CN117368818A - A parallel acquisition and reconstruction method for diffusion-weighted magnetic resonance spectra - Google Patents
A parallel acquisition and reconstruction method for diffusion-weighted magnetic resonance spectra Download PDFInfo
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Abstract
本发明公开了一种扩散加权磁共振波谱并行采集和重建方法。方法包括在单体素波谱序列中添加一对扩散梯度，得到单体素扩散加权磁共振波谱序列；对各个感兴趣体素位置处的单波段射频脉冲进行逐点求和，得到多波段射频脉冲，通过单体素扩散加权磁共振波谱序列和多波段射频脉冲得到多体素扩散加权磁共振波谱信号；获取接收线圈每个通道的灵敏度图，根据灵敏度图进行多体素扩散加权磁共振波谱信号的分离和重建，最终得到分离重建后的多体素扩散加权磁共振波谱信号。本发明能够同时测量人体多个体素的扩散加权磁共振波谱信号，并获得和单体素扩散加权磁共振波谱信号相当的信噪比和表观扩散系数，节约了至少一半的扫描时间。
The invention discloses a parallel acquisition and reconstruction method of diffusion-weighted magnetic resonance spectra. The method includes adding a pair of diffusion gradients to the single voxel spectrum sequence to obtain a single voxel diffusion weighted magnetic resonance spectrum sequence; summing the single-band radio frequency pulses at each voxel position of interest point by point to obtain a multi-band radio frequency pulse. , obtain multi-voxel diffusion-weighted magnetic resonance spectrum signals through single-voxel diffusion-weighted magnetic resonance spectrum sequences and multi-band radio frequency pulses; obtain the sensitivity map of each channel of the receiving coil, and perform multi-voxel diffusion-weighted magnetic resonance spectrum signals based on the sensitivity map separation and reconstruction, and finally obtain the multi-voxel diffusion-weighted magnetic resonance spectrum signal after separation and reconstruction. The invention can simultaneously measure the diffusion-weighted magnetic resonance spectrum signals of multiple voxels of the human body, and obtains a signal-to-noise ratio and apparent diffusion coefficient equivalent to those of a single voxel diffusion-weighted magnetic resonance spectrum signal, saving at least half of the scanning time.
Description
Technical Field
The invention belongs to a diffusion weighted magnetic resonance spectrum acquisition and reconstruction method in the field of magnetic resonance spectrums, and particularly relates to a diffusion weighted magnetic resonance spectrum parallel acquisition and reconstruction method.
Background
Diffusion magnetic resonance imaging has achieved great success in detecting cell microstructures and the like, but because water molecules widely exist in biological tissues, diffusion weighting signals of the water molecules are numerous in sources, and differences of diffusion motions of the water molecules in different cell microenvironments are difficult to distinguish. While metabolite molecules are typically within a defined cell or tissue space, diffusion weighted measurements of them can provide more and more information that is not detectable by diffuse magnetic resonance imaging. Diffusion weighted magnetic resonance spectroscopy has proven to have great potential in exploring various tissue microstructures and physiological information at the cellular and subcellular level. However, diffusion weighted magnetic resonance spectroscopy requires acquisition of multiple diffusion weighted signals to obtain apparent diffusion coefficients of metabolite molecules, resulting in longer scan times, which is a major application limitation of current diffusion weighted magnetic resonance spectroscopy. Several methods have been proposed for diffusion weighted magnetic resonance spectroscopy to accelerate acquisition, such as diffusion weighted magnetic resonance spectroscopy imaging techniques, to acquire metabolite information for multiple voxels of the whole brain region at a time, but the scan times tend to be long. In addition, due to the limited K-space sampling and instability factors such as the motion under test, the magnetic resonance spectroscopy imaging technique is also negatively affected by the correlation with the point spread function.
In some application scenarios, such as the human brain, the subject may be defined as a number of brain regions associated with disease or brain activity, which may reduce the area scanned, and may be used for a limited time to improve the accuracy of the scan. Whereas acquisition of signals from multiple brain regions in multiple lesions often requires sequential acquisition of monomeric spectra, this acquisition method is time consuming with a relatively large number of voxels. Therefore, it is highly desirable to achieve simultaneous acquisition and reconstruction of multi-voxel magnetic resonance spectroscopy signals for non-magnetic resonance spectroscopy imaging techniques. With higher main magnetic field strengths and the use of phased array receive coils with multiple channels, it has been demonstrated that spatial location information can be encoded with spatial variations in the receive coil sensitivity profiles. The invention realizes the simultaneous acquisition of a plurality of voxels of the human body by utilizing multiband radio frequency pulse, separates and reconstructs the diffusion weighted signals of the voxels by utilizing the sensitivity information of the receiving coil, and quantitatively compares the signal to noise ratio and apparent diffusion coefficient with the corresponding single element diffusion weighted magnetic resonance spectrum.
Existing diffusion weighted magnetic resonance spectroscopy techniques are still based on single voxel sequential measurements or on global measurements based on magnetic resonance spectroscopy imaging techniques rather than simultaneous measurements of multiple specific voxels of the region of interest.
Disclosure of Invention
In order to solve the problems in the background art, the invention aims to provide a diffusion weighted magnetic resonance spectrum parallel acquisition and reconstruction method. The invention realizes the simultaneous acquisition of a plurality of voxels of the human body through the multiband radio frequency pulse, and then separates and reconstructs the diffusion weighted signals of the voxels by utilizing the sensitivity information of the receiving coil, thereby obtaining the signal-to-noise ratio and the apparent diffusion coefficient which are equivalent to the diffusion weighted magnetic resonance spectrum of the monomeric element and saving at least half of the spectrum scanning time.
In order to achieve the above purpose, the technical scheme adopted by the invention comprises the following steps:
s1, adding a pair of diffusion gradients into a basic monomeric element spectrum sequence to obtain a monomeric element diffusion weighted magnetic resonance spectrum sequence;
the diffusion gradient adopts a pair of completely symmetrical diffusion gradients with opposite polarities;
s2, carrying out point-by-point summation on the single-band radio frequency pulse at each interested voxel position to obtain multi-band radio frequency pulse, replacing the single-band radio frequency pulse in the single-element diffusion weighted magnetic resonance spectrum sequence with the multi-band radio frequency pulse to obtain a multi-element diffusion weighted magnetic resonance spectrum sequence, and further obtaining a multi-element diffusion weighted magnetic resonance spectrum signal;
and S3, acquiring a sensitivity map of each channel of the receiving coil, and separating and reconstructing the multi-voxel diffusion weighted magnetic resonance spectrum signals according to the sensitivity map to finally obtain the separated and reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals.
The specific steps of the step S2 are as follows:
s2.1, determining the number of voxels to be acquired as N, and selecting initial positions of the N voxels;
s2.2, carrying out point-by-point summation on the single-band radio frequency pulse corresponding to the N voxel positions to obtain a multi-band radio frequency pulse;
s2.3, comparing excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error of the two is within the preset error range, the waveform of the multiband radio frequency pulse is considered to be correct, and the step S2.4 is continued;
otherwise, the waveform of the multiband radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire a new multiband radio frequency pulse again;
s2.4, changing the position of each voxel, and repeating the steps S2.2-S2.3 to obtain a plurality of multiband radio frequency pulses with correct waveforms;
s2.5, adding all the multiband radio frequency pulses obtained in the step S2.4 into a pulse file of a single voxel diffusion weighted magnetic resonance spectrum sequence, and selecting one multiband radio frequency pulse from the pulse file to replace an initial single-band radio frequency pulse so as to obtain a multiband diffusion weighted magnetic resonance spectrum sequence;
s2.6, importing the multi-voxel diffusion weighted magnetic resonance spectrum sequence into a magnetic resonance scanner, setting the number, the size and the acquisition times of b values, and then acquiring multi-voxel diffusion weighted magnetic resonance spectrum signals by using the magnetic resonance scanner.
The acquisition times of each b value are specifically the acquisition times of multi-voxel diffusion weighted magnetic resonance spectrum signals under the same b value; under the condition of closing water inhibition, the collection times are 2-16 times; under the condition of starting water inhibition, the collection times of each b value are 16-64 times.
The specific steps of the step S3 are as follows:
s3.1, acquiring a gray level image of each channel of the receiving coil by using a gradient echo sequence, and then acquiring a sensitivity map corresponding to each channel by using the gray level image of each channel;
s3.2, carrying out voxel segmentation and registration on the sensitivity maps of all channels to obtain sensitivity maps corresponding to each voxel position, and carrying out space dimension averaging on the sensitivity maps to obtain a sensitivity matrix S with the dimension of R x N, wherein R is the total number of channels of the receiving coil;
s3.3, Q pixel points are taken at the same edge position of each channel gray level image, and the respective noise vector eta of each channel image is obtained according to the gray level value of each pixel point r ：
η r ＝[h 1 h 2 …h q …h Q ]
Wherein eta r For the noise vector corresponding to the r channel, h q The gray value at the q pixel point in the r channel;
in the invention, the pixel points are selected to be positioned at the most edge position of the channel image and are positioned in corner areas outside the brain image;
s3.4, then, the noise vector is processed according to the following formula to obtain a receiving coil noise covariance matrix ψ with the dimension of R:
wherein, psi is i,j Values of the j-th column in the i-th row for the receive coil noise covariance matrix ψ, η i For the noise vector corresponding to the ith channel, η j The noise vector corresponding to the j-th channel;
s3.5, processing according to the following formula to obtain an unfolding matrix U:
U＝(S H Ψ -1 S) -1 S h Ψ -1
wherein, the superscript H represents that the sensitivity matrix S is subjected to conjugate transposition;
s3.6, multiplying the expansion matrix U by the multi-voxel diffusion weighted magnetic resonance spectrum signal with the dimension of R, P and A to obtain the multi-voxel diffusion weighted magnetic resonance spectrum signal with the dimension of N, P is the sampling point number of the multi-voxel diffusion weighted magnetic resonance spectrum signal, and A is the acquisition time number of the multi-voxel diffusion weighted magnetic resonance spectrum signal;
s3.7, carrying out frequency and phase correction on all multi-voxel diffusion weighted magnetic resonance spectrum signals corresponding to the same b value;
s3.8, arranging all multi-voxel diffusion weighted magnetic resonance spectrum signals corresponding to the same b value according to the peak height of a given magnetic resonance spectrum, selecting three multi-voxel diffusion weighted magnetic resonance spectrum signals with the peak heights of a second, a third and a fourth from all multi-voxel diffusion weighted magnetic resonance spectrum signals, taking 60% -80% of the peak height average value of the three multi-voxel diffusion weighted magnetic resonance spectrum signals as a threshold value, discarding multi-voxel diffusion weighted magnetic resonance spectrum signals lower than the threshold value, averaging the rest multi-voxel diffusion weighted magnetic resonance spectrum signals, and carrying out frequency and phase correction again to obtain the reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals under the b value.
The diffusion time of the diffusion gradient is 10-300 ms, and the duration of the diffusion gradient is 2-50 ms.
In the step S2.6, after the multi-voxel diffusion weighted magnetic resonance spectrum sequence is introduced, the number of b values is set to be 2-10, and the size of the b values is set to be 0-3333S/mm 2 Under the condition of closing water inhibition, acquiring 2-16 times of multi-voxel diffusion weighted magnetic resonance spectrum signals from each b value; under the condition of starting water inhibition, each b value is used for collecting 16-64 times of multi-voxel diffusion weighted magnetic resonance spectrum signals.
The magnitude of the b value is obtained by processing according to the following formula:
where γ is the gyromagnetic ratio of the protons, g is the magnitude of the diffusion gradient, δ is the duration of the diffusion gradient, and Δ is the diffusion time.
In the step S2.2, the multiband radio frequency pulse is obtained according to the following formula:
wherein RF N Time domain waveform, RF, of multi-band radio frequency pulses s Is the time domain waveform of the initial single-band radio frequency pulse in the sequence, f n For the offset frequency at the nth voxel position, the subscript n is the ordinal number of the voxel and t is time.
The step S2.3 specifically comprises the following steps:
s2.3.1, the single-band radio frequency pulse and the multi-band radio frequency pulse are simulated by using a Bloch equation to obtain simulated excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse, and the excitation performance of the multi-band radio frequency pulse is initially verified:
comparing simulation excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error between the simulated excitation profile of the single-band radio frequency pulse and the simulated excitation profile of the multi-band radio frequency pulse is smaller than the preset simulated profile error value, the waveform of the multi-band radio frequency pulse is considered to be initially correct, and the step S2.3.2 is entered;
otherwise, the waveform of the multiband radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire the multiband radio frequency pulse again;
s2.3.2, using a magnetic resonance scanner to perform actual test on the single-band radio frequency pulse and the multi-band radio frequency pulse respectively, so as to obtain actual excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse, and further verify excitation performance of the multi-band radio frequency pulse:
comparing the actual excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error between the actual excitation profile of the single-band radio frequency pulse and the actual excitation profile of the multi-band radio frequency pulse is smaller than the preset actual profile error value, the waveform of the multi-band radio frequency pulse is considered to be correct, and the step S2.4 is carried out;
otherwise, the waveform of the multiband radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to retrieve the multiband radio frequency pulse.
In the step S3.1, the sensitivity map of each channel of the receiving coil is obtained by processing according to the following formula:
wherein S is r (x, y) is the sensitivity map corresponding to the r-th channel, I r (x, y) is a gray scale image received by the R-th channel, and R represents the total number of channels of the receiving coil.
In the step S3.8, if the multi-voxel diffusion weighted magnetic resonance spectrum signal is collected under the condition of water inhibition, the given magnetic resonance spectrum peak adopts N-acetyl aspartic acid; if the multi-voxel diffusion weighted magnetic resonance spectroscopy signal is acquired under off water suppression conditions, then the given magnetic resonance spectroscopy peak employs a water peak.
An electronic device: comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the method described above.
The method can simultaneously obtain the diffusion weighted magnetic resonance spectrum of a plurality of voxels in the human body and separate and reconstruct different voxel signals by utilizing the sensitivity information of the receiving coil; the relative concentration of the metabolite molecules of interest in the human body is obtained and the apparent diffusion coefficient of the corresponding metabolite molecules is calculated from the decay of the relative concentration. The invention can simultaneously measure the diffusion weighted magnetic resonance spectrum of a plurality of voxels of the human body, obtain the signal to noise ratio and apparent diffusion coefficient equivalent to the single element diffusion weighted magnetic resonance spectrum, and save at least half of scanning time.
The invention relates to a method for realizing simultaneous acquisition of a plurality of voxels of a human body by utilizing multiband radio frequency pulse, which is used for separating and reconstructing diffusion weighted signals of aliasing of the plurality of voxels by utilizing sensitivity information of a receiving coil, and under the condition of saving at least half of scanning time, the signal-to-noise ratio and apparent diffusion coefficient equivalent to those of single element diffusion weighted magnetic resonance spectrum are obtained.
Compared with the prior art, the invention has the innovation that:
the method realizes parallel acquisition and separation of the multi-voxel diffusion weighted magnetic resonance spectrum for the first time, has smaller signal leakage, obtains the signal-to-noise ratio and apparent diffusion coefficient equivalent to those of the single-voxel diffusion weighted magnetic resonance spectrum measurement result, and can save at least half of scanning time.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a simplified diagram of a multi-voxel diffusion weighted magnetic resonance spectroscopy sequence;
FIG. 3 is a schematic view of the voxel locations under test;
FIG. 4 is a schematic illustration of a parameter tab of an added multi-voxel diffusion weighted magnetic resonance spectroscopy sequence of the present invention;
FIG. 5 is a schematic diagram showing metabolite peak spectra of a semicircular central region of a normal subject brain after reconstruction according to the present invention; wherein, (a) is a spectrum diagram obtained by exciting only voxel 1 at voxel 1, (b) is a spectrum diagram obtained by exciting only voxel 1 at voxel 2, (c) is a spectrum diagram obtained by exciting only voxel 2 at voxel 1, (d) is a spectrum diagram obtained by exciting only voxel 2 at voxel 2, (e) is a spectrum diagram obtained by exciting two voxels at voxel 1 at the same time, and (f) is a spectrum diagram obtained by exciting two voxels at voxel 2 at the same time.
Detailed Description
The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
The embodiment of the invention takes human brain as an example. Specific examples are as follows, as shown in fig. 1:
step S1, recruiting 4 normal subjects (with the age of 23.5+/-2.6 years).
Adding a pair of diffusion gradients into the basic monomeric element spectrum sequence to obtain a monomeric element diffusion weighted magnetic resonance spectrum sequence; wherein the diffusion gradient adopts a pair of completely symmetrical diffusion gradients with opposite polarities.
As shown in fig. 3, a corresponding diffusion parameter tab is added;
s2, carrying out point-by-point summation on single-band radio frequency pulses at each voxel position of interest to obtain multi-band radio frequency pulses, realizing simultaneous acquisition of a plurality of voxels, replacing the single-band radio frequency pulses in the single-element diffusion weighted magnetic resonance spectrum sequence with the multi-band radio frequency pulses to obtain a multi-element diffusion weighted magnetic resonance spectrum sequence, and further obtaining a multi-element diffusion weighted magnetic resonance spectrum signal; adding a multiband radio frequency pulse tab;
the specific steps of the step S2 are as follows:
s2.1, determining the number of voxels to be acquired as N, and selecting initial positions of the N voxels;
in this example, a half-oval center region with white matter in large majority is selected, and two voxels are placed 29.9mm to the left and 29.9mm to the right in the middle, respectively, with specific locations shown in fig. 3. In fig. 3, the black box represents voxel 1, the white box represents voxel 2, and the white dashed box represents the shim position;
s2.2, carrying out point-by-point summation on the single-band radio frequency pulse corresponding to the N voxel positions (namely, formula 1) to obtain multi-band radio frequency pulse;
the multiband radio frequency pulse is obtained according to the following formula:
wherein RF N Is a time domain waveform of multiband radio frequency pulse, N is the number of voxels needing to be excited simultaneously, RF s Is the time domain waveform of the initial single-band radio frequency pulse in the sequence, f n For the offset frequency at the n-th voxel position, n is the ordinal number of the voxel, t is time, j is an imaginary unit, and e is a natural number;
s2.3, comparing excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error of the two is within the preset error range, the multi-band radio frequency pulse excitation performance is better, the waveform of the multi-band radio frequency pulse is considered to be correct, and the step S2.4 is continued;
otherwise, the multi-band radio frequency pulse excitation performance is poor, the waveform of the multi-band radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire a new multi-band radio frequency pulse again;
the specific operation in step S2.3 is as follows:
s2.3.1, the single-band radio frequency pulse and the multi-band radio frequency pulse are simulated by using a Bloch equation to obtain simulated excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse, and the excitation performance of the multi-band radio frequency pulse is initially verified:
comparing simulation excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error between the simulated excitation profile of the single-band radio frequency pulse and the simulated excitation profile of the multi-band radio frequency pulse is smaller than the preset simulated profile error value, the multi-band radio frequency pulse excitation performance is better, the waveform of the multi-band radio frequency pulse is considered to be initially correct, and the next verification is carried out in step S2.3.2;
otherwise, the multi-band radio frequency pulse excitation performance is poor, the waveform of the multi-band radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire the multi-band radio frequency pulse again;
s2.3.2, using a magnetic resonance scanner to perform actual test on the single-band radio frequency pulse and the multi-band radio frequency pulse respectively, so as to obtain actual excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse, and further verify excitation performance of the multi-band radio frequency pulse:
comparing the actual excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error between the actual excitation profile of the single-band radio frequency pulse and the actual excitation profile of the multi-band radio frequency pulse is smaller than the preset actual profile error value, the multi-band radio frequency pulse excitation performance is better, the waveform of the multi-band radio frequency pulse is considered to be correct, and the step S2.4 is performed;
otherwise, the multi-band radio frequency pulse excitation performance is poor, the waveform of the multi-band radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire the multi-band radio frequency pulse again.
S2.4, changing the position of each voxel, and repeating the steps S2.2-S2.3 to obtain a plurality of multiband radio frequency pulses with correct waveforms;
s2.5, adding all the multiband radio frequency pulses obtained in the step S2.4 into a pulse file of a single voxel diffusion weighted magnetic resonance spectrum sequence for selection, selecting one multiband radio frequency pulse from the pulse file, and replacing the multiband radio frequency pulse in the single voxel diffusion weighted magnetic resonance spectrum sequence with the selected multiband radio frequency pulse to obtain a multiband diffusion weighted magnetic resonance spectrum sequence;
s2.6, importing the multi-voxel diffusion weighted magnetic resonance spectrum sequence into a magnetic resonance scanner, setting the number, the size and the acquisition times of b values, and then acquiring multi-voxel diffusion weighted magnetic resonance spectrum signals by using the magnetic resonance scanner.
In step S2.6, a multi-voxel diffusion weighted magnetic resonance spectroscopy signal is acquired using a multi-voxel diffusion weighted magnetic resonance spectroscopy sequence, the steps comprising:
s2.6.1 placing a normal subject in a supine position with the head advanced with a magnet into a magnetic resonance scanner;
s2.6.2 scanning position image and three-dimensional T 1 Weighted magnetization prepares a fast gradient echo sequence (imaging field of view 187 x 240 x 247 mm) 3 Repetition time 2300ms, echo time 3ms, flip angle 9 °, resolution 0.96mm x 1.06 mm) was used to locate the spectral voxel position.
S2.6.3 the repetition time of the multi-voxel diffusion weighted magnetic resonance spectrum sequence is set to be 1500ms, the echo time is 64ms, the mixing time is 45.6ms, and the voxel size is 20 x 28 x 26mm 3 The bandwidth is 2000Hz, the diffusion time is 80ms, the duration of the diffusion gradient is 20ms, and the diffusion gradient is applied to the surface of the substrate at 0-3333 s/mm 2 Uniformly increasing 5 b values over the range. When water inhibition is closed, collecting 16 wave spectrums under each b value condition; with water inhibition on, 32 spectra were collected for each b value. Selecting a large shimming frame to cover two voxel positions excited simultaneously for shimming;
figure 4 is a parametric tab of the added multi-voxel diffusion weighted magnetic resonance spectroscopy sequence of the present invention. The diffusion time can be adjusted by the diffusion time, the duration time can be adjusted by the diffusion time, the number of b values acquired in a staggered way can be adjusted by the b_number, the amplitude of the diffusion gradient corresponding to different b values can be adjusted by the g, and the single-band radio frequency pulse or the multiband radio frequency pulse with different separation distances can be selected by the choose RF.
The magnitude of the diffusion gradient is periodically changed, so that the magnitude of the b value is changed, and the cyclic staggered acquisition of corresponding multi-voxel diffusion weighted magnetic resonance spectrum signals under different b values is realized;
specifically, the magnitude of the b value is processed according to the following formula:
taking the stimulated echo acquisition mode sequence as an example, in fig. 2, G1 and G2 are an added pair of diffusion gradients, γ is the gyromagnetic ratio of protons, G is the amplitude of the diffusion gradients, δ is the duration of the diffusion gradients, Δ is the diffusion time, and ε is the rising edge (or falling edge) time of the diffusion gradients;
s2.6.4 the same experimental parameters as in S2.6.3 were used to acquire the co-located single element diffusion weighted magnetic resonance spectrum signals.
S3, separating and reconstructing human brain spectrum signals acquired by the multi-voxel diffusion weighted magnetic resonance spectrum sequence by using the sensitivity information of the receiving coil, wherein the steps comprise:
s3.1, obtaining a low-resolution gray level image of each channel of the receiving coil by using a small-angle gradient echo sequence, and then obtaining a sensitivity map corresponding to each channel by using the gray level image of each channel;
the gray image in step S3.1 is obtained by: after all diffusion weighted mr spectrum signals are acquired, a gradient echo sequence (224 x 224mm imaging field of view) is scanned 3 The repetition time is 10ms, the echo time is 2.83ms, the flip angle is 25 degrees, the resolution is 3.5mm by 3.5 mm), the amplitude and the phase diagram of each channel of the receiving coil are obtained, and the amplitude and the phase diagram of each channel are multiplied to obtain a final gray image;
the sensitivity map of each channel of the receiving coil is obtained by processing according to the following formula:
wherein S is r (x, y) is the sensitivity map corresponding to the r-th channel, I r (x, y) is a low resolution gray scale image received by the R-th channel, R represents the total number of channels of the receiving coil;
and S3.2, carrying out voxel segmentation and registration on the sensitivity maps of all channels to obtain sensitivity maps corresponding to each voxel position, carrying out spatial dimension averaging on the sensitivity maps to obtain a sensitivity matrix S with the dimension of R x N, wherein the value of the ith row and the jth column in the sensitivity matrix S = the corresponding sensitivity value at the jth voxel position of the ith channel, wherein R is the total number of channels of a receiving coil, and N is the number of voxels excited simultaneously. In this embodiment, r=64, n=2.
S3.3, Q pixel points are taken at the same edge position of each channel gray level image, and the respective noise vector eta of each channel image is obtained according to the gray level value of each pixel point r ：
η r ＝[h 1 h 2 …h q …h Q ]
Wherein eta r For the noise vector corresponding to the r channel, h q The gray value at the q pixel point in the r channel;
in the specific implementation, the pixel points are selected to be positioned at the most edge position of the channel image and are positioned in corner areas outside the human brain image;
s3.4, then, the noise vector is processed according to the following formula to obtain a receiving coil noise covariance matrix ψ with the dimension of R:
wherein, psi is i,j Values of the j-th column in the i-th row for the receive coil noise covariance matrix ψ, η i For the noise vector corresponding to the ith channel, η j The noise vector corresponding to the j-th channel;
s3.5, processing according to the following formula to obtain an unfolding matrix U:
U＝(S H Ψ -1 S) -1 S H Ψ -1
wherein, the superscript H represents that the sensitivity matrix S is subjected to conjugate transposition;
s3.6, multiplying the expansion matrix U with the multi-voxel diffusion weighted magnetic resonance spectrum signals with the dimension of R, P and A to obtain multi-voxel diffusion weighted magnetic resonance spectrum signals with the dimension of N, P and A to finish the separation of the diffusion weighted magnetic resonance spectrum signals at different voxel positions, wherein P is the sampling point number of the multi-voxel diffusion weighted magnetic resonance spectrum signals, and A is the scanning times of the multi-voxel diffusion weighted magnetic resonance spectrum signals;
in this example, p=1024, a=80 when the water peak is scanned, and a=160 when the metabolite peak is scanned.
S3.7, carrying out frequency and phase correction on all multi-voxel diffusion weighted magnetic resonance spectrum signals corresponding to the same b value;
s3.8, arranging all multi-voxel diffusion weighted magnetic resonance spectrum signals corresponding to the same b value according to the peak height of a given magnetic resonance spectrum, selecting three multi-voxel diffusion weighted magnetic resonance spectrum signals with the peak heights of a second, a third and a fourth from all multi-voxel diffusion weighted magnetic resonance spectrum signals, taking 60 percent (under the condition of water inhibition on) or 80 percent (under the condition of water inhibition off) of the peak height average value of the three multi-voxel diffusion weighted magnetic resonance spectrum signals as a threshold value, discarding multi-voxel diffusion weighted magnetic resonance spectrum signals with the peak height lower than the threshold value, averaging the rest multi-voxel diffusion weighted magnetic resonance spectrum signals, and carrying out frequency and phase correction again to obtain the reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals under the b value.
S3.9, repeating the steps S3.7-S3.8 to obtain reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals under all b values;
s4, carrying out quantitative calculation on related parameters of the reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals, comparing the parameters with the performance of the single voxel diffusion weighted magnetic resonance spectrum, and verifying the feasibility of the technology:
s4.1, calculating g factors to evaluate the difference of the sensitivity profiles of the receiving coils corresponding to the positions of the voxels, wherein the formula is as follows:
according to the above formula, g= 1.0122 +/-0.0047 is obtained, which shows that the sensitivity profile difference of the receiving coils corresponding to the positions of the two voxels is large enough, so that the signal separation is facilitated;
s4.2, calculating the signal leakage ratio by obtaining the area under the peak of the water peak, and observing 3.92% +/-0.56% signal leakage at the voxel 2 when only the voxel 1 is excited. When only voxel 2 was excited, 7.05% ± 1.16% signal leakage was observed at voxel 1.
The specific calculation method of the leakage ratio is as follows:
when only one voxel is excited by using the single voxel diffusion weighted magnetic resonance spectrum sequence to obtain a water peak, the signal of each voxel is obtained by using the reconstruction method, the observed signal at the unexcited voxel is a leakage signal, and the leakage ratio of the unexcited voxel to the excited voxel can be obtained by dividing the area under the water peak of each unexcited voxel by the area under the water peak of the excited voxel.
S4.3, dividing the area under the peak of water by the standard deviation of the last 256 points of the corresponding metabolite spectrum time domain signal to obtain the signal to noise ratio. The spectral signal-to-noise ratio obtained for multi-voxel excitation divided by the signal-to-noise ratio obtained for the corresponding monomeric excitation is shown in table 1 below:
table 1 compares the spectral signal to noise ratios obtained for multi-voxel excitation and for monomeric excitation
It was thus possible to verify that the signal-to-noise ratio obtained for the multi-voxel diffusion weighted magnetic resonance spectroscopy sequences developed by the present invention is comparable to that obtained for the single-voxel diffusion weighted magnetic resonance spectroscopy sequences.
S4.4, quantifying the relative concentration of each metabolite molecule by using LCM odel software, and calculating the apparent diffusion coefficient of each metabolite molecule according to the following formula:
wherein S is 0 S (b) is a signal received when the diffusion gradient is not applied, D is an apparent diffusion coefficient, and b represents the magnitude of the diffusion weight.
Apparent diffusion coefficient (in μm of water molecules and main metabolite molecules 2 The results of the/ms) calculations are shown in Table 2 below:
TABLE 2 apparent diffusion coefficients for different molecules obtained by comparing multimeric and monomeric excitations
After t-test of paired samples, the multi-voxel diffusion weighted magnetic resonance spectrum sequence developed by the invention has no obvious difference between water molecules and main metabolite molecules ADC obtained at the voxels 1 and 2 and the single voxel diffusion weighted magnetic resonance spectrum sequence.
FIG. 5 is a schematic diagram showing metabolite peak spectra of a normal subject brain semicircle central region after reconstruction according to the present invention, with less leakage of the spectrum signal at different voxels, and substantially identical multi-voxel diffusion weighted MR spectrum signal and corresponding single-voxel diffusion weighted MR spectrum signal. Exciting only voxel 1 by using a single element diffusion weighted magnetic resonance spectrum sequence, obtaining a spectrum after reconstruction, obtaining a graph (a) at the voxel 1, and obtaining a signal leakage graph (b) at the voxel 2; exciting only voxel 2 by using a single element diffusion weighted magnetic resonance spectrum sequence, obtaining a spectrum after reconstruction, obtaining a signal leakage diagram 5 (c) at the voxel 1, and obtaining a diagram 5 (d) at the voxel 2; the use of the multi-voxel diffusion weighted magnetic resonance spectroscopy sequence developed by the present invention excites voxels 1 and 2 simultaneously, and spectra are obtained after reconstruction, with fig. 5 (e) at voxel 1 and fig. 5 (f) at voxel 2. The spectra in fig. 5 (a) to 5 (f) are in the same scale.
The multi-voxel diffusion weighted magnetic resonance spectrum parallel acquisition and reconstruction method provided by the invention can obtain the signal to noise ratio and apparent diffusion coefficient equivalent to those of the single voxel diffusion weighted magnetic resonance spectrum under the condition of saving at least half of scanning time, and has the clinical application prospect and feasibility.
Claims (10)
1. The diffusion weighted magnetic resonance spectrum parallel acquisition and reconstruction method is characterized by comprising the following steps of:
s1, adding a pair of diffusion gradients into a basic monomeric element spectrum sequence to obtain a monomeric element diffusion weighted magnetic resonance spectrum sequence;
the diffusion gradient adopts a pair of completely symmetrical diffusion gradients with opposite polarities;
s2, carrying out point-by-point summation on the single-band radio frequency pulse at each interested voxel position to obtain multi-band radio frequency pulse, replacing the single-band radio frequency pulse in the single-element diffusion weighted magnetic resonance spectrum sequence with the multi-band radio frequency pulse to obtain a multi-element diffusion weighted magnetic resonance spectrum sequence, and further obtaining a multi-element diffusion weighted magnetic resonance spectrum signal;
and S3, acquiring a sensitivity map of each channel of the receiving coil, and separating and reconstructing the multi-voxel diffusion weighted magnetic resonance spectrum signals according to the sensitivity map to finally obtain the separated and reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals.
2. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 1, wherein: the specific steps of the step S2 are as follows:
s2.1, determining the number of voxels to be acquired as N, and selecting initial positions of the N voxels;
s2.2, carrying out point-by-point summation on the single-band radio frequency pulse corresponding to the N voxel positions to obtain a multi-band radio frequency pulse;
s2.3, comparing excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error of the two is within the preset error range, the waveform of the multiband radio frequency pulse is considered to be correct, and the step S2.4 is continued;
otherwise, the waveform of the multiband radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire a new multiband radio frequency pulse again;
s2.4, changing the position of each voxel, and repeating the steps S2.2-S2.3 to obtain a plurality of multiband radio frequency pulses with correct waveforms;
s2.5, adding all the multiband radio frequency pulses obtained in the step S2.4 into a pulse file of a single voxel diffusion weighted magnetic resonance spectrum sequence, and selecting one multiband radio frequency pulse from the pulse file to replace an initial single-band radio frequency pulse so as to obtain a multiband diffusion weighted magnetic resonance spectrum sequence;
s2.6, importing the multi-voxel diffusion weighted magnetic resonance spectrum sequence into a magnetic resonance scanner, setting the number, the size and the acquisition times of b values, and then acquiring multi-voxel diffusion weighted magnetic resonance spectrum signals by using the magnetic resonance scanner.
3. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 1, wherein: the specific steps of the step S3 are as follows:
s3.1, acquiring a gray level image of each channel of the receiving coil by using a gradient echo sequence, and then acquiring a sensitivity map corresponding to each channel by using the gray level image of each channel;
s3.2, registering voxels on the sensitivity maps of all channels to obtain sensitivity maps corresponding to each voxel position, and carrying out spatial dimension averaging on the sensitivity maps to obtain a sensitivity matrix S with the dimension of R x N, wherein R is the total number of channels of the receiving coil;
s3.3, Q pixel points are taken at the same edge position of each channel gray level image, and the respective noise vector eta of each channel image is obtained according to the gray level value of each pixel point r ：
η r ＝[h 1 h 2 ... h q ... h Q ]
Wherein eta r For the noise vector corresponding to the r channel, h q The gray value at the q pixel point in the r channel;
s3.4, then, the noise vector is processed according to the following formula to obtain a receiving coil noise covariance matrix ψ with the dimension of R:
wherein, psi is i，j Values of the j-th column in the i-th row for the receive coil noise covariance matrix ψ, η i For the noise vector corresponding to the ith channel, η j The noise vector corresponding to the j-th channel;
s3.5, processing according to the following formula to obtain an unfolding matrix U:
U＝(S H Ψ -1 S) -1 S H Ψ -1
wherein, the superscript H represents that the sensitivity matrix S is subjected to conjugate transposition;
s3.6, multiplying the expansion matrix U by the multi-voxel diffusion weighted magnetic resonance spectrum signal with the dimension of R, P and A to obtain the multi-voxel diffusion weighted magnetic resonance spectrum signal with the dimension of N, P is the sampling point number of the multi-voxel diffusion weighted magnetic resonance spectrum signal, and A is the acquisition time number of the multi-voxel diffusion weighted magnetic resonance spectrum signal;
s3.7, carrying out frequency and phase correction on all multi-voxel diffusion weighted magnetic resonance spectrum signals corresponding to the same b value;
s3.8, arranging all multi-voxel diffusion weighted magnetic resonance spectrum signals corresponding to the same b value according to the peak height of a given magnetic resonance spectrum, selecting three multi-voxel diffusion weighted magnetic resonance spectrum signals with the peak heights of a second, a third and a fourth from all multi-voxel diffusion weighted magnetic resonance spectrum signals, taking 60% -80% of the peak height average value of the three multi-voxel diffusion weighted magnetic resonance spectrum signals as a threshold value, discarding multi-voxel diffusion weighted magnetic resonance spectrum signals lower than the threshold value, averaging the rest multi-voxel diffusion weighted magnetic resonance spectrum signals, and carrying out frequency and phase correction again to obtain the reconstructed multi-voxel diffusion weighted magnetic resonance spectrum signals under the b value.
4. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 2, wherein: in the step S2.6, after the multi-voxel diffusion weighted magnetic resonance spectrum sequence is introduced, the number of b values is set to be 2-10, and the size of the b values is set to be 0-3333S/mm 2 Under the condition of closing water inhibition, acquiring 2-16 times of multi-voxel diffusion weighted magnetic resonance spectrum signals from each b value; under the condition of starting water inhibition, each b value is used for collecting 16-64 times of multi-voxel diffusion weighted magnetic resonance spectrum signals.
5. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 2, wherein: the magnitude of the b value is obtained by processing according to the following formula:
where γ is the gyromagnetic ratio of the protons, g is the magnitude of the diffusion gradient, δ is the duration of the diffusion gradient, and Δ is the diffusion time.
6. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 2, wherein: in the step S2.2, the multiband radio frequency pulse is obtained according to the following formula:
wherein RF N Time domain waveform, RF, of multi-band radio frequency pulses s Is the time domain waveform of the initial single-band radio frequency pulse in the sequence, f n For the offset frequency at the nth voxel position, the subscript n is the ordinal number of the voxel and t is time.
7. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 2, wherein: the step S2.3 specifically comprises the following steps:
s2.3.1, the single-band radio frequency pulse and the multi-band radio frequency pulse are simulated by using a Bloch equation to obtain simulated excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse, and the excitation performance of the multi-band radio frequency pulse is initially verified:
comparing simulation excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error between the simulated excitation profile of the single-band radio frequency pulse and the simulated excitation profile of the multi-band radio frequency pulse is smaller than the preset simulated profile error value, the waveform of the multi-band radio frequency pulse is considered to be initially correct, and the step S2.3.2 is entered;
otherwise, the waveform of the multiband radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to acquire the multiband radio frequency pulse again;
s2.3.2, using a magnetic resonance scanner to perform actual test on the single-band radio frequency pulse and the multi-band radio frequency pulse respectively, so as to obtain actual excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse, and further verify excitation performance of the multi-band radio frequency pulse:
comparing the actual excitation profiles of the single-band radio frequency pulse and the multi-band radio frequency pulse:
if the error between the actual excitation profile of the single-band radio frequency pulse and the actual excitation profile of the multi-band radio frequency pulse is smaller than the preset actual profile error value, the waveform of the multi-band radio frequency pulse is considered to be correct, and the step S2.4 is carried out;
otherwise, the waveform of the multiband radio frequency pulse is considered to be incorrect, and the step S2.2 is returned to retrieve the multiband radio frequency pulse.
8. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 3, wherein: in the step S3.1, the sensitivity map of each channel of the receiving coil is obtained by processing according to the following formula:
wherein S is r (x, y) is the sensitivity map corresponding to the r-th channel, I r (x, y) is a gray scale image received by the R-th channel, and R represents the total number of channels of the receiving coil.
9. A diffusion weighted magnetic resonance spectroscopy parallel acquisition and reconstruction method according to claim 3, wherein: in the step S3.8, if the multi-voxel diffusion weighted magnetic resonance spectrum signal is collected under the condition of water inhibition, the given magnetic resonance spectrum peak adopts N-acetyl aspartic acid; if the multi-voxel diffusion weighted magnetic resonance spectroscopy signal is acquired under off water suppression conditions, then the given magnetic resonance spectroscopy peak employs a water peak.
10. An electronic device, characterized in that: comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which, when executed by the processor, perform the steps of the method of any of claims 1-9.
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Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1499218A (en) * | 2002-10-31 | 2004-05-26 | 西门子公司 | A Method for Determining ADC Coefficients Using Steady-State Sequences in Diffusion-Weighted Magnetic Resonance Imaging |
| EP1449004A1 (en) * | 2001-11-20 | 2004-08-25 | Koninklijke Philips Electronics N.V. | Multiple gradient echo type projection reconstruction sequence for mri and especially diffusion weighted mri |
| US20050237057A1 (en) * | 2004-04-13 | 2005-10-27 | Porter David A | Movement-corrected multi-shot method for diffusion-weighted imaging in magnetic resonance tomography |
| US20090273344A1 (en) * | 2008-04-22 | 2009-11-05 | Fain Sean B | Method for simultaneously measuring t2* and diffusion with magnetic resonance imaging |
| US20110254548A1 (en) * | 2010-04-15 | 2011-10-20 | Kawin Setsompop | Method for simultaneous multi-slice magnetic resonance imaging |
| CN102928796A (en) * | 2012-09-28 | 2013-02-13 | 清华大学 | Fast-diffused magnetic resonance imaging and restoring method |
| US20140266195A1 (en) * | 2013-03-12 | 2014-09-18 | Pavel Levin | Method and apparatus for magnetic resonance imaging |
| US20170212199A1 (en) * | 2016-01-25 | 2017-07-27 | Siemens Healthcare Gmbh | Method and apparatus for diffusion-weighted magnetic resonance imaging |
| US20170322280A1 (en) * | 2016-05-03 | 2017-11-09 | Siemens Healthcare Gmbh | Method and apparatus for parallel magnetic resonance data acquisition |
| CN107430179A (en) * | 2015-04-10 | 2017-12-01 | 通用电气公司 | Use the apparatus and method for repeatedly exciting Diffusion-Weighted MR Imaging of array manifold puppet sensitivity encoding techniques |
| US20180136297A1 (en) * | 2015-05-15 | 2018-05-17 | The Medical College Of Wisconsin, Inc. | Systems and methods for diffusion-weighted multi-spectral magnetic resonance imaging |
| US20180164395A1 (en) * | 2016-12-12 | 2018-06-14 | Siemens Healthcare Gmbh | Method and apparatus for accelerated magnetic resonance imaging |
| CN109085522A (en) * | 2018-07-02 | 2018-12-25 | 上海东软医疗科技有限公司 | A kind of acquisition method and device of Diffusion-weighted imaging and spectroscopic signal |
| WO2020037814A1 (en) * | 2018-08-23 | 2020-02-27 | 清华大学 | Equal voxel magnetic resonance diffusion imaging method and apparatus based on multi-plate simultaneous excitation |
| CN110889897A (en) * | 2019-11-21 | 2020-03-17 | 厦门大学 | Method and system for reconstructing incoherent motion magnetic resonance imaging parameters in voxel |
| CN114879110A (en) * | 2022-04-20 | 2022-08-09 | 浙江大学 | 2D plane echo diffusion imaging method, medium and equipment based on dual-frequency orthogonal gradient |
| CN115867817A (en) * | 2020-07-31 | 2023-03-28 | 马克思-普朗克科学促进协会 | Method and apparatus for acquiring and reconstructing a sequence of diffusion-weighted magnetic resonance images covering a volume |
-
2023
- 2023-09-14 CN CN202311184467.1A patent/CN117368818B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1449004A1 (en) * | 2001-11-20 | 2004-08-25 | Koninklijke Philips Electronics N.V. | Multiple gradient echo type projection reconstruction sequence for mri and especially diffusion weighted mri |
| CN1499218A (en) * | 2002-10-31 | 2004-05-26 | 西门子公司 | A Method for Determining ADC Coefficients Using Steady-State Sequences in Diffusion-Weighted Magnetic Resonance Imaging |
| US20050237057A1 (en) * | 2004-04-13 | 2005-10-27 | Porter David A | Movement-corrected multi-shot method for diffusion-weighted imaging in magnetic resonance tomography |
| US20090273344A1 (en) * | 2008-04-22 | 2009-11-05 | Fain Sean B | Method for simultaneously measuring t2* and diffusion with magnetic resonance imaging |
| US20110254548A1 (en) * | 2010-04-15 | 2011-10-20 | Kawin Setsompop | Method for simultaneous multi-slice magnetic resonance imaging |
| CN102928796A (en) * | 2012-09-28 | 2013-02-13 | 清华大学 | Fast-diffused magnetic resonance imaging and restoring method |
| US20140266195A1 (en) * | 2013-03-12 | 2014-09-18 | Pavel Levin | Method and apparatus for magnetic resonance imaging |
| CN107430179A (en) * | 2015-04-10 | 2017-12-01 | 通用电气公司 | Use the apparatus and method for repeatedly exciting Diffusion-Weighted MR Imaging of array manifold puppet sensitivity encoding techniques |
| US20180136297A1 (en) * | 2015-05-15 | 2018-05-17 | The Medical College Of Wisconsin, Inc. | Systems and methods for diffusion-weighted multi-spectral magnetic resonance imaging |
| US20170212199A1 (en) * | 2016-01-25 | 2017-07-27 | Siemens Healthcare Gmbh | Method and apparatus for diffusion-weighted magnetic resonance imaging |
| US20170322280A1 (en) * | 2016-05-03 | 2017-11-09 | Siemens Healthcare Gmbh | Method and apparatus for parallel magnetic resonance data acquisition |
| US20180164395A1 (en) * | 2016-12-12 | 2018-06-14 | Siemens Healthcare Gmbh | Method and apparatus for accelerated magnetic resonance imaging |
| CN109085522A (en) * | 2018-07-02 | 2018-12-25 | 上海东软医疗科技有限公司 | A kind of acquisition method and device of Diffusion-weighted imaging and spectroscopic signal |
| WO2020037814A1 (en) * | 2018-08-23 | 2020-02-27 | 清华大学 | Equal voxel magnetic resonance diffusion imaging method and apparatus based on multi-plate simultaneous excitation |
| CN110889897A (en) * | 2019-11-21 | 2020-03-17 | 厦门大学 | Method and system for reconstructing incoherent motion magnetic resonance imaging parameters in voxel |
| CN115867817A (en) * | 2020-07-31 | 2023-03-28 | 马克思-普朗克科学促进协会 | Method and apparatus for acquiring and reconstructing a sequence of diffusion-weighted magnetic resonance images covering a volume |
| CN114879110A (en) * | 2022-04-20 | 2022-08-09 | 浙江大学 | 2D plane echo diffusion imaging method, medium and equipment based on dual-frequency orthogonal gradient |
Non-Patent Citations (3)
| Title |
|---|
| BAMMER, R等: "Parallel imaging strategies for high-speed Magnetic Resonance diffusion imaging", 《MEDICAL IMAGING 2000: PHYSIOLOGY AND FUNCTION FORM MULTIDIMENSIONAL IMAGES》, 31 December 2000 (2000-12-31) * |
| LIU, YY 等: "Diagnostic performance of T2-weighted imaging and intravoxel incoherent motion diffusion-weighted MRI for predicting metastatic axillary lymph nodes in T1 and T2 stage breast cancer", 《ACTA RADIOLOGICA》, vol. 63, no. 4, 30 April 2022 (2022-04-30) * |
| 高嵩;张怀岑;包尚联;: "基于磁共振扩散加权波谱技术的水分子扩散测量方法", 航天医学与医学工程, no. 02, 15 April 2008 (2008-04-15) * |
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