CN112669406A - Projection estimation-based phased array coil magnetic resonance image non-uniformity correction method - Google Patents
Projection estimation-based phased array coil magnetic resonance image non-uniformity correction method Download PDFInfo
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Abstract
The invention discloses a projection estimation-based non-uniformity correction method for a phased array coil magnetic resonance image, which comprises the following steps: obtaining a binary mask image by a threshold method to further obtain a region of interest (ROI) image, projecting the ROI image, fitting a projection curve by a nonlinear fitting method, reconstructing the fitted curve by a Radon inverse transformation method to obtain a bias field estimation image, processing the mask and the bias field estimation image based on a low-pass filtering method, and fusing an original magnetic resonance image, the mask image and the bias field estimation image by a designed image fusion algorithm to obtain a non-uniformity corrected image. The method effectively solves the problem of uneven image gray scale caused by uneven magnetic field of the radio frequency coil and spatial difference of the sensitivity of the phased array coil in the conventional magnetic resonance system, corrects the magnetic resonance image by using the method, and displays the corrected image, thereby greatly improving the uniformity of the image while ensuring the signal-to-noise ratio and having high clinical application value.
Description
Technical Field
The invention belongs to the field of magnetic resonance imaging, and particularly relates to a projection estimation-based method for correcting the nonuniformity of a magnetic resonance image of a phased array coil.
Background
The magnetic resonance imaging has the advantages of no radioactivity, clear imaging, good soft tissue resolution, free selection of imaging surfaces, no interference of gas and bone artifacts and the like, and is widely applied to the fields of medical diagnosis, scientific research and the like. However, due to the non-uniform magnetic field generated by the radio frequency coil and the spatial difference of the coil sensitivity, the MR image has non-uniform gray scale, and even the judgment of the doctor on the disease condition is affected.
In order to solve the problem of gray-scale inhomogeneity of the MR images, many experts in the magnetic resonance field have studied and proposed various correction algorithms. Generally, the method can be divided into a prospective method and a retrospective method, wherein the prospective method focuses on correction and optimization in the image acquisition process, and the prospective method comprises methods such as a water model, a multi-coil method, a special sequence method and the like. The method based on the water model needs to extract an uneven field from the same position of the water model to correct a surface coil image, the method based on the multi-coil corrects the surface coil image by using a body coil, and the method based on the special sequence calculates the uneven field by using the difference of sequence parameters. The above methods all require additional scan data, which increases the burden of hardware to some extent, and the scan time is also increased. Retrospective methods rely on the image itself or some a priori knowledge, including methods of filtering, segmentation, surface fitting, histograms, etc. The filtering method is to regard the uneven field as the low frequency component of the image, directly filter the uneven field of gray scale by using a high-pass filter, the segmentation method is to introduce uneven correction of gray scale in the image segmentation process, and finally achieve the purpose of segmentation and correction at the same time, the curved surface fitting method is to regard the uneven field of gray scale as a curved surface, the uneven field of gray scale is fitted by using the curved surface fitting method, and the histogram method is to process the histogram of the image by combining the characteristics of the uneven field of gray scale, so that the uneven image is corrected.
Disclosure of Invention
The invention aims to: the projection estimation-based non-uniformity correction method for the magnetic resonance image of the phased array coil is high in image signal-to-noise ratio and good in uniformity.
The technical scheme of the invention is as follows: a phased array coil magnetic resonance image non-uniformity correction method based on projection estimation is characterized by comprising the following steps:
(1) carrying out threshold processing on original magnetic resonance image data to obtain a mask image, and fusing the mask image and the original magnetic resonance image to obtain an image of a region of interest;
(2) carrying out Radon projection transformation on the image of the region of interest within 0-179 degrees to obtain projection curves at all angles;
(3) carrying out nonlinear fitting on the target area of the projection curve at each angle to remove high-frequency signals of human tissues;
(4) carrying out Radon inverse transformation on the fitted integral curve to reconstruct an estimated bias field estimation image and carrying out low-pass filtering;
(5) and (3) carrying out low-pass filtering processing on the mask image obtained in the step (1), and then fusing the mask image with the original magnetic resonance image and the bias field estimation image to obtain a corrected magnetic resonance image.
Further, the method for threshold processing in step (1) is an OTSU method.
Further, the OTSU method comprises: in a gray scale space L of an original image, an image is divided into a foreground type and a background type by using a gray scale value i for one image, and G is respectively used0And G1To represent G0And G1Between-class variance between two classes
Can be expressed as:
in the formula, ω0Is the proportion of pixels in the foreground region, omega1Is the proportion of pixels in the background area, mu0Is the mean value of pixels in the foreground region, mu1Is the mean value of pixels in the background area, muTOptimal threshold i required for original image pixel mean, OTSU methodoptShould satisfy the calculated between-class variance
Maximum:
according to the optimum threshold value ioptA binarized foreground image can be obtained, a mask image can be obtained by hole filling of the image, and a region of interest (ROI) image can be obtained by mask processing of the original image.
Further, the method for Radon projection transform in step (2) is as follows: for a region of interest (ROI) image f (x, y), its Radon transform can be viewed as a one-dimensional projection of the image f (x, y) along a certain line l, or f (x, y) is defined as a line integral along a certain line l, so the mathematical expression of the Radon transform can be expressed as:
in the formula, the straight line l may represent a straight line which is away from the origin ρ and forms an angle θ with the negative direction of the y-axis in the form of polar coordinates, where ρ is x cos θ + y sin θ, and the Radon transform may be further expressed as follows by using the characteristic of the impulse function δ (x):
further, in the step (3), a 4 th-order polynomial is adopted to fit the projection curve, tissue details in the projection curve are removed, then a Radon inverse transformation is adopted to reconstruct an image of the projection curve within 0 to 179 degrees, and specifically, the method can be solved according to a Radon transformation formula:
writing the above equation in polar form has:
in the formula, Rf' (p, theta) is Rf(ρ, θ) partial derivative of ρ.
The magnetic resonance image V (x, y) due to the non-uniform gray scale in step 4 is usually made up of a uniform image U (x, y) multiplied by a non-uniform bias field B (x, y) plus noise N (x, y), i.e.:
V(x，y)＝U(x，y)B(x，y)+N(x，y)
where the bias field B (x, y) is generally considered to be a slowly varying, approximately smooth multiplicative field. The purpose of the gray scale inhomogeneity correction is to recover the true image U (x, y) from the image V (x, y) observed in the magnetic resonance apparatus, and if N (x, y) is sufficiently small relative to U (x, y) B (x, y) and the bias field B (x, y) is known, then the homogeneous image can be obtained by:
because the image tissue edge artifact is obvious, a Gaussian low-pass filtering method is adopted to filter the mask, and the two-dimensional Gaussian filtering kernel function is known to be as follows according to Gaussian distribution:
further, in the step 5, the original magnetic resonance image, the mask and the bias field estimation image are subjected to image fusion by using an image fusion algorithm, wherein the designed image fusion algorithm is as follows:
wherein V (x, y) is the original MR image, M (x, y) is the mask image obtained in step 1, BLPF(x, y) is the low-pass filtered estimated field image reconstructed in step 4, MLPF(x, y) is the gaussian filtered mask image in step 5.
The invention has the advantages that: the method adopts a projection estimation method to reconstruct a bias field estimation image of a phased array coil magnetic resonance image, and designs an image fusion method to fuse the corrected interested region image and an original image, thereby effectively solving the problems of unobvious correction effect, easy amplification of image noise and the like of the traditional correction method, simultaneously, the method does not depend on a sequence, does not need additional scanning data, and can ensure the signal-to-noise ratio and simultaneously improve the non-uniformity of the image.
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The invention is further described with reference to the following figures and examples:
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a magnetic resonance image of the original abdomen to be corrected according to the present invention.
Fig. 3 is a mask image of the present invention.
FIG. 4 shows a 90 ° projection original curve and a fitting curve according to the present invention.
Fig. 5 is a gaussian filtered offset field estimation image of the present invention.
Figure 6 is a corrected magnetic resonance image of the present invention.
FIG. 7 is a comparison of an original image of the present invention and a corrected image.
Detailed Description
Example (b): the method for correcting the inhomogeneity of the magnetic resonance image of the phased array coil based on projection estimation, which is shown in figure 1, comprises the following steps: (1) carrying out threshold processing on original magnetic resonance image data (original images) to obtain mask images (masks), and fusing the mask images (masks) and the original magnetic resonance images to obtain region-of-interest images (ROI); (2) carrying out Radon projection transformation on the image of the region of interest within 90 degrees to obtain projection curves at all angles; (3) carrying out nonlinear fitting on the target area of the projection curve at each angle to remove high-frequency signals of human tissues; (4) carrying out Radon inverse transformation on the fitted integral curve to reconstruct an estimated bias field estimation image and carrying out low-pass filtering; (5) and (3) carrying out low-pass filtering processing on the mask image obtained in the step (1), and then fusing the mask image with the original magnetic resonance image and the bias field estimation image to obtain a corrected magnetic resonance image.
In this embodiment, as shown in fig. 2, the original abdominal magnetic resonance image to be corrected is subjected to thresholding by using an OTSU method, specifically, in a gray scale space L of the original image, an image is divided into a foreground and a background by using a gray scale value i, and G is used for dividing the image into the foreground and the background respectively0And G1To represent G0And G1Between-class variance between two classes
Can be expressed as:
in the formula, ω0Is the proportion of pixels in the foreground region, omega1Is the proportion of pixels in the background area, mu0Is the foreground area pixel mean value, mu 1 is the background area pixel mean value, muTOptimal threshold i required for original image pixel mean, OTSU methodoptShould satisfy the calculated between-class variance
Maximum:
according to the optimum threshold value ioptA binarized foreground image can be obtained, and a mask image can be obtained by filling holes in the image, this embodimentIn an example, a portion of the original image larger than the threshold is set to be 1, a portion of the original image smaller than or equal to the threshold is set to be 0, and hole filling is performed to obtain a mask, as shown in fig. 3, and a region of interest (ROI) image can be obtained by performing mask processing on the original image.
Then, the Radon projection transform is performed on the ROI image within 90 °, specifically, for the ROI image f (x, y), the Radon transform can be regarded as a one-dimensional projection of the image f (x, y) along a certain straight line l, or f (x, y) is defined as a line integral along a certain straight line l, so the mathematical expression of the Radon transform can be expressed as:
in the formula, the straight line l may represent a straight line which is away from the origin ρ and forms an angle θ with the negative direction of the y-axis in the form of polar coordinates, where ρ is x cos θ + y sin θ, and the Radon transform may be further expressed as follows by using the characteristic of the impulse function δ (x):
then, carrying out nonlinear fitting on the projection curve within 90 degrees, carrying out Radon inverse transformation on the fitted curve to obtain an offset field estimation image, specifically, fitting the projection curve by adopting a 4-order polynomial, removing tissue details in the projection curve, then carrying out image reconstruction on the projection curve within 90 degrees by adopting Radon inverse transformation, and specifically solving the following steps according to a Radon transformation formula:
writing the above equation in polar form has:
in the formula, Rf' (p, theta) is Rf(ρ, θ) partial derivative of ρ. In this embodiment, the principle of determining the left and right boundary points is to take the mean k times of the curve, where k is 0.2, and only fit the inner regions of the left and right boundary points, taking 90 ° curve projection as an example, and the original curve and the fitted curve are shown in fig. 4.
The magnetic resonance image V (x, y) due to the non-uniform gray scale is usually made up of a uniform image U (x, y) multiplied by a non-uniform bias field B (x, y) plus noise N (x, y), i.e.:
V(x，y)＝U(x，y)B(x，y)+N(x，y)
where the bias field B (x, y) is generally considered to be a slowly varying, approximately smooth multiplicative field. The purpose of the gray scale inhomogeneity correction is to recover the true image U (x, y) from the image V (x, y) observed in the magnetic resonance apparatus, and if N (x, y) is sufficiently small relative to U (x, y) B (x, y) and the bias field B (x, y) is known, then the homogeneous image can be obtained by:
because the image tissue edge artifact is obvious, a Gaussian low-pass filtering method is adopted to filter the mask, and the two-dimensional Gaussian filtering kernel function is known to be as follows according to Gaussian distribution:
carrying out image fusion on the original magnetic resonance image, the mask and the bias field estimation image by using an image fusion algorithm, wherein the designed image fusion algorithm is as follows:
wherein V (x, y) is an original magnetic resonance image, M (x, y) is a mask image obtained in step (1), and BLPFThe result of (x, y) representing the gaussian-filtered estimated offset field image reconstructed in step (4) is shown in fig. 5, where the standard deviation σ of the gaussian kernel is taken to be 5. MLPF(x, y) is the gaussian filtered mask image in step 5.
The fused correction images are shown in fig. 6 and 7, and it is clear from the comparison of fig. 7 that the algorithm of the present invention can greatly improve the inhomogeneity of the nuclear magnetic image while ensuring the signal-to-noise ratio.
Claims (6)
1. A phased array coil magnetic resonance image non-uniformity correction method based on projection estimation is characterized by comprising the following steps:
(1) carrying out threshold processing on original magnetic resonance image data to obtain a mask image, and fusing the mask image and the original magnetic resonance image to obtain an image of a region of interest;
(2) carrying out Radon projection transformation on the image of the region of interest within 0-179 degrees to obtain projection curves at all angles;
(3) carrying out nonlinear fitting on the target area of the projection curve at each angle to remove high-frequency signals of human tissues;
(4) carrying out Radon inverse transformation on the fitted integral curve to reconstruct an estimated bias field estimation image and carrying out low-pass filtering;
(5) and (3) carrying out low-pass filtering processing on the mask image obtained in the step (1), and then fusing the mask image with the original magnetic resonance image and the bias field estimation image to obtain a corrected magnetic resonance image.
2. A projection estimation-based phased array coil magnetic resonance image non-uniformity correction method according to claim 1, wherein the thresholding method in step (1) is OTSU method.
3. The projection estimation-based phased array coil magnetic resonance image non-uniformity correction method according to claim 2, wherein the OTSU method is: in the gray scale space L of the original magnetic resonance image, for oneDividing the image into foreground and background by using gray value i, and respectively using G0And G1To represent G0And G1Between-class variance between two classes
Can be expressed as:
in the formula, ω0Is the proportion of pixels in the foreground region, omega1Is the proportion of pixels in the background area, mu0Is the mean value of pixels in the foreground region, mu1Is the mean value of pixels in the background area, muTOptimal threshold i required for original image pixel mean, OTSU methodoptShould satisfy the calculated between-class variance
Maximum:
according to the optimum threshold value ioptA binarized foreground image can be obtained, a mask image can be obtained by filling holes in the image, and an image of a region of interest can be obtained by performing mask processing on an original image.
4. The method for correcting inhomogeneity of a magnetic resonance image of a phased array coil based on projection estimation as claimed in claim 2, wherein the method of Radon projection transformation in the step (2) is as follows: for the region of interest image f (x, y), the line integral along a certain line l can be expressed as:
in the formula, a straight line l represents a straight line which is away from an origin ρ in a polar coordinate form and has an angle θ with a negative direction of a y axis, and ρ is xcos θ + ysin θ.
5. A projection estimation-based method for correcting inhomogeneity of a phased array coil magnetic resonance image according to claim 2, wherein in the step (3), a 4 th order polynomial is used to perform non-linear fitting on the target region of the projection curve at each angle.
6. The method for correcting inhomogeneity of a magnetic resonance image of a phased array coil based on projection estimation as claimed in claim 2, wherein the fusion algorithm in step (5) is:
v (x, y) is the original magnetic resonance image, M (x, y) is the mask image, BLPF(x, y) is the bias field estimate image after Gaussian filter reconstruction, MLPF(x, y) is the low-pass filtered mask image.
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