CN110652296A - Method for removing magnetic resonance head image motion artifact - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005070 sampling Methods 0.000 claims abstract description 20
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- 210000003625 skull Anatomy 0.000 claims description 2
- 230000001737 promoting effect Effects 0.000 abstract 1
- 238000013441 quality evaluation Methods 0.000 abstract 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 7
- 238000005457 optimization Methods 0.000 description 5
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Abstract
The invention discloses a method for removing magnetic resonance head image motion artifacts, wherein a non-reference image quality evaluation index QI is adopted in the methodTVThe method is used for reflecting the image quality, and the method reconstructs the k-space data after each sampling through compressed sensing and calculates the QI of a reconstructed imageTVValue by observing the image QITVAnd (3) finding the movement moment of the patient in the magnetic resonance scanning process according to the variation trend, and performing compressed sensing reconstruction by using the unmoved k-space data so as to optimize the full-acquisition motion artifact image. The invention can effectively improve the quality of the magnetic resonance motion artifact image, thereby promoting the clinical application of the magnetic resonance image.
Description
Technical Field
The invention relates to the technical field of magnetic resonance imaging, in particular to a method for removing a magnetic resonance head image motion artifact.
Background
The magnetic resonance imaging technology is one of the commonly used technologies in medical imaging, and has the advantages of no ionizing radiation, good soft tissue contrast, imaging at any angle and the like. The imaging speed is slow and is susceptible to artifacts, which is one of the main challenges facing magnetic resonance imaging, and during long-time scanning of a patient, autonomous or involuntary motion can introduce motion artifacts in final imaging, so that the image quality is reduced, and the diagnostic value of the image is affected. The current methods for suppressing motion artifacts mainly include motion detection on target tissues, and scanning is performed only when the target tissues are in specific positions, for example, abdominal scanning is performed by setting cardiac gating and respiratory gating, however, the method prolongs the scanning time; the image reconstruction is performed after phase correction of the data by means of a special sequence, such as a BLADE sequence, by means of repeated sampling of the k-space center, phase estimation with low-frequency data. The method needs k-space interpolation calculation, which can cause image gridding artifacts, and the image resolution is influenced to a certain extent.
The compressed sensing can be used for reconstructing a high-quality magnetic resonance image by adopting partial k-space data and through sparse constraint and a nonlinear algorithm in a non-uniform random sampling mode. And (3) based on the inspiration of the compressed sensing algorithm, the row or column which is less influenced by the motion in the k space is found out through an optimization algorithm, and the data are reconstructed by the compressed sensing algorithm to obtain a high-quality image.
Disclosure of Invention
The invention aims to provide a method for removing motion artifacts of a magnetic resonance head image, which can be used for inhibiting the problem of the motion artifacts caused by various conditions of the magnetic resonance head image based on the motion artifacts generated by the spontaneous or involuntary motion of a patient which can be kept still for a period of time before the patient is in the magnetic resonance scanning process and at the later period.
The specific technical scheme for realizing the purpose of the invention is as follows:
a method for removing motion artifact of magnetic resonance head image, which does not depend on external equipment, and performs high-quality magnetic resonance image reconstruction by analyzing k-space data on the premise of not increasing scanning time (assuming that the size of the reconstructed image is MxN, k-space data can be collected in rows or columns, k-space data is collected in columns in the invention); the method specifically comprises the following steps:
step 1: selecting a pseudo-random sampling sequence under a k-space Cartesian coordinate system, and determining a sampling sequence of a k-space;
step 2: according to the sampling sequence, sequentially obtaining k space data k acquired at the t timet(ii) a Wherein t is 1,2,3 … N;
and step 3: using k-space data k acquired t times beforetReconstructing by using a compressed sensing algorithm to obtain a reconstructed image It；
And 4, step 4: calculating a reconstructed image ItIs of QITVValue QITV|t；
And 5: repeating the step 2-4 until all k-space data are acquired;
step 6: compare all QIsTVValue, select the maximum QITVThe image corresponding to the value is taken as the image from which the motion artifact is removed.
Compared with the prior art, the invention has the following beneficial effects: cartesian sampling can be used in magnetic resonance scanning, so that the gridding process in the later stage of data is avoided, the requirement on system gradient transformation is reduced, and the magnetic resonance scanning method is more favorable for implementation on equipment. The method is simple and easy to operate, and only requires that the patient can keep still within a period of time after the scanning starts, so that high-quality data with a certain proportion exist in all k spaces, and a magnetic resonance image with higher quality can be reconstructed by the method; a higher quality image is obtained without the need to correct the moving k-space data.
Drawings
FIG. 1 is a k-space fill sequence diagram;
figure 2 is a flow chart for removing magnetic resonance brain image motion artifacts;
figure 3 is a ROI region representation of a magnetic resonance brain image;
FIG. 4 is QI of all images in the image optimization processTVThe scatter plot of (a);
fig. 5 is a comparison between the original motion artifact image and the optimized image.
Detailed description of the preferred embodiments
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, and the present invention is not particularly limited, except for those specifically mentioned below.
The invention better optimizes the magnetic resonance head motion artifact image and greatly improves the image quality which keeps still in a period of time after the scanning starts. The following examples describe the design of the scanning sequence of the present invention, full acquisition of moving k-space data, reconstruction of images using compressed sensing, and image quality determination and optimization processes.
Examples
In this embodiment, the acquired magnetic resonance imaging data is two-dimensional cranial magnetic resonance imaging data. The method is realized on a sequence integration development platform IDEA of Siemens, and magnetic resonance imaging data are acquired from a Siemens prism 3T superconducting magnetic resonance imaging system, and a gradient echo sequence is used. The specific imaging parameters are: the repetition Time (TR) is 250ms, the echo Time (TE) is 4.8ms, the Flip Angle (FA) is 70 °, the field of view (FOV) is 320mm × 320mm (frequency coding × phase coding), the layer thickness is 5mm, the Bandwidth (BW) is 330HZ/pixel, and the scan matrix (matrix) is 320 × 320 (frequency coding × phase coding).
The motion artifact image optimization process according to the present embodiment will be described in detail below with reference to fig. 1 to 5.
Referring to fig. 1, fig. 1 is a k-space data fill sequence diagram implemented in accordance with the present embodiment. The method comprises the steps of adopting a Monte Carlo pseudo-random Cartesian sampling mode, preferentially and continuously acquiring 15% of data in the center of k space, then generating a pseudo-random sampling sequence through a Probability Density Function (PDF) based on energy distribution of the k space, and randomly sampling a high-frequency part of the k space. FIG. 1(a) is a sample probability function; FIG. 1(b) is a 15% continuous sampling of the center of k-space at the time of sampling; FIG. 1(c) is a sampling mask when the k-space sampling rate is 30%; fig. 1(d) is a sampling mask when the k-space sampling rate is 50%.
Referring to FIG. 2, which is a flow chart of the present invention, after the original k-space data of the image is obtained, the k-space data obtained by the first scanning is obtained according to the filling sequence of the scanning sequence to the k-space dataAnd performing image reconstruction according to the compressed sensing, and then calculating QI (quality index) of the reconstructed imageTVValue, QITVThe calculation method of (a) is as follows:
wherein TVinTotal Variation (TV), TV, representing a Region of Interest (ROI)outRepresenting the total variation of the background region, where the region of interest refers to the magnetic resonance brain map skull and its internal region. When the image quality is better, there are fewer artifacts in the region of interest, but because of the brain tissue information, TVinHaving a certain value, no brain tissue information outside the ROI area, TVoutThe value is small and therefore the ratio of the two is large. The ROI is here distinguished from the background by the greater body algorithm, as shown in fig. 3. Then filling a second line of k-space data according to the k-space filling sequence, compressing the perceptually reconstructed image and then calculating an image QITVAnd repeating the steps until the k space data is completely filled, and calculating QI of all the imagesTVThe value is obtained. Since the patient remains still during the initial scan, there are no artifacts outside the ROI, and as the scan progresses, the tissue information contained in the ROI becomes richer and richer, TVinRise, and TVoutNot subject to major changes and therefore QITVThe value is in the rising trend, after the patient moves in the later period, the motion artifact is generated outside the ROI, and the TVoutThe value becomes larger, TVinAnd TVoutReduced ratio, i.e. QITVDecrease, so when calculating QI of all reconstructed imagesTVAfter value, QITVThe variation trend of (1) is ascending first and then descending, and the highest point is QI of a reconstructed image immediately before the patient movesTVThe value is obtained. QITVThe trend of change of (c) is shown in fig. 4.
Fig. 5(a) is an original motion artifact image, and the image quality is poor due to the motion of the patient during the scanning process, and does not meet the medical diagnosis requirement, and as shown in fig. 5(c), the image obtained by the algorithm optimization of the present invention is approximately reduced in brain tissue structure compared with the gold standard image obtained by the resting state scanning, and compared with the original motion artifact image, the quality is greatly improved, and the medical diagnosis requirement is met.
Claims (2)
1. A method for removing motion artifacts of a magnetic resonance head image is characterized by comprising the following specific steps:
step 1: selecting a pseudo-random sampling sequence under a k-space Cartesian coordinate system, and determining a sampling sequence of a k-space;
step 2: according to the sampling sequence, sequentially obtaining k space data k acquired at the t timet(ii) a Wherein t is 1,2,3 … N; n is the total sampling times of the k space data when the data are sampled according to the rows or the columns;
and step 3: using k-space data k acquired t times beforetReconstructing by using a compressed sensing algorithm to obtain a reconstructed image It；
And 4, step 4: calculating a reconstructed image ItIs of QITVValue QITV|t；
And 5: repeating the step 2-4 until all k-space data are acquired;
step 6: compare all QIsTVValue, select the maximum QITVThe image corresponding to the value is taken as the image from which the motion artifact is removed.
2. The method for removing motion artifacts of magnetic resonance head images according to claim 1, characterized in that the QI of the computed images isTVThe values are specifically:
TVinrepresenting the total variation of the region of interest of the image; TV (television)outRepresenting the total variation of the background area; the region of interest refers to a magnetic resonance head image skull and an internal region thereof, and is obtained through a Dajin algorithm and a hole filling algorithm of a binary image.
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| CN113558600A (en) * | 2020-04-28 | 2021-10-29 | 株式会社日立制作所 | Magnetic resonance imaging apparatus and image processing method |
| CN115359144A (en) * | 2022-10-19 | 2022-11-18 | 之江实验室 | Magnetic resonance plane echo imaging artifact simulation method and system |
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