CN103714521A - Liver R2* graph measuring method based on inquiry table - Google Patents

Liver R2* graph measuring method based on inquiry table Download PDF

Info

Publication number
CN103714521A
CN103714521A CN201310742267.3A CN201310742267A CN103714521A CN 103714521 A CN103714521 A CN 103714521A CN 201310742267 A CN201310742267 A CN 201310742267A CN 103714521 A CN103714521 A CN 103714521A
Authority
CN
China
Prior art keywords
liver
question blank
represent
measuring method
method based
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201310742267.3A
Other languages
Chinese (zh)
Other versions
CN103714521B (en
Inventor
王常青
冯衍秋
陈武凡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southern Medical University
Original Assignee
Southern Medical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southern Medical University filed Critical Southern Medical University
Priority to CN201310742267.3A priority Critical patent/CN103714521B/en
Publication of CN103714521A publication Critical patent/CN103714521A/en
Application granted granted Critical
Publication of CN103714521B publication Critical patent/CN103714521B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

A liver R2* graph measuring method based on an inquiry table comprises the steps of obtaining a magnetic resonance liver graph, drawing an area, comprising the liver, of interest, carrying out spline interpolation on a confluent hyper-geometric function with the given number of channels of a receiving coil, setting up the corresponding inquiry table, enabling the gray level and echo time of each pixel in the liver area of interest to be fitted in a first-moment model of a first-moment model under the influence of non-central Chi noise, and obtaining an R2* graph comprising the liver. The liver R2* graph measuring method can measure the R2* graph comprising the liver accurately and rapidly.

Description

Liver R2* figure measuring method based on question blank
Technical field
The invention belongs to the technical field that in magnetic resonance, transverse relaxation rate R2* figure measures, be specifically related to a kind of R2* figure of can be fast and measuring exactly liver to measure the liver R2* figure measuring method based on question blank of liver concentration of iron.
Background technology
If occur in human body, too much deposition of iron easily causes serious pathology when the endocrine organs such as liver, heart, and particularly for sickle anaemia and patients with thalassemia, long-term treatment of blood transfusion can cause too much deposition of iron phenomenon.Because 70%～90% too much iron in human body will be deposited in liver, conventionally using liver concentration of iron as an important indicator of reacting iron content in body.
Compared to the direct method of liver organization biopsy, the liver concentration of iron measuring method based on magnetic resonance imaging R2* figure can effectively be avoided the complication such as liver is hemorrhage, and measuring accuracy is not affected by the factors such as sample size, position and liver iron uneven distribution can.
By magnetic resonance imaging parameters R 2*, measure liver concentration of iron at present, mainly contain two kinds of methods: a kind of is based on a plurality of little liver area-of-interests, the method first averages the gray scale of pixel in area-of-interest, then by obtain average after gray-scale value according to suitable curve model, carry out matching with the corresponding echo time, obtain representing the R2* value of liver.Because liver iron is not to be evenly distributed in liver, and there is artificial difference in the size of area-of-interest and the selection of position, tends to cause final measurement result inaccurate.
Another kind method is the R2* figure based on magnetic resonance imaging, and the method, to each pixel in liver, is fitted to suitable curve model by its gray scale and echo time, obtains the R2* figure of whole liver.Owing to will each pixel being carried out to matching, therefore calculated amount is huge, calculate comparatively consuming time, particularly especially obvious in some curve models.
There is researcher to propose to adopt noise correction first moment model to measure R2* value (Feng Y; He T; Gatehouse PD; Li X; Harith Alam M; Pennell DJ, Chen W, Firmin DN. Improved MRI R2* relaxometry of iron-loaded liver with noise correction. Magn Reson Med 2013; 70:1765-1774.), obtained the R2* estimated value of better accuracy and precision.Because the method has only been considered a plurality of little liver area-of-interests, signal in region is averaged and then carries out once fitting, in the artificial selection of area-of-interest, sometimes can not get rid of the impact that some focus or artifact are measured R2*; In addition, in matching, the calculating of confluent hypergeometric function has taken most of the time, carry out single matching relatively consuming time but still belong to the scope that can accept, if schemed and carry out calculating liver R2* by Pixel fit, estimating needs tens of hours consuming time, has limited its application in clinical practice.
Therefore, not enough for prior art, provide a kind of R2* of liver fast and accurately figure measuring method very necessary to overcome the deficiencies in the prior art.
Summary of the invention
The present invention proposes a kind of liver R2* figure measuring method based on question blank, the method can effectively avoid confluent hypergeometric function in the area-of-interest improper R2* measuring error causing of sampling and noise correction first moment model to calculate problem consuming time, can in several minutes, obtain full liver R2* figure accurately.
Above-mentioned purpose of the present invention realizes by following technological means.
A liver R2* figure measuring method based on question blank, in turn includes the following steps:
(1), gather magnetic resonance liver image, and on obtained magnetic resonance liver image, draw the area-of-interest that comprises liver;
(2), to known receiving coil port number
Figure 481841DEST_PATH_IMAGE001
confluent hypergeometric function
Figure 2013107422673100002DEST_PATH_IMAGE002
carry out spline interpolation, and set up by interpolation knot and with the interpolation sub-range question blank that interpolating function coefficient forms one to one;
(3), the gray scale of each pixel and echo time are fitted in the first moment modular form (I) of Single-Index Model under non-central Chi noise effect and obtain the R2* value corresponding with each pixel, the R2* value by each pixel obtains the R2* that comprises liver and schemes;
Wherein, formula (I) is:
Figure 721192DEST_PATH_IMAGE003
……（I）;
In formula (I),
Figure 285029DEST_PATH_IMAGE004
represent expectation,
Figure 376875DEST_PATH_IMAGE005
represent observes signal values, the standard deviation that represents the Gaussian noise of each receiving coil passage,
Figure 574955DEST_PATH_IMAGE007
represent two factorials (
Figure 840851DEST_PATH_IMAGE008
,
Figure 184108DEST_PATH_IMAGE001
represent receiving coil port number;
Figure 826442DEST_PATH_IMAGE009
the spline interpolation function that represents confluent hypergeometric function,
Figure 305965DEST_PATH_IMAGE010
represent the echo time,
Figure 8341DEST_PATH_IMAGE011
represent
Figure 573315DEST_PATH_IMAGE012
time muting true signal value,
Figure 252296DEST_PATH_IMAGE013
represent transverse relaxation rate; Due at muting image background regions signal
Figure 586325DEST_PATH_IMAGE014
therefore, the standard deviation in formula (I)
Figure 459603DEST_PATH_IMAGE015
can obtain by through type (II):
Figure 511873DEST_PATH_IMAGE016
?……（II）。
Above-mentioned steps (1) specifically adopts many echo gradient echo sequence to obtain magnetic resonance liver image.
Above-mentioned steps (2) specifically adopts cubic spline interpolation method under non-node boundary condition to be similar to confluent hypergeometric function, and sets up corresponding question blank.
The equally spaced interpolation knot of the right and wrong of choosing concrete selection of interpolation knot in above-mentioned steps (2).
Preferred interpolation knot
Figure 230430DEST_PATH_IMAGE017
,
Figure 684545DEST_PATH_IMAGE018
; When
Figure 463145DEST_PATH_IMAGE019
time interval be chosen as 0.1, when
Figure 2711DEST_PATH_IMAGE020
time interval be chosen as 50, boundary condition is selected the boundary condition of non-node.
Above-mentioned steps (3) specifically adopts and based on question blank, confluent hypergeometric function is carried out the curve of approximate noise correction first moment model.
The present invention is based on the liver R2* figure measuring method of question blank, the method can effectively avoid in prior art confluent hypergeometric function in the area-of-interest improper R2* measuring error causing of sampling and noise correction first moment model to calculate defect consuming time, can in several minutes, obtain the R2* figure of whole liver accurately.
Accompanying drawing explanation
The present invention is further illustrated to utilize accompanying drawing, but content in accompanying drawing does not form any restriction to people of the present invention.
Fig. 1 is the schematic flow sheet of the inventive method;
Fig. 2 is question blank structural representation corresponding to the cubic spline functions of confluent hypergeometric function;
Fig. 3 is that the emulated data of different signal to noise ratio (S/N ratio)s is used method of the present invention and used noise correction first moment model M 1r2* figure and comparative result thereof that NCM method obtains respectively, comprise R2* value from 100 ~ 1000 s in emulating image -1;
Fig. 4 is that the emulated data of different signal to noise ratio (S/N ratio)s and receiving coil port number is used method of the present invention and M 1nCM method is calculated respectively the comparative result form that R2* schemes the time used;
Fig. 5 is that two groups of true liver data are used method of the present invention and M 1r2* figure and comparative result thereof that NCM method obtains respectively;
Fig. 6 is that two groups of true liver data are used method of the present invention and M 1nCM method is calculated respectively the comparative result form that R2* schemes the time used.
Embodiment
The invention will be further described with the following Examples.
embodiment 1.
A liver R2* figure measuring method based on question blank, as shown in Figure 1, in turn includes the following steps:
(1), gather magnetic resonance liver image, and on obtained magnetic resonance liver image, draw the area-of-interest that comprises liver.
(2), to known receiving coil port number
Figure 14705DEST_PATH_IMAGE001
confluent hypergeometric function
Figure 588906DEST_PATH_IMAGE021
carry out spline interpolation, and set up by interpolation knot and with the interpolation sub-range question blank that interpolating function coefficient forms one to one, as shown in Figure 2.
(3), the gray scale of each pixel and echo time are fitted in the first moment modular form (I) of Single-Index Model under non-central Chi noise effect and obtain the R2* value corresponding with each pixel, the R2* value by each pixel obtains the R2* that comprises liver and schemes.
Wherein, formula (I) is:
Figure 538408DEST_PATH_IMAGE003
……（I）;
In formula (I), represent expectation,
Figure 891209DEST_PATH_IMAGE005
represent observes signal values,
Figure 54337DEST_PATH_IMAGE006
the standard deviation that represents the Gaussian noise of each receiving coil passage,
Figure 174739DEST_PATH_IMAGE007
represent two factorials (
Figure 954477DEST_PATH_IMAGE008
,
Figure 818527DEST_PATH_IMAGE001
represent receiving coil port number;
Figure 101741DEST_PATH_IMAGE009
the spline interpolation function that represents confluent hypergeometric function,
Figure 157160DEST_PATH_IMAGE010
represent the echo time,
Figure 893035DEST_PATH_IMAGE011
represent time muting true signal value,
Figure 698497DEST_PATH_IMAGE013
represent transverse relaxation rate.
Due at muting image background regions signal
Figure 426281DEST_PATH_IMAGE014
therefore, the standard deviation in formula (I) can obtain by through type (II):
?……（II）。
Wherein, step (1) can adopt many echo gradient echo sequence to obtain magnetic resonance liver image.
Wherein, step (3) specifically adopts and based on question blank, confluent hypergeometric function is carried out the curve of approximate noise correction first moment model.
The present invention is based on the liver R2* figure measuring method of question blank, the method can effectively avoid in prior art confluent hypergeometric function in the area-of-interest improper R2* measuring error causing of sampling and noise correction first moment model to calculate defect consuming time, can in several minutes, obtain the R2* figure of whole liver accurately.
embodiment 2.
A liver R2* figure measuring method based on question blank, as shown in Figure 1, take human liver as measuring object, specifically comprises the steps.
Step 1, obtains 12 echo liver magnetic resonance image (MRI), draws out full liver area-of-interest along liver image edge.Its imaging parameters is set to: the echo time is chosen respectively 0.93,2.27,3.61,4.95,6.29,7.63,8.97,10.30,11.64,12.98,14.32 and 15.66 ms, and the repetition time is 200 ms, and matrix size is 64 * 128, and flip angle is 20 o, bed thickness is 10 mm, receiving coil passage number is 8.
It should be noted that, the method for drawing full liver area-of-interest can adopt manual drawing, also can adopt other modes to draw, as programmed control is automatically drawn or draws by other equipment.
Step 2, to known receiving coil passage number
Figure 113111DEST_PATH_IMAGE022
confluent hypergeometric function
Figure 277377DEST_PATH_IMAGE021
carry out cubic spline interpolation, set up by interpolation knot and with the interpolation sub-range question blank that interpolating function coefficient forms one to one.
The parameter that cubic spline interpolation adopts is set to: interpolation knot
Figure 722264DEST_PATH_IMAGE017
,
Figure 233274DEST_PATH_IMAGE018
; When
Figure 345586DEST_PATH_IMAGE019
time interval be chosen as 0.1, when time interval be chosen as 50, boundary condition is selected the boundary condition of non-node.Herein cubic spline functions be calculated as general knowledge known in this field, do not repeat them here.
Step 3, the question blank obtaining by step 2 replaces the calculating of confluent hypergeometric function in noise correction first moment model of fit, each pixel in the liver area-of-interest that step (1) is obtained, its gray scale and echo time are fitted to the R2* value that obtains each pixel in the first moment modular form (I) of Single-Index Model under non-central Chi noise effect, then obtain according to the R2* value of each pixel the R2* figure that comprises liver.
Figure 347357DEST_PATH_IMAGE003
……（I）;
In formula (I), represent expectation,
Figure 596253DEST_PATH_IMAGE005
represent observes signal values,
Figure 836741DEST_PATH_IMAGE006
the standard deviation that represents the Gaussian noise of each receiving coil passage, represent two factorials (
Figure 371683DEST_PATH_IMAGE008
,
Figure 458587DEST_PATH_IMAGE001
represent receiving coil port number,
Figure 869977DEST_PATH_IMAGE009
the spline interpolation function that represents confluent hypergeometric function,
Figure 776753DEST_PATH_IMAGE010
represent the echo time,
Figure 931791DEST_PATH_IMAGE011
represent time muting true signal value,
Figure 455493DEST_PATH_IMAGE013
represent effect transverse relaxation rate.
Due at muting image background regions signal
Figure 849566DEST_PATH_IMAGE014
, the standard deviation in formula (I)
Figure 808294DEST_PATH_IMAGE015
that through type (II) obtains:
Figure 604212DEST_PATH_IMAGE016
……（II）。
Research shows, the calculated amount of confluent hypergeometric function is large and consuming time, the present invention replaces confluent hypergeometric function with its spline interpolation function, adopt the first moment model (formula (I)) of Single-Index Model under non-central Chi noise effect to carry out by Pixel fit, experiment shows that carried model can obtain almost (being called for short M with the noise correction single order moments method of direct calculating confluent hypergeometric function 1nCM) R2* of identical liver figure, and accelerated nearly two orders of magnitude computing time.
First method of the present invention draws out area-of-interest to liver image, then for given receiving coil port number, in advance confluent hypergeometric function is carried out to cubic spline interpolation, set up corresponding question blank for the quick approximate treatment of this function, first moment model of fit in conjunction with noise correction is the first moment model (formula (I)) of Single-Index Model under non-central Chi noise effect, accelerate the speed of curve, shortened the time of calculating liver R2* figure.
In order further to verify that method of the present invention is (hereinafter to be referred as M 1nCM-LUT method) effect, by M 1nCM-LUT method (is called for short M with the noise correction first moment that directly calculates confluent hypergeometric function 1nCM) method is tested relatively, and result is as follows:
Fig. 2 has shown question blank structural drawing corresponding to confluent hypergeometric function that second step of the present invention is set up.
Fig. 3 is that the emulated data that receiving coil port number is 8, signal to noise ratio (S/N ratio) is respectively under 15,30 and 60 is used method of the present invention and M 1r2* figure and comparative result thereof that NCM method obtains, can find out for different signal to noise ratio (S/N ratio)s, and two kinds of methods obtain almost identical R2* figure, and error is 10 -5s -1the order of magnitude.
Fig. 4 has shown that the emulated data under different signal to noise ratio (S/N ratio)s and receiving coil port number used method of the present invention and M 1nCM method is calculated respectively the comparative result table that R2* schemes the time used.In table, shown that emulated data is used method of the present invention and M under different signal to noise ratio (S/N ratio)s (signal to noise ratio (S/N ratio) is 15,30,60) and different receiving coil port number (coil channel number is 1,2,4,8,16,32,64,128) combination 1the accuracy comparison diagram of two kinds of methods of NCM method, can find out that method of the present invention accelerated 95～418 times, can within the time of a few minutes, obtain R2* figure.
Fig. 5 is that two groups of true liver data are used method of the present invention and M 1the R2* that NCM method obtains respectively schemes and relatively, one group is serious liver iron overload, and one group is slight liver iron overload.Can find out, two kinds of methods obtain almost identical R2* figure, and error is 10 -4s -1the order of magnitude.
Fig. 6 has provided 6 groups of true liver data and has used method of the present invention and M 1the time comparison of NCM method, respectively corresponding liver iron overload in various degree.Can find out, method of the present invention can be accelerated 120～162 times equally.
From above result, the liver R2* figure measuring method that the present invention is based on question blank can obtain and M 1the liver R2* figure that NCM is identical, but nearly two orders of magnitude in speed, improved, can in several minutes, obtain a complete R2* figure.
It should be noted that, the measuring method that the present invention is based on the liver R2* figure of question blank is not only applicable to the measurement of human liver R2* figure, is equally applicable to the measurement of other animal's livers R2* figure yet.
Finally should be noted that; above embodiment is only in order to illustrate technical scheme of the present invention but not limiting the scope of the invention; although the present invention is explained in detail with reference to preferred embodiment; those of ordinary skill in the art is to be understood that; can modify or be equal to replacement technical scheme of the present invention, and not depart from essence and the scope of technical solution of the present invention.

Claims (6)

1. the liver R2* figure measuring method based on question blank, is characterized in that: in turn include the following steps:
(1), gather magnetic resonance liver image, and on obtained magnetic resonance liver image, draw the area-of-interest that comprises liver;
(2), to known receiving coil port number
Figure 455242DEST_PATH_IMAGE001
confluent hypergeometric function
Figure 372383DEST_PATH_IMAGE002
carry out spline interpolation, and set up by interpolation knot and with the interpolation sub-range question blank that interpolating function coefficient forms one to one;
(3) each pixel in the liver area-of-interest, step (1) being obtained, the gray scale of each pixel and echo time are fitted in the first moment modular form (I) of Single-Index Model under non-central Chi noise effect and obtain the R2* value corresponding with each pixel, and the R2* value by each pixel obtains the R2* that comprises liver and schemes;
Wherein, formula (I) is:
Figure 2013107422673100001DEST_PATH_IMAGE003
……（I）;
In formula (I),
Figure 89803DEST_PATH_IMAGE004
represent expectation,
Figure 2013107422673100001DEST_PATH_IMAGE005
represent observes signal values,
Figure 173428DEST_PATH_IMAGE006
the standard deviation that represents the Gaussian noise of each receiving coil passage,
Figure 2013107422673100001DEST_PATH_IMAGE007
represent two factorials (
Figure 987800DEST_PATH_IMAGE008
,
Figure 2013107422673100001DEST_PATH_IMAGE009
represent receiving coil port number,
Figure 747946DEST_PATH_IMAGE010
the spline interpolation function that represents confluent hypergeometric function,
Figure 14979DEST_PATH_IMAGE011
represent the echo time, represent
Figure 2013107422673100001DEST_PATH_IMAGE013
time muting true signal value,
Figure 69709DEST_PATH_IMAGE014
represent transverse relaxation rate, due at muting image background regions signal therefore, the standard deviation in formula (I)
Figure 755DEST_PATH_IMAGE016
can obtain by through type (II):
?……（II）。
2. the liver R2* figure measuring method based on question blank according to claim 1, is characterized in that: described step (1) specifically adopts many echo gradient echo sequence to obtain magnetic resonance liver image.
3. the liver R2* figure measuring method based on question blank according to claim 1, it is characterized in that: described step (2) specifically adopts cubic spline interpolation method under non-node boundary condition to be similar to confluent hypergeometric function, and sets up corresponding question blank.
4. the liver R2* figure measuring method based on question blank according to claim 3, is characterized in that: the equally spaced interpolation knot of the right and wrong of choosing concrete selection of interpolation knot in described step (2).
5. the liver R2* figure measuring method based on question blank according to claim 4, is characterized in that: interpolation knot
Figure 492097DEST_PATH_IMAGE018
; When
Figure 700968DEST_PATH_IMAGE019
time interval be chosen as 0.1, when time interval be chosen as 50, boundary condition is selected the boundary condition of non-node.
6. the liver R2* figure measuring method based on question blank according to claim 1, is characterized in that: described step (3) specifically adopts and based on question blank, confluent hypergeometric function carried out the curve of approximate noise correction first moment model.
CN201310742267.3A 2013-12-30 2013-12-30 Liver R2* figure measuring method based on inquiry table Active CN103714521B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201310742267.3A CN103714521B (en) 2013-12-30 2013-12-30 Liver R2* figure measuring method based on inquiry table

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201310742267.3A CN103714521B (en) 2013-12-30 2013-12-30 Liver R2* figure measuring method based on inquiry table

Publications (2)

Publication Number Publication Date
CN103714521A true CN103714521A (en) 2014-04-09
CN103714521B CN103714521B (en) 2016-08-17

Family

ID=50407465

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201310742267.3A Active CN103714521B (en) 2013-12-30 2013-12-30 Liver R2* figure measuring method based on inquiry table

Country Status (1)

Country Link
CN (1) CN103714521B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105608681A (en) * 2016-02-14 2016-05-25 电子科技大学 Liver magnetic resonance R2* parameter graph drafting method
CN113240078A (en) * 2021-04-26 2021-08-10 南方医科大学 Deep learning network-based magnetic resonance R2*Parameter quantization method, medium, and apparatus

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110338832B (en) * 2019-06-19 2020-06-02 上海全景医学影像诊断中心有限公司 18F-FDG-based simulated cerebral blood perfusion parameter imaging method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102439474A (en) * 2009-03-25 2012-05-02 皇家飞利浦电子股份有限公司 Quantification of intracellular and extracellular spio agents with r2 and r2* mapping
CN102737392A (en) * 2012-06-07 2012-10-17 南方医科大学 Non-partial regularization prior reconstruction method for low-dosage X-ray captive test (CT) image
CN103218788A (en) * 2013-04-24 2013-07-24 南方医科大学 Method for measuring liver magnetic resonance crosswise relaxation rate R2* parameter
US20130208969A1 (en) * 2012-02-10 2013-08-15 Duke University Systems and methods for automated magnetic resonance imaging

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102439474A (en) * 2009-03-25 2012-05-02 皇家飞利浦电子股份有限公司 Quantification of intracellular and extracellular spio agents with r2 and r2* mapping
US20130208969A1 (en) * 2012-02-10 2013-08-15 Duke University Systems and methods for automated magnetic resonance imaging
CN102737392A (en) * 2012-06-07 2012-10-17 南方医科大学 Non-partial regularization prior reconstruction method for low-dosage X-ray captive test (CT) image
CN103218788A (en) * 2013-04-24 2013-07-24 南方医科大学 Method for measuring liver magnetic resonance crosswise relaxation rate R2* parameter

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ANDERS KRISTOFFERSEN: "Estimating Non-Gaussian Diffusion Model Parameters in the Presence of Physiological Noise and Rician Signal Bias", 《JOURNAL OF MAGNETIC RESONANCE IMAGING》, vol. 35, no. 1, 3 October 2011 (2011-10-03) *
YANQIU FENG 等: "Improved MRI R2* Relaxometry of Iron-Loaded Liver with Noise Correction", 《MAGNETIC RESONANCE IN MEDICINE》, vol. 70, no. 6, 20 August 2013 (2013-08-20) *
胡粟 等: "磁共振 -R2*值无创评估铁过载肝脏铁含量的初步研究", 《临床放射学杂志》, vol. 31, no. 5, 20 May 2012 (2012-05-20) *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105608681A (en) * 2016-02-14 2016-05-25 电子科技大学 Liver magnetic resonance R2* parameter graph drafting method
CN105608681B (en) * 2016-02-14 2018-12-21 电子科技大学 A kind of liver magnetic resonance R2*Parameter drawing drawing method
CN113240078A (en) * 2021-04-26 2021-08-10 南方医科大学 Deep learning network-based magnetic resonance R2*Parameter quantization method, medium, and apparatus
CN113240078B (en) * 2021-04-26 2024-03-19 南方医科大学 Magnetic resonance R2 based on deep learning network * Parameter quantization method, medium and device

Also Published As

Publication number Publication date
CN103714521B (en) 2016-08-17

Similar Documents

Publication Publication Date Title
O'callaghan et al. Tissue magnetic susceptibility mapping as a marker of tau pathology in Alzheimer's disease
CN110033486B (en) Method and system for monitoring edge and volume of transparent crystal in growth process in real time
CN112462311B (en) Method and device for correcting and measuring transverse magnetization vector decay time constant, computer equipment and non-uniform field magnetic resonance system
US7276904B2 (en) Method for generating T1-weighted magnetic resonance images and quantitative T1 maps
CN104323775A (en) Magnetic resonance imaging apparatus, image processing apparatus, and image processing method
Bertram et al. Why and how we determine nephron number
US20170076449A1 (en) Free-breathing parameter mapping with high-contrast image registration
JP6765972B2 (en) Methods, instruments, and computer software for quantifying the amount of iron in tissues using diffusion-weighted magnetic resonance imaging.
CN103714521A (en) Liver R2* graph measuring method based on inquiry table
CN103135082B (en) The method of control sequence during for determining transmitted in parallel
WO2011034004A1 (en) Magnetic resonance imaging device and method for applying a gradient magnetic field
Donker et al. Quantitative 1H-NMR imaging of water in white button mushrooms (Agaricus bisporus)
JP2017517325A5 (en)
DE102014223734B4 (en) Correction of imaging techniques in a magnetic resonance device
Ebbers et al. Higher order weighted least-squares phase offset correction for improved accuracy in phase-contrast MRI
DE102020212173A1 (en) Method for acquiring reference data for phase correction in magnetic resonance technology
DE102014206398B4 (en) Magnetic resonance imaging method for at least two separate radio-frequency transmitting coils with time-delayed slice-selective excitation pulses
DE102017207128A1 (en) Method for recording a magnetic resonance data set, data carrier and magnetic resonance system
Bidault et al. Rapid MR imaging of cryoprotectant permeation in an engineered dermal replacement
CN105608681B (en) A kind of liver magnetic resonance R2*Parameter drawing drawing method
CN109814056B (en) Method for acquiring accurate quantitative magnetic resonance image
DE102014219780B4 (en) Determination of time windows in a measurement sequence
CN106659420A (en) Magnetic resonance imaging device
Amiri et al. Novel imaging phantom for accurate and robust measurement of brain atrophy rates using clinical MRI
CN104331681B (en) Method for obtaining contrast agent relaxation time

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant