CN111445412A - Two-dimensional geometric correction method for magnetic resonance image - Google Patents
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Abstract
A two-dimensional geometric correction method for magnetic resonance images is characterized in that when geometric distortion occurs in a magnetic resonance water model image, an image obtained by using an improved automatic window adjusting algorithm is used, the water model is used for displaying the image, basic parameters of longitudinal and transverse stretching states are further obtained by combining the displayed definition and contrast with the automatic window adjusting algorithm, and the automatic window adjusting algorithm is used for carrying out audit correction so that the shape deviation caused by organ self-weight is improved. Moreover, the problems can be effectively solved, and clear and rich-level magnetic resonance images can be quickly and conveniently obtained.
Description
Technical Field
The invention relates to the field of magnetic resonance imaging, in particular to an algorithm for two-dimensional geometric correction of a magnetic resonance image.
Background
An algorithm for two-dimensional geometric correction of magnetic resonance images is a digital image processing algorithm that corrects images that are geometrically distorted in magnetic resonance. The geometric distortion exists widely at the edge of a television screen, the edge part of a camera lens and a magnetic resonance image, and the invention particularly refers to the geometric distortion of the magnetic resonance image. The geometric distortion of the magnetic resonance image is mainly caused by the nonlinearity of a gradient field generated on the hardware of a magnetic resonance imaging instrument, the distortion is more serious in a region which is farther away from the center of a magnet, and the difference is also large according to the personal size. Geometric correction algorithms for magnetic resonance images are of great help for medical diagnosis.
The general algorithm for solving the problem of geometric distortion of the magnetic resonance image is to utilize magnetic resonance to perform imaging experiment, estimate the deformation quantity of a control point by adopting a proper algorithm and further perform geometric correction by utilizing geometric transformation of digital image processing. The algorithm is characterized in that the algorithm can be predicted according to the position information of the control points of the magnetic resonance image, the actual positions of the control points are used for determining the primary positioning of the positions of the control points in the image, and the positions of the control points in the image are accurately searched near the positions. However, when the visual field of the image is large, the image deformation is also large, the control points may deform from the line where the control points are located to another line, and at this time, the above algorithm cannot effectively extract the corresponding control points, and may use the horizontal and vertical control points of another line as the control points of the current line, which is the first defect, the algorithm can perform initial positioning on the control points with small deformation amount, and when the initial positioning is performed on the points with large deformation amount, the obtained points are the points of the adjacent lines or the points of the adjacent columns; this geometry correction algorithm cannot estimate the amount of deformation outside the control point coverage using a simple interpolation algorithm, which is a second drawback.
Disclosure of Invention
Aiming at the defects that the prior art can not effectively extract the control points of the magnetic resonance image and can not estimate the deformation field outside the coverage range of the control points of the magnetic resonance image, the invention provides an algorithm for geometric correction of the magnetic resonance image, which comprises the following steps:
the method comprises the following steps: acquiring experimental data and recording the number and position information of grid points of a water model by using a magnetic resonance imaging image and the water model as the number and position information of initial transverse and longitudinal control points of the image;
step two: acquiring a plurality of water model images by utilizing different areas of the water model effect of the magnetic resonance image, and extracting the number and position information of control points in each image, wherein the position information of the transverse control points and the longitudinal control points is the position of the control points in the image after the image is deformed; the control points of the image correspond to the stretching grid points of the transverse direction and the longitudinal direction of the water model image;
step three: combining the plurality of images according to different regions of the magnetic resonance image, calculating position information and deformation quantity of the transverse and longitudinal stretching grid points, namely the control points, and estimating a deformation field according to the deformation quantity;
step four: and correcting the magnetic resonance image with geometric deformation by using an improved automatic window adjusting algorithm according to the deformation field.
Wherein, the step of using the number and position information of the distortion control points of the magnetic resonance image in the magnetic resonance water model image in the step two comprises the following steps:
step A1: performing convolution processing on the magnetic resonance water model image by adopting a cross template, wherein the convolution processing is used for highlighting signals at the centers of the grid cross points, so that the accurate position of the control point of the magnetic resonance image can be conveniently determined in the subsequent steps;
step A2: setting a threshold value to filter the background of the water model image, and setting the initial value of a control point of the background smaller than the threshold value as 0;
step A3: recording the number and position information of control points according to the maximum brightness value in a rectangular window range with a certain size in the magnetic resonance water model image as the control points;
step A4: if the number of the magnetic resonance image control points is larger than the number of the grid points, increasing the threshold value and re-executing the step A2; if the number of control points is less than the number of grid points, decreasing the threshold and re-executing step A2; and if the number of the control points is equal to the number of the grid points, finishing extracting the number and the position information of the control points.
In the third step, the deformation quantity of each control point within the range of the control point of the magnetic resonance water model image is estimated by adopting a bilinear interpolation algorithm to estimate the transverse and longitudinal variables of each control point, so that a deformation field is formed; and fitting and estimating the distortion degree of each control point by adopting a least square method for each point outside the range of the transverse and longitudinal control points, and finally forming a deformation field containing distortion values.
The step of correcting the magnetic resonance image in the fourth step is a process of calculating the intensity of each pixel point on the final image (i.e., the corrected image) by using the two-dimensional geometric correction algorithm of the magnetic resonance image with geometric distortion and combining an automatic window adjustment algorithm, and specifically includes:
step C1: for a control point on the final image, calculating the position of the control point in the magnetic resonance image corresponding to the geometric deformation according to the deformation field;
step C2: assigning the last control point of the final magnetic resonance water model image as a specific value of the deformation position in the magnetic resonance image with the geometric deformation;
step C3: and repeating the steps C1 to C2 until all control points on the final image are processed and then the process is terminated.
And when the real position of the control point exceeds the magnetic resonance image, the variable value of the pixel point is assigned to be 0.
When the true positions of the transverse control point and the longitudinal control point are within the image range and exceed the deformation field and the distortion description range, at least two distortion data points are selected in the adjacent area of the pixel point, and the deformation position of the pixel point is obtained by adopting least square normal fitting. This is the second innovation of the present invention.
Wherein the two-dimensional geometric correction algorithm is expressed by the following formula of a least square method:
f(x，y)＝bx+y+a
in the formula, x represents the abscissa of the deformation position, y represents the ordinate of the deformation position, and xi, yi represent the fitted numerical values.
The invention adopts the iterative search algorithm of the local maximum value on the extraction of the control point of the magnetic resonance water model image, and overcomes the defect that the conventional algorithm is difficult to extract the control point with larger deformation; according to the method, the deformation field is estimated for the region outside the control point range by adopting the least square normal fitting algorithm, so that the defect of region condition limitation in the conventional algorithm is overcome effectively, and a good correction effect is obtained.
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FIG. 1 is a flow chart of the two-dimensional geometry correction algorithm of the present invention.
Fig. 2 is a schematic view of a positioning plate used in the water model data acquisition process.
FIG. 3 is a flowchart illustrating the processing of the water model image according to the present embodiment.
Detailed Description
The invention is further described with reference to the following figures and examples.
The present invention will be described in further detail with reference to the following specific examples and the accompanying drawings. The procedures, conditions, experimental methods and the like for carrying out the present invention are general knowledge and common general knowledge in the art except for the contents specifically mentioned below, and the present invention is not particularly limited.
The algorithm flow of the two-dimensional geometric correction of the magnetic resonance image of the present invention, as shown in fig. 1, includes:
the method comprises the following steps: and forming a water model according to the magnetic resonance image, and recording the number and the position information of grid points of the magnetic resonance water model image as the number and the position information of the initial magnetic resonance image simulation control points, wherein the grid points are selected as the control points.
Step two: acquiring a plurality of magnetic resonance water model images according to different magnetic resonance imaging areas by using the magnetic resonance water model images, and extracting the number and position information of control points in each magnetic resonance water model image; and the control points of the magnetic resonance water model image correspond to grid points of each water model.
Step three: and combining a plurality of magnetic resonance water model images according to different areas of magnetic resonance imaging, calculating the deformation quantity between the position information of the grid points and the position information of the control points, namely the distortion effect, and estimating the deformation field according to the deformation quantity.
Step four: and correcting the magnetic resonance water model image according to the deformation field.
In this embodiment, the internal size of the magnetic resonance water-mode image designed based on the magnetic resonance image is 190mm × 190mm, the external size is 210mm × 210mm, the water-mode is filled with the aqueous solution to form 18 rows and 18 columns, the grid points are selected as control points, and the number of the grid points is 324 after the magnetic resonance water-mode images are spliced together.
In this embodiment, it is preferable that the direction of the main magnetic field is a depth direction, the left-right direction of the human body is an x-axis direction, and the front-back direction of the human body is a y-axis direction, as shown in fig. 2. The experiment is divided into four times, and the water model is placed at four positions, namely, the upper left position, the lower left position, the upper right position and the lower right position of the position O where the cross point of the bed surface of the instrument and the central positioning line of the magnet is positioned, and the water model can be spliced together without gaps. The four magnetic resonance water model image experiments are completed on a Siemens 3.0T magnetic resonance scanner by adopting the same imaging protocol. Four water model images with y being 0 layers are respectively selected from four experimental data. 324 x 4 control points of the magnetic resonance water model image cover the range of 380mm x 380mm on the plane with y being 0 and centered at the center of the magnet. The acquired water model image is then processed by the following steps, including:
step A1: and (3) convolving the magnetic resonance water mode image by adopting the normalized mean template of the cross-shaped templates of 3 rows and 9 columns and 9 rows and 3 columns to emphasize the variable values on the grid points.
Step A2: and setting a threshold T to filter the background of the magnetic resonance water model image, and setting the variable value of the background smaller than the threshold T as 0.
Step A3: and recording the number and position information of the control points by combining the maximum brightness value in a rectangular window range with a certain size on the magnetic resonance water model image as the control points. The rectangular range is a rectangular range of the automatic window adjusting window W × Wmm, and the interval between the control points in this embodiment is theoretically 10mm × 10mm, so that W is set to 9. Therefore, only one control point exists in each rectangular range, and the highest brightness value in the rectangular range is obtained as the control point. And sequencing the control points from top to bottom and from left to right, and storing the positions of the transverse control points and the longitudinal control points.
Step A4: if the number of the control points of the magnetic resonance water model image is larger than that of the grid points, increasing the threshold value T and executing the step A2 again; if the number of control points is less than the number of grid points, decreasing the threshold and re-executing step A2; and if the number of the control points of the magnetic resonance image is equal to the number of the control points of the magnetic resonance image, finishing extracting the number and the position information of the control points.
The step of estimating the deformation field in the present invention comprises the step B: estimating a deformation field by combining deformation quantities of all points within the coverage range of the control points by adopting a double-line interpolation method; and fitting and estimating the deformation field by adopting a least square method for each point outside the control point range. The control point range is an area surrounded by the outermost control points.
The step of correcting the magnetic resonance image in the fourth step of the present invention is a process of calculating each control point on the final image (i.e., the corrected image) using the magnetic resonance image with geometric deformation, and specifically includes:
step C1: calculating the position of a pixel point on the final image, which corresponds to the magnetic resonance image with geometric deformation, according to the deformation field;
step C2: assigning the variable value of the pixel point on the final magnetic resonance image to the variable value of the deformation position in the magnetic resonance image with geometric deformation;
step C3: and C1 to C2 are repeatedly executed until all the pixels on the final image are processed and then the process is terminated.
And when the deformation positions of the transverse control points and the longitudinal control points exceed the magnetic resonance water model image, the variable value of the control points is assigned to be 0.
When the real positions of the transverse control points and the longitudinal control points exceed the range of the deformation field, data points of at least two deformation fields are selected in the adjacent areas of different control points. In this embodiment, 10 data points of the deformation field are selected, and the deformation position of the control point is obtained by least square normal linear fitting. And correcting the points in the control point range by adopting an algorithm of automatic two-dimensional geometric correction of the magnetic resonance image, and correcting the deformation equivalent extension of the control point boundary by adopting the points outside the control point range.
And the result obtained by the steps is the result corrected by the two-dimensional geometric correction square algorithm of the magnetic resonance image. Compared with the acquired corrected result image, the acquired corrected result image has better correction effect at a position far away from the center of the magnet, the geometric deformation can cause variable unevenness, the gray correction, the image enhancement and other operations need to be carried out on the image, the variable correction is introduced into the geometric correction by the current geometric correction algorithm, and the invention aims to improve the geometric correction algorithm.
Example (b):
in this embodiment, the magnetic resonance water model image is placed at the upper left position of the positioning plate shown in fig. 2, the outer contour of the water model is attached to the cross mark position line, the scanning sequence adopted is tr _ mpr _ tra _ p2_ iso, the voxel is 1.0mm × 1.0mm, and the image size is: 256mm by 256mm, image center shifted from magnet center r101.7mm, p18.8mm, and h101.0mm, and the number of collected layers was 144, resulting in Data 1.
The magnetic resonance water model image is placed at the upper right position of the positioning plate shown in fig. 2, the outer contour of the magnetic resonance water model image is attached to the cross mark bit line, the adopted scanning sequence is tr _ mpr _ tra _ p2_ iso, the voxel is 1.0mm, the image size is 256mm, the image center is deviated from the magnet center by L120.7.7 mm, P18.8mm and H109.0mm, the number of collected layers is 144, and the Data set Data2 is obtained.
The magnetic resonance water model image is placed at the lower right position of the positioning plate shown in fig. 2, the outer contour of the magnetic resonance water model image is attached to the cross mark bit line, the adopted scanning sequence is tr _ mpr _ tra _ p2_ iso, the voxel is 1.0mm, the image size is 256mm, the image center is deviated from the magnet center by L120.7.7 mm, P18.8mm and F118.0mm, and the number of collected layers is 144, so that a Data set Data3 is obtained.
The magnetic resonance water model image is placed at the lower left position of the positioning plate image shown in fig. 2, the outer contour of the magnetic resonance water model image is attached to the cross mark position line, the adopted scanning sequence is tr _ mpr _ tra _ p2_ iso, the voxel is 1.0mm, and the image size is as follows: 256mm, image center shifted from the magnet center r123.7mm, a12.3mm, and f123.7mm, and the number of collected layers was 144, yielding Data set Data 4.
And selecting a Data layer where y is 0 in the Data1 according to the parameter information Slice location of the image, extracting 324 control points according to the second step of the method, and obtaining the positions P2 after the deformation of the 324 control points according to the pixel spacing and the image orientation of the parameter image. The exact position of the control point P1 is measurable with the magnet center as the origin. The deformation amount of the upper left 324 control points can be calculated according to the formula D-P1-P2.
The deformation quantities of the four acquired magnetic resonance water model images are stored in a matrix map, and the storage mode is as follows: assuming that the interval between two adjacent control points in two same images is 9 data points, the distance between two adjacent control points in two same images is 1mm, the two adjacent images are the upper left and the upper right, the interval between the rightmost control point extracted by the upper left water model image and the leftmost control point extracted by the upper right magnetic resonance water model image is 40mm, and the interval is 39 data points. The values of the data points between the control points are filled in with neighboring data points by means of bilinear interpolation. The formula is shown below
Wherein, Q11(x1, y1), Q21(x2, y1), Q12(x1, y2) and Q22(x2, y2) are four corner points of a rectangular frame, I (x, y) refers to the variable value of the position (x, y), and x and y are the horizontal and vertical coordinates of the internal points of the rectangle. For example, I (Q11(1,2)) -1, I (Q21(2,2)) -2, I (Q12(1, 1)) -3, I (Q22(2, 1)) -4, can be obtained by calculation using the formula
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A two-dimensional geometric correction method for magnetic resonance images is characterized by comprising the following steps:
step 1: acquiring experimental data and recording the number and position information of grid points of a water model by using a magnetic resonance imaging image and the water model as the number and position information of initial transverse and longitudinal control points of the image;
step 2: acquiring a plurality of water model images by utilizing different areas of the water model effect of the magnetic resonance image, and extracting the number and the position information of control points in each image, wherein the position information of the transverse control points and the longitudinal control points is the position of the control points in the image after the image is deformed; the control points of the image correspond to the stretching grid points of the horizontal direction and the longitudinal direction of the water model image;
and step 3: combining the plurality of images according to different regions of the magnetic resonance image, calculating position information and deformation quantities of the transverse and longitudinal stretching and shrinking grid points, namely the control points, and estimating a deformation field according to the deformation quantities;
and 4, step 4: and correcting the magnetic resonance image with geometric deformation by using an improved automatic window adjusting algorithm according to the deformation field.
2. The method as claimed in claim 1, wherein the water model has a square shape, the grid of the water model image is a grid-like glass filler, the filler in the water model image is water or solution, the inner dimension of the water model has a side length of 190mm, the outer dimension has a side length of 210mm, the outer wall has a thickness of 10mm, and the number of the grid points of the magnetic resonance water model image is 81.
3. The method of claim 1, wherein the step of extracting the number and position information of the control points in each image comprises:
step A1: performing water modeling image convolution processing on the magnetic resonance image by adopting a cross template;
step A2: setting a threshold value to filter out the background of the magnetic resonance water model image;
step A3: traversing pixel points with the maximum brightness value in a rectangular window range with a certain size in the magnetic resonance water model image as the transverse and longitudinal initial control points, and recording the quantity and position information of the control points;
step A4: if the number of the transverse and longitudinal control points is larger than the number of the grid points, increasing the threshold value and executing the step A2 again; if the number of control points is less than the number of grid points, decreasing the threshold and re-executing the step a 2; and if the number of the control points is equal to the number of the grid points, finishing the extraction of the number and the position information of the transverse and longitudinal control points.
4. The method of claim 1, wherein the step of estimating the deformation field comprises: estimating the deformation quantity of each transverse and longitudinal control point by adopting an automatic window adjusting algorithm for the deformation quantity of each control point within the range of the transverse and longitudinal control points, calculating the stretching and shrinking quantities and forming a deformation field; and fitting and estimating the deformation quantity of each control point by adopting a least square method for each point outside the range of the control points, and calculating the stretching and shrinking quantities to form a deformation field.
5. The method of claim 1, wherein the step of modifying the geometrically deformed magnetic resonance image comprises:
step C1: calculating the position of a transverse and longitudinal control point on the final image, which corresponds to the magnetic resonance image with the geometric deformation, according to the deformation field;
step C2: assigning the stretching and shrinking variable quantity of the control point on the final magnetic resonance image as the variable quantity of the control point of the deformation position in the magnetic resonance image with the geometric deformation;
step C3: and repeating the steps C1 to C2 until all the image control points on the final image are processed and then the process is terminated.
6. The method of claim 5, wherein when the deformation position of the pixel point exceeds the magnetic resonance image, the initial value of the image control point is 0; and when the real position of each control point is in the image range and exceeds the deformation field description range, selecting at least two deformation field data points in the adjacent area of the control point, and obtaining the deformation positions of the transverse and longitudinal control points by adopting least square normal fitting.
7. The method of claim 6, wherein the least squares method is formulated as follows: f (x, y) ═ bx + y + a
In the formula, x represents the abscissa of the deformation position, y represents the ordinate of the deformation position, and xi, yi represent the fitted numerical values.
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