CN114601448B - Rapid magnetic resonance imaging method for visual quantitative evaluation of lung compliance - Google Patents
Rapid magnetic resonance imaging method for visual quantitative evaluation of lung compliance Download PDFInfo
- Publication number
- CN114601448B CN114601448B CN202210280982.9A CN202210280982A CN114601448B CN 114601448 B CN114601448 B CN 114601448B CN 202210280982 A CN202210280982 A CN 202210280982A CN 114601448 B CN114601448 B CN 114601448B
- Authority
- CN
- China
- Prior art keywords
- lung
- breath
- compliance
- ventilation
- cst
- 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.)
- Active
Links
- 210000004072 lung Anatomy 0.000 title claims abstract description 149
- 238000002595 magnetic resonance imaging Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000000007 visual effect Effects 0.000 title claims abstract description 12
- 238000011158 quantitative evaluation Methods 0.000 title abstract description 6
- 238000009826 distribution Methods 0.000 claims abstract description 31
- 238000009423 ventilation Methods 0.000 claims description 41
- 230000011218 segmentation Effects 0.000 claims description 9
- 230000000241 respiratory effect Effects 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 239000011261 inert gas Substances 0.000 abstract description 11
- 239000007789 gas Substances 0.000 abstract description 10
- 206010006322 Breath holding Diseases 0.000 abstract description 4
- 230000005865 ionizing radiation Effects 0.000 abstract description 4
- 238000005070 sampling Methods 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000003384 imaging method Methods 0.000 description 9
- 238000002591 computed tomography Methods 0.000 description 8
- 229910052756 noble gas Inorganic materials 0.000 description 5
- 241000700159 Rattus Species 0.000 description 4
- 230000029058 respiratory gaseous exchange Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 241000282412 Homo Species 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 210000000621 bronchi Anatomy 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000005415 magnetization Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001020 rhythmical effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/085—Measuring impedance of respiratory organs or lung elasticity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
Landscapes
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pulmonology (AREA)
- General Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Animal Behavior & Ethology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Theoretical Computer Science (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The invention discloses a rapid MRI method for visual quantitative evaluation of lung compliance, wherein a subject inhales hyperpolarized inert gas for n times according to set target breath-holding pressure to finish the inhalation process, and the breath-holding is performed for lung gas MRI scanning after each inhalation; obtaining n groups of airway breath-hold pressures and corrected lung volume distribution; adopting an image registration algorithm to the corrected lung volume distribution to obtain lung volume distribution registered to the minimum airway breath-hold pressure; the lung compliance distribution and the dependence coefficient R of the lung compliance on different directions are calculated by point-to-point fitting of the lung volume distribution under different airway breath-hold pressures. The invention directly images the gas in the lung based on hyperpolarized inert gas rapid MRI, has no ionizing radiation and radioactivity, short scanning time, high sampling speed and accurate calculation of the lung volume, and is more suitable for clinical popularization.
Description
Technical Field
The invention relates to the technical field of magnetic resonance imaging (Magnetic resonance imaging, MRI), in particular to a rapid MRI method for visualized quantitative evaluation of lung compliance. The method is suitable for quantitative evaluation of lung compliance by using hyperpolarized inert gas rapid magnetic resonance as a means.
Background
Lung compliance is an important indicator of the elasticity of the reflecting lung tissue, and is currently not clinically measurable locally. In the clinic, electrical sensors are now commonly used to measure lung respiratory airflow and pressure to calculate static compliance, while dynamic lung compliance is calculated by continuously monitoring lung compliance in rhythmic breathing [ Cotes J E et al, john Wiley & Sons,2009 ]. These techniques only achieve global compliance of the lungs and no visual study of compliance is possible. Since calculation of lung compliance is related to lung volume, it is important to develop a method for assessing lung compliance based on image means.
Computer Tomography (CT) is the most widely used lung imaging means in clinical practice at present, for example, human lung volume changes are obtained by CT, and further lung compliance distribution [ Nair G B et al, PHYSICS IN MEDICINE & Biology,2021,66 (21): 21NT06 ]. Similarly, the lung volume can also be estimated using the region of the lung cavity in conventional proton MRI [ Morgan, a.r. et al proc ISMRM 2010; p.2520 ]. However, CT and proton MRI do not image the lung cavity, so CT and proton MRI have difficulty in distinguishing the region of ventilation defect that contributes less to the calculation of lung volume, and thus cannot accurately calculate lung volume. Meanwhile, the ionizing radiation of CT makes the repeated scanning technique limited in clinical popularization.
GY Jang et al [ Jang G Y et al Biomedical engineering online,2019,18 (1): 1-18 ] obtained a lung compliance distribution using electrical impedance imaging. The method evaluates local lung volume changes by measuring lung tissue impedance changes, and further makes local evaluations of lung compliance, but the development of the method is limited by the inherent spatial resolution disadvantages of electrical impedance imaging.
Hyperpolarized noble gas rapid MRI has proven to be an effective technique for imaging lung cavities over the last decades, with extremely high sensitivity, rapid sampling and specificity, as compared to other clinical imaging techniques, making it inherently advantageous for direct measurement of lung cavity volume. For example, global lung compliance of rat lungs was measured using hyperpolarized 129 Xe gas MRI and hyperpolarized 3 He gas MRI at two pressures [ Fox M S et al Magnetic resonance IN MEDICINE,2014,71 (3): 1130-1136 ]; the lung compliance can be further obtained by measuring lung volumes under different inflation states using hyperpolarized 3 He gas MRI by S Choy et al to obtain a lung pressure-volume curve across the whole lung. However, neither of the above two studies achieved a localized distribution of lung compliance, and did not fundamentally address pain points of lung compliance measurements.
Disclosure of Invention
In view of the above-described problems in the prior art, the present invention provides a rapid MRI method for visual quantitative assessment of lung compliance, which can achieve accurate local assessment of lung compliance.
The above object of the present invention is achieved by the following technical solutions:
a rapid magnetic resonance imaging method for visual quantitative assessment of lung compliance, comprising the steps of:
Step 1, in a magnetic resonance scanner, in one respiratory cycle, a subject inhales n times, holds breath after each inhalation and performs image acquisition, and the subject exhales after the nth image acquisition is completed;
Step 2, dividing and calibrating a lung three-dimensional ventilation image Img (i) under n groups of breath-hold pressures P (i) to obtain lung ventilation region volume distribution V map (i, x, y, z), wherein i is { 1-n }, x, y, z is the pixel sequence number of the obtained lung three-dimensional ventilation image Img (i) in three dimensions;
Step 3, registering n groups of lung ventilation area volume distributions V map (i, x, y, z) to lung ventilation area volume distributions V map (1, x, y, z) acquired under the minimum breath-hold pressure P (1) by utilizing an image registration algorithm to obtain n groups of volume change factor graphs F (i, x, y, z), and multiplying the volume change factor graphs F (i, x, y, z) by V map (1, x, y, z) acquired under the minimum pressure point to obtain volume distribution graphs V reg_map (i, x, y, z) under different registered breath-hold pressures;
step 4, linearly fitting the ventilation volume distribution diagram V reg_map (i, x, y, z) under different breath-hold pressures and the breath-hold pressures according to the following formula to obtain lung compliance distribution Cst (x, y, z) of the lung voxel-by-voxel points,
Vreg_map(i,x,y,z)＝Cst(x,y,z)×P(i)
Wherein i is {1 to n };
Step 5, fitting the dependence coefficient R AB,RAP,RLR of the lung compliance on the above three directions in the tip-bottom direction A-B, the anterior lung-posterior direction A-P and the left lung-right lung direction L-R according to the following formula according to the lung compliance distribution Cst (x, y, z) obtained in step 4,
Wherein Cst Global is the mean value of Cst of whole lung, i AB,iAP,iLR is the layer number ,iAB∈{1～nAB},iAP∈{1～nAP},iLR∈{1～nLR},nAB,nAP in the direction of A-B, A-P, L-R and n LR is the layer number in the direction of A-B, A-P, L-R, and mean (Cst (i AB,AllAP,AllLR)) is the lung compliance mean value of the i AB layer in the direction of A-B; mean (Cst (All AB,iAP,AllLR)) is the mean lung compliance of the i AP th layer in the a-P direction; mean (Cst (All AB,AllAP,iLR)) is the mean lung compliance of the i LR th layer in the L-R direction, m is the coefficient of determination; im is the number of the determined coefficient, im is { 1-m }, R AB,RAP,RLR is the lung compliance dependence coefficient in the directions of A-B, A-P and L-R, R AB,RAP,RLR has zero order, first order to m-1 order values according to the size of m, L AB,LAP,LLR is the distance from each layer to the center of the lung in the directions of A-B, A-P and L-R, and All AB,AllAP,AllLR represents the traversal of All pixels in the directions of A-B, A-P and L-R.
The shield pressure P (i) is less than 10cm H 2 O as described above.
Segmentation of the three-dimensional ventilation image Img (i) of the lung as described above comprises the steps of: and averaging the first 1% signal intensity of the three-dimensional lung ventilation image Img (i) to obtain a signal mean value S, calculating a first derivative and a second derivative of the signal of the three-dimensional lung ventilation image Img (i) to obtain image edge information E, and performing staggered iteration of threshold segmentation and edge extraction on the three-dimensional lung ventilation image Img (i) according to the signal mean value S and the image edge information E to finally finish segmentation of the three-dimensional lung ventilation image Img (i).
The signal calibration of step 2 as described above includes longitudinal relaxation time (T1) calibration, and also includes apparent transverse relaxation time (T2), diffusion calibration (ADC), and radio frequency field (B1) uniformity calibration.
Compared with the prior art, the invention has the following characteristics:
1. The hyperpolarized inert gas lung ventilation rapid MRI can directly image the lung cavity area, and can rapidly and accurately acquire lung ventilation volume information.
2. Compared with clinical lung imaging means such as CT, the hyperpolarized inert gas lung ventilation rapid MRI measurement of lung compliance has no ionizing radiation and radioactivity, is more accurate, and is more suitable for clinical popularization.
3. By zoning the visualized lung compliance results, the direction dependent coefficient of lung compliance can be quantitatively obtained.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of the respiration and sampling triggering in step1 according to the embodiment of the present invention;
FIG. 3 shows the lung volume distribution at a certain level of the lung at different breath-hold pressures obtained in step 2 of the present embodiment;
FIG. 4 is a detailed view of step 3 of the embodiment of the present invention;
Fig. 5 is a representative result of the lung compliance profile obtained in step 4 of the present example.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, the following detailed description of the embodiments of the present invention will be given by way of example only with reference to figures 1 to 5 and 7TMRI for hyperpolarized inert gas rapid MRI of rats.
A rapid MRI method for visual quantitative assessment of lung compliance, comprising the steps of:
Step 1, as shown in fig. 2, the subject finishes the inhalation process by inhaling hyperpolarized inert gas n times in the magnetic resonance scanner, and performs breath-holding and lung gas MRI scanning after each inhalation. In the process, the lung pressure of the subject is gradually accumulated, and the quasi-static process of lung inhalation is realized in combination with rapid breath-holding sampling after inhalation. In addition, hyperpolarized noble gas in the lung and the residual longitudinal magnetization vector thereof after the previous breath-hold imaging provide the base pressure and the additional signal intensity in the lung for the next breath-hold imaging, so that the optimal utilization of the hyperpolarized noble gas is realized, the hyperpolarized noble gas is not required to be repeatedly breathed by a subject, and the examination time is shortened.
In the step, the gas quantity inhaled each time is determined according to the set breath-hold pressure P (i), wherein i is the number of times of inhalation in one respiratory period, i is { 1-n }, in one respiratory period, the subject inhales n times, breath-holds after each inhalation and performs image acquisition, and the subject exhales after finishing the nth image acquisition. I.e. there are n inhalations and 1 exhalation in one breathing cycle.
Preferably, the setting of the shield pressure P (i) is less than 10cm H 2 O. In this example, the subject is a rat, but may be another animal or human. The hyperpolarized noble gas includes 3He、83Kr、129 Xe, or 131 Xe, which have ultra-high magnetic resonance signal sensitivity. The device for realizing the delivery of hyperpolarized inert gas, shielding gas and measuring airway pressure during shielding gas is an existing breathing device.
Step 2, according to step 1, obtaining a lung three-dimensional ventilation image Img (i) under n groups of breath-hold pressures P (i), dividing and calibrating signals of the n groups of lung three-dimensional ventilation images Img (i), and obtaining a lung ventilation region volume distribution V map (i, x, y, z), wherein i is { 1-n }, x, y, z is a pixel sequence number of the obtained lung three-dimensional ventilation image Img (i) in three dimensions, and fig. 3 shows lung ventilation region volume distribution V map(1,x,y,5),Vmap(2,x,y,5),Vmap(3,x,y,5),Vmap (4, x, y, 5) when z is 5 under 4 different breath-hold pressures P (i) respectively.
Preferably, the segmentation method of the three-dimensional lung ventilation image Img (i) is an image segmentation algorithm based on a self-adaptive threshold and edge extraction, wherein the first 1% signal intensity of the three-dimensional lung ventilation image Img (i) is averaged to obtain a signal mean value S, in addition, the first-order derivative and the second-order derivative of the signal of the three-dimensional lung ventilation image Img (i) are calculated to obtain image edge information E, the threshold segmentation and the edge extraction of the three-dimensional lung ventilation image Img (i) are performed through staggered iteration according to the signal mean value S and the image edge information E, and finally the segmentation of the three-dimensional lung ventilation image Img (i) is completed, and the image segmentation algorithm fully utilizes the advantages of no background interference signal, clear signal area and clear noise area limit of the hyperpolarized inert gas image to segment the image.
The signal calibration of the three-dimensional ventilation image Img (i) of the lung comprises the calibration of factors such as longitudinal relaxation time T1, apparent transverse relaxation time T2, gas diffusion ADC, radio frequency field uniformity B1 and the like on the signal intensity of the three-dimensional ventilation image Img (i) of the lung.
And 3, registering n groups of V map (i, x, y, z) to the lung ventilation area volume distribution V map (1, x, y, z) acquired under the minimum breath-hold pressure P (1) by using an image registration algorithm to obtain n groups of volume change factor graphs F (i, x, y, z), wherein i is the number of times of inspiration in one respiratory cycle in the step 1, and i epsilon { 1-n }. Preferably, the image registration algorithm is a feature-based depth learning deformable image registration algorithm, and the algorithm selects a carina, a bronchus and a lung tip of a lung in an image as image features to perform image registration. The volume change factor graph F (i, x, y, z) is multiplied point-to-point by V map (1, x, y, z) acquired at the minimum breath-hold pressure P (i) to obtain a registered volume-integral graph V reg_map (i, x, y, z) at different breath-hold pressures, as shown in fig. 4.
Step 4, fitting the volume-point layout V reg_map (i, x, y, z) in step 3 according to equation 1 to obtain a lung compliance distribution Cst (x, y, z) for each voxel point of the lung, as shown in figure 5,
V reg_map (i, x, y, z) =cst (x, y, z) ×p (i) formula 1
Wherein i is {1 to n }, n is the number of inspiration in step 1.
Step 5, fitting the dependence coefficients R AB,RAP,RLR of the lung compliance on the above three directions according to the formula 2-4 in the tip-bottom direction A-B, the anterior lung-posterior direction A-P and the left lung-right lung direction L-R, respectively, according to the lung compliance distribution Cst (x, y, z) obtained in step 4,
Wherein Cst Global is the mean of the lung compliance distribution Cst of the whole lung. i AB,iAP,iLR is the number of layers ,iAB∈{1～nAB},iAP∈{1～nAP},iLR∈{1～nLR},nAB,nAP in the directions A-B, A-P and L-R, and n LR is the number of layers in the directions A-B, A-P and L-R. mean (Cst (i AB,AllAP,AllLR)) is the mean lung compliance of the i AB th layer in the a-B direction; mean (Cst (All AB,iAP,AllLR)) is the mean lung compliance of the i AP th layer in the a-P direction; mean (Cst (All AB,AllAP,iLR)) is the mean lung compliance of the i LR th layer in the L-R direction. m is a determining coefficient, typically 3 for humans and rats; im is the number of the determined coefficient, im is { 1-m }. R AB,RAP,RLR is the lung compliance dependence coefficient in A-B, A-P and L-R directions, and R AB,RAP,RLR has zero order and first order to m-1 order values according to m. L AB,LAP,LLR is the distance of each layer from the center of the lung in the directions A-B, A-P, L-R, all AB,AllAP,AllLR represents the traversal of All pixels in the three directions A-B, A-P, L-R, respectively.
In summary, the present invention is a rapid MRI method for visual quantitative evaluation of lung compliance, which uses a respiratory device to set and record airway pressure of a subject, and uses n times of inhalation of hyperpolarized inert gas to complete an inhalation process for another subject, and records a lung breath-hold pressure P (i) of the subject after the ith inhalation and a lung volume distribution V reg_map (i, x, y, z) obtained by corresponding calibration and registration using a hyperpolarized inert gas ventilation rapid magnetic resonance image, thereby obtaining a lung compliance distribution Cst of the subject, and a dependence coefficient R of lung compliance on different directions, wherein i is { 1-n }. The specific advantages are as follows: the lung cavity region can be imaged directly, and lung ventilation volume information can be acquired rapidly and accurately; compared with clinical lung imaging means such as CT, the method for measuring the lung compliance has the advantages of no ionizing radiation and radioactivity, more accuracy and suitability for clinical popularization; the visualized lung compliance results are divided into areas, and the direction dependent coefficient of lung compliance can be quantitatively obtained.
The foregoing is a part of the embodiments of the present invention, and the scope of the present invention is not limited thereto, and any changes and substitutions that are easily conceivable by those skilled in the art within the technical scope of the present invention are intended to be covered in the scope of the present invention.
Claims (4)
1. A rapid magnetic resonance imaging method for visual quantitative assessment of lung compliance, comprising the steps of:
Step 1, in a magnetic resonance scanner, in one respiratory cycle, a subject inhales n times, holds breath after each inhalation and performs image acquisition, and the subject exhales after the nth image acquisition is completed;
Step 2, dividing and calibrating a lung three-dimensional ventilation image Img (i) under n groups of breath-hold pressures P (i) to obtain lung ventilation region volume distribution V map (i, x, y, z), wherein i is { 1-n }, x, y, z is the pixel sequence number of the obtained lung three-dimensional ventilation image Img (i) in three dimensions;
Step 3, registering n groups of lung ventilation area volume distributions V map (i, x, y, z) to lung ventilation area volume distributions V map (1, x, y, z) acquired under the minimum breath-hold pressure P (1) by utilizing an image registration algorithm to obtain n groups of volume change factor graphs F (i, x, y, z), and multiplying the volume change factor graphs F (i, x, y, z) by V map (1, x, y, z) acquired under the minimum pressure point to obtain volume distribution graphs V reg_map (i, x, y, z) under different registered breath-hold pressures;
step 4, linearly fitting the ventilation volume distribution diagram V reg_map (i, x, y, z) under different breath-hold pressures and the breath-hold pressures according to the following formula to obtain lung compliance distribution Cst (x, y, z) of the lung voxel-by-voxel points,
Vreg_map(i,x,y,z)＝Cst(x,y,z)×P(i)
Wherein i is {1 to n };
Step 5, fitting the dependence coefficient R AB,RAP,RLR of the lung compliance on the above three directions in the tip-bottom direction A-B, the anterior lung-posterior direction A-P and the left lung-right lung direction L-R according to the following formula according to the lung compliance distribution Cst (x, y, z) obtained in step 4,
Wherein Cst Global is the mean value of Cst of whole lung, i AB,iAP,iLR is the layer number ,iAB∈{1～nAB},iAP∈{1～nAP},iLR∈{1～nLR},nAB,nAP in the direction of A-B, A-P, L-R and n LR is the layer number in the direction of A-B, A-P, L-R, and mean (Cst (i AB,AllAP,AllLR)) is the lung compliance mean value of the i AB layer in the direction of A-B; mean (Cst (All AB,iAP,AllLR)) is the mean lung compliance of the i AP th layer in the a-P direction; mean (Cst (All AB,AllAP,iLR)) is the mean lung compliance of the i LR th layer in the L-R direction, m is the coefficient of determination; im is the number of the determined coefficient, im is { 1-m }, R AB,RAP,RLR is the lung compliance dependence coefficient in the directions of A-B, A-P and L-R, R AB,RAP,RLR has zero order, first order to m-1 order values according to the size of m, L AB,LAP,LLR is the distance from each layer to the center of the lung in the directions of A-B, A-P and L-R, and All AB,AllAP,AllLR represents the traversal of All pixels in the directions of A-B, A-P and L-R.
2. A method of rapid magnetic resonance imaging for visual quantitative assessment of lung compliance according to claim 1, wherein the breath-hold pressure P (i) is less than 10cm H 2 O.
3. A method of rapid magnetic resonance imaging for visual quantitative assessment of lung compliance according to claim 1, wherein: segmentation of the three-dimensional ventilation image Img (i) of the lung comprises the steps of: and averaging the first 1% signal intensity of the three-dimensional lung ventilation image Img (i) to obtain a signal mean value S, calculating a first derivative and a second derivative of the signal of the three-dimensional lung ventilation image Img (i) to obtain image edge information E, and performing staggered iteration of threshold segmentation and edge extraction on the three-dimensional lung ventilation image Img (i) according to the signal mean value S and the image edge information E to finally finish segmentation of the three-dimensional lung ventilation image Img (i).
4. A method of rapid magnetic resonance imaging for visual quantitative assessment of lung compliance according to claim 1, wherein: the signal calibration in step 2 includes longitudinal relaxation time (T1) calibration, and also includes apparent transverse relaxation time (T2), diffusion calibration (ADC), and radio frequency field (B1) uniformity calibration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210280982.9A CN114601448B (en) | 2022-03-17 | 2022-03-17 | Rapid magnetic resonance imaging method for visual quantitative evaluation of lung compliance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210280982.9A CN114601448B (en) | 2022-03-17 | 2022-03-17 | Rapid magnetic resonance imaging method for visual quantitative evaluation of lung compliance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114601448A CN114601448A (en) | 2022-06-10 |
| CN114601448B true CN114601448B (en) | 2024-08-09 |
Family
ID=81865604
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210280982.9A Active CN114601448B (en) | 2022-03-17 | 2022-03-17 | Rapid magnetic resonance imaging method for visual quantitative evaluation of lung compliance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114601448B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107822632A (en) * | 2016-09-16 | 2018-03-23 | 德尔格制造股份两合公司 | Processing determines and visualized the equipment of pulmonary ventilation region characteristic with visualization data |
| CN108366753A (en) * | 2015-10-07 | 2018-08-03 | 生物质子有限责任公司 | Selective sampling for assessing the structure space frequency with specified contrast mechanisms |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7072706B2 (en) * | 2001-02-08 | 2006-07-04 | The Trustees Of The University Of Pennsylvania | Equilibration method for high resolution imaging of lung compliance and distribution of functional residual capacity |
| WO2006079036A2 (en) * | 2005-01-21 | 2006-07-27 | Northwestern University | Methods and compositions for encapsulation of cells |
| US7885702B2 (en) * | 2006-04-19 | 2011-02-08 | Wisconsin Alumni Research Foundation | Segmentation of the airway tree using hyperpolarized noble gases and diffusion weighted magnetic resonance imaging |
| EP3727587A4 (en) * | 2017-12-22 | 2022-02-23 | Massachusetts Institute Of Technology | Compositions and methods for williams syndrome (ws) therapy |
| US20190246949A1 (en) * | 2018-02-12 | 2019-08-15 | Timpel Medical B.V. | Systems and methods to determine a patient's responsiveness to an alveolar recruitment maneuver |
| EP3876832A4 (en) * | 2018-11-05 | 2022-08-17 | Autem Medical, LLC | Chemical exchange saturation transfer system for characterization, diagnosis, and treatment of a health condition |
| CN110623668A (en) * | 2019-09-18 | 2019-12-31 | 中国科学院武汉物理与数学研究所 | Rapid magnetic resonance imaging method for quantitative evaluation of lung compliance |
| DE102021000312A1 (en) * | 2020-02-07 | 2021-08-12 | Löwenstein Medical Technology S.A. | Method and device for determining a regional compliance of a lung during spontaneous breathing |
-
2022
- 2022-03-17 CN CN202210280982.9A patent/CN114601448B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108366753A (en) * | 2015-10-07 | 2018-08-03 | 生物质子有限责任公司 | Selective sampling for assessing the structure space frequency with specified contrast mechanisms |
| CN107822632A (en) * | 2016-09-16 | 2018-03-23 | 德尔格制造股份两合公司 | Processing determines and visualized the equipment of pulmonary ventilation region characteristic with visualization data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114601448A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Deninger et al. | Assessment of a single‐acquisition imaging sequence for oxygen‐sensitive 3He‐MRI | |
| Koumellis et al. | Quantitative analysis of regional airways obstruction using dynamic hyperpolarized 3He MRI—preliminary results in children with cystic fibrosis | |
| Klimeš et al. | Free‐breathing quantification of regional ventilation derived by phase‐resolved functional lung (PREFUL) MRI | |
| US7885702B2 (en) | Segmentation of the airway tree using hyperpolarized noble gases and diffusion weighted magnetic resonance imaging | |
| GB2426342A (en) | Measuring Functional Residual Capacity (FRC) using Electrical Impedance Tomography (EIT) | |
| US9008750B2 (en) | Method for determining the ventilation of a lung | |
| EP2496952A1 (en) | Lung function analysis method and apparatus | |
| Diaz et al. | Validity of apparent diffusion coefficient hyperpolarized 3He-MRI using MSCT and pulmonary function tests as references | |
| Pennati et al. | Diaphragm involvement in Duchenne muscular dystrophy (DMD): an MRI study | |
| JP2005028121A (en) | Method of measuring ventilation distribution by respiratory ct image | |
| McRobbie et al. | Studies of the human oropharyngeal airspaces using magnetic resonance imaging. I. Validation of a three-dimensional MRI method for producing ex vivo virtual and physical casts of the oropharyngeal airways during inspiration | |
| CN110652297A (en) | Lung function imaging processing method based on MRI technology | |
| Hamedani et al. | A hybrid multibreath wash‐in wash‐out lung function quantification scheme in human subjects using hyperpolarized 3He MRI for simultaneous assessment of specific ventilation, alveolar oxygen tension, oxygen uptake, and air trapping | |
| Guerrero et al. | Novel method to calculate pulmonary compliance images in rodents from computed tomography acquired at constant pressures | |
| US8660634B2 (en) | System and method for improved lung fourier decomposition magnetic resonance imaging | |
| CN110623668A (en) | Rapid magnetic resonance imaging method for quantitative evaluation of lung compliance | |
| US7072706B2 (en) | Equilibration method for high resolution imaging of lung compliance and distribution of functional residual capacity | |
| US20220254016A1 (en) | Regional Pulmonary V/Q via image registration and Multi-Energy CT | |
| CN114601448B (en) | Rapid magnetic resonance imaging method for visual quantitative evaluation of lung compliance | |
| US8447380B2 (en) | Method for diagnosis of functional lung illnesses | |
| Peterson et al. | Measurement of lung airways in three dimensions using hyperpolarized helium-3 MRI | |
| Obert et al. | Examining lung microstructure using 19F MR diffusion imaging in COPD patients | |
| Klimeš et al. | Comparison of Free‐Breathing 3D Phase‐Resolved Functional Lung (PREFUL) MRI With Dynamic 19F Ventilation MRI in Patients With Obstructive Lung Disease and Healthy Volunteers | |
| CN116430287A (en) | Dynamic imaging method for lung magnetic resonance | |
| Xue et al. | Effective lung ventilation estimation based on 4D CT image registration and supervoxels |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |