CN107783067B - Magnetic resonance imaging method - Google Patents
Magnetic resonance imaging method Download PDFInfo
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- CN107783067B CN107783067B CN201610766073.0A CN201610766073A CN107783067B CN 107783067 B CN107783067 B CN 107783067B CN 201610766073 A CN201610766073 A CN 201610766073A CN 107783067 B CN107783067 B CN 107783067B
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- 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
- G01R33/28—Details of apparatus provided for in groups G01R33/44 - G01R33/64
- G01R33/32—Excitation or detection systems, e.g. using radio frequency signals
- G01R33/34—Constructional details, e.g. resonators, specially adapted to MR
- G01R33/34046—Volume type coils, e.g. bird-cage coils; Quadrature bird-cage coils; Circularly polarised coils
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Abstract
The invention discloses a magnetic resonance imaging method, which comprises the following steps: exciting a target region of a detected person by using an imaging sequence, and acquiring a magnetic resonance signal generated by the target region by using a multi-channel RF coil to acquire multi-channel acquired K space data; performing Fourier transform on the K space data to obtain a multi-channel image; calculating a sensitivity of each channel RF coil from the multi-channel image; acquiring the signal-to-noise ratio of a pixel point of a multi-channel image, and weighting the sensitivity of each channel RF coil according to the signal-to-noise ratio; and weighting the multichannel images according to the sensitivity of each channel RF coil after weighting, and then combining the weighted multichannel images. The invention uses the signal-to-noise ratio of the multi-channel image to carry out weighting processing on the sensitivity of the RF coil, thereby reducing the influence of background noise.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of magnetic resonance imaging for medical diagnosis, in particular to a magnetic resonance imaging method based on multiple channels.
[ background of the invention ]
Compared with other medical imaging modes such as Computed Tomography (CT), Ultrasound (UT) and X-ray imaging (XR), the magnetic resonance imaging technology has the characteristics of non-invasiveness, no ionization, no radiation and the like, and the obtained image is clear and fine, has high resolution and good contrast, particularly has good soft tissue level display, and can greatly improve the diagnosis efficiency. The radio frequency receiving coil is used as the foremost end of a receiving chain, plays a very important role in imaging quality, and directly influences the signal-to-noise ratio of an image.
The multi-coil imaging technology is a new magnetic resonance imaging technology developed in recent years, is based on a multi-channel phased array coil, and has the characteristics of high signal-to-noise ratio, high image spatial resolution and the like. Generally, a phased array coil is a coil array composed of two or more coil units, each of which can receive signals of respective areas simultaneously. The magnetic resonance signals obtained by the multi-channel RF coil are effectively weighted sum of signals obtained by each coil, but the noise is only from a small area determined by each coil unit, so the image obtained by the multi-channel RF coil has higher signal-to-noise ratio. Multi-coil imaging combines the advantages of small coil imaging with the large scan field of view of large coil imaging.
The current multi-channel combination method mainly comprises a classical sum of squares algorithm (SOS) and a coil sensitivity-based channel combination method. The classical sum of squares algorithm cannot well inhibit external noise, and the synthesized image has signal deviation; the rectification effect of the noise makes the signal deviation more obvious in the region where the signal intensity is lower than the noise intensity, so that the signal-to-noise ratio of the image is reduced, the background noise is high, and the phase diagram cannot be reconstructed. The channel synthesis method based on coil sensitivity is mainly executed in an image domain, and images formed by a plurality of phased array coils are combined with respective special space sensitivity information in an image space to generate a full-field image without aliasing artifacts like a traditional imaging mode. However, the sensitivity information of the receiving coil and the information of the imaged object in the echo signals acquired by the magnetic resonance system are interwoven, and it is very difficult to effectively separate the MR signals of the imaged object and the sensitive signals reflecting the objective attributes of the coil in the actual imaging process, so the sensitivity map estimated in the imaging process often contains the tissue information of the imaged object, and the accuracy of map estimation is seriously affected. On the other hand, in areas where the phase is not smooth, especially in areas where the SNR is low, signal cancellation artifacts may be caused. In view of this, there is a need for an improvement of existing multi-channel magnetic resonance imaging methods.
[ summary of the invention ]
The invention aims to provide a magnetic resonance imaging method which has high signal-to-noise ratio and no destructive artifact.
The technical scheme adopted by the invention for solving the technical problems is as follows: a magnetic resonance imaging method comprising the steps of:
exciting a target region of a detected person by using an imaging sequence, and acquiring a magnetic resonance signal generated by the target region by using a multi-channel RF coil to acquire multi-channel acquired K space data;
performing Fourier transform on the K space data to obtain a multi-channel image;
calculating a sensitivity of each channel RF coil from the multi-channel image;
acquiring the signal-to-noise ratio of a pixel point of a multi-channel image, and weighting the sensitivity of each channel RF coil according to the signal-to-noise ratio;
and weighting the multichannel images according to the sensitivity of each channel RF coil after weighting, and then combining the weighted multichannel images.
Further, the method further comprises performing phase preprocessing on the multi-channel image, wherein the phase preprocessing comprises:
removing a phase difference between the multi-channel RF coils; or
And removing the signal phase of the multi-channel image to obtain image data only containing the noise phase.
Further, the K-space data is obtained by full sampling.
Further, the multi-channel K-space data is obtained by undersampling.
Furthermore, interpolation processing is carried out on the sensitivity of each channel RF coil after weighting processing, and the sensitivity of the RF coil corresponding to the virtual full-sampling K space data is obtained.
Further, the signal-to-noise ratio of the multi-channel image pixel point is obtained through the following steps:
calculating a norm value of each pixel point of the multi-channel image;
classifying the pixel points of the multi-channel image based on the norm values and the noise norm thresholds of the pixel points, wherein the noise norm threshold comprises a first noise norm threshold and a second noise norm threshold, and
when the norm value of the pixel point is larger than the first noise norm threshold value, the pixel point is made to be a first-class pixel point, and the first-class pixel point is distributed with a first signal-to-noise ratio;
and when the norm value of the pixel point is smaller than the second noise norm threshold value, enabling the pixel point to be a second type pixel point, and distributing a second signal-to-noise ratio to the second type pixel point.
Further, the multi-channel image further comprises a third type of pixel point, the norm value of the third type of pixel point is smaller than the first noise norm threshold and larger than the second noise norm threshold, and the signal-to-noise ratio of the third type of pixel point is obtained by the following method:
selecting a neighborhood where the third type pixel points are located in the multi-channel image;
determining a set of pixel points of which the norm values in the selected neighborhood are smaller than a first noise norm threshold;
and calculating the mean value of pixel point norms in the pixel point set, and acquiring the signal-to-noise ratio of the third type of pixel points according to the mean value of the pixel point norms.
Further, whether the number of the pixel points contained in the pixel point set exceeds a set range is judged, and if yes, a first signal-to-noise ratio is distributed to the third type of pixel points.
Further, the noise norm threshold is obtained by the following process:
acquiring the noise variance of a multi-channel image, and calculating the mean value of the noise norm of the multi-channel image according to the noise variance of the multi-channel image;
calculating the variance of the noise norm of the multi-channel image according to the noise variance of the multi-channel image and the mean value of the noise norm of the multi-channel image;
and acquiring a threshold value of the noise norm according to the mean value and the variance of the noise norm of the multi-channel image.
Further, the method also comprises the step of carrying out filtering processing on the multi-channel image.
Compared with the prior art, the invention has the beneficial effects that: acquiring a threshold of a noise norm of the multi-channel image in a norm domain, and classifying pixel points of the multi-channel image by comparing the threshold of the noise norm of the image with a norm value of pixel points of the multi-channel image, so that a region with low signal-to-noise ratio and a region with high signal-to-noise ratio can be identified; noise pixel points and signal pixel points of the multi-channel image are identified in a classified mode, a low signal-to-noise ratio is distributed to the noise pixel points, a high signal-to-noise ratio is distributed to the signal pixel points, and noise interference is suppressed to a certain extent; the sensitivity of each channel coil is weighted according to the signal-to-noise ratio of the multi-channel image, different weighted values are distributed to noise pixel points and signal pixel points, signal cancellation artifacts can be effectively inhibited, magnetic resonance signals after multi-channel combination are close to true values, and the multi-channel combination acquired image is more reliable.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only some embodiments of the invention, and it is obvious for a person skilled in the art that the invention can also be applied to other similar scenarios according to these drawings without inventive effort. Unless otherwise apparent from the context of language or otherwise indicated, like reference numerals in the figures refer to like structures and operations.
Figure 1 is a basic flow diagram of magnetic resonance imaging;
FIG. 2 is a flow chart of a magnetic resonance imaging method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a Hanning window structure used in accordance with an embodiment of the present invention;
FIG. 4 is a flowchart of a method for threshold acquisition of a noise norm of a multi-channel image;
FIG. 5 is a schematic diagram of a K-space structure according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a method for calculating SNR of pixels of a third type according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating a method for calculating SNR of pixels of a third type according to another embodiment of the present invention;
FIG. 8a is a head image formed by multi-pass merging using a prior art SOS method;
FIG. 8b is a head image formed by multi-channel merging using a conventional adaptive channel merging method;
FIG. 8c is a head image formed by multi-pass merging using the method of the present invention;
FIG. 9a is an abdomen image formed by multi-channel merging using the SOS method;
FIG. 9b is an abdomen image formed by multi-channel merging using the adaptive channel merging method;
fig. 9c is an abdominal image formed by multi-channel merging using the method of the present invention.
[ detailed description ] embodiments
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. It should be understood that these exemplary embodiments are given only for the purpose of enabling those skilled in the relevant art to better understand and to implement the present invention, and are not intended to limit the scope of the present invention in any way.
The Magnetic Resonance (MR) signals generated by the excitation of the body part of the subject by the radio frequency pulses contain information from the radio frequency coil, and therefore the acquired MR signals need to be frequency-encoded and phase-encoded to achieve spatial localization. The general procedure of magnetic resonance imaging mainly comprises the steps as shown in figure 1: pulse signals generated by radio frequency excitation are received by a radio frequency coil through space coding to obtain MR signals with space positioning coding information, and a filling space formed by original data of the MR signals is a K space; the data of the K space is subjected to Fourier transform, so that the spatial positioning coding information in the original data can be decoded, MR signals with different frequencies, phases and amplitudes are decomposed, the different frequencies and the phases represent different spatial positions, the amplitudes represent the MR signal intensity, and the MR digital information with different frequencies, phases and signal intensities is distributed to corresponding pixels to obtain MR image data, namely, an MR image is reconstructed.
The induced voltage of the magnetic resonance signal generated by the object in the receiving coil near the object is closely related to the corresponding spatial position, and the signal strength difference caused by the spatial position is called the sensitivity of the coil. The multi-coil (multi-channel) imaging technology comprising a multi-channel acquisition technology and a parallel imaging algorithm greatly improves the speed of acquiring and imaging magnetic resonance signals. The multi-coil imaging technology mainly utilizes the sensitivity (spatial sensitivity) difference of each receiving coil in the phased array coil to encode spatial information, reduces the gradient encoding step number (mainly phase encoding step number) required by imaging, and obtains faster scanning speed.
The acquisition of the MR image is actually the acquisition and filling of the phase encoding lines of K-space, the more phase encoding lines that the K-space needs to acquire, the longer the acquisition time of the image. Therefore, the density of the phase encoding lines determines the field of view (FOV) of the image in the phase encoding direction, the rectangular FOV technology can reduce the phase encoding lines required to be acquired, the acquired phase encoding lines are sparsely filled in the whole K space, the acquisition time can be proportionally shortened on the premise of keeping the resolution of the image space unchanged, and the FOV of the image in the phase encoding direction is reduced. For a 256 × 256 (pixel), if only 50% (i.e., 128) phase encoding lines are collected and filled in the entire K space, the collection time is only half of the original time, and the FOV of the image in the phase encoding direction is only 50% of the original FOV. The magnetic resonance imaging method of the invention adopts the multi-channel RF coils to acquire the magnetic resonance signals, each channel RF coil has corresponding (space) sensitivity, and the sensitivities of the channel RF coils are different, thereby the acquired magnetic resonance signals have extra image space information. For example, for a magnetic resonance imaging system with 32 independent (receive) channels, the image acquisition speed can theoretically be increased by a factor of 32 compared to a conventional magnetic resonance system that acquires with a single RF coil. However, the existing multi-channel imaging technology has the problems of poor signal-to-noise ratio, easy generation of destructive artifacts and the like.
Based on the above problem, a magnetic resonance imaging method according to an embodiment of the present invention is shown in fig. 2, and includes:
step 201, exciting a target region of a subject by using an imaging sequence, acquiring a magnetic resonance signal generated in the target region by using a multi-channel RF (radio frequency) coil, and acquiring K-space data acquired by multiple channels, wherein each channel of the multi-channel RF coil has a corresponding coil sensitivity to the magnetic resonance signal. A multi-channel RF (Radio Frequency) coil may comprise a plurality of Radio Frequency receive coils, which may also be referred to as a coil assembly. In this particular embodiment, each channel of the multi-channel RF coil may consist of a single coil in the array. Illustratively, the multi-channel RF coil composed of the transmitting coil or the receiving coil for magnetic resonance signal acquisition may be a local coil array such as a breast coil, a shoulder coil, a head coil, a spine coil or an abdomen coil, and may also be a body coil such as a VTC. The plurality of coils form a coil array for acquisition of parallel imaging data, and a single coil corresponds to a single channel of the multi-channel RF coil. If the head coil composed of 32 coils comprises the head coil, the number of the channels of the head coil is 32 channels; the spine coil comprises a 4 x 4 coil array consisting of 16 coils, and the number of channels of the spine coil is 16 channels.
Illustratively, in one embodiment, the coil array employed by the magnetic resonance system includes Q channels, specifically, Q may be 32, and B may bei(x,y)Representing the sensitivity information (1 ≦ i ≦ Q) corresponding to the ith channel coil at the position point (x, y), the frequency domain signal strength can be expressed as:
wherein S isi(kx,ky) Representing the K space data acquired by the ith channel coil; b isi(x, y) represents the sensitivity information corresponding to the ith channel coil at the position (x, y); c (x, y) represents the spin density at position (x, y); (k)x,ky) Is the K-space coordinate corresponding to the image space position (x, y). Alternatively, the above K-space data acquisition may employ conventional spin echo techniques, i.e. each 90-degree rf pulse only fills one phase encoding line in K-space, the length of the phase encoding line in the frequency encoding direction being proportional to the product of the readout gradient field amplitude and the action time, the position being determined by the product of the phase encoding gradient field amplitude and the action time.
The K space data acquired by multiple channels can be fully sampled or undersampled, and operations such as low-pass filtering processing and the like can also be performed. The K-space corresponding to the multi-channel RF coil may be filled as follows: magnetic resonance signal acquisition is carried out by using a Hermitian symmetry principle of K space, half of K space phase encoding lines are acquired firstly, and then the symmetry of the K space is used for calculating and supplementing the other half of the K space phase encoding lines.
K-space can also be sampled as follows: a multi-channel RF coil is used as a radio frequency receive coil and is placed in a configuration at different locations around a subject, each coil simultaneously acquiring magnetic resonance signals after application of a pulse sequence, each radio frequency coil having an independent receive channel. By increasing the distance between the phase encodings, the number of sampling steps in K-space can be reduced, thereby reducing the sampling density in K-space.
In addition, filtering processing can be performed on the K space data acquired by the multiple channels, a low-pass filter can be adopted for the filtering processing, a filtering window of the filter can be adjusted according to actual needs, and preferably, filtering can be performed by adopting the proportion of 10% -20% of the K space data of the multiple channels. Illustratively, in one embodiment, a Hanning filter window is used to filter the multi-channel K-space data, the width of the filter window being 20 pixels in size. And performing Fourier inversion on the filtered multi-channel K space data to an image domain to obtain a multi-channel image.
And step 202, performing Fourier transform on the K space data to obtain a multi-channel image. Magnetic resonance signals acquired by a coil array consisting of multi-channel RF coils can constitute a plurality of K-space data, wherein each K-space can obtain an image corresponding to the K-space through Fourier transform. Illustratively, when the multi-channel RF coil includes Q channels, Q K spaces are obtained through filling of the phase encoding lines, and Q images are obtained through fourier transform of the Q K spaces, wherein each image corresponds to a signal acquisition channel.
The "fourier transform" referred to in the present invention may also be referred to as "inverse fourier transform" and refers to transformation of the K-space data domain into the image domain. Illustratively, the signals of the multi-channel image have the following relationship with the K-space data:
Si(x,y)＝F-1(Si(kx,ky) (formula 2)
Wherein S isi(kx,ky) Denotes the ith channel RF coil in K-space (K)x,ky) And the K-space data can be fully sampled or undersampledOr may be subjected to filtering processing; si(x, y) denotes an image signal of the i-th channel RF coil at the image field position (x, y).
Step 203, calculating the sensitivity of each channel RF coil according to the multi-channel image, wherein the sensitivities of the plurality of channel RF coils can form a sensitivity map of the RF coil. The sensitivity maps of the multi-channel RF coils are obtained by computing a modulus map of the multi-channel image, wherein the multi-channel image may be filtered or unfiltered.
Illustratively, the sensitivity calculation method for each channel RF coil is: and taking the sum of the mean square of the full-FOV images obtained by the multi-channel RF coil as a reference, and obtaining the sensitivity of each channel RF coil according to the frequency domain signal intensity acquired by each channel coil. More specifically, the sensitivity calculation formula for each channel RF coil is:
wherein, Bi(x, y) represents sensitivity information corresponding to the ith channel RF coil at the image space position (x, y); si(x, y) represents an image signal of the i-th channel RF coil at an image space position (x, y); q is the number of channels of the multi-channel RF coil, and Q is more than or equal to 2.
In another embodiment of the present invention, the sensitivity of each channel RF coil can be obtained by:
the multichannel image is filtered to obtain the sensitivity of each channel RF coil, and specifically the following formula can be adopted:
wherein, BiRepresenting the sensitivity of the ith channel RF coil; siRepresenting image signals acquired by an i-th channel RF coil; fiA low-pass filter corresponding to the ith channel coil is shown,
representing a convolution operation. The low-pass filter may be a Hanning filter, a Tukey filter, or the like.
Fig. 3 is a schematic diagram of a Hanning filter window structure according to an embodiment of the present invention. Wherein PB represents the pass band width; TB represents the width of the transition belt; f (k) represents a function describing the shape of the transition zone; Δ r is a threshold value. After the amplitude is normalized, the intensity of the filter in the pass band is 1.0, and the width meets the condition that 2(PB + TB) is 1.0 after the amplitude is normalized. The threshold of the Hanning filter window is set to not less than 0.3 and the transition band TB is not less than 0.15. In yet another embodiment, the multi-channel K-space data is filtered by using a Tukey filter window, and the function f (K) describing the shape of the transition zone may be a trigonometric function, an exponential function or a polynomial function; f (k) may be a complex function of a trigonometric function and an exponential function, a complex function of an exponential function and a polynomial function, or a complex function of a trigonometric function and a polynomial function; f (k) can also be a composite function consisting of three types of functions, namely a trigonometric function, a polynomial function and an exponential function.
And 204, acquiring the signal-to-noise ratio of the pixel points of the multi-channel image, and weighting the sensitivity of the RF coil of each channel according to the signal-to-noise ratio. Illustratively, the signal-to-noise ratio of a pixel point of a multi-channel image is obtained by the following steps: acquiring the noise variance of the multi-channel image; acquiring a threshold value of a noise norm of the multi-channel image according to the noise variance of the multi-channel image and a set signal-to-noise ratio; classifying pixel points of the multi-channel image according to a noise norm threshold of the multi-channel image; and acquiring the signal-to-noise ratio corresponding to the classified pixel points.
In one embodiment, the signal-to-noise ratio of the pixel points of the multi-channel image is obtained by the following steps:
calculating the norm value of each pixel point in the multi-channel image; classifying the pixel points of the multi-channel image based on the norm value and the noise norm threshold value of the pixel points, wherein the noise norm threshold value comprises a first noise norm threshold value and a second noise norm threshold value. The classification of pixel points in the multi-channel image can be as follows: when the norm value of the pixel point is larger than the first noise norm threshold value, the pixel point is made to be a first-class pixel point, and the first-class pixel point is distributed with a first signal-to-noise ratio; and when the norm value of the pixel point is smaller than the second noise norm threshold value, enabling the pixel point to be a second type pixel point, and distributing a second signal-to-noise ratio to the second type pixel point.
In another embodiment, the multi-channel image may further include a third type of pixel points, wherein a norm value of the third type of pixel points is smaller than the first noise norm threshold and larger than the second noise norm threshold. The signal-to-noise ratio of the third type of pixel points can be obtained by the following method: selecting a neighborhood where the third type pixel points are located in the multi-channel image; determining a set of pixel points of which the norm values in the selected neighborhood are smaller than a first noise norm threshold; and calculating the mean value of pixel point norms in the pixel point set, and acquiring the signal-to-noise ratio of the third type of pixel points according to the mean value of the pixel point norms. In the process of acquiring the signal-to-noise ratio of the third-class pixel points, whether the number of the pixel points contained in the pixel point set exceeds a set range can be judged, if yes, the signal-to-noise ratio of the third-class pixel points is not acquired according to the mean value of pixel point norms, and the first signal-to-noise ratio is directly allocated to the third-class pixel points.
In yet another embodiment, the noise norm threshold is obtained by: firstly, acquiring the noise variance of a multi-channel image, and calculating the mean value of the noise norm of the multi-channel image according to the noise variance of the multi-channel image; then, calculating the variance of the noise norm of the multi-channel image according to the noise variance of the multi-channel image and the mean value of the noise norm of the multi-channel image; and finally, acquiring a threshold value of the noise norm according to the mean value and the variance of the noise norm of the multi-channel image. It should be noted that the noise norm threshold may include a first noise norm threshold, a second noise norm threshold, and the like, and the first noise norm threshold is greater than the second noise norm threshold.
And step 205, weighting the multichannel images according to the sensitivity of the RF coil of each channel after weighting, and then combining the weighted multichannel images, wherein the multichannel images can be processed by low-pass filtering or not.
It should be noted that, in the above process, the multi-channel image may be further subjected to phase preprocessing, and the phase preprocessing may include: removing a phase difference between the multi-channel RF coils; or removing signal phases of the multi-channel image to acquire image data containing only noise phases. Further, the phase preprocessing operation may be performed before the sensitivity of each channel RF coil is calculated in step 203, or may be performed after the sensitivity of each channel RF coil is calculated in step 203.
Illustratively, as shown in fig. 4, the threshold of the noise norm of the multi-channel image is obtained by the following procedure:
The method for estimating the noise variance in the K space can be obtained by estimating K space edge data and can also be obtained by collecting K space pure noise data. Estimating the K space noise variance is carried out by adopting the following method: the mean and variance of the noise are estimated using the sampled data points at the K-space edges. Illustratively, 5% -15% of the edge data points in the entire K-space may be selected as the region of noise estimation. More specifically, the position of the magnetic resonance signals or data in K-space is determined by the gradient timing structure of the imaging sequence, i.e. by the area of the gradient pulses within a specific time: the smaller the gradient pulse area is, the central part of the K space is filled with a phase encoding line obtained by encoding the obtained magnetic resonance signal; conversely, the larger the gradient pulse area is, the more the phase encoding lines obtained by encoding the obtained magnetic resonance signals are filled in the edge positions of the K space.
As shown in fig. 5, the entire K-space is divided into M1 and M2, where M1 contains data at the edge of K-space, M2 contains internal data points of K-space, and M1 contains data accounting for about 10% of the entire K-space data, and the noise mean and variance of the K-space data contained in the M1 region are calculated, respectively. The mean of the K-space noise can be estimated according to the noise mean of the K-space data contained in the M1 region, and the variance of the K-space noise can be estimated according to the noise variance of the K-space data contained in the M1 region. Optionally, the variance of K-space noise may also be estimated from the acquired pure noise data, i.e. the rf pulse is not turned on during the acquisition of K-space data.
Because the image domain data subjected to Fourier transform and the K space data are independent and identically distributed discrete variables, the noise variance of the multi-channel image can be obtained according to the noise variance of the multi-channel K space data.
And 402, calculating the mean value of the noise norm of the multi-channel image according to the noise variance of the multi-channel image. Illustratively, calculating the mean of the noise norm of the multi-channel image may be obtained by the following formula:
wherein F represents a hyper-geometric function (hyper-geometric function) containing three parametric variables; SNR represents the set signal-to-noise ratio, and parameter 1 in F represents the number of channels; σ represents the noise variance of the multi-channel image. The set SNR may comprise a first SNR (high SNR) respectivelyHAnd a second signal-to-noise ratio (low signal-to-noise ratio) SNRL. In this embodiment, the SNR is set separatelyH＝1.05，SNRL0.35. Correspondingly, the mean of the noise norm of the multi-channel image may be included with the SNRHCorresponding first noise norm mean mDHAnd SNRLCorresponding second noise norm mean mDL。
And step 403, calculating the variance of the noise norm of the multi-channel image according to the noise variance of the multi-channel image and the mean value of the noise norm of the multi-channel image. Illustratively, the variance of the multi-channel image noise norm may be expressed as:
wherein S represents multi-channel acquired K space data or magnetic resonance signals; σ represents the noise variance of the multi-channel image. More specifically, the variance of the noise norm of the multi-channel image includes the sum of mDHCorresponding first noise norm variance sDHAnd mDLCorresponding second noise norm variance sDL。
And step 404, obtaining a threshold value of the noise norm of the multi-channel image according to the mean value and the variance of the noise norm of the multi-channel image. Illustratively, the following formula may be used for calculation:
TH＝mD+f×sD(formula 7)
Wherein f represents a weighting factor, and the value range is 2-4; m isDMeans representing a noise norm of the multi-channel image; sDRepresenting the variance of the noise norm of the multi-channel image. The noise norm threshold may include a first noise norm threshold and a second noise norm threshold corresponding to a mean and a variance of a multi-channel image noise norm. Illustratively, m is a mean value from the imageDHSum variance sDHThe first noise norm threshold TH of the multi-channel image norm can be obtainedH(ii) a According to the mean value m of the imageDLSum variance sDLSecond noise norm threshold TH of multi-channel image norm can be obtainedL。
For each pixel point of the multi-channel image, Forbenius norms of a real part image and an imaginary part image of Gaussian distribution are met, and Chi distribution is met. Illustratively, the Forbenius representation may be as follows:
wherein a represents a multi-channel image; a isRijRepresenting the real parts of ith row and jth column pixel points in the multi-channel image a; a isIijAnd the imaginary parts of the ith row and jth column pixel points in the multichannel image a are represented.
Comparing the norm value of each pixel point of the multi-channel image with a first noise norm threshold value THHSecond, secondNoise norm threshold THLThe relation of (3) classifying pixel points of the multi-channel image:
if the Forbenius norm value of the multi-channel image pixel point is larger than the first noise norm threshold value THH(high noise threshold), then judge the pixel is the signal area with high SNR, can make the pixel be the first kind of pixel, and assign the SNR of the pixel to the first SNR (high SNR)H；
If the Forbenius norm value of the multi-channel image pixel is smaller than the second noise norm threshold value, judging that the pixel contains fewer signals, enabling the pixel to be a second type pixel, and assigning a second signal-to-noise ratio (low signal-to-noise ratio) SNR to the signal-to-noise ratio of the pixelL；
If the Forbenius norm value of the multi-channel image pixel point is smaller than the first noise norm threshold THH(high noise threshold) and greater than a second noise norm threshold THL(low noise threshold), then judge the pixel point as the pixel point containing high noise, make this kind of pixel point as the third kind of pixel point.
And for the third-class pixel point, calculating the signal-to-noise ratio corresponding to the pixel point according to the Forbenius norm value of the pixel point. Exemplarily, as shown in fig. 6, the method for calculating the signal-to-noise ratio of the third type of pixel point includes:
601, selecting a neighborhood where a third type of pixel points are located in the multi-channel image;
step 602, determining that the Forbenius norm value in the selected neighborhood is less than the first noise norm threshold THHAnd calculating the sum of Forbenius norm values in the pixel point set;
step 603, obtaining the Forbenius norm value smaller than the first noise norm threshold value TH in the selected field according to the sum of the Forbenius norm values in the pixel point setHThe average value of pixel norm of (1);
step 604, according to the Forbenius norm value in the selected field being smaller than the first noise norm threshold THHThe average value of the pixel norm of the third class pixel is obtained.
In one embodiment, the neighborhood where the third type of pixel points are located is set as: the range of the multi-channel image row i is [ -d, d ], the range of the multi-channel image column j is [ -d, d ], and d can be an integer of 2-5. The sum of Forbenius norm values in the pixel point set can be expressed as:
wherein, | | D2d||FExpressing the sum of Forbenius norm values in the pixel point set; a isRijRepresenting the real parts of the ith row and the jth column of pixel points in the pixel point set of the multi-channel image; a isIijThe imaginary parts of the ith row and the jth column of pixel points in the pixel point set of the multi-channel image are represented; i represents a row where the pixel point is located, and j represents a column where the pixel point is located; d represents the radius of the neighborhood (selected domain) where the third type of pixel points are located. The Forbenius norm value in the selected field is smaller than the first noise norm threshold THHThe norm mean of the pixel point set can be expressed as:
m2d＝||D2d||Fn (equation 10)
Wherein n represents that the Forbenius norm value in the selected domain is smaller than the first noise norm threshold THHThe number of the pixels. And according to the norm mean m of the pixel point set2dThe following relation exists between the SNR and the SNR of the third type pixel points:
wherein, (n-1)! 1 × 2 × 3 × … (n-2) × (n-1), n representing that the Forbenius norm value in the selected domain is smaller than the first noise norm threshold THHThe number of the pixels. Thus, the mean m from the set of pixel points2dThe signal-to-noise ratio of any third type of pixel is derived.
In another embodiment, the signal-to-noise ratio of the third type of pixel as shown in fig. 7 is obtained by the following steps:
firstly, calculating the sum of Forbenius norm values in the pixel point set, and then obtaining the Forbenius norm value smaller than a first noise norm threshold value TH in the selected field according to the sum of the Forbenius norm values in the pixel point setHMean of pixel point set.
On the basis of obtaining the corresponding signal ratios of the first type, the second type and the third type of pixel points through classification, a signal-to-noise ratio diagram of the multi-channel RF coil can be obtained, and the signal-to-noise ratio diagram of the multi-channel RF coil comprises the signal-to-noise ratio corresponding to each channel coil. Illustratively, the multi-channel image includes a plurality of pixel points, and each pixel point can obtain a corresponding signal-to-noise ratio. In accordance with the above method, a corresponding signal-to-noise ratio for each channel of the multi-channel RF coil can be obtained.
It should be noted that in order to obtain a relatively continuous signal-to-noise ratio map of the multi-channel RF coil, the signal-to-noise ratio map of the multi-channel RF coil may be low-pass filtered or normalized. In one embodimentIn (3), a second signal-to-noise ratio SNR can be employedLAnd carrying out normalization processing on the signal-to-noise ratios of the first type, the second type and the third type of pixel points.
The sensitivities of the multi-channel RF coils may be weighted according to the aforementioned signal-to-noise ratio maps for the multi-channel RF coils. Illustratively, the specific steps of the weighting process are:
Bi'＝SNRi×Bi(formula 12)
Wherein, Bi' represents the sensitivity of the ith channel RF coil weighted by the signal-to-noise ratio; b isiRepresenting the sensitivity of the ith channel RF coil; SNRiRepresenting the signal-to-noise ratio of the ith channel RF coil. It should be noted that, if a certain background noise needs to be retained in the weighting process, a lower threshold may be set for the signal-to-noise ratio of the coil channel.
In one embodiment, the multi-channel images are merged by:
or carrying out homogenization treatment:
wherein, I represents a magnetic resonance image after channel combination; q represents the number of channel coils; siRepresents the image signal acquired by the ith channel RF coil, and the image may be low pass filtered or not; b isi' denotes the coil sensitivity weighted by the i-th channel coil signal-to-noise ratio. The complex images of each channel are combined after being subjected to coil sensitivity weighting weighted by the signal-to-noise ratio, the phases of the noise areas of each channel are disordered, the phases of the signal areas are all the phases of signals, the noise values can be offset through complex addition, and the signal values are reserved to increase the signal-to-noise ratio.
In another embodiment, the magnetic resonance imaging method of the present invention comprises the steps of:
exciting a target region of a detected person by using an imaging sequence, and acquiring a magnetic resonance signal generated by the target region by using a multi-channel RF coil to acquire multi-channel acquired K space data; performing Fourier transform on the K space data to obtain a multi-channel image; performing phase preprocessing on the multi-channel image, and calculating the sensitivity of each channel RF coil according to the multi-channel image; acquiring the signal-to-noise ratio of each pixel point in the multi-channel image, and weighting the sensitivity of the RF coil of each channel according to the signal-to-noise ratio; the weighted multichannel images, which may be low pass filtered and phase preprocessed, are weighted (phase preprocessed) according to the sensitivity of each channel RF coil and then combined. It should be noted that the phase preprocessing for the multi-channel images can be performed before or after the sensitivity calculation of the RF coils, and can also be performed after the sensitivity weighting processing of the RF coils.
In another embodiment, the multichannel magnetic resonance imaging method of the present invention is further applicable to the case of undersampling K-space data, and specifically includes the following steps:
exciting a target region of a subject by using an imaging sequence, acquiring a magnetic resonance signal generated by the target region by using a multi-channel RF coil, and acquiring multi-channel undersampled K-space data, wherein the multi-channel RF coil has corresponding coil sensitivity to the magnetic resonance signal;
carrying out filtering processing on the K space data;
carrying out Fourier transform (inverse transform) on the K space data after filtering processing to obtain a multi-channel image, and calculating the sensitivity of each channel RF coil according to the multi-channel image;
acquiring the signal-to-noise ratio of each pixel point in the multi-channel image, and weighting the sensitivity of the RF coil of each channel according to the signal-to-noise ratio;
performing interpolation processing on the sensitivity of each channel RF coil after weighting processing to obtain the sensitivity of the RF coil corresponding to the virtual full sampling K space data;
weighting the multichannel images according to the sensitivity of the RF coil corresponding to the virtual fully-sampled K space data, and combining the weighted multichannel images, wherein the multichannel images can be processed by low-pass filtering or not, and can also be processed by phase preprocessing.
In contrast, the classical sum of squares (SOS) algorithm, the adaptive channel merging method, and the channel merging method based on snr weighting as shown in fig. 2 are respectively used to process header data with high snr. Classical SOS algorithms can be referred to Yan R, Erdogmus D, Larsson E G, et al, "Image combination for high-field phase-array". MRI [ C ]// ICASSP (5).2003:1-4. The adaptive channel algorithm may be referred to Ma Y J, Liu W, ZHao X, et al, "Improved adaptive recovery of multichannel MR images". medical Physics,2015,42(2): 637-644.
In the specific embodiment of the present invention, the parameters are set as follows: the low-pass filter adopts a Hanning window; first signal-to-noise ratio SNRH(high signal-to-noise ratio) is set to 1.05; second signal-to-noise ratio (low signal-to-noise ratio) SNRLSet to 0.35; the value of f is 2.5; the neighborhood radius d is set to 2.
FIG. 8a is a head image formed by multi-channel merging using an SOS method, and FIG. 8b is a head image formed by multi-channel merging using an adaptive channel merging method; fig. 8c is a head image formed by multi-pass merging using the method of the present invention. Through comparison of the three, the head image formed by combining the channels by adopting the SOS method is relatively high in image background noise (shown by a square window), and the image is relatively poor; the head image formed by adopting the self-adaptive channel combination method is shown in fig. 8B, and obvious signal cancellation (annular ripples in the figure) exists in the areas with discontinuous phases at positions A and B; the head image formed by combining the channels by adopting the method has high image signal-to-noise ratio and good image contrast, and the signal cancellation phenomenon is not generated at the position corresponding to the figure 8c, so that the structure of the image is well reserved in the discontinuous phase region.
In yet another embodiment, the abdomen image is processed using a classical sum of squares (SOS) algorithm, an adaptive channel merging method, and a channel merging method based on signal-to-noise ratio weighting according to an embodiment of the present invention as shown in fig. 2, respectively. Fig. 9a is an abdomen image formed by multi-channel combination using the SOS method, and fig. 9b is an abdomen image formed by multi-channel combination using the adaptive channel combination method; fig. 9c is an abdominal image formed by multi-channel merging using the method of the present invention. Through comparison, the abdomen image formed by adopting SOS to carry out multi-channel combination has poor signal-to-noise ratio, the square window area displays obvious background noise, and the whole abdomen image is seriously influenced by the background noise; the abdomen image formed by combining the adaptive channels has obvious artifacts at the C, D position in fig. 9 b; the abdomen image formed by combining channels by the method of the present invention has high signal-to-noise ratio and less background noise, and no artifact appears at the corresponding positions of the two points as shown in fig. 9c and C, D.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software service.
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (9)
1. A magnetic resonance imaging method, characterized by comprising the steps of:
exciting a target region of a detected person by using an imaging sequence, and acquiring a magnetic resonance signal generated by the target region by using a multi-channel RF coil to acquire multi-channel acquired K space data;
performing Fourier transform on the K space data to obtain a multi-channel image;
calculating a sensitivity of each channel RF coil from the multi-channel image;
acquiring the signal-to-noise ratio of a pixel point of a multi-channel image, and weighting the sensitivity of each channel RF coil according to the signal-to-noise ratio;
weighting the multi-channel images according to the sensitivity of the RF coil of each channel after weighting processing, and then combining the weighted multi-channel images;
the signal-to-noise ratio of the multi-channel image pixel point is obtained through the following steps:
calculating a norm value of each pixel point of the multi-channel image;
classifying the pixel points of the multi-channel image based on the norm values and the noise norm thresholds of the pixel points, wherein the noise norm threshold comprises a first noise norm threshold and a second noise norm threshold, and
when the norm value of the pixel point is larger than the first noise norm threshold value, the pixel point is made to be a first-class pixel point, and the first-class pixel point is distributed with a first signal-to-noise ratio;
and when the norm value of the pixel point is smaller than the second noise norm threshold value, enabling the pixel point to be a second type pixel point, and distributing a second signal-to-noise ratio to the second type pixel point.
2. The magnetic resonance imaging method of claim 1, further comprising phase preprocessing the multi-channel image, the phase preprocessing comprising:
removing a phase difference between the multi-channel RF coils; or
And removing the signal phase of the multi-channel image to obtain image data only containing the noise phase.
3. A magnetic resonance imaging method as claimed in claim 1, characterized in that the K-space data are obtained by full sampling.
4. A magnetic resonance imaging method as claimed in claim 1, characterized in that the multi-channel K-space data is obtained by undersampling.
5. The magnetic resonance imaging method according to claim 4, further comprising interpolating the sensitivities of the RF coils of each channel after the weighting process to obtain the sensitivities of the RF coils corresponding to the virtual fully-sampled K-space data.
6. The magnetic resonance imaging method of claim 1, wherein the multi-channel image further includes a third type of pixel points, the norm value of the third type of pixel points is smaller than the first noise norm threshold and larger than the second noise norm threshold, and the signal-to-noise ratio of the third type of pixel points is obtained by:
selecting a neighborhood where the third type pixel points are located in the multi-channel image;
determining a set of pixel points of which the norm values in the selected neighborhood are smaller than a first noise norm threshold;
and calculating the mean value of pixel point norms in the pixel point set, and acquiring the signal-to-noise ratio of the third type of pixel points according to the mean value of the pixel point norms.
7. The MRI method of claim 6, further comprising determining whether the number of pixels included in the set of pixels exceeds a predetermined range, and if so, assigning a first SNR to the third type of pixels.
8. The magnetic resonance imaging method of claim 1, wherein the noise norm threshold is obtained by:
acquiring the noise variance of a multi-channel image, and calculating the mean value of the noise norm of the multi-channel image according to the noise variance of the multi-channel image;
calculating the variance of the noise norm of the multi-channel image according to the noise variance of the multi-channel image and the mean value of the noise norm of the multi-channel image;
and acquiring a threshold value of the noise norm according to the mean value and the variance of the noise norm of the multi-channel image.
9. The magnetic resonance imaging method of claim 8, further comprising performing a filtering process on the multi-channel image.
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