WO2022198655A1 - Procédé d'imagerie du métabolisme de l'oxygène-17 par résonance magnétique, appareil, support de stockage et dispositif terminal - Google Patents

Procédé d'imagerie du métabolisme de l'oxygène-17 par résonance magnétique, appareil, support de stockage et dispositif terminal Download PDF

Info

Publication number
WO2022198655A1
WO2022198655A1 PCT/CN2021/083359 CN2021083359W WO2022198655A1 WO 2022198655 A1 WO2022198655 A1 WO 2022198655A1 CN 2021083359 W CN2021083359 W CN 2021083359W WO 2022198655 A1 WO2022198655 A1 WO 2022198655A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
radial
matrix data
sub
sampling data
Prior art date
Application number
PCT/CN2021/083359
Other languages
English (en)
Chinese (zh)
Inventor
王珊珊
荣楚誉
郑海荣
刘新
Original Assignee
中国科学院深圳先进技术研究院
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 中国科学院深圳先进技术研究院 filed Critical 中国科学院深圳先进技术研究院
Publication of WO2022198655A1 publication Critical patent/WO2022198655A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G06T5/94—Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning

Definitions

  • the present application belongs to the field of computer technology, and in particular relates to a magnetic resonance oxygen seventeen metabolic imaging method, device, computer-readable storage medium and terminal device.
  • the regulation of cerebral fluid plays an important role in the brain.
  • the dynamic image of cerebral fluid can be obtained by magnetic resonance oxygen seventeen (oxygen-17) metabolic imaging, which can be used as a basis for judging whether the brain is in a normal state or a diseased state.
  • an imaging method based on a gridding algorithm can be used. In this method, after density compensation is performed on the sampled data, the data is resampled to a large grid matrix by interpolation, and then the magnetic resonance image is reconstructed. Although this method can achieve fast imaging, the imaging accuracy is low.
  • embodiments of the present application provide a magnetic resonance oxygen seventeen metabolic imaging method, device, computer-readable storage medium, and terminal device, so as to solve the problem of low accuracy of the existing imaging method.
  • a first aspect of the embodiments of the present application provides a magnetic resonance oxygen seventeen metabolic imaging method, which may include:
  • Different radial interpolation networks are used to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition;
  • the use of different radial interpolation networks to respectively process the radial sampling data of each sub-region to obtain padded radial sampling data may include:
  • the augmented matrix data of each sub-region is restored to the form of radial spokes, and the filled radial sampling data is obtained.
  • the method may further include:
  • the training sample set of the gth radial interpolation network 1 ⁇ g ⁇ G, G is the number of frequency partitions;
  • the training sample set includes several training samples, and each training sample includes input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregion matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;
  • the g-th radial interpolation network is trained by using the training sample set to obtain the g-th radial interpolation network after training.
  • the construction of the training sample set of the gth radial interpolation network may include:
  • a training sample consisting of the input matrix data and the expected output matrix data is added to the training sample set.
  • the process of using different radial interpolation networks to process the matrix data of each sub-region to obtain the augmented matrix data of each sub-region may include:
  • the process of using different radial interpolation networks to process the matrix data of each sub-region to obtain the augmented matrix data of each sub-region may include:
  • the process of using different radial interpolation networks to process the matrix data of each sub-region to obtain the augmented matrix data of each sub-region may include:
  • the number of rows of each subregional matrix data is enlarged to ⁇ g times, and the number of columns is enlarged to ⁇ g times, and the augmented matrix data of each subregion is obtained.
  • performing imaging processing on the padded radial sampling data to obtain a target image may include:
  • a second aspect of the embodiments of the present application provides a magnetic resonance oxygen seventeen metabolic imaging device, which may include:
  • the sampling data division module is used to obtain the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to the preset frequency division;
  • the radial interpolation processing module is used to process the radial sampling data of each sub-region by using different radial interpolation networks to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition;
  • the imaging processing module is configured to perform imaging processing on the filled radial sampling data to obtain a target image.
  • the radial interpolation processing module may include:
  • the data rearrangement unit is used to rearrange the radial sampling data of each sub-region into a matrix form respectively, and obtain the matrix data of each sub-region;
  • the radial interpolation processing unit is used to process the matrix data of each sub-region by using different radial interpolation networks to obtain the augmented matrix data of each sub-region;
  • a data restoration unit configured to restore the augmented matrix data of each sub-region to the form of radial spokes, and obtain the filled radial sampling data.
  • the magnetic resonance oxygen seventeen metabolic imaging device may further include:
  • the training sample set building module is used to construct the training sample set of the gth radial interpolation network, 1 ⁇ g ⁇ G, G is the number of frequency partitions; the training sample set includes several training samples, each training sample Including input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregional matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;
  • the radial interpolation network training module is used for training the g th radial interpolation network by using the training sample set to obtain the g th radial interpolation network after training.
  • the training sample set building module may include:
  • the full sampling data acquisition unit is used to obtain fully sampled full sampling data
  • an under-sampling data processing unit configured to perform data deletion on the fully-sampled data to obtain under-sampled data corresponding to the fully-sampled data
  • a first rearranging unit used for rearranging the fully collected data to obtain the input matrix data
  • a second rearranging unit configured to rearrange the undersampled data to obtain the expected output matrix data
  • a training sample adding unit configured to add a training sample composed of the input matrix data and the expected output matrix data to the training sample set.
  • the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of rows of each subregional matrix data to ⁇ g times, respectively, to obtain each subregional matrix data.
  • Region augmentation matrix data where ⁇ g is the augmentation coefficient corresponding to the g-th frequency partition, and ⁇ g is an integer greater than 1.
  • the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of columns of each subregional matrix data to ⁇ g times, respectively, to obtain each subregional matrix data.
  • Region augmentation matrix data where ⁇ g is the augmentation coefficient corresponding to the g-th frequency partition, and ⁇ g is an integer greater than 1.
  • the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of rows and columns of each subregional matrix data to ⁇ g times, respectively. to ⁇ g times to obtain the augmented matrix data of each sub-region.
  • the imaging processing module is specifically configured to perform inverse fast Fourier transform processing on the padded radial sampling data to obtain the target image.
  • a third aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any one of the above-mentioned magnetic resonance oxygen seventeen metabolism is realized The steps of the imaging method.
  • a fourth aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program The steps of realizing any one of the above-mentioned magnetic resonance oxygen seventeen metabolic imaging methods.
  • a fifth aspect of the embodiments of the present application provides a computer program product, which, when the computer program product runs on a terminal device, enables the terminal device to perform any of the steps of the above-mentioned magnetic resonance oxygen seventeen metabolic imaging method.
  • the embodiment of the present application has the following beneficial effects: the embodiment of the present application obtains the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divides the radial sampling data according to preset frequency divisions is the radial sampling data of several sub-regions; different radial interpolation networks are used to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency Partition; perform imaging processing on the filled radial sampling data to obtain a target image.
  • different radial interpolation networks are introduced to fill in the sampled data in each frequency partition respectively, so that the sampled data in each different frequency partition has a stronger pertinence, and the filled data is more accurate, thereby effectively improving the the final imaging accuracy.
  • FIG. 1 is a flowchart of an embodiment of a magnetic resonance oxygen seventeen metabolic imaging method in an embodiment of the present application
  • Fig. 2 is the schematic diagram of golden angle radial sampling trajectory
  • Fig. 3 is the schematic flow chart of using different radial interpolation networks to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data;
  • Fig. 4 is the schematic diagram of frequency domain data filling process
  • FIG. 5 is a structural diagram of an embodiment of a magnetic resonance oxygen seventeen metabolic imaging device in an embodiment of the present application.
  • FIG. 6 is a schematic block diagram of a terminal device in an embodiment of the present application.
  • the term “if” may be contextually interpreted as “when” or “once” or “in response to determining” or “in response to detecting” .
  • the phrases “if it is determined” or “if the [described condition or event] is detected” may be interpreted, depending on the context, to mean “once it is determined” or “in response to the determination” or “once the [described condition or event] is detected. ]” or “in response to detection of the [described condition or event]”.
  • an embodiment of a magnetic resonance oxygen seventeen metabolic imaging method in the embodiment of the present application may include:
  • Step 101 Acquire the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to preset frequency partitions.
  • Magnetic Resonance Imaging Magnetic Resonance Imaging
  • MRI Magnetic Resonance Imaging
  • X-CT X-ray computed tomography
  • MRI does not produce harmful radiation to the human body, and can image human soft tissue at the same time.
  • X-CT X-ray computed tomography
  • MRI does not produce harmful radiation to the human body, and can image human soft tissue at the same time.
  • dynamic imaging which requires high temporal resolution, causes motion artifacts due to the slow imaging speed of MRI, which limits its wide application.
  • the original data collected by MRI is frequency domain data (that is, k-space data).
  • k-space data frequency domain data
  • researchers have done a lot of research on undersampling, non-uniform sampling, and sampling trajectory design.
  • the golden angle radial sampling targeted by the embodiments of the present application is different from the ordinary radial sampling. Because the ordinary radial sampling is densely sampled in the low-frequency region in the center, and sparsely sampled in the high-frequency region, this causes certain difficulties for reconstruction.
  • the golden angle radial sampling adopts different sampling densities for different annular areas according to the distance from the center, that is, reducing the sampling spokes in the low frequency area and increasing the sampling spokes in the high frequency area, which improves the sampling efficiency.
  • Figure 2 shows a schematic diagram of the golden angle radial sampling trajectory.
  • the golden angle radial sampling rotates a fixed angle of 111.25 degrees each time. This value is calculated based on the golden ratio, so it is called the golden angle.
  • the advantage of the golden angle radial sampling data is that the k-space at any time in the sampling process presents a state of relatively uniform sampling, which is conducive to image reconstruction based on the data of any number of sampling lines.
  • the terminal device that executes the embodiments of the present application may be a magnetic resonance scanner, or may be other terminal devices that can acquire radial sampling data through wired communication, wireless communication, transfer storage media, or the like.
  • the executive body can be an image workstation, and the image workstation can communicate and connect with the magnetic resonance scanner through data lines, wifi, bluetooth, etc., to obtain radial sampling data, and perform magnetic resonance imaging work.
  • the radial sampling data may be divided into several sub-regional radial sampling data according to preset frequency partitions.
  • the specific number of frequency partitions and the frequency range of each frequency partition may be set according to actual conditions, which are not specifically limited in this embodiment of the present application.
  • three frequency partitions can be set in the low frequency area, the intermediate frequency area and the high frequency area, and the radial sampling data can be divided into the low frequency area radial sampling data, the intermediate frequency area radial sampling data and the high frequency area radial sampling data correspondingly.
  • Step 102 using different radial interpolation networks to process the radial sampling data of each sub-region respectively to obtain the filled radial sampling data.
  • step 102 may include the process shown in FIG. 3 :
  • Step 1021 Rearrange the radial sampling data of each sub-region into a matrix form to obtain matrix data of each sub-region.
  • the number of radial spokes is denoted as N
  • the number of sampling points on each spoke is denoted as M.
  • the sampling on each spoke is If the data is taken as a row of the matrix, a matrix data with a size of N ⁇ M (N rows and M columns) can be obtained, that is, subregional matrix data.
  • Step 1022 Use different radial interpolation networks to process the matrix data of each sub-region respectively to obtain the augmented matrix data of each sub-region.
  • radial interpolation networks corresponding to each frequency partition may be preset. For example, as shown in Figure 4, for the three frequency partitions of the low frequency region, the intermediate frequency region and the high frequency region, corresponding radial interpolation networks can be set respectively (that is, the radial interpolation module 1 shown in the figure, the radial interpolation module 2 and radial interpolation module 3).
  • any neural network in the prior art may be selected and used as the radial interpolation network according to the actual situation, which is not specifically limited here.
  • Size expansion can specifically include the following three situations:
  • Case 1 Use different radial interpolation networks to expand the number of rows of each sub-regional matrix data to ⁇ g times, that is, increase the number of spokes, and obtain the augmented matrix data of each sub-region ;
  • the augmentation coefficient corresponding to the frequency partition, and ⁇ g is an integer greater than 1, and its specific value can be set according to the actual situation, which is not specifically limited in this embodiment of the present application, 1 ⁇ g ⁇ G, G is the number of frequency partitions .
  • Case 2 Use different radial interpolation networks to expand the number of columns of each subregional matrix data to ⁇ g times, that is, increase the number of sampling points on each spoke, and obtain the augmented matrix data of each subregion; among them, ⁇ g is the augmentation coefficient corresponding to the g-th frequency partition, and ⁇ g is an integer greater than 1, and its specific value can be set according to the actual situation, which is not specifically limited in this embodiment of the present application.
  • Case 3 Using different radial interpolation networks to expand the number of rows of each subregional matrix data to ⁇ g times, and the number of columns to ⁇ g times, that is to say, the number of spokes and the number of sampling points on each spoke are increased at the same time.
  • Each subregion augments the matrix data.
  • any one of the above-mentioned situations may be selected according to the actual situation to process the matrix data of each subregion.
  • the radial interpolation network used in the embodiments of the present application has undergone the training process of deep learning in advance. Processing Radial Interpolation Network) as an example to describe its training process in detail.
  • the training sample set includes several training samples.
  • Each training sample includes input matrix data and expected output matrix data.
  • the size of the input matrix data is the same as that of the gth subregional matrix data, and the expected output matrix data is increased with the gth subregional data.
  • the size of the wide matrix data is consistent.
  • the fully sampled data of the corresponding frequency partition may be obtained first, and then the fully sampled data is deleted according to the preset undersampling ratio to obtain the same data as the fully sampled data.
  • the undersampled data corresponding to the data.
  • rearrange the fully sampled data to obtain input matrix data; and rearrange the undersampled data to obtain expected output matrix data.
  • the training samples consisting of the input matrix data and the expected output matrix data are added to the training sample set.
  • the training sample set is empty, that is, there is no training sample.
  • the g th radial interpolation network can be trained by using the training sample set to obtain the g th radial interpolation network after training.
  • the radial interpolation network can be used to process the input matrix data in the training sample to obtain the actual output matrix data, and then according to the expectations in the training sample
  • the output matrix data and the actual output matrix data calculate the training loss value.
  • the specific calculation method of the training loss value can be set according to the actual situation.
  • the squared error between the expected output matrix data and the actual output matrix data can be calculated, and the squared error can be determined. is the training loss value.
  • the model parameters of the radial interpolation network can be adjusted according to the training loss value.
  • the model parameter of the radial interpolation network is W1
  • the training loss value is back-propagated to modify the model parameter W1 of the radial interpolation network to obtain the modified model parameter W2.
  • the training loss value is back-propagated to modify the model parameter W1 of the radial interpolation network to obtain the modified model parameter W2.
  • the training conditions can be that the number of training times reaches the preset number of times threshold, the number of times
  • the threshold can be set according to the actual situation, for example, it can be set to thousands, tens of thousands, hundreds of thousands or even larger values; the training condition can also be that the radial interpolation network converges; it may occur that the number of training has not yet reached the number of times threshold value, but the radial interpolation network has converged, which may lead to unnecessary repetition of work; or the radial interpolation network can never converge, which may lead to an infinite loop and cannot end the training process.
  • the training condition can also be training. The degree reaches the degree threshold or the radial interpolation network converges. When the training conditions are met, the trained radial interpolation network can be obtained.
  • Step 1023 restore the augmented matrix data of each sub-region to the form of radial spokes, and obtain the filled radial sampling data.
  • step 1023 is the inverse process of step 1021.
  • N′ N′ rows and M′ columns
  • the process of data restoration Taking the data of each row of the matrix as the sampling data on one spoke, the radial sampling data after corresponding sub-region filling can be obtained, including N′ spokes, and the number of sampling points on each spoke is M′. After restoring and obtaining the filled radial sampling data of each sub-region, these data can be combined into complete filled radial sampling data.
  • Figure 4 shows a schematic diagram of the data rearrangement, radial interpolation network processing and data restoration process. After such a process, the data filling in the frequency domain is effectively realized, and the finally obtained radial sampling data after filling is compared with the original. The radially sampled data has higher data precision.
  • Step 103 Perform imaging processing on the padded radial sampling data to obtain a target image.
  • inverse fast Fourier transform processing may be performed on the padded radial sampling data, so as to obtain the final imaging result, that is, the target image.
  • the embodiment of the present application acquires the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divides the radial sampling data into several sub-regional radial sampling data according to preset frequency partitions; using Different radial interpolation networks respectively process the radial sampling data of each subregion to obtain filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition; The data is subjected to imaging processing to obtain the target image.
  • different radial interpolation networks are introduced to fill in the sampled data in each frequency partition respectively, so that the sampled data in each different frequency partition has a stronger pertinence, and the filled data is more accurate, thereby effectively improving the the final imaging accuracy.
  • FIG. 5 shows a structural diagram of an embodiment of a magnetic resonance oxygen seventeen metabolic imaging device provided in an embodiment of the present application.
  • a magnetic resonance oxygen seventeen metabolic imaging device may include:
  • the sampling data division module 501 is used to obtain the golden angle radial sampling data of magnetic resonance oxygen seventeen metabolism, and divide the radial sampling data into several sub-regional radial sampling data according to preset frequency divisions;
  • the radial interpolation processing module 502 is used to process the radial sampling data of each sub-region by using different radial interpolation networks to obtain the filled radial sampling data; wherein, each radial interpolation network corresponds to a frequency partition ;
  • the imaging processing module 503 is configured to perform imaging processing on the filled radial sampling data to obtain a target image.
  • the radial interpolation processing module may include:
  • the data rearrangement unit is used to rearrange the radial sampling data of each sub-region into a matrix form respectively, and obtain the matrix data of each sub-region;
  • the radial interpolation processing unit is used to process the matrix data of each sub-region by using different radial interpolation networks to obtain the augmented matrix data of each sub-region;
  • a data restoration unit configured to restore the augmented matrix data of each sub-region to the form of radial spokes, and obtain the filled radial sampling data.
  • the magnetic resonance oxygen seventeen metabolic imaging device may further include:
  • the training sample set building module is used to construct the training sample set of the gth radial interpolation network, 1 ⁇ g ⁇ G, G is the number of frequency partitions; the training sample set includes several training samples, each training sample Including input matrix data and expected output matrix data, the input matrix data is consistent with the size of the gth subregional matrix data, and the expected output matrix data is consistent with the size of the gth subregion augmented matrix data;
  • the radial interpolation network training module is used for training the g th radial interpolation network by using the training sample set to obtain the g th radial interpolation network after training.
  • the training sample set building module may include:
  • the full sampling data acquisition unit is used to obtain fully sampled full sampling data
  • an under-sampling data processing unit configured to perform data deletion on the fully-sampled data to obtain under-sampled data corresponding to the fully-sampled data
  • a first rearranging unit used for rearranging the fully collected data to obtain the input matrix data
  • a second rearranging unit configured to rearrange the undersampled data to obtain the expected output matrix data
  • a training sample adding unit configured to add a training sample composed of the input matrix data and the expected output matrix data to the training sample set.
  • the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of rows of each subregional matrix data to ⁇ g times, respectively, to obtain each subregional matrix data.
  • Region augmentation matrix data where ⁇ g is the augmentation coefficient corresponding to the g-th frequency partition, and ⁇ g is an integer greater than 1.
  • the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of columns of each subregional matrix data to ⁇ g times, respectively, to obtain each subregional matrix data.
  • Region augmentation matrix data where ⁇ g is the augmentation coefficient corresponding to the g-th frequency partition, and ⁇ g is an integer greater than 1.
  • the radial interpolation processing unit is specifically configured to: use different radial interpolation networks to expand the number of rows and columns of each subregional matrix data to ⁇ g times, respectively. to ⁇ g times to obtain the augmented matrix data of each sub-region.
  • the imaging processing module is specifically configured to perform inverse fast Fourier transform processing on the padded radial sampling data to obtain the target image.
  • FIG. 6 shows a schematic block diagram of a terminal device provided by an embodiment of the present application. For convenience of description, only parts related to the embodiment of the present application are shown.
  • the terminal device 6 in this embodiment includes: a processor 60 , a memory 61 , and a computer program 62 stored in the memory 61 and running on the processor 60 .
  • the processor 60 executes the computer program 62
  • the steps in each of the above embodiments of the magnetic resonance oxygen seventeen metabolic imaging method are implemented, for example, steps 101 to 103 shown in FIG. 1 .
  • the processor 60 executes the computer program 62
  • the computer program 62 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 61 and executed by the processor 60 to complete the this application.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the terminal device 6 .
  • FIG. 6 is only an example of the terminal device 6, and does not constitute a limitation on the terminal device 6, and may include more or less components than the one shown, or combine some components, or different components
  • the terminal device 6 may further include an input and output device, a network access device, a bus, and the like.
  • the processor 60 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processors). Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 61 may be an internal storage unit of the terminal device 6 , such as a hard disk or a memory of the terminal device 6 .
  • the memory 61 may also be an external storage device of the terminal device 6, such as a plug-in hard disk equipped on the terminal device 6, a smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc.
  • the memory 61 may also include both an internal storage unit of the terminal device 6 and an external storage device.
  • the memory 61 is used to store the computer program and other programs and data required by the terminal device 6 .
  • the memory 61 can also be used to temporarily store data that has been output or will be output.
  • the disclosed apparatus/terminal device and method may be implemented in other manners.
  • the apparatus/terminal device embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like.
  • the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only). Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer-readable Storage media exclude electrical carrier signals and telecommunications signals.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

Un procédé d'imagerie du métabolisme de l'oxygène-17 par résonance magnétique, un appareil, un support de stockage lisible par ordinateur et un dispositif terminal. Le procédé consiste : à obtenir des données d'échantillonnage radial d'angle d'or du métabolisme de l'oxygène-17 par résonance magnétique, et à diviser les données d'échantillonnage radial en une pluralité de données d'échantillonnage radial de domaine partitionné selon des partitions de fréquence prédéfinies (S101) ; à amener différents réseaux d'interpolation radiale à effectuer respectivement un traitement sur chaque donnée d'échantillonnage radial de domaine partitionné, et à obtenir des données d'échantillonnage radial remplies (S102) ; à réaliser un traitement d'imagerie sur les données d'échantillonnage radial remplies, et à obtenir une image cible (S103). L'introduction de différents réseaux d'interpolation radiale pour effectuer respectivement un remplissage sur des données d'échantillonnage sur chaque partition de fréquence permet une amélioration efficace de la précision d'imagerie finale.
PCT/CN2021/083359 2021-03-23 2021-03-26 Procédé d'imagerie du métabolisme de l'oxygène-17 par résonance magnétique, appareil, support de stockage et dispositif terminal WO2022198655A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN2021082440 2021-03-23
CN202110310782 2021-03-23
CN202110310782.9 2021-03-23
CNPCT/CN2021/082440 2021-03-23

Publications (1)

Publication Number Publication Date
WO2022198655A1 true WO2022198655A1 (fr) 2022-09-29

Family

ID=76278820

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/083359 WO2022198655A1 (fr) 2021-03-23 2021-03-26 Procédé d'imagerie du métabolisme de l'oxygène-17 par résonance magnétique, appareil, support de stockage et dispositif terminal

Country Status (2)

Country Link
CN (1) CN112967297B (fr)
WO (1) WO2022198655A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2005023108A1 (ja) * 2003-09-05 2007-11-01 株式会社日立メディコ 磁気共鳴イメージング装置
US20150077112A1 (en) * 2012-04-19 2015-03-19 New York University System, method and computer-accessible medium for highly-accelerated dynamic magnetic resonance imaging using golden-angle radial sampling and compressed sensing
US20190054194A1 (en) * 2017-08-17 2019-02-21 Case Western Reserve University System and Method For Evaluation of Subjects Using Magnetic Resonance Imaging and Oxygen-17
WO2020033566A1 (fr) * 2018-08-07 2020-02-13 Human Longevity, Inc. Réseaux neuronaux pour segmentation volumétrique et représentations de surface parcellées
CN111492256A (zh) * 2017-12-18 2020-08-04 皇家飞利浦有限公司 运动补偿的磁共振成像

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017202535A1 (de) * 2017-02-16 2018-08-16 Siemens Healthcare Gmbh Erstellen von MR-Bildern
EP3704667B1 (fr) * 2017-10-31 2021-05-26 Koninklijke Philips N.V. Prédiction basé sur l'apprentissage profond d'artefacts de mouvement pendant l'acquisition des données d'imagerie par résonance magnétique
CN110133558B (zh) * 2018-02-09 2021-04-30 深圳先进技术研究院 一种磁共振动态成像方法、装置及可读介质
CN111292388A (zh) * 2020-01-22 2020-06-16 上海东软医疗科技有限公司 磁共振成像方法、装置、电子设备、存储介质
CN111445547B (zh) * 2020-03-20 2023-05-09 中国科学院深圳先进技术研究院 一种基于神经网络的网格化磁共振图像重建方法和装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2005023108A1 (ja) * 2003-09-05 2007-11-01 株式会社日立メディコ 磁気共鳴イメージング装置
US20150077112A1 (en) * 2012-04-19 2015-03-19 New York University System, method and computer-accessible medium for highly-accelerated dynamic magnetic resonance imaging using golden-angle radial sampling and compressed sensing
US20190054194A1 (en) * 2017-08-17 2019-02-21 Case Western Reserve University System and Method For Evaluation of Subjects Using Magnetic Resonance Imaging and Oxygen-17
CN111492256A (zh) * 2017-12-18 2020-08-04 皇家飞利浦有限公司 运动补偿的磁共振成像
WO2020033566A1 (fr) * 2018-08-07 2020-02-13 Human Longevity, Inc. Réseaux neuronaux pour segmentation volumétrique et représentations de surface parcellées

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WU ZHANXIONG; LI XUN: "Construction of Brain Anatomical Connectivity Network Based on Q-ball Imaging", HANGTIAN YIXUE YU YIXUE GONGCHENG - SPACE MEDICINE AND MEDICALENGINEERING, GAI KAN BIANJIBU, BEIJING, CN, vol. 29, no. 6, 15 December 2016 (2016-12-15), CN , pages 428 - 433, XP009539802, ISSN: 1002-0837, DOI: 10.16289/j.cnki.1002-0837.2016.06.008 *

Also Published As

Publication number Publication date
CN112967297B (zh) 2024-05-03
CN112967297A (zh) 2021-06-15

Similar Documents

Publication Publication Date Title
CN111461983B (zh) 一种基于不同频度信息的图像超分辨率重建模型及方法
CN112881957B (zh) 用于磁共振成像的方法和系统
Wright et al. Non‐Cartesian parallel imaging reconstruction
CN111951344B (zh) 一种基于级联并行卷积网络的磁共振图像重建方法
WO2020114329A1 (fr) Imagerie paramétrique par résonance magnétique rapide et dispositif
CN110490832A (zh) 一种基于正则化深度图像先验方法的磁共振图像重建方法
CN103218795B (zh) 基于自适应双字典学习的部分k空间序列图像重构方法
CN111157933B (zh) 用于磁共振图像重建的系统和方法
Schaetz et al. Accelerated computing in magnetic resonance imaging: real‐time imaging using nonlinear inverse reconstruction
CN110264538B (zh) 用于核磁共振图像重建的方法、系统和装置
WO2023045482A1 (fr) Procédé et appareil d'imagerie par résonance magnétique multicouche basés sur la reconstruction d'un modèle d'attention à longue distance
CN106680748A (zh) 磁共振图像获取方法及磁共振系统
Shao et al. SPECTnet: a deep learning neural network for SPECT image reconstruction
WO2022236851A1 (fr) Procédé de reconstruction d'image de résonance magnétique de champ de vision panoramique, dispositif informatique et support de stockage
CN102096055B (zh) 一种用于磁共振成像非均匀采样数据的快速精确重建方法
CN111784792A (zh) 基于双域卷积神经网络的快速磁共振重建系统及其训练方法与应用
CN112213673B (zh) 动态磁共振成像方法、装置、重建计算机及磁共振系统
WO2022198655A1 (fr) Procédé d'imagerie du métabolisme de l'oxygène-17 par résonance magnétique, appareil, support de stockage et dispositif terminal
CN111445547B (zh) 一种基于神经网络的网格化磁共振图像重建方法和装置
CN117495992A (zh) 一种欠采样核磁共振图像的重建方法
Sabbagh et al. Cardiac MRI compressed sensing image reconstruction with a graphics processing unit
CN114913262B (zh) 采样模式与重建算法联合优化的核磁共振成像方法及系统
US20220139003A1 (en) Methods and apparatus for mri reconstruction and data acquisition
CN116070401A (zh) 一种基于变换域张量低秩先验深度展开网络的高维磁共振图像重建方法
WO2022193378A1 (fr) Procédé et appareil de génération de modèle de reconstruction d'image, procédé et appareil de reconstruction d'image, dispositif et support

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21932276

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21932276

Country of ref document: EP

Kind code of ref document: A1