CN112051531B - Multi-excitation navigation-free magnetic resonance diffusion imaging method and device - Google Patents
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Abstract
The invention provides a multiple-excitation non-navigation magnetic resonance diffusion imaging method and a device, wherein the method comprises the following steps: exciting a target to be detected for multiple times, and acquiring a diffusion magnetic resonance imaging signal without acquiring a navigation signal in the signal acquisition process; reconstructing and generating calculation navigation information by using the acquired imaging signals; performing joint interpolation on the k space by using the calculated navigation information to combine the signals of multiple channels and multiple excitations to recover the signals and reconstruct an image; in the process of recovering the signal, a calculation navigation information optimization algorithm taking phase smoothing as a prior constraint condition is provided, and the signal-to-noise ratio of the image is further improved.
Description
Technical Field
The invention belongs to the field of biophysics, and relates to a method and a device for multi-excitation non-navigation magnetic resonance diffusion imaging.
Background
Magnetic resonance imaging (dMRI) is the only non-invasive method capable of measuring microscopic diffusion information of water molecules in tissues in vivo. Under the condition of normal temperature, water molecules do random thermal motion (brownian motion) continuously, and in biological tissues, the motion of the water molecules is limited by the internal structure of the tissues. Therefore, the microstructure of the tissue can be revealed by measuring diffusion information of water molecules in the tissue. The magnetic resonance diffusion imaging can obtain unique information which can not be provided by other imaging modes such as diffusion coefficients of water molecules in biological tissues along all directions and the like, has the advantages of non-invasion, no need of contrast agents and the like, draws great attention in the fields of theoretical research and clinical practice, and has wide application prospect. Currently, magnetic resonance diffusion imaging is mainly applied to two aspects: the first is that in the aspect of pathological change diagnosis, besides the diagnosis of stroke, the medicine is also used for diagnosing tumors (brain lymphoma, epidermic and cholesteatoma cyst), infection (suppurative brain abscess and encephalitis gangrene), degenerative diseases (Creutzfeldt-Jakob disease), inflammatory diseases (multiple sclerosis), traumas (shock and fracture) and the like; the second is the fiber bundle structure used for researching biological tissues (white matter, cardiac muscle, tongue, etc.).
In diffusion imaging, the applied diffusion sensitive gradient field is a strong gradient field, which is particularly sensitive to motion, and can realize micron-scale motion detection sensitivity. This makes the diffusion imaging technique not only detect the diffusion movement of water molecules in the tissue, but also be sensitive to some physiological movements of the human body such as heartbeat, respiration, blinking, etc. (submillimeter-level movements), and subjective movements of the human body such as rotation and translation of the head, etc., which easily produce image artifacts. At present, the clinical routine magnetic resonance diffusion imaging method is a single-shot imaging method, and has low imaging resolution and large imaging size. The recently developed multi-excitation diffusion imaging method has high resolution and small deformation, but the problem of inconsistent phase caused by the combination of diffusion imaging and physiological motion exists between each excitation, and the acquisition of navigation signals is required to be used for image artifact correction caused by phase errors besides the acquisition of imaging signals, so that the data acquisition time is long.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention aims to provide a method and a device for non-navigation magnetic resonance diffusion imaging based on multiple excitations, so that the scanning time is short, the image artifact is small, and the signal-to-noise ratio is high.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a navigation-free magnetic resonance diffusion imaging method based on multiple times of excitation, which is characterized by comprising the following steps:
1) Data acquisition: exciting a target to be detected for multiple times, and acquiring signals of the target to be detected in each exciting process;
2) And (3) calculating navigation information generation: generating multi-channel k-space data of each excitation through a parallel imaging technology;
3) And (3) multi-excitation diffusion image reconstruction: and performing joint interpolation on the k space by using the calculated navigation information to combine the signals of multiple channels and multiple excitations to recover the signals and reconstruct an image.
Further, the signal acquisition in 1) comprises imaging echo signal acquisition.
Further, the parallel imaging technology in 2) adopts a reconstruction algorithm based on k-space data processing, including but not limited to AUTO-SMASH, VD-AUTO-SMASH, GRAPPA, SPIRiT algorithm.
In a specific embodiment of the present invention, the parallel imaging technique employs GRAPPA algorithm.
Further, step 3) further comprises performing inverse fourier transform on the k-space data obtained by the joint interpolation technique.
Further, the step 3) also comprises the step of obtaining a final diffusion image in a root-mean-square form.
Preferably, the root-mean-square calculation formula:
further, the method further comprises performing optimization of the calculation of the navigation information before step 3).
Further, the navigation information is optimized and calculated by taking phase smoothing as a priori constraint condition.
Further, the phase smoothing processing step is as follows:
1) Performing inverse Fourier transform on k-space data of the computed navigation, and performing channel synthesis on an obtained multi-channel image by adopting a parallel imaging technology to obtain a computed navigation complex image;
2) Performing two-dimensional low-pass filtering on the calculated navigation complex image, and extracting a complex phase image;
3) Carrying out complex multiplication on the multichannel b =0 image and the complex phase image in 2) to obtain a new computational navigation complex image;
4) And carrying out Fourier transform on the new computed navigation complex image to obtain new computed navigation information of the multi-channel coil and multiple times of excitation.
Further, the parallel imaging technique employs image domain reconstruction algorithms including, but not limited to, SENSE, SC-SENSE algorithms.
In an embodiment of the present invention, the reconstruction algorithm of the image domain is a SENSE algorithm.
Further, the formula for extracting the complex phase diagram is as follows: complex phase computation navigates complex images/abs (complex image) which represents taking the magnitude of the complex image.
A second aspect of the invention provides a navigator-free magnetic resonance diffusion imaging apparatus based on multiple excitations, comprising:
the acquisition module is used for exciting a target to be detected for multiple times and acquiring signals of the target to be detected in each excitation process;
the navigation information calculating module is used for generating multi-channel k-space data excited each time;
and the diffusion image reconstruction module is used for performing joint interpolation on the k space by using the optimized calculation navigation information in combination with the multi-channel and multi-excitation signals to recover the signals and reconstruct the image.
Further, the acquisition module includes:
a diffusion preparation unit for diffusion preparing gradient and diffusion coding;
and the image acquisition unit is used for acquiring imaging echo signal data.
Further, the module for calculating navigation information further adopts a parallel imaging technology to generate multi-channel k-space data of each excitation.
Further, the parallel imaging technique employs a reconstruction algorithm based on k-space data processing, including but not limited to AUTO-SMASH, VD-AUTO-SMASH, GRAPPA, spiit algorithms.
Preferably, the parallel imaging technique employs a GRAPPA algorithm.
Further, the device also comprises a calculation navigation information optimization module, wherein the calculation navigation information optimization module is used for optimizing the data generated by the calculation navigation information module;
further, the navigation information optimization calculation module optimizes the navigation information calculation by taking phase smoothing as a priori constraint condition.
Further, the phase smoothing processing step is as follows:
1) Performing inverse Fourier transform on k-space data of the computed navigation, and performing channel synthesis on an obtained multi-channel image by adopting a parallel imaging technology to obtain a computed navigation complex image;
2) Performing two-dimensional low-pass filtering on the calculated navigation complex image, and extracting a complex phase image;
3) Carrying out complex multiplication on the multichannel b =0 image and the complex phase image in 2) to obtain a new computational navigation complex image;
4) And carrying out Fourier transform on the new computed navigation complex image to obtain new computed navigation information of the multi-channel coil and multiple times of excitation.
Further, parallel imaging techniques employ image domain reconstruction algorithms including, but not limited to, SENSE, SC-SENSE algorithms.
In an embodiment of the invention, the reconstruction algorithm of the image domain is a SENSE algorithm.
In the invention, the diffusion image reconstruction module further comprises an inverse fourier transform module for performing inverse fourier transform on the k-space data obtained by the joint interpolation technique.
Further, the diffusion image reconstruction module also obtains a final diffusion image in a root-mean-square form.
Preferably, the root mean square calculation formula:
the term "low-pass filtering", also known as noise-removing mask method, has an action effect just opposite to that of high-pass filtering, and can effectively retain low-frequency information in a target image and weaken high-frequency information in the image. In general, the low frequency components in the image constitute the background region and the brightness gradient region of most of the area of the image, and the high frequency components form the noise of the image. The essence of the low pass filtering process is to perform a weighted summation, or simply convolution, of all the pixels of a given area in the image. The specific calculation process is that a neighborhood to be processed with a specific shape in an image is selected, then each pixel in the neighborhood is multiplied by a corresponding element in a convolution kernel, the result of product summation is a new value of a template center pixel, and the element in the convolution kernel is called a weighting coefficient. Two-dimensional low-pass filtering can be performed in the present invention using filtering methods conventional in the art, including but not limited to hanning windows, hamming windows, bartlett-Hann windows, blackman windows, kaiser windows, and the like.
The term "SENSE algorithm" mainly uses the coil sensitivity function to obtain the weight of opening the aliasing of the image, so that the quality of the final reconstructed image is closely related to the accuracy of the coil sensitivity function. The "SC-SENSE" algorithm is a more commonly used SENSE algorithm, with undersampled acquisition of the periphery of k-space and full sampling of the center of k-space. The data at the center of k-space is used to generate a low resolution but complete image as a function of coil sensitivity.
The term "GRAPPA" can be regarded as an extension of VD-AUTO-SMASH, which reconstructs the non-acquired data lines by a reconstruction block, taking the reconstruction of the data line with the jth coil offset from the sampled data line by m as an example, the reconstruction formula can be expressed as follows:
wherein N is b And the number of reconstruction blocks in the reconstruction process is shown, the reconstruction blocks are composed of data sampled by one line and data not sampled by R-1 line, n (j, b, l and m) is a weight coefficient, l is the index of a coil, and b is the index of the reconstruction blocks. The more reconstruction blocks contained in the formula, the better the reconstruction effect.
The invention has the advantages and beneficial effects that:
compared with the traditional multiple excitation diffusion imaging method, the magnetic resonance diffusion imaging method does not need to acquire any navigation signal, so that the image acquisition efficiency is improved; in addition, the method utilizes the reconstruction of the imaging signal to generate the calculation navigation information for the reconstruction of the diffusion image, and can remove the phase error in the multi-excitation diffusion imaging and the artifact generated by the phase error; meanwhile, the resonance diffusion imaging method of the invention uses phase smoothing as a prior constraint condition to update and calculate navigation information and reconstruct a diffusion image, and can improve the signal-to-noise ratio of the reconstructed diffusion image.
Drawings
Fig. 1 is a multi-shot diffusion imaging pulse plan.
FIG. 2 is a diffusion image map constructed by different methods.
Detailed Description
The present invention is further illustrated below with reference to specific examples, which are provided only for the purpose of illustration and are not meant to limit the scope of the present invention.
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified.
Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
Example 1 multiple excitation navigator-free magnetic resonance diffusion imaging method
1. Data acquisition
The method comprises the steps of exciting a detected target for multiple times, collecting signals of the detected target in the process of each excitation, and collecting imaging echo signals of multiple excitation diffusion imaging pulses, as shown in figure 1.
2. Data processing
1) And calculating and obtaining calculated navigation information from the imaging echo signal, wherein the imaging echo signal when the diffusion gradient is opened corresponds to diffusion image data, and the program echo signal when the diffusion gradient is closed corresponds to b =0 image data: and calculating navigation information, namely calculating a multichannel coil interpolation coefficient from k-space data of b =0 by using a GRAPPA parallel imaging technology and a k-space interpolation technology, interpolating data of each excitation in k-space of diffusion data, and filling k-space data which is not acquired, wherein the generated multichannel k-space data of each excitation is used for calculating navigation information.
2) And (3) performing multi-excitation diffusion image reconstruction by utilizing the calculated navigation information: after navigation information is generated and calculated in the previous step, a multi-channel coil and multi-excitation combined k-space interpolation technology is utilized, interpolation is carried out on data excited every time in k-space of diffusion data, k-space data which are not acquired are filled, complete k-space excited every time is generated, inverse Fourier transform is carried out on the k-space, and root-mean-square is used
In such a manner that a final diffusion image is obtained.
3) The method comprises the following steps of (1) calculating navigation information optimization technology by taking phase smoothing as a prior constraint condition: before multi-channel coil and multi-excitation joint k-space interpolation, the following phase smoothing processing is carried out on the calculated navigation information:
a) Performing inverse Fourier transform on k-space data for calculating navigation, and performing channel synthesis on the obtained multi-channel image by using a SENSE algorithm to obtain a complex image for calculating navigation;
b) Two-dimensional low-pass filtering [ e.g., using a two-dimensional hanning window, hamming window, etc. ] is performed on the computed navigation complex number image, and a complex phase map is extracted [ complex phase map extraction formula: complex phase = compute navigation complex image/abs (complex image) where abs (complex image) denotes taking the magnitude of the complex image ];
c) Carrying out complex multiplication on the multichannel b =0 image and the previous-step complex phase image to obtain a new calculation navigation complex image;
d) Carrying out Fourier transform on the new computational navigation complex number image to obtain a multi-channel coil and new computational navigation information excited for multiple times;
e) And 3) utilizing the k-space interpolation technology in the step 2) to reconstruct a diffusion image by using new calculated navigation information.
Embodiment 2 application of multiple excitation non-navigation magnetic resonance diffusion imaging method
1. Experimental materials:
A3.0T magnetic resonance experiment of a brain of a healthy volunteer is carried out, the experimental equipment is magnetic resonance of Philips 3.0T main field intensity, the model is Achieva TX, and data are acquired by 8-channel head coils of a machine original factory.
2. The experimental method comprises the following steps:
the experiment acquires the whole brain diffusion image of a healthy volunteer, and the imaging visual field is 210 multiplied by 210mm 2 Imaging voxel size of voxel size =1 × 1 × 4mm 3 Imaging 20 layers, interlayer spacing 1mm, echo Time (TE) =86ms, repetition Time (TR) =3500ms, scanning was performed using 6 excitation diffusion imaging sequences, and the number of scan repetitions =2.
The experiment will compare the artifact correction without image phase error, the artifact correction using the traditional GRAPPA method, the artifact correction using the computed navigation information and the artifact correction method using the computed navigation information optimized by taking phase smoothing as a priori constraint condition. And evaluating the image quality by using an image quality observation and image signal-to-noise ratio calculation method.
3. Results of the experiment
The experimental image reconstruction result is shown in fig. 2, and it can be seen that the result artifact obtained without image phase error artifact correction is obvious and cannot be used; most artifacts can be removed by using the traditional GRAPPA method for artifact correction, but the signal loss (black hole artifacts) still exists in the center of the human brain, and the image noise is serious; the result of artifact correction is calculated by using the navigation information acquired and calculated without navigation, so that the noise of the image is obviously reduced; after the phase smoothing is added as the calculated navigation information optimized by the prior constraint condition, the signal missing artifact is reduced, and the integral image quality is highest.
The signal-to-noise ratios of the 20 layers of images are shown in table 1, the average image signal-to-noise ratios of the artifact correction method using the conventional GRAPPA method, the artifact correction method using the non-navigation acquisition computed navigation information of the present patent, and the artifact correction method using the computed navigation information of the present patent optimized by using phase smoothing as a priori constraint condition are 6.67,7.03 and 7.77, the image signal-to-noise ratio of the method proposed in the present patent is significantly higher than that of the conventional method, and the image signal-to-noise ratio of the image reconstruction method using the computed navigation information optimized in the present patent is the highest.
TABLE 1 SNR for 20 layer images
The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.
Claims (11)
1. A multiple-excitation-based non-navigation magnetic resonance diffusion imaging method is characterized by comprising the following steps of:
1) Data acquisition: exciting a target to be detected for multiple times, and acquiring imaging echo signals of the target to be detected in each exciting process;
2) And (3) calculating navigation information generation: generating multi-channel k-space data of each excitation by using imaging echo signals through a parallel imaging technology; the imaging echo signal when the diffusion gradient is opened corresponds to diffusion image data, and the imaging echo signal when the diffusion gradient is closed corresponds to b =0 image data;
3) And (3) calculating navigation information optimization: and optimally calculating navigation information by taking phase smoothing as a prior constraint condition, wherein the phase smoothing processing steps are as follows:
i) Performing inverse Fourier transform on k-space data of the computed navigation information to obtain a multi-channel image, and performing channel synthesis on the multi-channel image by adopting a parallel imaging technology to obtain a computed navigation complex image;
ii) performing two-dimensional low-pass filtering on the computed navigation complex number image, and extracting a complex phase map, wherein the formula for extracting the complex phase map is as follows: complex phase = compute navigation complex image/abs (complex image) where abs (complex image) indicates taking the magnitude of the complex image;
iii) Carrying out complex multiplication on the multichannel b =0 image and the complex phase image in ii) to obtain a new calculation navigation complex image;
iv) carrying out Fourier transform on the new computed navigation complex image to obtain new computed navigation information which is multi-channel and excited for multiple times;
4) And (3) multi-excitation diffusion image reconstruction: and performing joint interpolation on the k space by using the optimized calculation navigation information in combination with the multi-channel and multi-excitation signals to recover the signals and reconstruct an image.
2. The method of claim 1, wherein the parallel imaging technique of 2) employs a reconstruction algorithm based on k-space data processing.
3. The method according to claim 2, wherein the k-space data processing based reconstruction algorithm is a GRAPPA algorithm.
4. The method of claim 1, wherein the parallel imaging technique in i) employs an image domain reconstruction algorithm.
5. The method according to claim 4, characterized in that the reconstruction algorithm of the image domain is a SENSE algorithm.
6. A multi-shot based navigator-less magnetic resonance diffusion imaging apparatus, comprising:
the acquisition module is used for exciting a target to be detected for multiple times and acquiring imaging echo signals of the target to be detected in each excitation process;
the calculation navigation information generation module is used for generating multi-channel k-space data excited each time by utilizing an imaging echo signal through a parallel imaging technology; the imaging echo signal when the diffusion gradient is opened corresponds to diffusion image data, and the imaging echo signal when the diffusion gradient is closed corresponds to b =0 image data;
the calculation navigation information optimization module is used for optimizing the data generated by the calculation navigation information generation module and optimizing the calculation navigation information by taking phase smoothness as a prior constraint condition; the phase smoothing processing steps are as follows:
1) Performing inverse Fourier transform on k-space data of the navigation information to obtain a multi-channel image, and performing channel synthesis on the multi-channel image by adopting a parallel imaging technology to obtain a navigation complex number image;
2) Performing two-dimensional low-pass filtering on the calculated navigation complex image, and extracting a complex phase image; the formula for extracting the complex phase diagram is as follows: complex phase = compute navigation complex image/abs (complex image) where abs (complex image) indicates taking the magnitude of the complex image;
3) Carrying out complex multiplication on a multichannel b =0 image and the complex phase image in 2) to obtain a new computational navigation complex image;
4) Carrying out Fourier transform on the new computational navigation complex image to obtain new computational navigation information which is multi-channel and excited for many times;
and the diffusion image reconstruction module is used for performing joint interpolation on the k space by using the optimized calculation navigation information in combination with the multi-channel and multi-excitation signals to recover the signals and reconstruct the image.
7. The apparatus of claim 6, wherein the acquisition module comprises:
a diffusion preparation unit for diffusion preparing gradient and diffusion coding;
and the image acquisition unit is used for acquiring imaging echo signal data.
8. The apparatus of claim 7, wherein the parallel imaging technique in generating multi-channel k-space data for each shot by a parallel imaging technique using imaging echo signals employs a reconstruction algorithm based on k-space data processing.
9. The apparatus of claim 8, wherein the k-space data processing based reconstruction algorithm is a GRAPPA algorithm.
10. The apparatus of claim 6, wherein the parallel imaging technique of 1) employs an image domain reconstruction algorithm.
11. The apparatus of claim 10, wherein the reconstruction algorithm of the image domain is a SENSE algorithm.
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