CN113985334A - Method for evaluating signal-to-noise ratio of magnetic resonance scanning image - Google Patents
Method for evaluating signal-to-noise ratio of magnetic resonance scanning image Download PDFInfo
- Publication number
- CN113985334A CN113985334A CN202111311767.2A CN202111311767A CN113985334A CN 113985334 A CN113985334 A CN 113985334A CN 202111311767 A CN202111311767 A CN 202111311767A CN 113985334 A CN113985334 A CN 113985334A
- Authority
- CN
- China
- Prior art keywords
- roi
- magnetic resonance
- image
- snr
- signal
- 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.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- High Energy & Nuclear Physics (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- General Physics & Mathematics (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The invention discloses an evaluation method of a magnetic resonance scanning image signal to noise ratio, which is applied to the field of magnetic resonance and aims at the problem that the prior art does not carry out accurate quantification aiming at the selection process and method of an ROI so as to cause lower SNR accuracy, and the invention obtains the ROI area occupation ratio with the maximum SNR by establishing the relationship between different ROI occupation ratios and the SNR; then, respectively searching the optimal ROI area ratio of two synthetic images obtained by magnetic resonance scanning, and solving the signal mean value S of the ROI areas of the two synthetic images; obtaining a background data mean value Sc obtained after subtraction of ROI areas of the two synthetic images; the SNR of the synthesized image in the ROI region was determined from the SNR of 1.414 × S/Sc.
Description
Technical Field
The invention belongs to the field of magnetic resonance, and particularly relates to a technology for evaluating the signal-to-noise ratio of a magnetic resonance scanning image.
Background
The image signal-to-noise ratio is an important index of the performance of a medical magnetic resonance system, is a quantitative index for technical judgment of the admission authentication of magnetic resonance equipment by various authentication organizations, and is a method basis for providing self-evaluation of the performance, particularly the performance of a coil, by a magnetic resonance manufacturer.
NEMA sets forth a series of criteria for magnetic resonance imaging, among which are the magnetic resonance imaging signal-to-noise ratio criteria NEMA MS1-2008, NEMA MS 6-2008, NEMA MS 9-2008, which operate on single channel body coils, single channel non-body coils (so-called "surface coils"), and phased array coils, respectively. The standard defines methods for evaluating magnetic resonance image signal-to-noise ratio imaging conditions and measuring the signal-to-noise ratio. The steps of measuring the signal-to-noise ratio all need to go through the following two steps:
in a first step, an image signal is determined. Performing a signal image scan must be performed according to standard-specified conditions, but allows for calibration of system parameters as in a conventional diagnostic scan. After scanning to obtain an image, the pixel value of a region of interest (MROI) is measured on average to obtain the image signal size.
And secondly, determining the noise size.
The ROI is generally selected from a water model area of an image or from 4 corners of a two-dimensional image, but the prior publication does not describe the precise quantification of the ROI selection process and method; .
Although the existing NEMA standard provides for the measurement of the signal-to-noise ratio of a multi-channel coil, the existing NEMA standard only adopts an evaluation method of a single-channel coil and does not provide for the multi-channel coil to be suitable for the characteristics of the multi-channel coil. With the development of multi-channel coils, the NEMA standard for the characteristics of multi-channel coils is urgently needed to be perfected.
The patent CN 110693498A "multi-core magnetic resonance system lung gas imaging signal-to-noise ratio/uniformity testing apparatus" discloses a multi-core magnetic resonance system lung gas imaging signal-to-noise ratio/uniformity testing apparatus, which includes a human body lung imaging coil, a human body lung hyperpolarized gas imaging signal-to-noise ratio/uniformity testing mold body, and a ring-cylindrical human body lung loading mold body sleeved outside the human body lung hyperpolarized gas imaging signal-to-noise ratio/uniformity testing mold body, wherein the human body lung imaging coil is arranged outside the human body lung loading mold body, and the human body lung hyperpolarized gas imaging signal-to-noise ratio/uniformity testing mold body includes an air chamber housing and an air bag arranged in the air chamber housing. The invention adopts hyperpolarized gas as imaging medium, which is closer to the actual situation of lung magnetic resonance imaging; the hyperpolarized gas in the human lung hyperpolarized gas imaging signal-to-noise ratio/uniformity testing model is fast in diffusion, and the imaging signal is strong and stable; a complex vacuumizing device is not needed, and the air suction and inflation operations are simple and convenient; the human lung imaging coil is effectively loaded by matching with a human lung loading mold body. However, the patent does not relate to the imaging image ROI selection and the calculation process of SNR of the water model ROI area of derivative scanning.
Disclosure of Invention
The invention provides an evaluation method for the signal-to-noise ratio of a magnetic resonance scanning image, which aims to solve the problem that the prior art lacks of effectively evaluating the signal-to-noise ratio of a synthesized image obtained by scanning a homogeneous water model by a multi-channel coil.
The technical scheme adopted by the invention is as follows: a method for evaluating the signal-to-noise ratio of a magnetic resonance scanning image comprises the following steps:
s1, performing two magnetic resonance scans on the water model through a nuclear magnetic resonance instrument to obtain two synthetic images;
s2, determining the respective mean water model ROI selection range of the two synthetic images;
and (3) the homogeneous water models of the two synthetic images are both circular, and the ROI selection range is determined in the following process: recording the radius of a circular homogeneous water mold as R, taking the center of the water mold as a base point to diffuse outwards, setting the radius as R/n ^2,2R/n ^2, … and R respectively, and setting n ^2 circular areas in total, wherein each circular area corresponds to a ROI range to be determined to obtain n ^2 ROIs so as to obtain n ^2 groups of SNR values;
s3, taking the proportion value of each ROI to be determined in the total area of the homogeneous water model image as an abscissa and the image SNR as an ordinate, thereby constructing a first graph;
s4, respectively selecting ROI ranges to be determined corresponding to the curve inflection points in the first graphs corresponding to the two synthetic images as ROI areas of the two synthetic images;
and S5, obtaining the imaging quality estimation of the nuclear magnetic resonance instrument according to the ROI of the two synthetic images.
The process of obtaining the composite image in step S1 is:
firstly, Fourier transform is carried out on K space data of a plurality of channels respectively to convert the K space data into image data, and an absolute value is taken for each pixel value in the image data;
then, squaring each pixel value of a plurality of receiving channel images respectively;
secondly, summing the pixel squares of the corresponding positions of the plurality of receiving channel images, then squaring to obtain the pixel value of a composite image, and finally outputting the composite image.
Step S5 specifically includes the following substeps:
s51, solving a signal mean value S of the ROI of each of the two synthetic images;
s52, solving a background data mean value Sc obtained by subtracting ROI areas of the two synthesized images;
s53, the SNR of the image after the nuclear magnetic resonance instrument synthesizes any water model in the ROI is obtained according to the following formula:
SNR=1.414*S/Sc。
the value of n in step S1 ranges from 4 to 10.
The invention has the beneficial effects that: according to the method, the ROI capable of measuring the maximum SNR is selected, so that the measured SNR error is smaller, and the imaging quality of the nuclear magnetic resonance instrument can be more accurately evaluated; the method of the invention comprises the following advantages:
1. the method selects the ROI capable of obtaining the highest SNR as the optimized scale for measuring the SNR of the image, reduces the randomness for calculating the SNR of the magnetic resonance image, and is convenient for different experimental groups to carry out transverse comparison on the SNR of the image;
2. aiming at sub-channel K space data acquired by a multi-receiving-channel coil, a process of synthesizing an image is provided; on the basis of optimizing ROI selection, the invention provides a method for calculating the signal-to-noise ratio of the synthesized image; an approach for SNR derivation of multi-receive channel derived subchannel K-space data into an image is indicated.
Drawings
FIG. 1 is a plot of area fraction of different ROIs versus SNR obtained from testing;
FIG. 2 is a schematic diagram of selecting the ROI area ratio that yields the highest SNR;
fig. 3 is a flowchart of converting a plurality of pieces of K-space data into a plurality of pieces of image data and synthesizing one image.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
The method can accurately evaluate the imaging quality of the nuclear magnetic resonance instrument, specifically adopts a prefabricated homogeneous water model as a scanning object, and obtains the imaging quality evaluation result of the nuclear magnetic resonance instrument by calculating the SNR of the homogeneous water model.
When calculating the signal-to-noise ratio of the homogeneous water model, selecting a proper ROI (region of interest) is a key first step. The two-dimensional image water model area formed by scanning the homogeneous water model is circular, and the radius of the circular homogeneous water model is R. The mean value water model ROI selection range is outward diffused by taking the center of a circular homogeneous water model as a base point, the radius is respectively set to be R/64,2R/64, … and R, 64 circular regions are totally set, 64 ROIs are obtained, and the proportion of the area of each ROI region to the total area of the water model image is respectively (1/64) ^2, (2/64) ^2, … and (64/64) ^ 2. At the water model ROI obtained at all radius settings, 64 sets of SNR values were obtained, respectively.
As will be understood by those skilled in the art, for a given test object in the present embodiment of the homogeneous water mode, the number of the circular areas is not limited to 64 given in the present embodiment, and in practical application, the number may be n ^2, where n is a value range: 4 to 10.
And taking the proportion value of the selected ROI in the total area of the homogeneous water model image as an abscissa and the image SNR as an ordinate to obtain a two-dimensional graph shown in the figure 1.
The front end of the acquisition signal is provided with a receiving coil, and the receiving coil acquires and obtains magnetic resonance induction signals of a plurality of receiving channels. The receiving coil transmits the received signals to the spectrometer receiving board, the spectrometer receiving board is responsible for analog-to-digital conversion of the received signals, then the digital signals are transmitted to the reconstruction unit, K space data are obtained in the reconstruction unit, and then the K space data are converted into image data. The water mold is placed in the center of the receiving coil.
When the water model is scanned, the coil with a plurality of receiving channels is used for receiving signals, each channel acquires K space data (the K space data is composed of a plurality of magnetic resonance echo signals, each phase encoding step corresponds to one echo signal), each K space data is converted into image data through Fourier transform, and then the sub-channel image data are synthesized into an image.
For a homogeneous water model image synthesized by a plurality of receiving channels, due to the near-coil effect, a signal close to the surface of a coil is stronger than a signal at the center of a water model, so that the rule that the closer the edge of the water model is, the larger the signal value is presented. If the ROI is chosen to be particularly large, especially near the water phantom edge, edge noise will be introduced due to the water phantom housing material. For the reasons set forth above, the ROI should be selected as large as possible, and the ROI that can obtain the highest SNR is selected as a scale for measuring the SNR of the image.
In this embodiment, the shell material of the water mold is acrylic plastic.
The method selects the ROI capable of obtaining the highest SNR as the scale for measuring the image SNR, and the radius is selected to be 62R/64 as shown in the example of figure 1, namely the ROI accounts for 93.85% of the total area of the homogeneous water model, the corresponding ordinate is the SNR inflection point of the image SNR, and the highest image SNR is obtained at the inflection point. The selected ROI is shown in fig. 2 with the area within the bold black circle. Of course, the above example data is only for illustrating the process of selecting the optimal ROI for calculating SNR, and does not mean that the radius 62R/64 or the scaling factor 93.85% must be selected, because the distribution of the image signal intensity after scanning is affected by different types of homogeneous water modes, different types of radio frequency coils, and different combinations of parameters of the magnetic resonance scanning sequence, so that the inflection point of fig. 1 may appear at other positions.
In the embodiment, the homogenizing water mold is spherical, and the diameter of the homogenizing water mold is 200 mm; the water model liquid comprises: 10mol/mL nickel chloride (NiCl4) +75mol/mL sodium chloride (NaCl).
The receiving radio frequency coil in this embodiment uses an 8-channel head coil. The specification of the receiving coil is an 8-channel phased array head coil (crown structure design, upper and lower structures are separable), the size and the shape of eight receiving units of the head coil are similar, the distribution is uniform, and the structural design of the coil is beneficial to improving the SNR and the uniformity of a scanned image. The coil size is 340mm long and 240mm diameter.
The scanning parameters in this embodiment are:
TR 250.0ms, TE 8.0ms, repetition scanning frequency (Average) 2, receiving channel number 8, radio frequency excitation selection layer number (excitation slices) 4, Phase Matrix (Phase Matrix) 220, Read Matrix (Read Matrix) 440, Phase FOV (Phase FOV) 220mm, Read FOV (Read FOV) 220mm, Phase Resolution (Phase Resolution) 1.0mm, Read Resolution (Read Resolution) 0.5mm, Flip angle (Flip angle) 70.0 degrees, Thickness (Thickness) 8.0mm, and layer gap (Slice) 3.0 mm.
The invention will also use the same model homogeneous water model as the scanning object, and use the same RF coil and the same magnetic resonance sequence scanning parameter combination, therefore, the following calculation of the scanning image SNR will select 93.85% of the water model area as the ROI.
Each time the magnetic resonance sequence is scanned, K-space data of a plurality of receiving channels are obtained, so that images of the plurality of receiving channels need to be combined into one image. As shown in fig. 3, a process of converting K space data obtained by scanning a plurality of receiving channels into 1 synthesized image includes, first, performing fourier transform on the K space data of the plurality of channels respectively to convert the K space data into image data, taking an absolute value of each pixel value, then squaring each pixel value of the image of the plurality of receiving channels respectively, then summing the pixel squares of corresponding positions of the image of the plurality of receiving channels, and then squaring to obtain a pixel value of a synthesized image, and finally outputting the synthesized image.
The evaluation process of the imaging quality of the nuclear magnetic resonance instrument according to the optimal ROI of the water model synthetic image comprises the following steps:
the following benefits are obtained by repeating the scanning and subtracting twice to obtain the signal-to-noise ratio: the background noise after the subtraction of the ROI area of the water model is scanned twice mainly reflects random noise, and structural noise errors can be eliminated, so that the method performs scanning twice, and subtracts the images synthesized by data obtained by 2 times of scanning to obtain the SNR. And repeatedly scanning the same layer of the water model twice to obtain a synthesized image a and a synthesized image b, and subtracting the two images to obtain Sc.
Definition S is: scanning the signal mean value of a water model ROI (region of interest) area twice before subtraction;
sc is defined as: and (4) scanning the background data mean value after subtraction of the water model ROI area twice.
The SNR of the synthesized image in the ROI region is finally obtained as:
SNR=1.414*S/Sc
the SNR of the composite image in the ROI region finally obtained in this embodiment is generally any composite image obtained by a specific test object.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (5)
1. A method for evaluating the signal-to-noise ratio of a magnetic resonance scanning image is characterized by comprising the following steps:
s1, performing two magnetic resonance scans on the water model through a nuclear magnetic resonance instrument to obtain two synthetic images;
s2, determining the respective mean water model ROI selection range of the two synthetic images;
s3, taking the proportion value of each ROI selection range in the total area of the homogeneous water model image as an abscissa, and taking the image SNR corresponding to each ROI selection range as an ordinate, thereby constructing a first graph;
s4, respectively selecting ROI selection ranges corresponding to the curve inflection points in the first graphs corresponding to the two synthetic images as ROI areas of the two synthetic images;
and S5, obtaining the imaging quality estimation of the nuclear magnetic resonance instrument according to the ROI of the two synthetic images.
2. The method for evaluating the signal-to-noise ratio of the magnetic resonance scan image according to claim 1, wherein the step S1 of obtaining the composite image comprises:
firstly, Fourier transform is carried out on K space data of a plurality of channels respectively to convert the K space data into image data, and an absolute value is taken for each pixel value in the image data;
then, squaring each pixel value of a plurality of receiving channel images respectively;
secondly, summing the pixel squares of the corresponding positions of the plurality of receiving channel images, then squaring to obtain the pixel value of a composite image, and finally outputting the composite image.
3. The method for evaluating the signal-to-noise ratio of the magnetic resonance scan image according to claim 2, wherein n in step S1 is in a range of 4 to 10.
4. The method according to claim 3, wherein the homogeneous water phantom of each of the two synthetic images is circular, and the ROI selection range is determined by: the radius of a circular homogeneous water mold is recorded as R, the water mold center is taken as a base point to diffuse outwards, the radii are respectively set as R/n ^2,2R/n ^2, … and R, n ^2 circular areas are set in total, each circular area corresponds to a ROI range to be determined, n ^2 ROIs are obtained, and therefore n ^2 groups of SNR values are obtained.
5. The method for evaluating the signal-to-noise ratio of the magnetic resonance scan image according to claim 4, wherein the step S5 comprises the following sub-steps:
s51, solving a signal mean value S of the ROI of each of the two synthetic images;
s52, solving a background data mean value Sc obtained by subtracting ROI areas of the two synthesized images;
s53, the SNR of the image after the nuclear magnetic resonance instrument synthesizes any water model in the ROI is obtained according to the following formula:
SNR=1.414*S/Sc。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111311767.2A CN113985334A (en) | 2021-11-08 | 2021-11-08 | Method for evaluating signal-to-noise ratio of magnetic resonance scanning image |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111311767.2A CN113985334A (en) | 2021-11-08 | 2021-11-08 | Method for evaluating signal-to-noise ratio of magnetic resonance scanning image |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113985334A true CN113985334A (en) | 2022-01-28 |
Family
ID=79746997
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111311767.2A Withdrawn CN113985334A (en) | 2021-11-08 | 2021-11-08 | Method for evaluating signal-to-noise ratio of magnetic resonance scanning image |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113985334A (en) |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101322648A (en) * | 2008-07-29 | 2008-12-17 | 四川大学华西医院 | Method for measuring stability and imaging index of nuclear magnetic resonance imaging equipment |
CN102749600A (en) * | 2012-05-30 | 2012-10-24 | 苏州安科医疗***有限公司 | Synthetic method of magnetic resonance multi-channel image |
CN102800057A (en) * | 2012-09-18 | 2012-11-28 | 苏州安科医疗***有限公司 | Image denoising method based on phase equalization for magnetic resonance imaging |
US20170371142A1 (en) * | 2015-01-13 | 2017-12-28 | Massachusetts Institute Of Technology | Circular Scanning Technique For Large Area Inspection |
US20180210053A1 (en) * | 2015-07-16 | 2018-07-26 | Synaptive Medical (Barbados) Inc. | Systems and Methods for Adaptive Multi-Resolution Magnetic Resonance Imaging |
CN110215209A (en) * | 2019-04-29 | 2019-09-10 | 上海联影医疗科技有限公司 | A kind of MR imaging method and magnetic resonance imaging system |
CN110322408A (en) * | 2019-06-11 | 2019-10-11 | 浙江大学 | Multicenter magnetic resonance image automated quality control method based on cloud platform |
-
2021
- 2021-11-08 CN CN202111311767.2A patent/CN113985334A/en not_active Withdrawn
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101322648A (en) * | 2008-07-29 | 2008-12-17 | 四川大学华西医院 | Method for measuring stability and imaging index of nuclear magnetic resonance imaging equipment |
CN102749600A (en) * | 2012-05-30 | 2012-10-24 | 苏州安科医疗***有限公司 | Synthetic method of magnetic resonance multi-channel image |
CN102800057A (en) * | 2012-09-18 | 2012-11-28 | 苏州安科医疗***有限公司 | Image denoising method based on phase equalization for magnetic resonance imaging |
US20170371142A1 (en) * | 2015-01-13 | 2017-12-28 | Massachusetts Institute Of Technology | Circular Scanning Technique For Large Area Inspection |
US20180210053A1 (en) * | 2015-07-16 | 2018-07-26 | Synaptive Medical (Barbados) Inc. | Systems and Methods for Adaptive Multi-Resolution Magnetic Resonance Imaging |
CN110215209A (en) * | 2019-04-29 | 2019-09-10 | 上海联影医疗科技有限公司 | A kind of MR imaging method and magnetic resonance imaging system |
CN110322408A (en) * | 2019-06-11 | 2019-10-11 | 浙江大学 | Multicenter magnetic resonance image automated quality control method based on cloud platform |
Non-Patent Citations (3)
Title |
---|
孙琦,马超,董敏俊等: "感兴趣区大小选择对多形性腺瘤和腺淋巴瘤ADC测量的影响", 《中国口腔颌面外科杂志》 * |
孟奥等: "基于Matlab实现MRI信噪比及图像均匀性自动检测***的研究", 《中国医学装备》 * |
杨克柽: "CT值均匀性评价方法研究", 《中国测试》 * |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Iima et al. | Diffusion MRI of the breast: Current status and future directions | |
JP5361160B2 (en) | Measurement sequence and magnetic resonance apparatus for three-dimensional magnetic resonance imaging | |
KR101604162B1 (en) | Method to generate magnetic resonance exposures | |
JP6084573B2 (en) | MR imaging using multipoint Dixon technology | |
US9146293B2 (en) | Methods and apparatus for accurate characterization of signal coil receiver sensitivity in magnetic resonance imaging (MRI) | |
US7495437B2 (en) | Method for MRT imaging on the basis of conventional PPA reconstruction methods | |
KR101663389B1 (en) | Reconstruction of raw magnetic resonance data | |
CN101636663B (en) | Magnetic resonance device and method | |
CN102670201B (en) | For producing method and the magnetic resonance tomography system of magnetic resonance image data | |
US8558547B2 (en) | System and method for magnetic resonance radio-frequency field mapping | |
US7265545B2 (en) | Method and apparatus for accelerated spiral-coded imaging in magnetic resonance tomography | |
JP2006297110A (en) | Magnetic resonance imaging method and device | |
US20120038673A1 (en) | Magnetic resonance imaging apparatus and method for displaying running direction of fibrous tissue | |
US20070241751A1 (en) | PPA MRT method and apparatus with scaled contribution of k-space data to the reconstructed image | |
JP4698231B2 (en) | Magnetic resonance diagnostic equipment | |
CN107479015B (en) | Method and system for magnetic resonance calibration scanning sequence configuration and image acquisition | |
CN113985334A (en) | Method for evaluating signal-to-noise ratio of magnetic resonance scanning image | |
US6483306B2 (en) | Magnetic resonance tomography apparatus and method for generating a magnetic resonance image of a subject with different pulse sequences in K-space | |
CN109471053A (en) | A kind of dielectric property iteration imaging method based on double constraints | |
CN109146802A (en) | A kind of uniformity correcting method and device of magnetic resonance image | |
CN113197566B (en) | In-vivo positioning method and device of portable nuclear magnetic resonance detection system, computer equipment and nuclear magnetic resonance detection system | |
CN115902733A (en) | Method for optimizing and accelerating chemical exchange saturation transfer Z spectrum magnetic resonance detection | |
CN114216920A (en) | Magnetic resonance molecular imaging method and system for dynamically reading CEST signal accumulation | |
US11698432B2 (en) | Magnetic resonance imaging system, and main magnetic field correction method therefor and storage medium | |
CN111090067B (en) | Method and system for sampling K-space data in magnetic resonance imaging |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WW01 | Invention patent application withdrawn after publication | ||
WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220128 |