CN110652297A - Lung function imaging processing method based on MRI technology - Google Patents
Lung function imaging processing method based on MRI technology Download PDFInfo
- Publication number
- CN110652297A CN110652297A CN201910958603.5A CN201910958603A CN110652297A CN 110652297 A CN110652297 A CN 110652297A CN 201910958603 A CN201910958603 A CN 201910958603A CN 110652297 A CN110652297 A CN 110652297A
- Authority
- CN
- China
- Prior art keywords
- lung
- magnetic resonance
- image
- sequence
- processing method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003384 imaging method Methods 0.000 title claims abstract description 41
- 230000004199 lung function Effects 0.000 title claims abstract description 29
- 238000005516 engineering process Methods 0.000 title claims abstract description 19
- 238000003672 processing method Methods 0.000 title claims abstract description 18
- 210000004072 lung Anatomy 0.000 claims abstract description 133
- 238000000034 method Methods 0.000 claims abstract description 57
- 238000009423 ventilation Methods 0.000 claims abstract description 35
- 230000010412 perfusion Effects 0.000 claims abstract description 31
- 230000033001 locomotion Effects 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims abstract description 24
- 230000029058 respiratory gaseous exchange Effects 0.000 claims abstract description 16
- 230000000241 respiratory effect Effects 0.000 claims abstract description 15
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 15
- 238000010586 diagram Methods 0.000 claims abstract description 14
- 230000008859 change Effects 0.000 claims abstract description 13
- 238000002595 magnetic resonance imaging Methods 0.000 claims description 28
- 238000012307 MRI technique Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 230000002685 pulmonary effect Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 230000001121 heart beat frequency Effects 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 230000010349 pulsation Effects 0.000 claims description 3
- 230000036391 respiratory frequency Effects 0.000 claims description 3
- 238000011524 similarity measure Methods 0.000 claims description 3
- 238000013535 dynamic contrast enhanced MRI Methods 0.000 claims description 2
- 210000001519 tissue Anatomy 0.000 description 22
- 230000006870 function Effects 0.000 description 16
- 238000001208 nuclear magnetic resonance pulse sequence Methods 0.000 description 6
- 238000002591 computed tomography Methods 0.000 description 5
- 238000000354 decomposition reaction Methods 0.000 description 5
- 239000007789 gas Substances 0.000 description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 239000001301 oxygen Substances 0.000 description 4
- 229910052760 oxygen Inorganic materials 0.000 description 4
- 208000006545 Chronic Obstructive Pulmonary Disease Diseases 0.000 description 3
- 230000000747 cardiac effect Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000009206 nuclear medicine Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 239000003623 enhancer Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000035790 physiological processes and functions Effects 0.000 description 2
- 230000009325 pulmonary function Effects 0.000 description 2
- 208000023504 respiratory system disease Diseases 0.000 description 2
- 208000000884 Airway Obstruction Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 208000006673 asthma Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 230000008081 blood perfusion Effects 0.000 description 1
- 230000035565 breathing frequency Effects 0.000 description 1
- 239000002872 contrast media Substances 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 230000005865 ionizing radiation Effects 0.000 description 1
- 229910052756 noble gas Inorganic materials 0.000 description 1
- 230000005408 paramagnetism Effects 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 239000012857 radioactive material Substances 0.000 description 1
- 239000000941 radioactive substance Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000284 resting effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000002603 single-photon emission computed tomography Methods 0.000 description 1
- 210000004872 soft tissue Anatomy 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- High Energy & Nuclear Physics (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Radiology & Medical Imaging (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The invention discloses a lung function imaging processing method based on an MRI technology, which comprises the following steps: firstly, rapidly acquiring a free-breathing lung dynamic magnetic resonance data sequence; secondly, reconstructing an MR image by using the magnetic resonance data sequence acquired in the first step, and reconstructing a lung dynamic MR image sequence with good spatial resolution and contrast; thirdly, segmenting the lung tissue of the magnetic resonance image and registering the free breathing lung dynamic MR image sequence, and extracting the geometric change information of the lung tissue in the breathing motion process; step four, establishing a time sequence and performing spectral analysis on voxels one by one according to the registered lung dynamic MR image sequence; and step five, according to the geometric change information of the lung tissues in the respiratory motion obtained by registration, and by combining the structural information of the lung MR image and the magnetic resonance quantitative parameters, constructing a quantitative lung ventilation function diagram and a lung perfusion diagram. The imaging processing method is safe and reliable, and a sensitive and accurate quantitative lung ventilation function diagram and a lung perfusion diagram are obtained.
Description
Technical Field
The invention relates to the technical field, in particular to a lung function imaging processing method based on an MRI technology.
Background
Respiratory diseases such as chronic obstructive pulmonary diseases and asthma are one of the main diseases causing death, and the incidence rate of people over 40 years old is up to 13.7 percent when nearly one hundred million patients with chronic obstructive pulmonary diseases (chronic obstructive pulmonary disease for short) exist in China. The respiratory diseases are mainly characterized by airway obstruction or tissue destruction and airflow limitation, lung volume, lung ventilation and air exchange functions are main objective analysis indexes, and lung function instruments such as spirometers, plethysmographs and the like are often used for measurement in clinic. These measurements only reflect the overall lung function and do not show the local ventilation and ventilation function of the lung and its differences, and various imaging methods of pulmonary function are powerful supplements. Nuclear medicine imaging based on radioactive substances and Computed Tomography (CT) are currently the main lung function imaging methods, and nuclear medicine imaging such as Single-Photon Emission Computed Tomography (SPECT), Scintigraphy (Scintigraphy), etc. are the gold standards for lung function imaging. However, nuclear medicine imaging requires the intake of radioactive materials and has low temporal and spatial resolution. In recent years, four-dimensional CT (4 division-CT, 4D-CT) has been widely studied for lung function imaging, but the CT technique has ionizing radiation and is not suitable for minors, pregnant women and patients requiring frequent follow-up examinations. Magnetic Resonance Imaging (MRI) technology has the advantages of no radiation, flexible Imaging, high soft tissue contrast and the like, and the rapid development of the technology promotes the research of magnetic resonance lung function Imaging.
In the prior art, the ventilation function and perfusion capability of the lung are imaged based on the MRI technology, and a lung ventilation function graph and perfusion graph are constructed to evaluate the ventilation capability of the lung and the blood perfusion condition of lung tissues.
MRI techniques have the unique advantage that pulmonary MRI has a high sensitivity to invasive and solid pathologies compared to CT techniques. However, conventional proton (1H) -based MRI techniques have difficulty imaging the lungs due to factors such as low proton density in lung tissue, increased local gradient magnetic field due to the large number of liquid-gas interfaces in the lungs, and shorter transverse relaxation times T2. In order to realize pulmonary magnetic resonance imaging, various aprotic MRI (e.g., hyperpolarized noble gas imaging, fluorinated gas imaging) techniques, oxygen enhanced proton imaging techniques, and the like have been proposed. However, the related art aprotic MRI technique requires additional equipment, requires complicated imaging protocol and measurement procedure design, is high in cost, and is not suitable for patients with poor lung function. Oxygen enhanced imaging techniques, which utilize the paramagnetism of oxygen to increase magnetic resonance signal intensity, are currently a widely studied method of pulmonary imaging. With the continuous improvement and optimization of MRI systems, various fast pulse sequences, extremely short and ultra-short echo time (UTE) pulse sequences are developed, data scanning strategies of different k-space (also known as fourier space, original acquisition space of magnetic resonance data) trajectories are applied to the lung, and the application of MRI to the lung is deeply studied under the push of various magnetic resonance fast imaging techniques.
It has been proposed to acquire free-breathing lung dynamic MR image sequences at a frame rate of 3 on a 0.35T MRI system, complete lung ventilation functional imaging by image registration and Fourier Decomposition (FD), and confirm that magnetic resonance ventilation functional imaging can be achieved without polarized gas, oxygen, and contrast agent.
However, the FD-MRI method for pulmonary function determination presents several fundamental problems: the measured lung ventilation and perfusion information is fuzzy and can not be clearly combined with objective indexes of clinical lung function examination; the reproducibility and repeatability of the measured lung ventilation function map and perfusion map need to be improved; there is a need to design a fast acquisition method of magnetic resonance data suitable for the lung function measurement process, which further improves the contrast and resolution of the lung ventilation map and the perfusion map.
Disclosure of Invention
In view of this, the invention provides a safe, reliable, sensitive and accurate lung function imaging processing method based on the MRI technology, which is used for solving the technical problems in the prior art.
The invention provides a lung function imaging processing method based on an MRI technology, which comprises the following steps:
firstly, rapidly acquiring a free-breathing lung dynamic magnetic resonance data sequence;
secondly, reconstructing an MR image by using the magnetic resonance data sequence acquired in the first step, and reconstructing a lung dynamic MR image sequence with good spatial resolution and contrast;
thirdly, segmenting the lung tissue of the magnetic resonance image and registering the free breathing lung dynamic MR image sequence, and extracting the geometric change information of the lung tissue in the breathing motion process;
step four, establishing a time sequence and performing spectral analysis on voxels one by one according to the registered lung dynamic MR image sequence;
for a set of free-breathing lung dynamic magnetic resonance image sequences S (t), a time sequence of certain voxels (xi, yi) is denoted as S (xi, yi; t), and the spectral analysis of the time sequence is as follows:
FFT|S(xi,yi；t)|＝s(xi,yi；ω) (1)
in the formula (1), fft (fast Fourier transform) is fast Fourier transform; omega is angular frequency;
and step five, according to the geometric change information of the lung tissues in the respiratory motion obtained by registration, and by combining the structural information of the lung MR image and the magnetic resonance quantitative parameters, constructing a quantitative lung ventilation function diagram and a lung perfusion diagram.
Optionally, collecting magnetic resonance signals of the lungs of the same subject at different periods, constructing a quantitative lung ventilation function graph and a quantitative lung perfusion graph, and analyzing and establishing the mutual relationship between the quantitative lung ventilation function graph, the quantitative lung perfusion graph and the objective index of lung function examination; and simultaneously, quantitatively analyzing the obtained lung perfusion image and a perfusion image obtained by dynamic contrast enhanced magnetic resonance imaging. .
Optionally, in the first step, the fast acquisition refers to that a radial trajectory data scanning mode is adopted, and the acquisition speed is increased by reducing the number of spokes (spokes).
Optionally, in the second step, the MR image reconstruction is to construct a solution model by using a regularization technique, and introduce constraint conditions according to sparsity and low rank characteristics of the magnetic resonance image sequence to form a numerical solution algorithm to reconstruct the lung magnetic resonance image sequence.
Alternatively, the mathematical model of the magnetic resonance imaging procedure is described as follows:
y＝Em (2)
in equation (2), y is the magnetic resonance data sequence acquired by multiple channels on each coil, the matrix E is a physical model expressing the magnetic resonance imaging process, and m is the magnetic resonance data of the region of interest, i.e. the magnetic resonance image to be reconstructed. Reconstruction m is an ill-defined inverse problem, and an optimization method can be applied to construct a solution model of the problem:
in equation (3), Ψ is a sparse transform operator (based on the sparsity of the magnetic resonance image), L is other prior information (including low rank characteristics), and λ 1 and λ 2 are regularization coefficients.
Equation (3) is solved by using an Alternating Direction multiplier Algorithm (ADMM).
Optionally, in step three, the reconstructed lung image sequence is registered to a reference image for motion correction, the adopted non-rigid registration method includes feature detection and matching, deformation model estimation and similarity measurement, and the reference image used for registration is extracted in the data acquisition process.
Optionally, in the free breathing process, the magnetic resonance images of the lung acquired at different times t are respectively recorded as m1(x)、m2(x)，m1(x) And m2(x) The deformation (denoted as h (x)) is calculated by a differential homomorphic method as follows:
in formula (4), x is a coordinate matrix of the image; lcc is m calculated based on similarity measure1(x) And m2(x) A local cross-correlation matrix; l (h (x)) is a regularization term; α is the regularization term control coefficient; ∑ is the gradient operator.
Optionally, in the fourth step, it can be known through spectrum analysis that the amplitude of each voxel of the lung at the respiratory frequency and the heartbeat frequency reflects the change of the magnetic resonance signal intensity caused by respiration and heartbeat; the respiratory motion and the heart pulsation have periodicity, and the lung tissue magnetic resonance signal time sequence is subjected to spectrum analysis in the respiratory process to extract ventilation and perfusion information of the lung.
Compared with the prior art, the technical scheme of the invention has the following advantages: the invention utilizes the rapid pulse sequence to improve the contrast of the lung MR image; realizing rapid data acquisition by acquiring partial radial trajectory k-space data; reconstructing a lung dynamic magnetic resonance image sequence with higher resolution by designing a radial trajectory undersampled k-space data reconstruction algorithm; the method provides a method item of imaging the lung ventilation function and the perfusion capability based on the MRI technology, which is based on the proton MRI technology, extracts the lung ventilation information and the blood flow perfusion information through image reconstruction, image segmentation, image registration and time series spectrum analysis under the condition of no contrast enhancer, and finally images the lung ventilation function and the perfusion capability; aiming at the problems of lung ventilation and lung perfusion imaging carried out by a Fourier decomposition MRI method, quantitative information (lung tissue geometric deformation information caused by respiratory motion, structural information of lung MR images and magnetic resonance quantitative parameters) is fused, a quantitative lung ventilation function diagram and a perfusion diagram are constructed, and the application of the Fourier decomposition MRI method is effectively promoted.
Drawings
FIG. 1 is a schematic flow chart of a lung function imaging processing method based on MRI technology according to the present invention;
FIG. 2 is a schematic diagram of images of the steps of the lung function imaging processing method based on MRI technology according to the present invention;
fig. 3 is a schematic diagram of fourier space k-space.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention is not limited to only these embodiments. The invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention.
In the following description of the preferred embodiments of the present invention, specific details are set forth in order to provide a thorough understanding of the present invention, and it will be apparent to those skilled in the art that the present invention may be practiced without these specific details.
The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. It should be noted that the drawings are in simplified form and are not to precise scale, which is only used for convenience and clarity to assist in describing the embodiments of the present invention.
Referring to fig. 1, the present invention provides a lung function imaging processing method based on MRI technology, including the following steps:
firstly, rapidly acquiring a free-breathing lung dynamic magnetic resonance data sequence;
secondly, reconstructing an MR image by using the magnetic resonance data sequence acquired in the first step, and reconstructing a lung dynamic MR image sequence with good spatial resolution and contrast;
thirdly, segmenting the lung tissue of the magnetic resonance image and registering the free breathing lung dynamic MR image sequence, and extracting the geometric change information of the lung tissue in the breathing motion process;
step four, establishing a time sequence and performing spectral analysis on voxels one by one according to the registered lung dynamic MR image sequence;
for a set of free-breathing lung dynamic magnetic resonance image sequences S (t), a time sequence of certain voxels (xi, yi) is denoted as S (xi, yi; t), and the spectral analysis of the time sequence is as follows:
FFT|S(xi,yi；t)|＝s(xi,yi；ω) (1)
in the formula (1), fft (fast Fourier transform) is fast Fourier transform; omega is angular frequency;
and step five, according to the geometric change information of the lung tissues in the respiratory motion obtained by registration, and by combining the structural information of the lung MR image and the magnetic resonance quantitative parameters, constructing a quantitative lung ventilation function diagram and a lung perfusion diagram.
The steps are completed by extracting a lung ventilation function map and a perfusion map without a contrast enhancer based on a proton MRI technology, and a Fourier decomposition algorithm is used, so that the method is also called Fourier decomposition MRI (FD-MRI).
Collecting lung magnetic resonance signals of the same object at different periods, constructing a quantitative lung ventilation function graph and a quantitative lung perfusion graph, and analyzing and establishing the mutual relation between the quantitative lung ventilation function graph, the quantitative lung perfusion graph and the objective index of lung function examination. The adopted lung function examination objective indexes are as follows: resting ventilation per minute, forced expiratory volume in one second.
According to the characteristics of lung tissues and the requirements of a dynamic image frame rate, namely at least 3-4 lung magnetic resonance images per second, the parameter optimization of the lung scanning rapid pulse sequence meeting the requirements of time and spatial resolution and the improvement of the imaging quality of the lung tissues is realized, and a rapid acquisition method of less radial trajectory magnetic resonance data is adopted. Specifically, in the first step, the fast acquisition refers to that a radial trajectory data scanning mode is adopted, and the acquisition speed is increased by reducing the number of spokes (spokes). The design process mainly considers the following factors: (1) the dynamic image frame rate is greater than 3; (2) the imaging parameter TE is relatively short. Lung tissue proton density is low and transverse relaxation time T2 is very short (T2 is 1.4 ± 0.4ms in field strength of 1.5T, 3.0T is 0.8 ± 0.1ms), short TE should be set to capture the rapidly decaying lung tissue signal; (3) the influence of respiratory motion and cardiac activity on data acquisition should be data acquisition that is insensitive to motion and facilitates motion correction. In summary, the present invention adopts the radial trajectory data scanning method, and simultaneously, the number of spokes (spokes) is reduced to increase the acquisition speed, because the radial scanning method has the unique advantages: (1) is not sensitive to movement; (2) radial scanning has no phase coding process, and TE can be effectively shortened; (3) when the k space filling mode is radial trajectory, the data is dense in the middle and sparse in the periphery, which is beneficial to the motion correction and image reconstruction process. The lung only contains about 800g of tissues and blood distributed in the volume of about 4-6L, and the MR signal intensity is far lower than that of other parts of a human body due to low proton density; the large liquid-gas interfaces in the lungs cause local gradient field increases and the field inhomogeneities cause the signal with a very short transverse relaxation time T2 to rapidly lose phase, which further attenuates the MR signal. These problems make proton MRI techniques difficult to apply in the lungs. To solve this problem, various proposed techniques of pulse sequence of very short and ultra-short TE capture MR signals by designing short TE fast pulse sequences; in the data scanning process, a partial k-space data acquisition mode of a radial track is designed to meet the requirement of the project on the frame rate of the dynamic image; and a stable and reliable lung dynamic MR image reconstruction algorithm is adopted to reconstruct a lung dynamic MR image sequence with the spatial resolution meeting the requirement.
Fig. 3 illustrates fourier space, i.e., k-space, where the radial lines emanating from the middle of k-space are spokes, which are data acquisition traces. The magnetic resonance data acquisition is sampled sequentially along the spokes, so that the data acquisition time can be reduced by reducing the number of spokes, thereby accelerating the acquisition speed.
In the second step, the MR image reconstruction refers to the construction of a solution model by using a regularization technology, and the construction of a stable numerical solution algorithm to reconstruct a lung magnetic resonance image sequence by introducing constraint conditions according to the sparsity and low-rank characteristics of the magnetic resonance image sequence. The invention acquires a magnetic resonance data sequence with less radial tracks in k space, and the problem of image reconstruction of the k space data is a solving process of an ill-defined inverse problem. A solution model is constructed by applying a regularization technology, and constraint conditions are introduced according to the sparsity and low-rank characteristic of a magnetic resonance image sequence, so that the reconstruction method has the characteristics of stability and high accuracy.
The mathematical model of the magnetic resonance imaging procedure is described as follows:
y＝Em (2)
in equation (2), y is the magnetic resonance data sequence acquired by multiple channels on each coil, the matrix E is a physical model expressing the magnetic resonance imaging process, and m is the magnetic resonance data of the region of interest, i.e. the magnetic resonance image to be reconstructed. Reconstruction m is an ill-defined inverse problem, and an optimization method can be applied to construct a solution model of the problem:
in equation (3), Ψ is a sparse transform operator (based on the sparsity of the magnetic resonance image), L is other prior information (including low rank characteristics), and λ 1 and λ 2 are regularization coefficients.
Equation (3) is solved by using an Alternating Direction multiplier Algorithm (ADMM).
In step three, the reconstructed lung image sequence is registered to a reference image for motion correction, and the adopted non-rigid registration method comprises characteristic detection andmatching, deformation model estimation and similarity measurement, and extracting a reference image adopted by registration in a data acquisition process. The method comprises the steps of collecting lung magnetic resonance signals of each phase of a respiratory motion cycle, wherein respiratory motion causes non-rigid deformation of a lung in the data scanning process, so that a reconstructed lung image sequence needs to be registered to a reference image for motion correction. In the free breathing process, the magnetic resonance images of the lung acquired at different times t are respectively recorded as m1(x)、m2(x)，m1(x) And m2(x) The deformation (denoted as h (x)) is calculated by a differential homomorphic method as follows:
in formula (4), x is a coordinate matrix of the image; lcc is m calculated based on similarity measure1(x) And m2(x) A local cross-correlation matrix; l (h (x)) is a regularization term; α is the regularization term control coefficient; ∑ is the gradient operator.
In the fourth step, the spectral analysis can know that the amplitude of each voxel of the lung on the respiratory frequency and the heartbeat frequency reflects the intensity change of the magnetic resonance signal caused by respiration and heartbeat; the respiratory motion and the heart pulsation have periodicity, and the lung tissue magnetic resonance signal time sequence is subjected to spectrum analysis in the respiratory process to extract ventilation and perfusion information of the lung. The lungs exchange gas through alternating ventilation and perfusion, a physiological process that causes changes in the intensity of the lung MR signal. During inspiration, the lung volume increases and the lung parenchymal tissue density and signal intensity decreases; the opposite is true during exhalation. Meanwhile, the lung tissue MR signal intensity is modulated by the heart cycle, and the signal intensity obtained in the systole phase is lower than that obtained in the diastole phase. The physiological processes of respiratory and cardiac cyclic motion have different frequencies, about 0.2 hz and 1.1 hz respectively. Therefore, the magnetic resonance signals of the lung tissue in the free breathing process are subjected to spectrum analysis, the amplitude values on the breathing frequency and the cardiac frequency are respectively calculated, the MR signal intensity change value of each voxel in the lung caused by lung ventilation and perfusion can be obtained, and then a quantitative lung ventilation function graph and a lung perfusion graph can be constructed by combining the geometric change information and the structural information of the lung tissue in the breathing process and the magnetic resonance quantitative parameters of the lung tissue.
Although the embodiments have been described and illustrated separately, it will be apparent to those skilled in the art that some common techniques may be substituted and integrated between the embodiments, and reference may be made to one of the embodiments not explicitly described, or to another embodiment described.
The above-described embodiments do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the above-described embodiments should be included in the protection scope of the technical solution.
Claims (8)
1. A lung function imaging processing method based on MRI technology is characterized in that: the method comprises the following steps:
firstly, rapidly acquiring a free-breathing lung dynamic magnetic resonance data sequence;
secondly, reconstructing an MR image by using the magnetic resonance data sequence acquired in the first step, and reconstructing a lung dynamic MR image sequence with good spatial resolution and contrast;
thirdly, segmenting the lung tissue of the magnetic resonance image and registering the free breathing lung dynamic MR image sequence, and extracting the geometric change information of the lung tissue in the breathing motion process;
step four, establishing a time sequence and performing spectral analysis on voxels one by one according to the registered lung dynamic MR image sequence;
for a set of free-breathing lung dynamic magnetic resonance image sequences S (t), a time sequence of certain voxels (xi, yi) is denoted as S (xi, yi; t), and the spectral analysis of the time sequence is as follows:
FFT|S(xi,yi；t)|＝s(xi,yi；ω) (1)
in the formula (1), fft (fast Fourier transform) is fast Fourier transform; omega is angular frequency;
and step five, according to the geometric change information of the lung tissues in the respiratory motion obtained by registration, and by combining the structural information of the lung MR image and the magnetic resonance quantitative parameters, constructing a quantitative lung ventilation function diagram and a lung perfusion diagram.
2. The lung function imaging processing method based on MRI technique according to claim 1, characterized in that: collecting lung magnetic resonance signals of the same object at different periods, constructing a quantitative lung ventilation function graph and a lung perfusion graph, and analyzing and establishing a mutual relation between the quantitative lung ventilation function graph and objective indexes of lung function examination; and simultaneously, quantitatively analyzing the obtained lung perfusion image and a perfusion image obtained by dynamic contrast enhanced magnetic resonance imaging.
3. The pulmonary imaging processing method based on MRI technique according to claim 1, characterized in that: in the first step, the fast acquisition means that a radial track data scanning mode is adopted, and the acquisition speed is improved by reducing the number of spokes.
4. The lung function imaging processing method based on MRI technique according to claim 1 or 3, characterized in that: in the second step, the MR image reconstruction refers to the construction of a solution model by using a regularization technology, and the construction of a stable numerical solution algorithm to reconstruct a lung magnetic resonance image sequence by introducing constraint conditions according to the sparsity and low-rank characteristics of the magnetic resonance image sequence.
5. The lung function imaging processing method based on the MRI technique according to claim 4, characterized in that: the mathematical model of the magnetic resonance imaging procedure is described as follows:
y＝Em (2)
in equation (2), y is the magnetic resonance data sequence acquired by multiple channels on each coil, the matrix E is a physical model expressing the magnetic resonance imaging process, and m is the magnetic resonance data of the region of interest, i.e. the magnetic resonance image to be reconstructed. Reconstruction m is an ill-defined inverse problem, and an optimization method can be applied to construct a solution model of the problem:
in equation (3), Ψ is a sparse transform operator (based on the sparsity of the magnetic resonance image), L is other prior information (including low rank characteristics), λ 1, λ 2 are regularization coefficients;
equation (3) is solved by using an Alternating Direction multiplier Algorithm (ADMM).
6. The lung function imaging processing method based on MRI technique according to claim 3, characterized in that: in the third step, the reconstructed lung image sequence is registered to a reference image for motion correction, the adopted non-rigid registration method comprises feature detection and matching, deformation model estimation and similarity measurement, and the reference image adopted for registration is extracted in the data acquisition process.
7. The lung function imaging processing method based on MRI technique according to claim 6, characterized in that: in the free breathing process, the magnetic resonance images of the lung acquired at different times t are respectively recorded as m1(x)、m2(x)，m1(x) And m2(x) The deformation (denoted as h (x)) is calculated by a differential homomorphic method as follows:
in formula (4), x is a coordinate matrix of the image; lcc is m calculated based on similarity measure1(x) And m2(x) A local cross-correlation matrix; l (h (x)) is a regularization term; α is the regularization term control coefficient;
is a gradient operator.
8. The lung function imaging processing method based on MRI technique according to claim 3, characterized in that: in the fourth step, the spectral analysis can know the amplitude of each voxel of the lung on the respiratory frequency and the heartbeat frequency, and reflects the intensity change of the magnetic resonance signal caused by respiration and heartbeat; the respiratory motion and the heart pulsation have periodicity, and the lung tissue magnetic resonance signal time sequence is subjected to spectrum analysis in the respiratory process to extract ventilation and perfusion information of the lung.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910958603.5A CN110652297A (en) | 2019-10-10 | 2019-10-10 | Lung function imaging processing method based on MRI technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910958603.5A CN110652297A (en) | 2019-10-10 | 2019-10-10 | Lung function imaging processing method based on MRI technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110652297A true CN110652297A (en) | 2020-01-07 |
Family
ID=69040395
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910958603.5A Pending CN110652297A (en) | 2019-10-10 | 2019-10-10 | Lung function imaging processing method based on MRI technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110652297A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113327274A (en) * | 2021-04-15 | 2021-08-31 | 中国科学院苏州生物医学工程技术研究所 | Lung CT image registration method and system integrating segmentation function |
| CN113592804A (en) * | 2021-07-27 | 2021-11-02 | 东软医疗系统股份有限公司 | Image processing method, device and equipment |
| CN113808176A (en) * | 2021-09-18 | 2021-12-17 | 浙江大学 | MR image registration method, analysis method and device based on device imaging coordinate |
| CN114236443A (en) * | 2021-12-03 | 2022-03-25 | 中国科学院精密测量科学与技术创新研究院 | Gas MRI method for rapid quantitative assessment of lung dynamic ventilation function |
| CN115439478A (en) * | 2022-11-07 | 2022-12-06 | 四川大学 | Lung lobe perfusion strength evaluation method, system, equipment and medium based on lung perfusion |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330953A (en) * | 2017-07-06 | 2017-11-07 | 桂林电子科技大学 | A kind of Dynamic MRI method for reconstructing based on non-convex low-rank |
| CN108447102A (en) * | 2018-02-11 | 2018-08-24 | 南京邮电大学 | A kind of dynamic magnetic resonance imaging method of low-rank and sparse matrix decomposition |
| CN108577840A (en) * | 2018-02-11 | 2018-09-28 | 南京邮电大学 | A kind of steady PCA imaging methods of dynamic magnetic resonance |
| CN109247939A (en) * | 2018-08-29 | 2019-01-22 | 中国科学院武汉物理与数学研究所 | A kind of adaptive high lack sampling hyperpolarized gas lung Dynamic MRI method for reconstructing |
-
2019
- 2019-10-10 CN CN201910958603.5A patent/CN110652297A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330953A (en) * | 2017-07-06 | 2017-11-07 | 桂林电子科技大学 | A kind of Dynamic MRI method for reconstructing based on non-convex low-rank |
| CN108447102A (en) * | 2018-02-11 | 2018-08-24 | 南京邮电大学 | A kind of dynamic magnetic resonance imaging method of low-rank and sparse matrix decomposition |
| CN108577840A (en) * | 2018-02-11 | 2018-09-28 | 南京邮电大学 | A kind of steady PCA imaging methods of dynamic magnetic resonance |
| CN109247939A (en) * | 2018-08-29 | 2019-01-22 | 中国科学院武汉物理与数学研究所 | A kind of adaptive high lack sampling hyperpolarized gas lung Dynamic MRI method for reconstructing |
Non-Patent Citations (3)
| Title |
|---|
| GRZEGORZ BAUMAN ET AL: "Non-Contrast-Enhanced Perfusion and Ventilation Assessment of the Human Lung by Means of Fourier Decomposition in Proton MRI", 《MAGNETIC RESONANCE IN MEDICINE》 * |
| 胡吉波: "磁共振肺通气-灌注功能成像及其应用", 《国外医学.临床放射学分册》 * |
| 郭悦等: "肺功能成像:猪肺的傅里叶分解磁共振成像和SPECT/CT成像的定性比较", 《中国医疗设备》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113327274A (en) * | 2021-04-15 | 2021-08-31 | 中国科学院苏州生物医学工程技术研究所 | Lung CT image registration method and system integrating segmentation function |
| CN113327274B (en) * | 2021-04-15 | 2024-01-30 | 中国科学院苏州生物医学工程技术研究所 | Lung CT image registration method and system integrating segmentation function |
| CN113592804A (en) * | 2021-07-27 | 2021-11-02 | 东软医疗系统股份有限公司 | Image processing method, device and equipment |
| CN113592804B (en) * | 2021-07-27 | 2024-05-07 | 东软医疗系统股份有限公司 | Image processing method, device and equipment |
| CN113808176A (en) * | 2021-09-18 | 2021-12-17 | 浙江大学 | MR image registration method, analysis method and device based on device imaging coordinate |
| CN113808176B (en) * | 2021-09-18 | 2024-01-26 | 浙江大学 | MR image registration method, analysis method and device based on device imaging coordinates |
| CN114236443A (en) * | 2021-12-03 | 2022-03-25 | 中国科学院精密测量科学与技术创新研究院 | Gas MRI method for rapid quantitative assessment of lung dynamic ventilation function |
| CN115439478A (en) * | 2022-11-07 | 2022-12-06 | 四川大学 | Lung lobe perfusion strength evaluation method, system, equipment and medium based on lung perfusion |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110652297A (en) | Lung function imaging processing method based on MRI technology | |
| AU2017248470B2 (en) | Heart imaging method | |
| Scott et al. | Motion in cardiovascular MR imaging | |
| Martinez-Möller et al. | Dual cardiac–respiratory gated PET: implementation and results from a feasibility study | |
| Runge et al. | Respiratory gating in magnetic resonance imaging at 0.5 Tesla. | |
| US20120245453A1 (en) | Respiratory interval-based correlation and processing of dynamic imaging data | |
| Sá et al. | Validating the distribution of specific ventilation in healthy humans measured using proton MR imaging | |
| EP2163201A1 (en) | List mode-based respiratory and cardiac gating in positron emission tomography | |
| Goolaub et al. | Multidimensional fetal flow imaging with cardiovascular magnetic resonance: a feasibility study | |
| JP2009537201A (en) | Perfusion imaging method | |
| US10517509B2 (en) | Assessment of the lung parenchyma by means of magnetic resonance images | |
| Falcão et al. | Pilot tone navigation for respiratory and cardiac motion‐resolved free‐running 5D flow MRI | |
| US8774898B2 (en) | Oxygen-enhanced MR imaging technique | |
| US8660634B2 (en) | System and method for improved lung fourier decomposition magnetic resonance imaging | |
| Visvikis et al. | Respiratory motion in positron emission tomography for oncology applications: Problems and solutions | |
| Goo | Advanced functional thoracic imaging in children: from basic concepts to clinical applications | |
| Ding et al. | Dynamic pulmonary MRI using motion‐state weighted motion‐compensation (MostMoCo) reconstruction with ultrashort TE: a structural and functional study | |
| Graf et al. | Electrical impedance tomography: Amplitudes of cardiac related impedance changes in the lung are highly position dependent | |
| CN206209898U (en) | Three-dimensional cardiac image re-construction system | |
| CN116430287A (en) | Dynamic imaging method for lung magnetic resonance | |
| Berman et al. | Volumetric MRI of the lungs during forced expiration | |
| Papp et al. | Lung parenchyma and structure visualisation in paediatric chest MRI: a comparison of different short and ultra-short echo time protocols | |
| US20150265165A1 (en) | System and Method For Non-Contrast Magnetic Resonance Imaging of Pulmonary Blood Flow | |
| Papp et al. | Deep learning for improving ZTE MRI images in free breathing | |
| JP3198721B2 (en) | MR imaging device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200107 |