CN111415315A - Radial collection diffusion weighted imaging motion artifact correction method - Google Patents
Radial collection diffusion weighted imaging motion artifact correction method Download PDFInfo
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- 238000002597 diffusion-weighted imaging Methods 0.000 title claims abstract description 24
- 230000033001 locomotion Effects 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 15
- 238000012937 correction Methods 0.000 title claims abstract description 12
- 238000001914 filtration Methods 0.000 claims abstract description 5
- 230000004927 fusion Effects 0.000 claims abstract description 4
- 229910052704 radon Inorganic materials 0.000 claims abstract description 4
- SYUHGPGVQRZVTB-UHFFFAOYSA-N radon atom Chemical compound [Rn] SYUHGPGVQRZVTB-UHFFFAOYSA-N 0.000 claims abstract description 4
- 230000009466 transformation Effects 0.000 claims abstract description 4
- 230000007704 transition Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract 2
- 238000003384 imaging method Methods 0.000 description 10
- 238000009792 diffusion process Methods 0.000 description 8
- 238000002591 computed tomography Methods 0.000 description 5
- 230000005284 excitation Effects 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000005415 magnetization Effects 0.000 description 3
- 230000002776 aggregation Effects 0.000 description 1
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- 238000002595 magnetic resonance imaging Methods 0.000 description 1
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- 238000013421 nuclear magnetic resonance imaging Methods 0.000 description 1
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Abstract
The invention discloses a radial collection diffusion weighted imaging motion artifact correction method which comprises damaged data detection, damaged data restoration and filtering back projection reconstruction. The damaged data detection step comprises the steps of converting original K space data into projection data space to obtain an original projection data set, reconstructing an original image from the original projection data set through filtering back projection, then obtaining a new projection data set from the original image through Radon transformation, and detecting motion damaged data by comparing the difference between the new projection data set and the original projection data set. And the damaged data restoration comprises the steps of replacing the data of the damaged area with new projection data, and carrying out linear fusion on the data of the edge of the damaged area with the original projection data and the new projection data to obtain corrected projection data. And finally, reconstructing the corrected projection data into a final image by utilizing filtered back projection reconstruction. The scheme of the invention can reduce motion artifacts and improve image quality.
Description
Technical Field
The invention relates to the field of nuclear magnetic resonance imaging, in particular to a radial acquisition diffusion weighted imaging motion artifact correction method.
Background
Diffusion Weighted Imaging (DWI) is an Imaging method which reflects the water molecule Diffusion movement of a living body noninvasively at the molecular level, and is the only image means for measuring the water molecule Diffusion movement of the living body at present. Diffusion weighted imaging relies primarily on the movement of water molecules rather than the proton density of tissue, T1 or T2 relaxation time. The diffusion weighted imaging is suitable for detecting the micro-dynamic state and the microstructure change of biological tissues at the living cell level, and plays a significant role in the identification of benign and malignant tumors, the evaluation of curative effect and the prediction.
Currently, a diffusion Imaging method widely used in clinic is generally single shot planar echo Imaging (EPI). Single shot EPI imaging is characterized by short scan times and less influence by subject motion. Single shot imaging techniques also have their own deficiencies. Firstly, because the acquisition bandwidth along the phase encoding direction is small, serious image deformation can be generated at the junction of different tissues with large difference of magnetic medium rates; second, when acquiring high resolution images in a single shot mode, a long echo chain is required, which means large T2 attenuation, resulting in image blurring and greatly reduced noise-to-noise ratio.
In order to reduce image deformation and improve image resolution and signal-to-noise ratio, multiple excitation diffusion weighted imaging becomes a new research hotspot in recent years. Based on different acquisition modes, the multi-excitation diffusion weighted imaging is mainly divided into multi-excitation EPI diffusion weighted imaging, multi-excitation helical acquisition trajectory (helical) diffusion weighted imaging, multi-excitation Fast Spin Echo (FSE) diffusion weighted imaging and multi-excitation radial acquisition diffusion weighted imaging. The first three imaging modes all need complex algorithms to process phase errors generated among multiple excitations, and have slow reconstruction time and poor stability. Based on diffusion weighted imaging of radial collection, K space data can be converted into Projection data through one-dimensional Fourier transform, reconstruction is carried out by utilizing a Computed Tomography (CT) reconstruction technology after the Projection data are subjected to modulus, such as an image is reconstructed by a Filtered Back Projection (FBP) algorithm, so that the influence of phase errors among multiple excitations is completely not considered, the algorithm is faster and more stable, and the method has great application potential.
In diffusion weighted imaging, a huge motion sensitive gradient is applied, on one hand, diffusion weighted contrast can be formed due to different diffusion rates of water molecules, and on the other hand, motion artifacts can be caused by being very sensitive to various macroscopic motions. For example, the autonomous movement, physiological movement and mechanical vibration of the patient can cause incomplete proton phase aggregation, thus randomly destroying the original data and forming artifacts. Fig. 1 shows the volunteer head diffusion weighted projection data acquired based on radial acquisition, each line of data is the projection data acquired by one-time excitation, and it can be seen that a plurality of lines of data are damaged in different degrees and are caused by motion dephasing.
Disclosure of Invention
The invention aims to provide a radial collection diffusion weighted imaging motion artifact correction method which can reduce motion artifacts and improve image quality.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention discloses a radial collection diffusion weighted imaging motion artifact correction method, which comprises the following steps:
s100, collecting radial K space data;
s200, performing one-dimensional Fourier transform along the reading direction, and taking a module value to obtain an original radial projection data set PrawRandom corrupted data exists in the data set;
s300, based on the original projection data set PrawObtaining an original image M by filtered back-projection reconstructionraw；
S400, based on original image MrawObtaining a new projection data set P by Radon transformationnew；
S500, comparing the original projection data set PrawAnd a new projection data set PnewObtaining a damaged data mask area S;
s600, data fusion: covering the projection data of the damaged area with a new projection data set, linearly fusing the points adjacent to the damaged area with the original projection data and the new projection data to achieve smooth transition, and finally obtaining a repaired projection data set Pc；
S700, repairing the projection data set PcAnd carrying out filtering back projection transformation to obtain a final reconstructed image.
Preferably, in step S500, the formula for S is
Preferably, T is 0.2.
Preferably, in step S600, PcIs calculated by the formula
Where α is the weight corresponding to the distance of the projected data point from the nearest corrupted data point along a single projection data one-dimensional direction.
Preferably, α is d/N, d being the distance of the projected data point from the nearest damaged data point.
Preferably, N is 5.
The invention has the beneficial effects that:
the invention provides a correction method for detecting and repairing damaged data aiming at motion artifacts in diffusion weighted imaging acquired in a radial mode, so that the motion artifacts can be reduced, and the image quality is improved.
Drawings
FIG. 1 is radial diffusion weighted projection data before repair;
FIG. 2 is a schematic flow chart of the present invention;
fig. 3 is the repaired radial diffusion weighted projection data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
In the present application:
k-space: k-space, the frequency domain space of the magnetic resonance signal
DWI: diffusion Weighted Imaging, Diffusion Weighted Imaging or Diffusion Weighted Imaging
Multi-Shot: multiple excitation
Multi-Shot DWI: multi-shot diffusion weighted imaging
T1: time constant for growing of longitudinal magnetization after RF-pulse, longitudinal magnetization vector recovery Time constant
T2: time constant for decay of transverse magnetization after RF-pulse
TR: repetition Time, Repetition Time or Repetition period
EPI: echo planar imaging, planar Echo imaging technique
Radial imaging: radial K space imaging technology (traditional magnetic resonance imaging based on Cartesian K space acquisition technology)
CT: computer Tomography, computed Tomography
FBP: filtered Back Projection reconstruction
As shown in fig. 2 and 3, the present invention includes the following steps:
step 1: collecting radial K space data;
step 2: one-dimensional Fourier transform is carried out along the reading direction, and the modulus is taken to obtain an original radial projection data set PrawRandom corrupted data exists in the data set;
and 3, step 3: based on the original projection data set PrawObtaining an original image M by filtered back-projection reconstructionraw；
And 4, step 4: based on the original image MrawObtaining a new projection data set P by Radon transformationnew；
And 5, step 5: comparing the original projection data set PrawAnd a new projection data set PnewTo obtain the damaged data mask region S,
specifically, S is obtained by the following formula
Where T is a threshold that can be set empirically. Preferably, T ═ 0.2 can achieve better results, but is not limited thereto;
step 6, data fusion, namely covering the projection data of the damaged area by using a new projection data set, linearly fusing the points adjacent to the damaged area by using the original projection data and the new projection data to achieve smooth transition, and finally obtaining a repaired projection data set PcIn particular
In this embodiment, α ═ d/N, d is the distance between the projection data point and the nearest damaged data point, and the distance is in pixels, where the value of N can take on the value of 5, but is not limited thereto;
step 7, the restored projection data set P is processedcAnd carrying out filtering back projection transformation to obtain a final reconstructed image.
The present invention is capable of other embodiments, and various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention.
Claims (6)
1. A radial acquisition diffusion weighted imaging motion artifact correction method is characterized by comprising the following steps:
s100, collecting radial K space data;
s200, performing one-dimensional Fourier transform along the reading direction, and taking a module value to obtain an original radial projection data set PrawRandom corrupted data exists in the data set;
s300, based on the original projection data set PrawObtaining an original image M by filtered back-projection reconstructionraw；
S400, based on original image MrawObtaining a new projection data set P by Radon transformationnew；
S500, comparing the original projection data set PrawAnd a new projection data set PnewObtaining a damaged data mask area S;
s600, data fusion: covering the projection data of the damaged area with a new projection data set, linearly fusing the points adjacent to the damaged area with the original projection data and the new projection data to achieve smooth transition, and finally obtaining a repaired projection data set Pc；
S700, repairing the projection data set PcAnd carrying out filtering back projection transformation to obtain a final reconstructed image.
2. The correction method according to claim 1, characterized in that: in step S500, the formula for S is
3. The correction method according to claim 2, characterized in that: t is 0.2.
4. The correction method according to claim 2 or 3, characterized in that: in step S600, PcIs calculated by the formula
Where α is the weight corresponding to the distance of the projected data point from the nearest corrupted data point along a single projection data one-dimensional direction.
5. The calibration method according to claim 4, wherein α is d/N, and d is the distance between the projected data point and the nearest damaged data point.
6. The correction method according to claim 5, characterized in that: n is 5.
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| CN102005031A (en) * | 2010-11-03 | 2011-04-06 | 宁波鑫高益磁材有限公司 | Method and device for eliminating motion artifact of K spacial sampled data in MRI system |
| CN105469366A (en) * | 2015-11-23 | 2016-04-06 | 山东科技大学 | Analytic method for eliminating metal artifact of CT image |
| US20160113617A1 (en) * | 2013-06-19 | 2016-04-28 | Koninklijke Philips N.V. | Calibration of imagers with dynamic beam shapers |
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| CN104952039B (en) * | 2015-06-14 | 2017-10-10 | 中国电子科技集团公司第十研究所 | Distributed image compressed sensing method for reconstructing |
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| CN102005031A (en) * | 2010-11-03 | 2011-04-06 | 宁波鑫高益磁材有限公司 | Method and device for eliminating motion artifact of K spacial sampled data in MRI system |
| US20160113617A1 (en) * | 2013-06-19 | 2016-04-28 | Koninklijke Philips N.V. | Calibration of imagers with dynamic beam shapers |
| CN105469366A (en) * | 2015-11-23 | 2016-04-06 | 山东科技大学 | Analytic method for eliminating metal artifact of CT image |
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