CN117368817A - Image reconstruction method, magnetic resonance imaging method and computer device - Google Patents
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Abstract
The present application relates to an image reconstruction method, a magnetic resonance imaging method and a computer device, the method comprising: acquiring a K space calibration data set corresponding to the target part, wherein the K space calibration data set is fully sampled in a central area of the K space; acquiring a plurality of undersampled K space data sets corresponding to a target part, wherein each undersampled K space data set is data acquired by the target part in one excitation; based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets; performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets; and reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times. In this way, the magnetic resonance image quality of the target region generated from the target K-space dataset is better.
Description
Technical Field
The present disclosure relates to the field of magnetic resonance imaging, and in particular, to an image reconstruction method, a magnetic resonance imaging method, and a computer device.
Background
The parallel magnetic resonance imaging technology adopts a multi-element coil array to collect K space data at the same time, allows undersampling of K space to reduce the number of phase encoding steps, thereby greatly shortening the magnetic resonance scanning time and improving the imaging speed under the condition of keeping the spatial resolution of an image unchanged.
In the related art, in the image reconstruction process based on the K-space data, the collected small-range fully-sampled calibration data (calibration lines) of the K-space central region is used as a recovery reference of the non-sampled data, so as to synthesize complete K-space data. To speed up the acquisition process or to be limited by factors such as sequence design, one calibration data is often shared when reconstructing multiple magnetic resonance images.
However, the above method may cause artifacts in the reconstructed image during image reconstruction, and the quality of the reconstructed image is poor.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an image reconstruction method, a magnetic resonance imaging method, and a computer device that can improve the quality of reconstructed images in parallel magnetic resonance imaging.
In a first aspect, the present application provides an image reconstruction method, the method comprising:
Acquiring a K space calibration data set corresponding to the target part, wherein the K space calibration data set is fully sampled in a central area of the K space;
acquiring a plurality of undersampled K space data sets corresponding to a target part, wherein each undersampled K space data set is data acquired by the target part in one excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets;
and reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
In one embodiment, based on the K-space calibration data set, performing a first fitting recovery on each undersampled K-space data set at an undersampled point of a central region of K-space, respectively, includes:
constructing a data recovery matrix according to the K space calibration data set and the undersampled K space data set aiming at any undersampled K space data set;
and carrying out first fitting recovery on the non-sampling points of each sampling K space data set in the central area of the K space based on the data recovery matrix corresponding to each undersampled K space data set.
In one embodiment, constructing a data recovery matrix from the K-space calibration dataset and the undersampled K-space dataset includes:
based on a K space calibration data set, a first low-rank matrix is constructed by adopting a preset low-rank matrix construction mode;
constructing a second low-rank matrix by adopting a low-rank matrix construction mode based on the undersampled K space data set;
and generating a data recovery matrix according to the first low-rank matrix and the second low-rank matrix.
In one embodiment, the process of constructing a low rank matrix includes:
extracting a preset number of different first data points from a target data set, and acquiring coordinate information of each first data point; the target data set is a K space calibration data set or an undersampled K space data set;
for any one first data point, acquiring a plurality of second data points with the distance smaller than the preset length from the first data point to obtain a data point set corresponding to the first data point;
acquiring signal values of a plurality of second data points in each data point set;
and constructing a low-rank matrix according to the coordinate information of each first data point and the signal values of a plurality of second data points in the data point set corresponding to each first data point.
In one embodiment, performing a second fit recovery on non-sampled points of the plurality of intermediate K-space datasets in a non-central region of K-space, includes:
Calculating a weight kernel of an un-sampled point of each intermediate K space data set in a non-central area of the K space by taking fitting full-sampled data of each intermediate K space data set in the central area of the K space as a reference;
and aiming at any intermediate K space data set, carrying out second fitting recovery on the non-sampling points of the non-central area according to the weight kernels of the non-sampling points.
In a second aspect, the present application also provides a magnetic resonance imaging method comprising:
filling a central region of the K space by adopting a partial sampling technology to obtain a K space calibration data set corresponding to the target part;
exciting the target part for multiple times, and collecting undersampled K space data sets corresponding to each excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on non-sampling points of a plurality of intermediate K space data sets in a non-central area of the K space to obtain a plurality of target K space data sets
And reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
In one embodiment, the direction of the dispersion gradient applied by each of the multiple excitations is different.
In one embodiment, the physiological phase of the target site for each of the multiple excitations is different.
In one embodiment, a marker pulse is applied to the target site for each of a plurality of excitations, and an undersampled K-space dataset is acquired at different delay times after the marker pulse is applied.
In a third aspect, the present application also provides an image reconstruction apparatus, the apparatus comprising:
the calibration data acquisition module is used for acquiring a K space calibration data set corresponding to the target part, and the K space calibration data set is fully sampled in the central area of the K space;
the undersampled data acquisition module is used for acquiring a plurality of undersampled K space data sets corresponding to the target part, and each undersampled K space data set is data acquired by the target part in one excitation;
the first data recovery module is used for carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space based on the K space calibration data set so as to acquire a plurality of intermediate K space data sets;
the second data recovery module is used for carrying out second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets;
And the image reconstruction module is used for reconstructing a plurality of target K space data sets and acquiring magnetic resonance images corresponding to the target parts excited for a plurality of times.
In a fourth aspect, the present application also provides a magnetic resonance imaging apparatus, the apparatus comprising:
the data acquisition module is used for filling the central area of the K space by adopting a partial sampling technology to acquire a K space calibration data set corresponding to the target part;
the scanning module is used for exciting the target part for multiple times and collecting undersampled K space data sets corresponding to each excitation;
the first data recovery module is used for carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space based on the K space calibration data set so as to acquire a plurality of intermediate K space data sets;
a second data recovery module for performing a second fitting recovery on the non-sampling points of the plurality of intermediate K-space data sets in the non-central region of the K-space to obtain a plurality of target K-space data sets
And the imaging module is used for reconstructing a plurality of target K space data sets and acquiring magnetic resonance images corresponding to the target parts excited for a plurality of times.
In a fifth aspect, the present application further provides a computer device comprising a memory storing a computer program and a processor implementing the steps of an embodiment of any one of the methods of the first and second aspects described above when the computer program is executed by the processor.
In a sixth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of an embodiment of any of the methods of the first and second aspects described above.
In a seventh aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of an embodiment of any one of the methods of the first and second aspects described above.
In the above-described image reconstruction method, magnetic resonance imaging method, and computer apparatus, first, a K-space calibration dataset corresponding to a target portion and a plurality of undersampled K-space datasets corresponding to the target portion are acquired. The K space calibration data set is fully sampled in the central area of the K space, and each undersampled K space data set is data acquired by the target part in one excitation. Then, based on the K space calibration data sets, first fitting recovery is carried out on the non-sampling points of each undersampled K space data set in the central area of the K space respectively so as to obtain a plurality of intermediate K space data sets. Further, second fitting recovery is carried out on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space, and a plurality of target K space data sets are obtained. And finally, reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target parts excited for a plurality of times. That is, the present application does not directly use the K-space calibration dataset as a reference to fit and recover the non-sampled data in the undersampled K-space dataset. And according to the K space calibration data set, first fitting recovery is carried out on the non-sampling points in the central region of each undersampled K space data set, and full-sampling data of the central region can be obtained through the first fitting recovery. Further, taking the fully sampled data of the central area obtained by the first fitting recovery as a reference, and carrying out the second fitting recovery on the non-sampled data points of the non-central area to obtain a target K space data set. Therefore, the intermediate K space data set obtained after the first fitting recovery is carried out on the non-sampling points of the central area of the K space is taken as a reference, the fully sampled data of the intermediate K space data set in the central area of the K space and the non-sampling points of the undersampled K space data set in the non-central area of the K space can be guaranteed, the data obtained by the same excitation belong to the data, the data matching degree is high, the target K space data set after the second fitting recovery also accords with the data situation obtained by actual fully sampling, and the magnetic resonance image quality of the target part generated based on the target K space data set is better.
Drawings
FIG. 1 is a flow chart of an image reconstruction method in one embodiment;
FIG. 2 is a schematic diagram of a K-space standard dataset in one embodiment;
FIG. 3 is a schematic diagram of a central region data fit restoration of an undersampled K-space dataset in one embodiment;
FIG. 4 is a flow diagram of a first fit recovery operation in one embodiment;
FIG. 5 is a schematic diagram of constructing a data recovery matrix in one embodiment;
FIG. 6 is a flow diagram of a second fit recovery operation in one embodiment;
FIG. 7 is a flow chart of a method of magnetic resonance imaging in one embodiment;
FIG. 8 is a block diagram of an image reconstruction apparatus in one embodiment;
figure 9 is a block diagram of a magnetic resonance imaging apparatus in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Magnetic resonance imaging (magnetic resonance imaging, MRI) is a technique for tomographic imaging of the human body using the principles of nuclear magnetic resonance. It can provide various images of human soft tissues, and has rapidly been developed as an important application technology in biomedicine. The imaging process has no radioactive pollution and high resolution, and can be used for imaging at any level. Moreover, different from the existing various imaging technologies, the factors involved in the magnetic resonance imaging are more, the obtained image information is large in quantity, and the imaging method has great superiority and application potential in medical diagnosis.
However, in some clinical applications, in addition to requiring high spatial resolution of the image, there is a need to reduce imaging time to reduce motion artifacts. For example, in the fields of real-time imaging of the cardiovascular system and imaging of brain functions, the effects of factors such as heart motion, respiration and blood flow must be considered. Magnetic resonance imaging requires continuous spatial encoding of gradients by encoding spatial information of fourier images with gradient fields, and imaging speed is greatly dependent on the performance of the gradient system of the magnetic resonance apparatus, such as gradient field strength and switching rate. To meet the need for fast imaging, the performance of gradient fields has been greatly enhanced, and at the same time, new problems have arisen, and the cost of gradient hardware systems is increasing. In addition, too high a gradient field switching rate can cause neuromuscular electromagnetic stimulation. The increase in imaging speed, which depends on gradient field performance, reaches a limit, and it is clinically desirable to give a more efficient method to increase imaging speed.
In order to improve the imaging speed, magnetic resonance Parallel imaging (parallell MRI) adopts a plurality of phased array coils to simultaneously receive induction signals, so that the number of gradient encoding times can be reduced, thereby greatly shortening the scanning time and improving the imaging speed.
Specifically, the parallel magnetic resonance imaging uses the small-range full-sampling calibration data (calibration lines) acquired in the central region of the K space as a reference for the recovery of the non-acquired data, calculates a weight kernel (weighting kernel) capable of fitting to obtain the non-sampled data, and applies the weight kernel to the undersampled data to be reconstructed in the next data synthesis process to synthesize the complete K space data.
In this process, accurate full sample calibration data is critical to imaging quality. If the acquired full-sampling calibration data are not matched with the undersampled data to be reconstructed, or the quality of the full-sampling calibration data is poor, errors are generated in the calculated weight cores, and the errors are introduced into a subsequent data synthesis stage, so that the reconstructed image has the image quality reduction phenomena such as artifact or blurring.
In some applications of sequence scanning, in order to accelerate the data acquisition speed or be limited by factors such as sequence design, it is often necessary to share one full-sampling calibration data when reconstructing a plurality of magnetic resonance images. The motion of the scanned object in the scanning process or the influence of other factors can cause deviation between the fully sampled calibration data and the undersampled data obtained by scanning, thereby influencing the quality of the reconstructed image.
Based on the above, the application provides an image reconstruction method, based on the fully adopted calibration data acquired before scanning, for undersampled K-space data sets acquired by scanning a target part of a scanned object each time, corresponding target calibration data are constructed, and the undersampled data are recovered by using the target calibration data corresponding to each undersampled K-space data set, so that complete K-space data are obtained, and the quality of a reconstructed image is better.
The image reconstruction method provided by the application can be applied to an image reconstruction device, the image reconstruction device can be realized in a software and/or hardware mode, and the device can be integrated in a computer device with a medical image processing function, for example: an imaging device in the magnetic resonance system, or any computer device outside the magnetic resonance system.
The imaging device in the magnetic resonance system is used for filling magnetic resonance signals into the K space and carrying out image reconstruction according to the K space data to obtain a target magnetic resonance image. The computer device outside the magnetic resonance system may be any terminal or server, wherein the terminal may include, but is not limited to, software running in a physical device, such as an application or a client installed on the device, and may also include, but is not limited to, a personal computer, a notebook, a smart phone, a tablet, and a portable wearable device in which the application is installed. Servers may include, but are not limited to, at least one standalone server, distributed servers, cloud servers, and server clusters.
The following will specifically describe, by way of examples and with reference to the accompanying drawings, how the technical solutions of the embodiments of the present application solve the above technical problems. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. It should be noted that, in the image reconstruction method provided in the embodiment of the present application, the execution body may be a magnetic resonance imaging device, or may be a computer device, or may be an image reconstruction apparatus provided in the present application. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present application.
In one embodiment, as shown in fig. 1, there is provided an image reconstruction method, which is exemplified as the method applied to a computer device, including the steps of:
step 110: and acquiring a K space calibration data set corresponding to the target part, wherein the K space calibration data set is fully sampled in the central area of the K space.
The target portion may be any portion to be detected of the scan object, and if the scan object is a human body, the target portion may be a head, a chest, an abdomen, or the like. Of course, the object to be scanned may be another organism, which is not limited in this embodiment.
It should be noted that, the K-space calibration data set may be acquired in a pre-scan process, or may be acquired in a positioning process, or may be acquired in a sequence scan process, which in this embodiment does not limit the acquisition timing.
Further, for image reconstruction, the reduction of scan time is typically achieved by reducing the number of points used, so that, in the embodiment of the present application, when acquiring the K-space calibration data set, it may be achieved by a partial sampling technique, where only the central region of the K-space, which determines the contrast of the image, is fully sampled. Whereas for non-central regions of K-space, i.e. peripheral regions, undersampling may be performed as shown in fig. 2 (a); sampling may not be performed as shown in fig. 2 (b).
It should be understood that (b) in fig. 2 is only a data set obtained by undersampling in an interlaced manner in a non-central region of K space, and the undersampling interval may be multiple lines, which is not limited.
Step 120: and acquiring a plurality of undersampled K space data sets corresponding to the target part, wherein each undersampled K space data set is data acquired by the target part in one excitation.
Wherein the plurality of undersampled K-space data sets are acquired in a dynamic or multi-phase scan. For example, in blood oxygen level dependent functional imaging (BOLD fMRI) applications, due to data acquisition time constraints and the requirement for planar echo imaging (Echo Planar Imaging, EPI) phase encoding, a K-space calibration dataset is pre-acquired during parallel imaging, followed by acquisition of multiple undersampled K-space datasets.
Step 130: based on the K space calibration data sets, first fitting recovery is carried out on the non-sampling points of each undersampled K space data set in the central area of the K space respectively, so that a plurality of intermediate K space data sets are obtained.
The intermediate K-space dataset after the first fitting recovery is equivalent to full sampling in the K-space center region compared to the undersampled K-space dataset.
That is, through the fully sampled data of the K-space calibration data set in the central region of the K-space, the first fitting recovery is performed on the non-sampled points of the undersampled K-space data set in the central region of the K-space, and the data values of the non-sampled points are fitted to obtain the fully sampled data of the undersampled K-space data set in the central region of the K-space.
In one possible implementation, the first fitting recovery may be implemented by constructing a Low Rank matrix and solving for a zero space matrix using Low Rank (Low Rank) property to determine an intermediate K-space dataset corresponding to each undersampled K-space dataset. The intermediate K-space dataset serves as a reference for recovering non-sampled data points in the undersampled K-space dataset.
The K-space standard data set and the center region of each undersampled K-space data set are identical in position/size. In other words, based on the fully sampled K-space center region locations in the K-space calibration dataset, a first fit recovery is performed on the same location of the non-sampled points in each of the sampled K-space datasets.
As an example, referring to fig. 3, where the K-space standard dataset is 3*3 in size in the central region of K-space, then the data range in which the first fitting recovery is performed in the undersampled K-space dataset is also the 3*3 range of non-sampled points in the central region of K-space.
Step 140: and carrying out second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space, and obtaining a plurality of target K space data sets.
It should be appreciated that the phase-encoding lines filling the central region of the K-space mainly determine the contrast of the image, while the phase-encoding lines of the peripheral region mainly determine the anatomical details of the image, the phase-encoding lines on either side of the zero fourier line being mirror symmetrical. That is, the K-space phase encoding direction and the frequency encoding direction exhibit mirror symmetry.
Based on the above, after all data of the undersampled K-space dataset in the central region of the K-space is restored by the first fitting, the target K-space dataset under the condition of full sampling can be obtained by the second fitting restoration based on the characteristic of mirror symmetry of the K-space filling data.
In one possible implementation, when performing image reconstruction based on the K-space domain, the first fitting recovery may be implemented using a spatial harmonic synchronized acquisition (Simultaneous Acquisition of Spatial Harmonics, SMASH) reconstruction method or a global Auto-calibrated partially parallel acquisition (GRAPPA) reconstruction method, a sensitivity encoding (Sensitivity Encoding, SENSE) reconstruction method.
Specifically, the basic idea of the SMASH reconstruction method is to recover K-space phase encoded line data lost due to undersampling by linear combination of receiver coil sensitivities. Instead of fitting the data of each array coil to the combined signal, GRAPPA is fitted to ACS rows of individual coils, resulting in a series of linear weights to reconstruct the missing K-space data rows of each array coil.
Step 150: and reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
In this step, image reconstruction is performed on each target K-space dataset, resulting in a corresponding magnetic resonance image. Specifically, the image reconstruction is to fourier transform the target K-space dataset to generate a reconstructed image. The reconstructed images of the plurality of target K space data sets are magnetic resonance images obtained after the scanning part is scanned for a plurality of times.
In the image reconstruction method, according to the K space calibration data set, first fitting recovery is performed on the non-sampling points of the central region in each undersampled K space data set, and full-sampling data of the central region can be obtained through the first fitting recovery. Further, taking the fully sampled data of the central area obtained by the first fitting recovery as a reference, and carrying out the second fitting recovery on the non-sampled data points of the non-central area to obtain a target K space data set. Therefore, the intermediate K space data set obtained after the first fitting recovery is carried out on the non-sampling points of the central area of the K space is taken as a reference, the fully sampled data of the intermediate K space data set in the central area of the K space and the non-sampling points of the undersampled K space data set in the non-central area of the K space can be guaranteed, the data obtained by the same excitation belong to the data, the data matching degree is high, the target K space data set after the second fitting recovery also accords with the data situation obtained by actual fully sampling, and the magnetic resonance image quality of the target part generated based on the target K space data set is better.
In one embodiment, as shown in fig. 4, based on the K-space calibration data set in step 130, the first fitting recovery is performed on the non-sampled points of each undersampled K-space data set in the central region of the K-space, respectively, including the following steps:
step 410: and constructing a data recovery matrix according to the K space calibration data set and the undersampled K space data set aiming at any undersampled K space data set.
In one possible implementation, the implementation procedure of step 410 may be: based on a K space calibration data set, a first low-rank matrix is constructed by adopting a preset low-rank matrix construction mode; constructing a second low-rank matrix by adopting a low-rank matrix construction mode based on the undersampled K space data set; and generating a data recovery matrix according to the first low-rank matrix and the second low-rank matrix.
The process of constructing the low-rank matrix comprises the following steps: extracting a preset number of different first data points from a target data set, and acquiring coordinate information of each first data point; for any one first data point, acquiring a plurality of second data points with the distance smaller than the preset length from the first data point to obtain a data point set corresponding to the first data point; acquiring signal values of a plurality of second data points in each data point set; and constructing a low-rank matrix according to the coordinate information of each first data point and the signal values of a plurality of second data points in the data point set corresponding to each first data point.
That is, the first low-rank matrix and the second low-rank matrix are constructed based on the same low-rank matrix construction mode, and the target data set is a K-space calibration data set or an undersampled K-space data set.
As one example, the construction operation of the low rank matrix is denoted as P c (. Cndot.) the construction steps are:
(1) Taking L different first data points from the K space center area of the target data set, wherein K represents the coding index of the selected first data points, and K is more than or equal to 1 and less than or equal to L and n x 、n y Representing the abscissa and ordinate of a certain first data point respectively, the signal value of the first data point can be expressed as
Further, for each first data point selected from the target data set, selecting a first data pointOther N with a distance within a radius R R A set of second data points. This N R Coordinate index reference of the second data point +.>And (3) representing.
Wherein m=1, 2, …, N R . The corresponding signal value isGenerally L.gtoreq.N R 。
(2) The low rank C matrix is formed by the following arrangement of formula (1):
where k=1, 2, …, L, m=1, 2, …, N R ，Abscissa of mth point representing nearest neighbor of kth point, +.>The ordinate of the mth point representing the nearest neighbor of the kth point. The size of the C (k, m) matrix is L N R 。
Referring to fig. 5, the process of generating the data recovery matrix in step 410 may be: partial data K from a K-space calibration data set in a central region of K-space acs Constructing a first low-rank matrix P by the low-rank matrix construction mode c (k acs ) The method comprises the steps of carrying out a first treatment on the surface of the At the same time, from the undersampled K-space dataset Q i Get the data k of the corresponding position i Constructing a second low-rank matrix P by the low-rank matrix construction mode c (k i ) The method comprises the steps of carrying out a first treatment on the surface of the And then generating a data recovery matrix [ P ] according to the first low-rank matrix and the second low-rank matrix c (k acs )P c (k i )]。
Wherein the K-space dataset Q is undersampled i For any of a plurality of undersampled K-space data sets.
Step 420: and carrying out first fitting recovery on the non-sampling points of each sampling K space data set in the central area of the K space based on the data recovery matrix corresponding to each undersampled K space data set.
In one possible implementation, the implementation procedure of step 420 may be: solving a zero space matrix N of a data recovery matrix, and N H N=i, which represents the full sample data and the Q-th of the K-space calibration data set acquired in the central region of K-space i And combining the data corresponding to the undersampled K space data together, and estimating the data of the undersampled points of each undersampled K space data set in the central region of the K space.
Specifically, the problem to be solved is formulated as:
wherein η represents the specific gravity of the fully sampled data in the K-space calibration data set, and the larger η represents the fully sampled data of the undersampled K-space data set in the central region of the K-space, which is recovered by fitting, and is more similar to the fully sampled data in the K-space calibration data set.
In this embodiment, through the K-space calibration data set, a first fitting recovery is performed on the non-sampled point of each undersampled K-space data set in the central region of the K-space, so as to obtain a plurality of intermediate K-space data sets. In this way, the problem that the K-space calibration data set is not matched with the undersampled K-space data set can be avoided, and the problem that artifacts or reconstructed image quality are reduced can be reduced by determining the intermediate K-space data set corresponding to each undersampled K-space data set under the condition that the central region of the K space is fully sampled.
Based on the above embodiments of the method, through the K-space calibration data set, a first fitting recovery is performed on the non-sampled point of each undersampled K-space data set in the central region of the K-space, so as to obtain a plurality of intermediate K-space data sets. The intermediate K space data set is obtained after the corresponding undersampled K space data set supplements data of non-sampling points in the central area of the K space, so that the intermediate K space data set is fully sampled in the central area of the K space.
Further, for each intermediate K-space dataset, fitting recovery can be performed on the non-sampled points of the non-central region of K-space, with reference to the complete data of the central region of K-space.
In one embodiment, as shown in fig. 6, the second fitting recovery of the non-sampled points of the plurality of intermediate K-space data sets in the non-central region of the K-space in the step 140 includes the following steps:
step 610: and calculating a weight kernel of an un-sampled point of each intermediate K space data set in a non-central region of the K space by taking fitting full-sampled data of each intermediate K space data set in the central region of the K space as a reference.
For the non-sampling points of the non-central area of the K space, the weights of the sampling points in the central area can be calculated and recovered according to the full sampling data of the central area. In other words, it is necessary to calculate the contribution value of each full sample data of the central region of the K-space to the recovery of the non-sampled points of the non-central region.
As an example, the weight kernel may be calculated by using a GRAPPA reconstruction method, which is not described herein.
Step 620: and aiming at any intermediate K space data set, carrying out second fitting recovery on the non-sampling points of the non-central area according to the weight kernels of the non-sampling points.
The method comprises the steps of calculating the weight kernel according to the fitting full-sampling data of each intermediate K space data set in the K space center region, obtaining complete K space data by using a GRAPPA reconstruction mode on non-sampling points of a non-center region, improving the data quality of a target K space data set obtained after second fitting recovery, and better inhibiting possible artifacts in images.
Based on the same inventive concept, the present application further provides a magnetic resonance imaging method, as shown in fig. 7, where the method is applied to a computer device, and the computer device may be specifically one or more devices in a magnetic resonance imaging system, and includes the following steps:
step 710: and filling the central region of the K space by adopting a partial sampling technology, and acquiring a K space calibration data set corresponding to the target part.
That is, a K-space calibration data set is acquired during a pre-scan or positioning procedure prior to multiple scans of the target site with a set number of excitations.
It will be appreciated that each magnetic resonance signal acquired during the pre-scan contains a full layer of information and therefore spatial location encoding, i.e. frequency encoding and phase encoding, of the magnetic resonance signals is required. The magnetic resonance signals collected by the receiving coil in the magnetic resonance scanning equipment are actually radio waves with space coding information, belong to analog signals instead of digital information, and need to be converted into digital information through analog-to-digital conversion (ADC), and the digital information is filled into K space to obtain a K space digital lattice.
In this step, acquiring the K-space calibration dataset by undersampling means refers to the fully sampled data of the K-space central region, the non-central region being undersampled.
Step 720: and (3) exciting the target part for multiple times, and collecting an undersampled K space data set corresponding to each excitation.
The number of excitation is determined by a predetermined number of sequences, and the number of excitation is not limited in this embodiment. Each excitation results in an undersampled K-space dataset that can be used to generate a magnetic resonance image.
Step 730: based on the K space calibration data sets, first fitting recovery is carried out on the non-sampling points of each undersampled K space data set in the central area of the K space respectively, so that a plurality of intermediate K space data sets are obtained.
Step 740: and carrying out second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space, and obtaining a plurality of target K space data sets.
Step 750: and reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
Based on the magnetic resonance imaging method, the target site is excited for multiple times, which can comprise any one of the following settings:
(1) The direction of the dispersion gradient applied by each of the multiple excitations is different.
This arrangement can be used for Diffusion-Weighted Imaging (DWI) and Diffusion tensor Imaging (Diffusion Tensor Imaging, DTI) of a target site by a magnetic resonance Imaging apparatus.
(2) The physiological phase of the target part corresponding to each excitation in the plurality of excitations is different.
(3) Each of the plurality of excitations applies a marker pulse to the target site and an undersampled K-space dataset is acquired at different delay times after the marker pulse is applied.
This arrangement may be used for arterial spin labeling (Arterial Spin Labeling, ASL) of a target site by a magnetic resonance imaging apparatus.
It should be noted that, the implementation principle and the technical effect of the image reconstruction step in the magnetic resonance imaging method provided in this embodiment are similar to those of the previous embodiments of the image reconstruction method, and specific definitions and explanations can refer to the previous embodiments of the method, which are not repeated herein.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an image reconstruction device for realizing the image reconstruction method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the image reconstruction apparatus provided in the following may be referred to the limitation of the image reconstruction method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 8, there is provided an image reconstruction apparatus 800 comprising: a calibration data acquisition module 810, an undersampled data acquisition module 820, a first data recovery module 830, a second data recovery module 840, and an image reconstruction module 850, wherein:
the calibration data acquisition module 810 is configured to acquire a K-space calibration data set corresponding to the target portion, where the K-space calibration data set is fully sampled in a central region of the K-space;
an undersampled data acquisition module 820 configured to acquire a plurality of undersampled K-space data sets corresponding to the target portion, where each undersampled K-space data set is data acquired by the target portion during one excitation;
a first data recovery module 830, configured to perform a first fitting recovery on an un-sampled point of each undersampled K-space dataset in a central region of K-space based on the K-space calibration dataset, so as to obtain a plurality of intermediate K-space datasets;
A second data recovery module 840, configured to perform a second fitting recovery on the non-sampling points of the plurality of intermediate K-space data sets in the non-central region of the K-space, to obtain a plurality of target K-space data sets;
the image reconstruction module 850 is configured to reconstruct a plurality of target K-space datasets, and acquire magnetic resonance images corresponding to multiple excitations of the target region.
In one embodiment, the first data recovery module 830 includes:
the matrix construction unit is used for constructing a data recovery matrix according to the K space calibration data set and the undersampled K space data set aiming at any undersampled K space data set;
and the first recovery unit is used for carrying out first fitting recovery on the non-sampling points of each sampling K space data set in the central area of the K space based on the data recovery matrix corresponding to each undersampled K space data set.
In one embodiment, the matrix construction unit comprises:
the first construction subunit is used for constructing a first low-rank matrix by adopting a preset low-rank matrix construction mode based on the K space calibration data set;
the second construction subunit is used for constructing a second low-rank matrix by adopting a low-rank matrix construction mode based on the undersampled K space data set;
and the matrix construction subunit is used for generating a data recovery matrix according to the first low-rank matrix and the second low-rank matrix.
In one embodiment, the process of constructing a low rank matrix includes:
extracting a preset number of different first data points from a target data set, and acquiring coordinate information of each first data point; the target data set is a K space calibration data set or an undersampled K space data set;
for any one first data point, acquiring a plurality of second data points with the distance smaller than the preset length from the first data point to obtain a data point set corresponding to the first data point;
acquiring signal values of a plurality of second data points in each data point set;
and constructing a low-rank matrix according to the coordinate information of each first data point and the signal values of a plurality of second data points in the data point set corresponding to each first data point.
In one embodiment, the second data recovery module 840 includes:
the weight calculation unit is used for calculating the weight kernel of the non-sampling point of each intermediate K space data set in the non-central area of the K space by taking the fitting full-sampling data of each intermediate K space data set in the central area of the K space as a reference;
and the second recovery unit is used for carrying out second fitting recovery on the non-sampling points of the non-central area according to the weight kernels of the non-sampling points aiming at any intermediate K space data set.
The various modules in the image reconstruction apparatus 800 described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Based on the same inventive concept, the embodiments of the present application also provide a magnetic resonance imaging apparatus for implementing the above-mentioned related magnetic resonance imaging method. The implementation of the solution provided by the apparatus is similar to that described in the above method, so specific limitations in one or more embodiments of the magnetic resonance imaging apparatus provided below may be found in the limitations of the magnetic resonance imaging method described above, and will not be repeated here.
In one embodiment, as shown in fig. 9, there is provided a magnetic resonance imaging apparatus 900 comprising: a data acquisition module 910, a scanning module 920, a first data recovery module 930, a second data recovery module 940, and an imaging module 950, wherein:
the data acquisition module 910 is configured to fill a central region of the K space by using a partial sampling technique, and acquire a K space calibration data set corresponding to the target portion;
The scanning module 920 is configured to excite the target site multiple times, and collect an undersampled K-space dataset corresponding to each excitation;
a first data recovery module 930, configured to perform a first fitting recovery on the non-sampled points of each undersampled K-space dataset in the central region of the K-space based on the K-space calibration dataset, so as to obtain a plurality of intermediate K-space datasets;
a second data recovery module 940, configured to perform a second fitting recovery on the non-sampled points of the plurality of intermediate K-space data sets in the non-central region of the K-space, to obtain a plurality of target K-space data sets
The imaging module 950 is configured to reconstruct a plurality of target K-space data sets, and acquire magnetic resonance images corresponding to the target site excited multiple times.
In one embodiment, the direction of the dispersion gradient applied by each of the multiple excitations is different.
In one embodiment, the physiological phase of the target site for each of the multiple excitations is different.
In one embodiment, a marker pulse is applied to the target site for each of a plurality of excitations, and an undersampled K-space dataset is acquired at different delay times after the marker pulse is applied.
The various modules in the magnetic resonance imaging apparatus 900 described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In an embodiment, a computer device is provided, which may be an image reconstruction device, a magnetic resonance imaging device, or other terminals for generating magnetic resonance images, and an internal structure diagram thereof may be shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement the image reconstruction method and/or the magnetic resonance imaging method provided herein. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring a K space calibration data set corresponding to the target part, wherein the K space calibration data set is fully sampled in a central area of the K space;
acquiring a plurality of undersampled K space data sets corresponding to a target part, wherein each undersampled K space data set is data acquired by the target part in one excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets;
And reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
In another embodiment, the processor, when executing the computer program, further performs the steps of:
filling a central region of the K space by adopting a partial sampling technology to obtain a K space calibration data set corresponding to the target part;
exciting the target part for multiple times, and collecting undersampled K space data sets corresponding to each excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on non-sampling points of a plurality of intermediate K space data sets in a non-central area of the K space to obtain a plurality of target K space data sets
And reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
The computer device provided in the foregoing embodiments has similar implementation principles and technical effects to those of the foregoing method embodiments, and will not be described herein in detail.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Acquiring a K space calibration data set corresponding to the target part, wherein the K space calibration data set is fully sampled in a central area of the K space;
acquiring a plurality of undersampled K space data sets corresponding to a target part, wherein each undersampled K space data set is data acquired by the target part in one excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets;
and reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
In another embodiment, the processor, when executing the computer program, further performs the steps of:
filling a central region of the K space by adopting a partial sampling technology to obtain a K space calibration data set corresponding to the target part;
exciting the target part for multiple times, and collecting undersampled K space data sets corresponding to each excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
Performing second fitting recovery on non-sampling points of a plurality of intermediate K space data sets in a non-central area of the K space to obtain a plurality of target K space data sets
And reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
The foregoing embodiment provides a computer readable storage medium, which has similar principles and technical effects to those of the foregoing method embodiment, and will not be described herein.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
acquiring a K space calibration data set corresponding to the target part, wherein the K space calibration data set is fully sampled in a central area of the K space;
acquiring a plurality of undersampled K space data sets corresponding to a target part, wherein each undersampled K space data set is data acquired by the target part in one excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets;
And reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
In another embodiment, the processor, when executing the computer program, further performs the steps of:
filling a central region of the K space by adopting a partial sampling technology to obtain a K space calibration data set corresponding to the target part;
exciting the target part for multiple times, and collecting undersampled K space data sets corresponding to each excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to obtain a plurality of intermediate K space data sets;
performing second fitting recovery on non-sampling points of a plurality of intermediate K space data sets in a non-central area of the K space to obtain a plurality of target K space data sets
And reconstructing a plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target part excited for a plurality of times.
The foregoing embodiment provides a computer program product, which has similar principles and technical effects to those of the foregoing method embodiment, and will not be described herein.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of image reconstruction, the method comprising:
acquiring a K space calibration data set corresponding to a target part, wherein the K space calibration data set is fully sampled in a central area of a K space;
acquiring a plurality of undersampled K space data sets corresponding to the target part, wherein each undersampled K space data set is data acquired by the target part in one excitation;
Based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to acquire a plurality of intermediate K space data sets;
performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets;
and reconstructing the plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target parts excited for a plurality of times.
2. The method of claim 1, wherein the performing a first fit recovery for each undersampled K-space dataset at an undersampled point of a central region of K-space based on the K-space calibration dataset, respectively, comprises:
constructing a data recovery matrix according to the K space calibration data set and the undersampled K space data set aiming at any undersampled K space data set;
and carrying out first fitting recovery on the non-sampling points of each sampling K space data set in the central area of the K space based on the data recovery matrix corresponding to each undersampled K space data set.
3. The method of claim 2, wherein said constructing a data recovery matrix from said K-space calibration data set and said undersampled K-space data set comprises:
Constructing a first low-rank matrix by adopting a preset low-rank matrix construction mode based on the K space calibration data set;
constructing a second low-rank matrix by adopting the low-rank matrix construction mode based on the undersampled K space data set;
and generating the data recovery matrix according to the first low-rank matrix and the second low-rank matrix.
4. A method according to claim 3, wherein the process of constructing a low rank matrix comprises:
extracting a preset number of different first data points from a target data set, and acquiring coordinate information of each first data point; the target data set is the K-space calibration data set or the undersampled K-space data set;
for any one of the first data points, acquiring a plurality of second data points with the distance smaller than a preset length from the first data point to obtain a data point set corresponding to the first data point;
acquiring signal values of a plurality of second data points in each data point set;
and constructing a low-rank matrix according to the coordinate information of each first data point and the signal values of a plurality of second data points in the data point set corresponding to each first data point.
5. The method of any of claims 1 to 4, wherein the second fitting recovery of the plurality of intermediate K-space datasets to non-sampled points in a non-central region of K-space comprises:
calculating a weight kernel of an un-sampled point of each intermediate K space data set in a non-central area of the K space by taking fitting full-sampled data of each intermediate K space data set in the central area of the K space as a reference;
and aiming at any intermediate K space data set, carrying out second fitting recovery on the non-sampling points of the non-central area according to the weight kernel of the non-sampling points.
6. A method of magnetic resonance imaging, the method comprising:
filling a central region of the K space by adopting a partial sampling technology to obtain a K space calibration data set corresponding to the target part;
exciting the target part for multiple times, and collecting undersampled K space data sets corresponding to each excitation;
based on the K space calibration data sets, respectively carrying out first fitting recovery on the non-sampling points of each undersampled K space data set in the central area of the K space so as to acquire a plurality of intermediate K space data sets;
performing second fitting recovery on the non-sampling points of the plurality of intermediate K space data sets in the non-central area of the K space to obtain a plurality of target K space data sets
And reconstructing the plurality of target K space data sets, and acquiring magnetic resonance images corresponding to the target parts excited for a plurality of times.
7. The method of claim 6, wherein the direction of the dispersion gradient applied by each of the plurality of excitations is different.
8. The method of claim 6, wherein the physiological phase of the target site for each of the plurality of excitations is different.
9. The method of claim 6, wherein each of the plurality of excitations applies a marker pulse to the target site, and the undersampled K-space dataset is acquired at different delay times after the marker pulse is applied.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 9 when the computer program is executed.
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