CN113933332A - Fat pressing method for magnetic resonance spectrum imaging - Google Patents

Fat pressing method for magnetic resonance spectrum imaging Download PDF

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CN113933332A
CN113933332A CN202111220147.8A CN202111220147A CN113933332A CN 113933332 A CN113933332 A CN 113933332A CN 202111220147 A CN202111220147 A CN 202111220147A CN 113933332 A CN113933332 A CN 113933332A
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张志勇
刘沂玲
陈浩
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Shanghai Jiaotong University
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Abstract

The invention discloses a fat pressing method for magnetic resonance spectrum imaging, which comprises the following steps: acquiring unpressurized fat spectrum imaging data: acquiring the unpressurized fat spectrum imaging data by using a CSI sequence; data preprocessing: preprocessing the data of the unpressurized fat spectrum imaging data to obtain data XCSI(ii) a Preparing a mask: preparation of lipid Signal mask MIipidAnd metabolite signal mask Mbrain(ii) a Carrying out double-weight signal reconstruction to obtain a double-weight signal Xdual(ii) a L2 constrained reconstruction was performed to obtain metabolite spectral signal X after removal of the lipid signal. The data acquisition sequence adopted by the method is a clinical common sequence, and multiple acquisition is not needed, so that the data acquisition time is short; a large amount of prior information is not needed; lipid signals can be rapidly and robustly removed and metabolite signals can be retained.

Description

Fat pressing method for magnetic resonance spectrum imaging
Technical Field
The invention relates to the technical field of magnetic resonance spectrum imaging, in particular to a fat pressing method for magnetic resonance spectrum imaging.
Background
Magnetic Resonance Spectroscopy (MRSI) techniques combine spatial location information provided by Magnetic Resonance Imaging (MRI) with metabolic compound information provided by proton chemical shift phenomena of Magnetic Resonance Spectroscopy (MRS) while providing anatomical information and compound information. The method can sensitively observe the energy metabolism condition of tissues and organs, characterize neurological and metabolic diseases before the change of the physiological structure of the tissues or the organs, diagnose and monitor diseases, and is a noninvasive detection method which can quantitatively analyze the tissue metabolism, physiological and biochemical environments and metabolic products of living bodies at present.
The MRSI technique is able to quantify different metabolites because the same nucleus can produce chemical shift phenomena in different chemical environments. However, the metabolite of interest is generally present in much lower amounts than the water and lipid content of the tissue, and without any treatment, the metabolite signal of interest tends to be buried in the water and lipid signals, thus necessitating a hydrostatic pressure lipolysis of the MRSI data. There are well established protocols for water signal suppression, but the problem of lipid signal suppression (i.e., the problem of lipid suppression) remains to be solved.
The currently used magnetic resonance spectroscopy imaging fat pressing technology mainly comprises two types:
1) method based on data acquisition sequence: for example, the majority of clinical applications use the Outer Volume Suppression (OVS), which places the selective saturation band on the scalp region with stronger lipid signal during data acquisition, and then dephasing the signals of these regions by gradient to achieve the effect of suppressing the lipid signal. However, the placement of the saturation zone for layer selection is not only cumbersome, but also causes problems of linear distortion and signal-to-noise ratio reduction of the collected brain internal metabolite signals.
2) The post-treatment method comprises the following steps: lipid signals were separated using a priori knowledge as Hankel matrix based singular value decomposition (HSVD); the Subspace reconstruction method (Union of Subspace, UOS) based on Chemical Shift Imaging (CSI) and plane Echo spectroscopy Imaging (EPSI) data combines the characteristics of high temporal resolution of CSI and high spatial resolution of EPSI to further optimize the lipid signal removal effect on the basis of HSVD; the low-frequency part of the high-resolution signal and the high-frequency part of the low-resolution signal are combined to generate a double-density signal by an L2 constraint method based on spiral sampling sequence data, and then a lipid signal base is generated by the double-density signal to carry out L2 constraint reconstruction so as to achieve the purpose of removing the lipid signal. However, the HSVD method and the UOS method based on the CSI and EPSI mixed signals are time consuming to calculate and require a lot of a priori information such as T2 values and chemical shifts of water, lipids and various metabolites. The L2 constraint method based on the spiral sampling sequence has the sampling sequence which is an unusual sequence, realizes the complex preprocessing that the data obtained by adopting the sequence needs gridding and the like, has long acquisition time, and is only used for scientific research at present.
Accordingly, those skilled in the art have endeavored to develop a method of liposuction for magnetic resonance spectroscopy imaging. To solve the technical problems in the prior art.
Disclosure of Invention
In order to achieve the above object, the present application provides a fat pressing method for magnetic resonance spectroscopy imaging, which is characterized by comprising the following steps:
step 1, collecting unpressurized fat spectrum imaging data: acquiring the unpressurized fat spectrum imaging data by using a CSI sequence;
step 2, data preprocessing: preprocessing the data of the unpressurized fat spectrum imaging data to obtain data XCSI；
Step 3, preparing a mask: preparation of lipid Signal mask MlipidAnd metabolite signal mask Mbrain；
Step 4, carrying out double-weight signal reconstruction to obtain a double-weight signal Xdual；
And 5, carrying out L2 constrained reconstruction to obtain a metabolite spectrum signal X after the lipid signal is removed.
Furthermore, in step 1, water pressing treatment is performed during data acquisition, and grease pressing is not performed.
Further, step 2 specifically includes the following steps:
step 2.1, channel compression is carried out on the unpressurized original spectrum imaging data by adopting a singular value decomposition method to obtain a three-dimensional matrix;
step 2.2, truncating and filtering the third dimension of the three-dimensional matrix;
2.3, performing inverse Fourier transform on the first dimension and the second dimension of the three-dimensional matrix, and performing Fourier transform on the third dimension of the three-dimensional matrix;
step 2.4, carrying out normalization processing on the three-dimensional matrix to obtain the data XCSI。
Further, in step 2.1, the size of the three-dimensional matrix is Nx×Ny×NpWherein N isx×NyIs the image size, NpThe number of sampling points.
Further, in step 2.2, the three-dimensional matrix is truncated to change the size to Nx×Ny×NtWherein N istAnd counting the number of the points after the third dimension is cut off.
Further, step 3 specifically includes the following steps:
step 3.1, for the data XCSISolving the mean square sum along the third dimension to obtain a two-dimensional magnetic resonance spectrum image;
step 3.2, calibrating the brain area in the two-dimensional magnetic resonance spectrum image to obtain the size Nx×NyBrain image mask Mimage；
Step 3.3, calibrating the internal metabolic substance area of the brain in the two-dimensional magnetic resonance spectrum image to obtain the size Nx×NyMetabolite mask M ofbrain；
Step 3.4, by MimageMinus MbrainObtaining a lipid mask Mlipid。
Further, in step 4, the double-weighted signal XdualCan be calculated from the following formula:
Xdual＝F-1[Phigh·F(Mlipid·XCSI)+αPlow·F(XCSI)]
wherein F () stands for Fourier transform, F-1() Representing an inverse Fourier transform, PhighAnd PlowMasks for the peripheral region and the central region of k-space, respectively, and α is the weight of the central region of k-space, representing a dot product operation.
Further, in step 5, the metabolite spectrum signal X after the lipid signal is removed is solved by the following formula:
Figure BDA0003312293650000031
wherein λ is a regularization parameter, B is a lipid-based matrix, and H represents a conjugate transpose of the matrix.
Further, the formula for solving the metabolite spectral signal X after removal of the lipid signal has a closed solution:
Figure BDA0003312293650000032
wherein I is a unit array.
Compared with the prior art, the fat pressing method for magnetic resonance spectrum imaging provided by the application has at least the following technical advantages:
1. the data acquisition sequence adopted by the method is a clinical common sequence, and multiple acquisition is not needed, so that the data acquisition time is short;
2. the method does not need a large amount of prior information;
3. the method can rapidly and robustly remove lipid signals and retain metabolite signals.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a flow chart of the steps of one embodiment of the present application;
FIG. 2 is an exemplary diagram of pre-processed CSI data as employed in one embodiment of the present application;
FIG. 3 is a schematic illustration of a metabolite mask prepared in one embodiment of the present application;
FIG. 4 is a schematic illustration of a lipid mask prepared in one embodiment of the present application;
FIG. 5 is a graphical representation of the results after removal of the lipid signal in one embodiment of the present application.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
Example one
The application provides a fat pressing method for magnetic resonance spectrum imaging, which specifically comprises the following steps:
step 1, collecting unpressurized fat spectrum imaging data: and acquiring the unpressurized fat spectrum imaging data by using a CSI sequence.
In this embodiment, it is preferable to perform acquisition by using a 32-channel head coil, perform only pressurized water treatment without using OVS grease, and use WET technology, where the specific acquisition parameters are as follows: the repetition time TR is 1500ms, the echo time TE is 30ms, and the imaging field of view FOV is 200X 200mm2VOI of interest of 80X 80mm2The excitation layer thickness is 10mm, the image size is 32 x 32, the number of sampling points is 2048, the spectrum width is 880Hz, and the sampling interval time is 0.57 ms.
Step 2, data preprocessing: preprocessing the data of the unpressurized fat spectrum imaging data to obtain data XCSI。
In this embodiment, it is preferable to perform channel compression on the image data by using a singular value decomposition method to obtain a matrix with a size of 32 × 32 × 2048, where 32 × 32 is an image size, and 2048 is a sampling point number. Cutting the third dimension to 256 th point to obtain matrix with size of 32 × 32 × 256, filtering the third dimension, performing inverse Fourier transform on the first dimension, performing Fourier transform on the third dimension, and dividing the first dimension by the maximum valueRow normalization to obtain preprocessed data XCSI. Preprocessed data X obtained in this exampleCSIAs shown in fig. 2, the left part of fig. 2 is a CSI image, the right part of fig. 2 is a spectrum signal at a coordinate point (16,15) in the image, and the signal pointed by the arrow in the right part is a lipid signal to be suppressed or eliminated.
Step 3, preparing a mask: preparation of lipid Signal mask MlipidAnd metabolite signal mask Mbrain。
In the present embodiment, preferably, X to be obtainedCSIObtaining a two-dimensional magnetic resonance spectrum image by calculating the sum of the mean squares along the third dimension, firstly calibrating the brain region in the two-dimensional magnetic resonance spectrum image to obtain a brain image mask M with the index value of the brain region being 1 and the size being 32 multiplied by 32 and the index value of the other region being 0image. Similarly, calibrating metabolite regions within the brain in two-dimensional magnetic resonance spectroscopy images yields a metabolite mask M of size 32 × 32brain(as shown in FIG. 3), and finally by MimageMinus MbrainObtaining a lipid mask Mlipid(as shown in fig. 4).
Step 4, carrying out double-weight signal reconstruction to obtain a double-weight signal Xdual。
In the present embodiment, preferably, the double weight signal XdualCan be calculated from the following formula:
Xaual＝F-1[Phigh·F(Mlipid·XCSI)+αPlow·F(XCSI)]
wherein F () stands for Fourier transform, F-1() Representing an inverse Fourier transform, PhighAnd PlowMasks for the peripheral region and the central region of k-space, respectively, and α is the weight of the central region of k-space, representing a dot product operation.
According to the formula, firstly, a full 1 matrix with the size of 32 multiplied by 32 is generated, the area with the central diameter of 18 is assigned with 1, and the rest areas are assigned with 0 to obtain a mask P in the central area of the k spacelowThen, the full 1 matrix with size of 32 × 32 is used to subtract PlowObtaining a k-space peripheral region mask Phigh. Assigning 3 to alpha, and obtaining XCSI、Mlipid、PlowAnd PhighSubstituting into a formula to calculate to obtain a double-weight signal Xdual。
And 5, carrying out constrained reconstruction by L2 to obtain a metabolite spectrum signal X after the lipid signal is removed.
The metabolite spectral signal X after removal of the lipid signal is solved by the following formula:
Figure RE-GDA0003354118590000051
wherein λ is a regularization parameter, B is a lipid-based matrix, and H represents a conjugate transpose of the matrix.
The formula has a closed solution:
Figure BDA0003312293650000052
wherein I is a unit array.
Preferably, in this embodiment, a lipid mask M is usedlipidFor dual weight signal XdualAnd performing signal modulation to generate a lipid matrix B, substituting the lipid matrix B into a solving formula, assigning the lambda to be 0.65, and calculating to obtain the metabolite spectrum signal X after the lipid signal is removed. The spectrum signals at coordinates (16,15) in the metabolite spectrum signal X calculated in this example are shown in fig. 5, and the positions indicated by arrows are the positions of the lipid signals. Compared to the right part of fig. 2, it is clearly seen that the lipid signal is effectively suppressed.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (9)

1. A fat pressing method for magnetic resonance spectrum imaging is characterized by comprising the following steps:
step 1, collecting unpressurized fat spectrum imaging data: acquiring the unpressurized fat spectrum imaging data by using a CSI sequence;
step 2, data preprocessing: preprocessing the data of the unpressurized fat spectrum imaging data to obtain data XCSI；
Step 3, preparing a mask: preparation of lipid Signal mask MlpidAnd metabolite signal mask Mbrain；
Step 4, carrying out double-weight signal reconstruction to obtain a double-weight signal Xdual；
And 5, carrying out L2 constrained reconstruction to obtain a metabolite spectrum signal X after the lipid signal is removed.
2. The method of claim 1, wherein the data is collected without pressure treatment during step 1.
3. The fat reduction method for magnetic resonance spectroscopy as defined in claim 2, wherein step 2 comprises the steps of:
step 2.1, channel compression is carried out on the unpressurized fat spectrum imaging data by adopting a singular value decomposition method to obtain a three-dimensional matrix;
step 2.2, truncating and filtering the third dimension of the three-dimensional matrix;
2.3, performing inverse Fourier transform on the first dimension and the second dimension of the three-dimensional matrix, and performing Fourier transform on the third dimension of the three-dimensional matrix;
step 2.4, carrying out normalization processing on the three-dimensional matrix to obtain the data XCSI。
4. The method of claim 3, wherein in step 2.1, the three-dimensional matrix has a size Nx×Ny×NpWherein N isx×NyIs the image size, NpThe number of sampling points.
5. The method of claim 4, wherein in step 2.2, the third dimension of the three-dimensional matrix is truncated to a size of Nx×Ny×NtWherein N istAnd counting the number of the points after the third dimension is cut off.
6. The method of fat reduction for magnetic resonance spectroscopy as defined in claim 5, wherein step 3 comprises the steps of:
step 3.1, for the data XCSISolving the mean square sum along the third dimension to obtain a two-dimensional magnetic resonance spectrum image;
step 3.2, calibrating the brain area in the two-dimensional magnetic resonance spectrum image to obtain the size Nx×NyBrain image mask Mimage；
Step 3.3, calibrating the internal metabolic region of the brain in the two-dimensional magnetic resonance spectrum image to obtain the size Nx×NyMetabolite mask M ofbrain；
Step 3.4, by MimageMinus MbrainObtaining a lipid mask Mlipid。
7. The method of claim 6, wherein in step 4, the dual weight signal X is used to generate a fat reduction signaldualCan be calculated from the following formula:
Xdual＝F-1[Phigh·F(Mlipid·XCSI)+αPlow·F(XCSI)]
wherein F () stands for Fourier transform, F-1() Representing an inverse Fourier transform, PhighAnd PlowMasks for the peripheral region and the central region of k-space, respectively, and α is the weight of the central region of k-space, representing a dot product operation.
8. The method of claim 7, wherein in step 5, the metabolite spectral signal X after the removal of the lipid signal is solved by the following formula:
Figure FDA0003312293640000021
wherein λ is a regularization parameter, B is a lipid-based matrix, and H represents a conjugate transpose of the matrix.
9. The method of claim 8, wherein the formula for solving for the metabolite spectral signal X after removal of the lipid signal has a closed-form:
Figure FDA0003312293640000022
wherein I is a unit array.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5274329A (en) * 1989-04-29 1993-12-28 Bruker Medizintechnik Gmbh Method for recording spin resonance spectra and for spin resonance imaging
US20090230960A1 (en) * 2008-03-13 2009-09-17 Michael Deimling Method and magnetic resonance system to generate a fat-reduced, spatially resolved magnetic resonance spectrum
US20120197104A1 (en) * 2009-10-24 2012-08-02 Stc.Unm System and methods for automatic placement of spatial supression regions in mri and mrsi
CN103654786A (en) * 2012-09-26 2014-03-26 三星电子株式会社 Method and apparatus for removing distortion by lipids from magnetic resonance spectroscopic image
US20190369185A1 (en) * 2018-06-01 2019-12-05 The General Hospital Corporation Method for echo planar time-resolved magnetic resonance imaging
CN112862950A (en) * 2021-01-22 2021-05-28 南昌大学 Rapid myocardial perfusion magnetic resonance imaging method based on low-rank tensor coding

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5274329A (en) * 1989-04-29 1993-12-28 Bruker Medizintechnik Gmbh Method for recording spin resonance spectra and for spin resonance imaging
US20090230960A1 (en) * 2008-03-13 2009-09-17 Michael Deimling Method and magnetic resonance system to generate a fat-reduced, spatially resolved magnetic resonance spectrum
US20120197104A1 (en) * 2009-10-24 2012-08-02 Stc.Unm System and methods for automatic placement of spatial supression regions in mri and mrsi
CN103654786A (en) * 2012-09-26 2014-03-26 三星电子株式会社 Method and apparatus for removing distortion by lipids from magnetic resonance spectroscopic image
US20140085304A1 (en) * 2012-09-26 2014-03-27 Industry-Academic Cooperation Foundation, Yonsei University Method and apparatus for removing distortion by lipids from magnetic resonance image
US20190369185A1 (en) * 2018-06-01 2019-12-05 The General Hospital Corporation Method for echo planar time-resolved magnetic resonance imaging
CN112862950A (en) * 2021-01-22 2021-05-28 南昌大学 Rapid myocardial perfusion magnetic resonance imaging method based on low-rank tensor coding

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