CN107154025A - A kind of blood flow artifact minimizing technology being imaged for arteria carotis magnetic resonance vascular wall - Google Patents
A kind of blood flow artifact minimizing technology being imaged for arteria carotis magnetic resonance vascular wall Download PDFInfo
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- 230000017531 blood circulation Effects 0.000 title claims abstract description 34
- 238000005516 engineering process Methods 0.000 title claims abstract description 19
- 230000002792 vascular Effects 0.000 title claims abstract description 15
- 239000008280 blood Substances 0.000 claims abstract description 123
- 210000004369 blood Anatomy 0.000 claims abstract description 122
- 238000003384 imaging method Methods 0.000 claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000006243 chemical reaction Methods 0.000 claims abstract description 4
- 238000000354 decomposition reaction Methods 0.000 claims description 7
- 210000004204 blood vessel Anatomy 0.000 claims description 2
- 208000037396 Intraductal Noninfiltrating Carcinoma Diseases 0.000 description 7
- 208000028715 ductal breast carcinoma in situ Diseases 0.000 description 7
- 230000001629 suppression Effects 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 2
- 238000002595 magnetic resonance imaging Methods 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- PQSUYGKTWSAVDQ-ZVIOFETBSA-N Aldosterone Chemical compound C([C@@]1([C@@H](C(=O)CO)CC[C@H]1[C@@H]1CC2)C=O)[C@H](O)[C@@H]1[C@]1(C)C2=CC(=O)CC1 PQSUYGKTWSAVDQ-ZVIOFETBSA-N 0.000 description 1
- PQSUYGKTWSAVDQ-UHFFFAOYSA-N Aldosterone Natural products C1CC2C3CCC(C(=O)CO)C3(C=O)CC(O)C2C2(C)C1=CC(=O)CC2 PQSUYGKTWSAVDQ-UHFFFAOYSA-N 0.000 description 1
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 206010007687 Carotid artery stenosis Diseases 0.000 description 1
- 229960002478 aldosterone Drugs 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000002308 calcification Effects 0.000 description 1
- 210000001715 carotid artery Anatomy 0.000 description 1
- 208000006170 carotid stenosis Diseases 0.000 description 1
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- 230000003247 decreasing effect Effects 0.000 description 1
- 238000000331 delays alternating with nutation for tailored excitation Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
- G06T2207/30104—Vascular flow; Blood flow; Perfusion
Abstract
The invention discloses a kind of blood flow artifact minimizing technology being imaged for arteria carotis magnetic resonance vascular wall.First, the consistent black blood in space and grey blood image are obtained using grey black blood double-contrast imaging technique, is then based on two-dimensional discrete wavelet conversion and image is pre-processed, suppress high-frequency sub-band；And black blood and grey blood image are handled using different preemphasis, the black blood image after preemphasis is handled subtracts the grey blood image after preemphasis processing, the image after being subtracted each other；Image after to subtracting each other carries out brightness adjustment, makes its mean flow rate consistent with the mean flow rate of initial black blood image, obtains removing the vascular wall MRI of blood flow artifact.This method can reduce tube chamber signal to noise ratio, improve tube wall tube chamber difference and make an uproar and compare, and more thoroughly remove the blood flow artifact among former black blood imaging.
Description
Technical field
The present invention relates to magnetic resonance vascular wall imaging technique, more particularly to a kind of blood flow being imaged for arteria carotis magnetic resonance vascular wall
Artifact minimizing technology, belongs to medical imaging field.
Background technology
Cardiovascular and cerebrovascular disease turns into the primary disease for threatening human health.The black blood imaging technique (Black-blood of magnetic resonance
Magnetic resonance imaging) magnetic resonance vascular wall imaging technique is also known as, it is a kind of conventional the diagnosis of vascular diseases technology, it
Hemadostewnosis and occlusion site can be imaged.It suppresses blood signal, therefore resulting figure by black blood prepulsing
Dark signal is presented in blood as in.Black blood imaging technique can clearly display the plaque of Endovascular, and the black blood of high-resolution
Imaging can distinguish the composition of patch, be to face at present so playing the role of positive for the stability and risk profile for assessing patch
A kind of effective technology being imaged in bed for patch.
The suppression of blood flow artifact is the key of the black blood imaging of arteria carotis.A variety of blood flow artifact suppressing methods have been proposed in forefathers, bag
Include double overturn and recover (DIR), three upset recoveries (TIR), four upset recoveries (QIR), motion sensitive driving balance (MSDE)
(DANTE) technology etc. is excited with the delay alternating customization with nutating.These methods make use of different blood flows to suppress prepulsing,
While surrounding static tissue signal is retained, suppress the blood signal of flowing.
However, due to the unique texture of arteria carotis, the flow pattern of blood flow is complicated, in carotid bifuracation it sometimes appear that flow velocity mistake
Slowly, the blood flow for stopping and flowing back, causes blood flow artifact, and these blood flow artifacts are difficult to suppress prepulsing by blood flow merely to make a return journey
Remove.Remaining blood flow artifact can disturb the imaging results of lumen of vessels, vascular wall and patch, make vascular wall and patch obscurity boundary,
It is difficult to differentiate.This can cause the vessel wall thickness measured from image partially thick, can also influence the identification of patch, so as to influence clinic
Diagnosis.
Grey blood imaging technique is a kind of new vascular wall imaging technique derived by black blood imaging technique.The image of the technical limit spacing
In, intermediate light is presented in blood flow signal, thus is referred to as " grey blood ".Pass through the design of the filling mode in K spaces, grey blood imaging
Technology can obtain the image of black blood picture and grey two kinds of contrasts of blood picture simultaneously among single pass.Such grey black blood double-contrast into
The suppression of blood flow artifact provides new enlightenment in being imaged as technology for black blood.
The content of the invention
In order to obtain relatively sharp reliable carotid artery vascular wall image, the present invention proposes a kind of based on double-contrast image subtraction
The blood flow artifact suppressing method of (Dual-Contrast Images Subtraction, DICS).The present invention using grey black blood double-contrast into
As technology, and a kind of method that double-contrast image preemphasis is subtracted each other again is devised, can more thoroughly removed in original black blood picture
Remaining blood flow signal.
Technical scheme is as follows：
A kind of blood artifact minimizing technology of magnetic resonance vascular wall imaging, comprises the following steps：
1) the black blood and ash of spatial alignment are obtained simultaneously in single pass to blood vessel imaging using grey black blood double-contrast imaging technique
Two kinds of contrast images of blood；
2) black blood image and grey blood image are pre-processed respectively based on two-dimensional discrete wavelet conversion, suppresses its high-frequency sub-band；
3) to step 2) pretreated black blood image and grey blood image carry out different preemphasis processing respectively, then by pre-add
Black blood image after weight subtracts the grey blood image after preemphasis, the image after being subtracted each other；
4) to subtracting each other after image carry out brightness adjustment, make its mean flow rate consistent with the mean flow rate of initial black blood image, obtain
To the vascular wall MRI for removing blood flow artifact.
Above-mentioned steps 1) the consistent black blood image in space and grey blood image are obtained by the echo based on double-contrast, it is preferred that institute
State grey black blood double-contrast imaging technique and be based on RECS-3D MERGE imaging techniques, utilize imaging layer saturation blood in the phase between imaging
The feature that outflow, new blood are flowed into, using front and rear two 3D FSPGR imaging sequences arranged side by side, is respectively intended to obtain black blood
Image and grey blood image.
To suppress the amplification of the picture noise caused by follow-up preemphasis and image subtraction, in step 2) it is small using two-dimensional discrete
Wave conversion is pre-processed to black blood image and grey blood image.It is preferred that, step 2) db8 mother wavelet functions are selected to black blood figure
Picture and grey blood image carry out wavelet decomposition, and the wavelet decomposition number of plies is 2~4 layers；After wavelet decomposition, by the high frequency wavelet of grey blood image
Subband is thoroughly removed, and the high frequency wavelet subband of black blood image is multiplied byWherein α values are 0.5~1.5.Further, if
A small echo hard -threshold T=λ σ is put, amplitude in the high frequency wavelet subband of black blood image is less than to T coefficient zero setting, further suppression
The small coefficient of high-frequency sub-band processed.Wherein, σ is the noise estimated from most fine dimension subband using median absolute deviation method
Variance, coefficient lambda value is 3~6.
Step 3) the grey blood image after preemphasis is subtracted with the black blood image after preemphasis, so as to utilize the blood flow in grey blood image
Signal eliminates blood flow artifact remaining in black blood image.Above-mentioned steps 3) in, the pre emphasis factor of grey blood image is α, black blood figure
The pre emphasis factor of picture is α+1, and α values are 0.5~1.5, with step 2).
In step 4) carry out before brightness adjustment, the image intermediate value after must subtracting each other is set to zero for the pixel value of negative.
Step 5) it is that the mean flow rate based on image is adjusted, by setting gain coefficient to the image after subtracting each other, put down it
Equal brightness is consistent with the mean flow rate of original black blood image.
The present invention has advantages below：
Method of the invention based on grey black blood double-contrast image subtraction, using the blood flow signal in grey blood image, eliminates black blood figure
The remaining blood flow signal as in.Compared to the simple method for suppressing prepulsing by blood flow, the present invention can be removed more thoroughly
Blood flow artifact among black blood imaging.Test result indicates that, in the black blood image that this method is obtained, reflection blood flow artifact suppresses effect
Signal to noise ratio have decreased to the 1% of the black blood image directly obtained by black blood sequence in the tube chamber of fruit, and tube wall-tube chamber difference is made an uproar than improving
91.4%.
Brief description of the drawings
Fig. 1 is the sequence chart of grey black blood double-contrast imaging sequence.
Fig. 2 is the process chart of blood flow artifact suppressing method of the present invention.
Fig. 3 shows that α in the embodiment of the present invention takes 1 imaging results, illustrate image that typical DICS methods obtain and
Corresponding original black blood image (BB), grey blood image (GB), 3D TOF and 2D QIR-FSE images.
Fig. 4 is shown when different α values are chosen in the embodiment of the present invention, the tube chamber signal to noise ratio (a) for the image that DCIS technologies are obtained,
It is poorer (c) than (b) and noise criteria that tube wall-tube chamber difference is made an uproar.
Embodiment
The present invention will be further described by the following examples, to more fully understand technical scheme, but the present invention
It is not limited thereto.
1. grey black blood double-contrast imaging sequence
The imaging of grey black blood double-contrast is based on RECS-3D MERGE imaging techniques, and its sequence chart is as shown in Figure 1.Employ two
3D FSPGR imaging sequences, the image of two kinds of contrasts of black blood image and grey blood image can be obtained simultaneously during single pass.
The present embodiment is imaged for arteria carotis, and the sequential parameter of selection is as follows：
TR/TE=6.4ms/3.1ms, flip angle=6 °, FOV=150 × 150mm, picture size=256 × 256, thickness=1.4
Mm, the number of plies=48, receiver bandwidth=31.25kHz, signal acquisition times=1, compressed sensing accelerates multiple=3, time delay
=800ms.
Sweep time amounts to 2 points 12 seconds.
2. image preprocessing step
From db8 mother wavelet functions, 3 layers of wavelet decomposition are carried out to original black blood image and grey blood image, as shown in Figure 2；So
The high-frequency sub-band of grey blood image is thoroughly removed afterwards, the high-frequency sub-band of black blood image is multiplied byα values are 0.5~1.5,
Value is 1 in the present embodiment.In addition, the high-frequency sub-band to black blood image sets a small echo hard -threshold T=λ σ, further suppression
The small coefficient of high-frequency sub-band processed.Wherein, σ is that median absolute deviation (median absolute are used from most fine dimension subband
Deviation, MAD) method estimation noise variance, λ values be 3~6, in embodiment value be 5.After above-mentioned processing
Image carry out discrete wavelet inverse transformation, obtain after the grey blood image after high-frequency sub-band is completely removed and high-frequency sub-band be suppressed
Black blood image.
3. preemphasis and image subtraction
1) black blood image and grey blood image are subjected to preemphasis respectively, grey blood image is multiplied by factor alpha, and black blood image is multiplied by factor alpha+1
(referring to Fig. 2).
2) grey blood image is subtracted with the black blood image after preemphasis.
3) referring to Fig. 2, the negative value zero setting of obtained image will be made the difference, obtains image after final processing.
4. brightness adjustment
As shown in Fig. 2 the average gray value for the image that step 3 is obtained is set to and former black blood image identical average gray value,
I.e.：
IBRepresent original black blood image, ISThe image that step 3 is obtained is represented,Represent the image after brightness adjustment, mean
Expression is averaged operation.
The image and original black blood image that a obtains DCIS methods of the present invention in Fig. 3 are compared, it is seen that original black blood figure
Still there is the blood flow artifact of residual as in, and blood flow artifact has obtained abundant suppression in the image after the processing of DCIS methods.B in Fig. 3
Representational DICS images and original black blood image, grey blood image, 3D TOF figures, 2D QIR-FSE images are carried out with c
Compare.B illustrates the image of an Aldosterone in Fig. 3, it is seen that the remaining blood flow signal in original black blood image is effective
Remove, blood vessel-tube chamber border becomes apparent from.C illustrates an image for carrying calcified plaque in Fig. 3, it is seen that due to DICS
Technology more thoroughly inhibits blood flow artifact, compared to original black blood image, and the border of calcification and tube chamber becomes more easy to identify.
The present embodiment has carried out arteria carotis imaging to 20 patients, wherein 12 have carotid artery stenosis, 8 are Healthy subjects.
Its image data is analyzed, result as shown in Figure 4 is obtained.
Fig. 4 show selection different α values when, the tube chamber signal to noise ratio for the image that DCIS technologies are obtained, tube wall-tube chamber difference make an uproar than with
Noise criteria is poor.Compared to original black blood image, the tube chamber signal to noise ratio for the image that DCIS technologies are obtained, which has, significantly to be reduced.When
α>When 1, tube chamber signal to noise ratio is intended to 0, and this shows that blood flow artifact has obtained sufficient removal.And the image that DCIS technologies are obtained
Tube wall-tube chamber difference make an uproar than increasing 63.5%-126.1% compared to original black blood image.The noise for the image that DCIS technologies are obtained
Level has also reduced compared to original black blood image.
Claims (7)
1. a kind of blood artifact minimizing technology of magnetic resonance vascular wall imaging, comprises the following steps：
1) the black blood and ash of spatial alignment are obtained simultaneously in single pass to blood vessel imaging using grey black blood double-contrast imaging technique
Two kinds of contrast images of blood；
2) black blood image and grey blood image are pre-processed respectively based on two-dimensional discrete wavelet conversion, suppresses its high-frequency sub-band；
3) to step 2) pretreated black blood image and grey blood image carry out different preemphasis processing respectively, then by pre-add
Black blood image after weight subtracts the grey blood image after preemphasis, the image after being subtracted each other；
4) to subtracting each other after image carry out brightness adjustment, make its mean flow rate consistent with the mean flow rate of initial black blood image, obtain
To the vascular wall MRI for removing blood flow artifact.
2. the method as described in claim 1, it is characterised in that the step 1) RECS-3D MERGE imaging techniques are based on,
Using the feature that the outflow of imaging layer saturation blood, new blood are flowed into the phase between imaging, using front and rear two 3D arranged side by side
FSPGR imaging sequences, are respectively intended to obtain black blood image and grey blood image.
3. the method as described in claim 1, it is characterised in that step 2) pretreatment in, from db8 mother wavelet functions to black
Blood image and grey blood image carry out wavelet decomposition, and the wavelet decomposition number of plies is 2~4 layers；After wavelet decomposition, by the height of grey blood image
Frequency wavelet sub-band is thoroughly removed, and the high frequency wavelet subband of black blood image is multiplied byWherein α values are 0.5~1.5.
4. method as claimed in claim 3, it is characterised in that step 2) in, the high frequency wavelet subband of black blood image is multiplied by
Afterwards, a small echo hard -threshold T=λ σ is set, amplitude in the high frequency wavelet subband of black blood image is less than to T coefficient zero setting,
Wherein σ is the noise variance estimated from most fine dimension subband using median absolute deviation method, and coefficient lambda value is
3~6.
5. method as claimed in claim 3, it is characterised in that step 3) in, the pre emphasis factor of grey blood image is α, black blood
The pre emphasis factor of image is α+1, and α value is with step 2).
6. the method as described in claim 1, it is characterised in that in step 4) image is carried out before brightness adjustment, after subtracting each other
Image intermediate value is set to zero for the pixel value of negative.
7. the method as described in claim 1, it is characterised in that step 5) it is that the mean flow rate based on image is adjusted, pass through
Gain coefficient is set to the image after subtracting each other, makes its mean flow rate consistent with the mean flow rate of original black blood image.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110276725A (en) * | 2018-03-15 | 2019-09-24 | 北京大学 | A kind of quick minimizing technology freely breathing lower abdomen magnetic resonance imaging artifact |
| CN110367985A (en) * | 2019-07-18 | 2019-10-25 | 惠仁望都医疗设备科技有限公司 | A kind of method that Low Magnetic field MRI line sweeps Diffusion Imaging removal blackstreak |
| CN111202519A (en) * | 2020-01-17 | 2020-05-29 | 首都医科大学宣武医院 | Method and system for detecting hardness of in-vivo thrombus |
| CN113419202A (en) * | 2020-12-29 | 2021-09-21 | 苏州朗润医疗系统有限公司 | Method for acquiring carotid magnetic resonance blood vessel image by adopting Radial 3DTOF and magnetic resonance imaging system |
| WO2022105623A1 (en) * | 2020-11-23 | 2022-05-27 | 西安科锐盛创新科技有限公司 | Intracranial vascular focus recognition method based on transfer learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6430430B1 (en) * | 1999-04-29 | 2002-08-06 | University Of South Florida | Method and system for knowledge guided hyperintensity detection and volumetric measurement |
| CN102119400A (en) * | 2008-08-08 | 2011-07-06 | 汤姆逊许可证公司 | Method and apparatus for detecting dark noise artifacts |
| CN102551717A (en) * | 2010-12-31 | 2012-07-11 | 深圳迈瑞生物医疗电子股份有限公司 | Method and device for removing blood vessel splicing image artifacts in magnetic resonance imaging |
| CN102800073A (en) * | 2012-06-28 | 2012-11-28 | 西北工业大学 | Automatic judgment and correction method of cone beam CT annulus artifact |
| CN104931903A (en) * | 2014-03-18 | 2015-09-23 | 上海联影医疗科技有限公司 | Method and device for eliminating motion artifact through magnetic resonance |
-
2016
- 2016-03-04 CN CN201610124252.4A patent/CN107154025B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6430430B1 (en) * | 1999-04-29 | 2002-08-06 | University Of South Florida | Method and system for knowledge guided hyperintensity detection and volumetric measurement |
| CN102119400A (en) * | 2008-08-08 | 2011-07-06 | 汤姆逊许可证公司 | Method and apparatus for detecting dark noise artifacts |
| CN102551717A (en) * | 2010-12-31 | 2012-07-11 | 深圳迈瑞生物医疗电子股份有限公司 | Method and device for removing blood vessel splicing image artifacts in magnetic resonance imaging |
| CN102800073A (en) * | 2012-06-28 | 2012-11-28 | 西北工业大学 | Automatic judgment and correction method of cone beam CT annulus artifact |
| CN104931903A (en) * | 2014-03-18 | 2015-09-23 | 上海联影医疗科技有限公司 | Method and device for eliminating motion artifact through magnetic resonance |
Non-Patent Citations (2)
| Title |
|---|
| 张娜，等: ""MIR检测易损斑块的优势与不足"", 《视点聚焦》 * |
| 李宁: ""fMRI伪影去除方法研究的进展"", 《中国医疗设备》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110276725A (en) * | 2018-03-15 | 2019-09-24 | 北京大学 | A kind of quick minimizing technology freely breathing lower abdomen magnetic resonance imaging artifact |
| CN110276725B (en) * | 2018-03-15 | 2022-03-25 | 北京大学 | Method for rapidly removing lower abdominal magnetic resonance imaging artifacts in free breathing |
| CN110367985A (en) * | 2019-07-18 | 2019-10-25 | 惠仁望都医疗设备科技有限公司 | A kind of method that Low Magnetic field MRI line sweeps Diffusion Imaging removal blackstreak |
| CN111202519A (en) * | 2020-01-17 | 2020-05-29 | 首都医科大学宣武医院 | Method and system for detecting hardness of in-vivo thrombus |
| WO2022105623A1 (en) * | 2020-11-23 | 2022-05-27 | 西安科锐盛创新科技有限公司 | Intracranial vascular focus recognition method based on transfer learning |
| CN113419202A (en) * | 2020-12-29 | 2021-09-21 | 苏州朗润医疗系统有限公司 | Method for acquiring carotid magnetic resonance blood vessel image by adopting Radial 3DTOF and magnetic resonance imaging system |
| CN113419202B (en) * | 2020-12-29 | 2022-06-21 | 苏州朗润医疗系统有限公司 | Method for acquiring carotid magnetic resonance blood vessel image by adopting Radial 3DTOF and magnetic resonance imaging system |
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