the two-dimensional projection data are accurately supplemented; and then, performing three-dimensional reconstruction by using the corrected two-dimensional projection data to obtain a reconstructed image. The method can accurately correct the truncated two-dimensional projection data, supplements the missing projection data, does not make an overuse or insufficient correction, eliminates truncation artifacts in the reconstructed image on the basis of ensuring that the original image quality is not damaged, and improves the quality of the reconstructed image.
As shown in fig. 2, the present specification provides an image reconstruction apparatus, which may perform the image reconstruction method according to any embodiment of the present specification. The apparatus may include a boundary determination module 201, a truncated curve fitting module 202, a truncated correction module 203, and an image reconstruction module 204. Wherein:
a boundary determining module 201, configured to determine a boundary of a scanned object according to two-dimensional projection data of the scanned object;
a truncated curve fitting module 202, configured to fit a truncated curve of the scanned object according to the boundary;
a truncation correction module 203, configured to perform truncation correction on the two-dimensional projection data according to the truncation curve;
an image reconstruction module 204, configured to perform three-dimensional reconstruction based on the truncated and corrected two-dimensional projection data, so as to obtain a reconstructed image of the scanned object.
Optionally, the boundary determining module 201 is configured to, when determining the boundary of the scanned object according to the two-dimensional projection data of the scanned object, include: determining at least one boundary pixel block according to the two-dimensional projection data, wherein the gray value of the boundary pixel block is equal to a preset gray threshold value; and fitting according to the at least one boundary pixel block to obtain the boundary of the scanning object.
Optionally, the truncated curve fitting module 202, configured to fit a truncated curve of the scanned object according to the boundary, includes: and fitting a truncated curve of the scanning object by utilizing a spline interpolation method according to the boundary.
Optionally, the truncation correction module 203 is configured to, when performing truncation correction on the two-dimensional projection data according to the truncation curve, include: for each row of pixels within the truncated curve: determining the supplement length of a line of pixels according to the truncation curve; and determining the gray value of the pixel in the supplement length according to the gray value of the pixel in the same row in the two-dimensional projection data and the supplement length.
Optionally, the truncation correction module, when determining the gray-scale value of the pixel in the supplemental length, includes: and determining the gray value of the pixel in the complementary length by using a binomial interpolation method.
The implementation process of the functions and actions of each module in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of at least one embodiment of the present specification. One of ordinary skill in the art can understand and implement it without inventive effort.
The present specification also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being capable of implementing the image reconstruction method of any of the embodiments of the specification when executing the program.
The present specification also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, is capable of implementing the image reconstruction method of any of the embodiments of the present specification.
The non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc., which is not limited in this application.
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 will be understood that the present description is not limited to the precise arrangements described above and shown in the drawings, 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 above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (12)
1. A method of image reconstruction, the method comprising:
determining the boundary of a scanning object according to two-dimensional projection data of the scanning object;
fitting a truncated curve of the scanned object according to the boundary;
performing truncation correction on the two-dimensional projection data according to the truncation curve;
and performing three-dimensional reconstruction based on the two-dimensional projection data after truncation correction to obtain a reconstructed image of the scanning object.
2. The method of claim 1, wherein determining the boundary of the scanned object from the two-dimensional projection data of the scanned object comprises:
determining at least one boundary pixel block according to the two-dimensional projection data, wherein the gray value of the boundary pixel block is equal to a preset gray threshold value;
and fitting according to the at least one boundary pixel block to obtain the boundary of the scanning object.
3. The method of claim 1, wherein fitting a truncated curve of the scanned object according to the boundary comprises:
and fitting a truncated curve of the scanning object by utilizing a spline interpolation method according to the boundary.
4. The method of claim 1, wherein the truncating the two-dimensional projection data according to the truncation curve comprises:
for each row of pixels within the truncated curve:
determining the supplement length of a line of pixels according to the truncation curve;
and determining the gray value of the pixel in the supplement length according to the gray value of the pixel in the same row in the two-dimensional projection data and the supplement length.
5. The method of claim 4, wherein determining the gray scale value for the pixels within the supplemental length comprises:
and determining the gray value of the pixel in the complementary length by using a binomial interpolation method.
6. An image reconstruction apparatus, characterized in that the apparatus comprises:
the boundary determining module is used for determining the boundary of the scanning object according to the two-dimensional projection data of the scanning object;
the truncated curve fitting module is used for fitting a truncated curve of the scanned object according to the boundary;
the truncation correction module is used for performing truncation correction on the two-dimensional projection data according to the truncation curve;
and the image reconstruction module is used for performing three-dimensional reconstruction on the basis of the two-dimensional projection data after truncation correction to obtain a reconstructed image of the scanning object.
7. The apparatus of claim 6, wherein the boundary determining module, when determining the boundary of the scanned object according to the two-dimensional projection data of the scanned object, comprises:
determining at least one boundary pixel block according to the two-dimensional projection data, wherein the gray value of the boundary pixel block is equal to a preset gray threshold value;
and fitting according to the at least one boundary pixel block to obtain the boundary of the scanning object.
8. The apparatus of claim 6, wherein the truncated curve fitting module, when fitting the truncated curve of the scanned object according to the boundary, comprises:
and fitting a truncated curve of the scanning object by utilizing a spline interpolation method according to the boundary.
9. The apparatus of claim 6, wherein the truncation correction module, when performing truncation correction on the two-dimensional projection data according to the truncation curve, comprises:
for each row of pixels within the truncated curve:
determining the supplement length of a line of pixels according to the truncation curve;
and determining the gray value of the pixel in the supplement length according to the gray value of the pixel in the same row in the two-dimensional projection data and the supplement length.
10. The apparatus of claim 9, wherein the truncation correction module, when determining the gray scale value for the pixels within the supplemental length, comprises:
and determining the gray value of the pixel in the complementary length by using a binomial interpolation method.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-5 when executing the program.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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| CN113628232A (en) * | 2021-05-11 | 2021-11-09 | 深圳市汇川技术股份有限公司 | Method for eliminating interference points in fit line, visual recognition equipment and storage medium |
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