Method, system, terminal and medium for extracting respiratory and cardiac signals based on projection selection in magnetic resonance

Abstract

The method, system, terminal and medium for extracting respiratory and cardiac signals based on projection selection in magnetic resonance of the present application calculate the respiratory and cardiac signal values at each frequency below the set sampling frequency, calculate the respiratory and cardiac signal values at each frequency below the set sampling frequency, calculate the respiratory and cardiac ratio of each projection position point of each coil, and respectively calculate the PCA of the signals on each respiratory-sensitive projection position point and each cardiac-sensitive projection position point. The first principal component signals obtained are used as the final extracted respiratory and cardiac signals. The respiratory and cardiac signals calculated by the present application have high accuracy, and the frequency band is independent of the individual being scanned. Moreover, the calculation amount of the PCA is small, and there is no need to select the corresponding respiratory and cardiac signals from the multiple components of the PCA. The respiratory and cardiac signals obtained by the present application retain complete respiratory phase and cardiac phase information, and can guide the cardiac phase division of the data scanned for patients with irregular heartbeats.

Description

Technical Field

[0001] This invention relates to the field of magnetic resonance imaging technology, and in particular to a method, system, terminal, and medium for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging. Background Technology

[0002] In traditional cardiac MRI scans, due to the coexistence of respiration and heartbeat, additional equipment is used to monitor respiratory and cardiac signals to assist in the cardiac MRI scan (if breath-holding occurs, only cardiac signals need to be acquired). Generally, respiratory signals captured by a breathing belt (millimeter-wave radar) are used as respiratory gating signals, and cardiac signals acquired by an ECG (electrocardiogram) are used as cardiac signals. However, in practice, changes in the tightness and position of the breathing belt, detection failures of millimeter-wave radar, severe interference from MHD (magnetohydrodynamic effects) on ECG in high fields, and weak ECG signals in patients with excessive chest hair or obesity all affect cardiac MRI scans. Therefore, a self-gated MRI scanning method has been proposed. Self-gated MRI technology refers to analyzing the acquired MRI signals to extract respiratory and cardiac signals. Among these methods, the extraction of respiratory and cardiac signals based on a repetitive one-dimensional signal along the SI (superior-inferior) head-foot direction through the center of k-space is widely used.

[0003] Numerous studies have previously explored using signals passing through the center of k-space to simultaneously acquire respiratory and cardiac signals during free breathing. In 2007, Uribe pioneered the use of k-space center data along the SI direction to extract motion signals, but his work only analyzed respiratory signals. In 2010, Liu acquired k-space center data along the SI direction, selected a coil with a stable heart rate, calculated the centroid of the signal acquired from this coil, and used iterative filtering to obtain both respiratory and cardiac signals. However, the obtained cardiac signal showed a 20ms uncertainty compared to the ECG signal. In 2014, Pang also acquired k-space data along the SI direction. He first concatenated all coil signals and then used PCA to select signals consistent with pre-defined respiratory and cardiac frequencies from the top 10 results of the PCA analysis, using them as the respiratory and cardiac signals respectively. His obtained cardiac signal showed a 30ms uncertainty compared to the ECG signal. In 2017, Han also acquired k-space data in the SI direction and calculated respiratory signals using cross-correlation. Cardiac signals were obtained using centroid filtering. The heart rate signal calculated using this method had an uncertainty of 13 ms compared to the ECG signal. In 2019, Di Sopra also acquired k-space data in the SI direction at similar intervals and obtained respiratory and cardiac signals using the same PCA method as Pang. He further used filtering to obtain the cardiac gating time points, and the resulting signal had an uncertainty of 17-39 ms.

[0004] Therefore, because respiratory and cardiac signals are mixed together, current PCA (principal component analysis) methods produce cardiac signals with an uncertainty of 20-30 ms. This means that if a cardiac time resolution of 50 ms is used to acquire cardiac images, approximately half of the acquired data will be temporally inaccurate. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, system, terminal and medium for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, so as to solve the above technical problems in the prior art.

[0006] To achieve the above and other related objectives, this invention provides a method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging. The method includes: calculating the respiratory-cardioresonance ratio (PCA) at each projection location of each coil based on the signal values ​​at each frequency below a set sampling frequency obtained by performing Fourier transform on the magnetic resonance signal data collected by each coil along the k-space center line, the calculated signal values ​​at each frequency below the set sampling frequency of the respiratory signal, and the signal values ​​at each frequency below the set sampling frequency of the cardiac signal; calculating PCA at the projection locations of multiple respiratory-sensitive coils and multiple cardiac-sensitive coils selected from the respiratory-cardioresonance ratios at each projection location of all coils, and using the first principal component signal obtained from each as the final extracted respiratory and cardiac signals.

[0007] In one embodiment of the present invention, the method for calculating the signal values ​​of the respiratory signal at each frequency below the set sampling frequency and the signal values ​​of the cardiac signal at each frequency below the set sampling frequency includes: calculating the respiratory-cardioresonance ratio at each projection position point of each coil based on the signal values ​​of each projection position point at each frequency below the set sampling frequency obtained by performing Fourier transform on the magnetic resonance signal data collected by each coil through the center line of k space, the initial signal values ​​of the respiratory signal and the cardiac signal at each frequency below the set sampling frequency; based on the respiratory-cardioresonance ratio at all coil projection position points, filtering the projections at the projection position points of each respiratory-sensitive coil and the projections at the projection position points of each cardiac-sensitive coil, calculating PCA respectively, and using the signal values ​​of the first principal component signals obtained respectively as the signal values ​​corresponding to each frequency below the set sampling frequency and the signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal.

[0008] In one embodiment of the present invention, the method of obtaining the signal values ​​corresponding to each projection position point below a set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space includes: performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scanning area; performing a time-wise Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal values ​​corresponding to each projection position point of each coil below the set sampling frequency.

[0009] In one embodiment of the present invention, the method for calculating the respiratory-cardiac ratio at each coil position of each coil includes: calculating the integral of the signal value of each coil position point in a set respiratory frequency range and the integral of the signal value of each coil position point in a set cardiac frequency range based on the signal value of each coil position point at each frequency set below, and the ratio of the two integrals is used as the respiratory-cardiac ratio of each coil and each coil position.

[0010] In one embodiment of the present invention, calculating PCA based on signals at projection points of multiple respiratory-sensitive coils and signals at projection points of multiple cardiac-sensitive coils, selected from the respiratory-cardioresonance ratios at each projection point of all coils, includes: selecting signals at projection points of multiple respiratory-sensitive coils and signals at projection points of multiple cardiac-sensitive coils based on screening rules and according to the respiratory-cardioresonance ratios at each projection point of all coils; performing Z-Score normalization on the selected signals at projection points of each respiratory-sensitive coil and performing Z-Score normalization on the selected signals at projection points of each cardiac-sensitive coil to obtain Z-Score normalization results for respiratory-sensitive signals and Z-Score normalization results for cardiac-sensitive signals; and performing PCA calculation on the Z-Score normalization results for respiratory-sensitive signals and Z-Score normalization results for cardiac-sensitive signals to obtain the corresponding first principal component signals.

[0011] In one embodiment of the present invention, the screening rules include: sorting the respiratory-to-cardiopulmonary ratio at each projection point of all coils in descending order, selecting the projections of the projection points of the coils with the highest respiratory-to-cardiopulmonary ratio as the signals at the projection points of the respiratory-sensitive coils, and selecting the respiratory-to-cardiopulmonary ratios of the projection points of the coils with the lowest respiratory-to-cardiopulmonary ratio as the signals at the projection points of the cardiac-sensitive coils.

[0012] In one embodiment of the present invention, the respiratory-cardiac ratio at each projection point of each coil is calculated based on a respiratory-cardiac ratio calculation formula; wherein, the respiratory-cardiac ratio calculation formula includes:

[0013]

[0014] And among them, n c n is the coil's serial number. x For n c The projection position point R is obtained by performing a Fourier transform on the magnetic resonance signal data collected along the center line of space k. u and R l C represents the upper and lower limits of the effective frequency band for the preset respiratory rate. u and Cl The upper and lower limits of the effective frequency band for the pre-set cardiac rate, where f is the frequency, and s(n c n x f) is the coil n c The projection position point n x The signal value at frequency f, s r (f) represents the respiratory signal value at frequency f, s c (f) represents the cardiac signal value at frequency f.

[0015] To achieve the above and other related objectives, the present invention provides a system for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging (MRI). The system includes: a respiratory-cardioresonance ratio (RCR) calculation module, used to calculate the RCR of each projection point of each coil based on the signal values ​​corresponding to each frequency below a set sampling frequency obtained by performing Fourier transform on the MRI signal data collected by each coil along the k-space center line, the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the respiratory signal, and the signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal; and a respiratory and cardiac signal extraction module, connected to the RCR calculation module, used to calculate PCA based on the signals at the projection points of multiple respiratory-sensitive coils and the signals at the projection points of multiple cardiac-sensitive coils selected from the RCRs at each projection point of all coils, and to use the first principal component signal obtained as the final extracted respiratory and cardiac signals.

[0016] To achieve the above and other related objectives, the present invention provides a terminal for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, comprising: one or more memories and one or more processors; the one or more memories are used to store a computer program; the one or more processors are connected to the memories and are used to run the computer program to execute the method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging.

[0017] To achieve the above and other related objectives, the present invention provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, performs the method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging.

[0018] As described above, this invention provides a method, system, terminal, and medium for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging (MRI). It offers the following advantages: This invention calculates the respiratory-cardioresonance ratio (RCR) at each projection point of each coil by performing Fourier transform on data collected from the center line of k-space. The RCR is obtained from the signal values ​​at each projection point below a set sampling frequency, along with the calculated respiratory and cardiac signals at the same frequencies below the set sampling frequency. PCA is then calculated on signals at selected respiratory-sensitive and cardiac-sensitive projection points, and the first principal component signal obtained is used as the final extracted respiratory and cardiac signals. The respiratory and cardiac signals calculated by this invention have high accuracy, and their frequency bands are independent of the individual being scanned. Furthermore, the computational load of PCA calculation is low, and there is no need to filter corresponding respiratory and cardiac signals from multiple components of the PCA. The respiratory and cardiac signals obtained by this invention retain complete respiratory and cardiac phase information, which can guide cardiac phase segmentation in data scanned from patients with irregular heartbeats. Attached Figure Description

[0019] Figure 1 The diagram shown is a flowchart illustrating a method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging according to an embodiment of the present invention.

[0020] Figure 2 The diagram shown is a flowchart illustrating the extraction of respiratory and cardiac signals according to an embodiment of the present invention.

[0021] Figure 3 The diagram shown is a schematic representation of screening respiratory and cardiac signals in one embodiment of the present invention.

[0022] Figure 4 The diagram shown is a schematic diagram of data acquisition for the center line of k-space in one embodiment of the present invention.

[0023] Figure 5 This diagram illustrates a comparison between the cardiac signal calculated by PCA and ECG R-peaks in one embodiment of the present invention.

[0024] Figure 6 This diagram illustrates a comparison of the accuracy of ECG triggering and self-gating time in experiments conducted on different subjects according to an embodiment of the present invention.

[0025] Figure 7 The diagram shown is a schematic representation of a projection-selective respiratory and cardiac signal extraction system in magnetic resonance imaging according to an embodiment of the present invention.

[0026] Figure 8 The diagram shown is a schematic representation of a projection-selective respiratory and cardiac signal extraction terminal in magnetic resonance imaging according to an embodiment of the present invention. Detailed Implementation

[0027] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0028] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the invention. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of the invention. The following detailed description should not be considered limiting, and the scope of the embodiments of the invention is defined only by the claims of the published patents. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. Spatially related terms, such as “upper,” “lower,” “left,” “right,” “below,” “below,” “lower part,” “above,” “upper part,” etc., may be used herein to illustrate the relationship between one element or feature shown in the figures and another element or feature.

[0029] Throughout this specification, when it is said that a part is "connected" to another part, this includes not only "direct connection" but also "indirect connection" by placing other elements in between. Furthermore, when it is said that a part "includes" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather means that other constituent elements may also be included.

[0030] The terms "first," "second," and "third," etc., used herein are for the purpose of describing various parts, components, regions, layers, and / or segments, but are not limiting. These terms are used only to distinguish one part, component, region, layer, or segment from others. Therefore, the "first part," "component," "region," "layer," or "segment" described below may refer to a "second part," "component," "region," "layer," or "segment" without departing from the scope of this invention.

[0031] Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated feature, operation, element, component, item, kind, and / or group, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition arise only when combinations of elements, functions, or operations are inherently mutually exclusive in some manner.

[0032] Because respiratory and cardiac signals are mixed together, current PCA (principal component analysis) methods produce cardiac signals with an uncertainty of 20-30 ms. This means that if a cardiac time resolution of 50 ms is used to acquire cardiac images, approximately half of the acquired data will be temporally inaccurate.

[0033] Based on the shortcomings of existing technologies, this invention proposes the idea of ​​separately filtering the signals from coils and projection points that are sensitive to breathing and heartbeat, taking into account the characteristic that signals from different coils and different locations contain different respiratory and cardiac signals.

[0034] Therefore, this invention provides a method, system, terminal, and medium for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging (MRI). The method calculates the respiratory-cardiopulmonary ratio (RCR) at each projection point of each coil by performing Fourier transform on data collected from each coil along the k-space centerline, obtaining signal values ​​at each frequency below a set sampling frequency, and calculating the respiratory and cardiac signals at each frequency below the set sampling frequency. PCA is then performed on signals at selected respiratory-sensitive and cardiac-sensitive projection points, and the first principal component signal obtained is used as the final extracted respiratory and cardiac signals. The respiratory and cardiac signals calculated by this invention have high accuracy, and their frequency bands are independent of the individual being scanned. Furthermore, the computational load of PCA calculation is low, and it eliminates the need to filter corresponding respiratory and cardiac signals from multiple components of the PCA. The respiratory and cardiac signals obtained by this invention retain complete respiratory and cardiac phase information, which can guide cardiac phase segmentation in data scanned from patients with irregular heartbeats.

[0035] The present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0036] like Figure 1 This invention presents a schematic flowchart of a method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, according to an embodiment of the present invention.

[0037] The method includes:

[0038] Step S1: Based on the magnetic resonance signal data collected by each coil through the center line of k space, perform Fourier transform to obtain the signal values ​​corresponding to each projection position point below the set sampling frequency, calculate the signal values ​​corresponding to each frequency below the set sampling frequency of the respiratory signal, and calculate the signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal. Calculate the respiratory-cardiotonic ratio at each projection position point of each coil.

[0039] In one embodiment, the method of obtaining the signal values ​​corresponding to each frequency below the set sampling frequency for each projection position point by performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of space k includes:

[0040] Fourier transform is performed on the magnetic resonance signal data collected by each coil along the center line of k space at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scan area. Specifically, in magnetic resonance scanning, the center line of k space can be collected at set time intervals, and the magnetic resonance signal data collected multiple times along the center line of k space can be Fourier transformed to obtain one-dimensional projection data of the image space of the corresponding scan area for each collection.

[0041] Perform a time-time Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal value corresponding to each projection position point of each coil at each frequency below the set sampling frequency.

[0042] Specifically, obtain the signal value s(n) corresponding to each projection position point of each coil at each frequency below the set sampling frequency. c n x The method f) is achieved by the following steps:

[0043]

[0044] S(n c k x ,t) is magnetic resonance n c The coil in time t and space k x The signal value collected above, Indicates along k x Perform the inverse Fourier transform to obtain n c At time t, the coil is in image space n x Projection I(n) at position c n x ,t), This means that performing a Fourier transform along t yields n. c coil n x Signal value s(n) at position frequency f c n x f).

[0045] It should be noted that this invention can collect magnetic resonance signal data in the SI direction passing through the center line of k-space, as well as magnetic resonance signal data in the AP (front-back), LR (left-right), short axis, long axis, and other RO (readout) directions.

[0046] In one embodiment, the method for calculating the respiratory-cardiac ratio at each coil position for each coil includes:

[0047] The respiratory-cardiotonic ratio at each coil position of each coil is calculated based on the signal values ​​of each projection position point of the current coil at each frequency below the set sampling frequency, the signal values ​​of the calculated respiratory signal at each frequency below the set sampling frequency, the signal values ​​of the calculated cardiac signal at each frequency below the set sampling frequency, the effective frequency band of the respiratory frequency, and the effective frequency band of the cardiac frequency.

[0048] In one embodiment, the respiratory-cardiac ratio at each projection location point of each coil is calculated based on the respiratory-cardiac ratio calculation formula;

[0049] The formula for calculating the respiratory-to-cardiac ratio includes:

[0050]

[0051] And among them, n c n is the coil's serial number. x For n c The projection position point R is obtained by performing a Fourier transform on the magnetic resonance signal data collected along the center line of space k. u and R l The upper and lower limits of the effective frequency band for respiratory rate are preset, i.e., the upper and lower limits of the effective frequency band for the respiratory rate of the measured subject. For normal individuals, this is generally taken as [0.01, 0.5] Hz; C u C1 and C2 are the pre-set upper and lower limits of the effective frequency band for cardiac rate, which are [0.5, 2] Hz for normal individuals ([0.5, 3] Hz for animals with high heart rates, such as pigs); f is the frequency, s(n cn x f) is the coil n c The projection position point n x The signal value at a set frequency f, s r (f) represents the respiratory signal value at frequency f, s c (f) represents the cardiac signal value at frequency f.

[0052] Step S2: Calculate PCA based on the signals at the projection points of multiple respiratory-sensitive coils and multiple cardiac-sensitive coils selected from the respiratory-cardioresonance ratios at each projection point of all coils, and use the first principal component signal obtained as the final extracted respiratory signal and cardiac signal.

[0053] In one embodiment, step S2 includes:

[0054] Based on the filtering rules, multiple respiratory-sensitive signals and multiple cardiac-sensitive signals are filtered from the respiratory-cardiac ratio at each projection point of all coils.

[0055] The signals at the projection points of each selected respiratory-sensitive coil and the signals at the projection points of each selected cardiac-sensitive coil are Z-Score standardized to obtain the Z-Score standardized results of the respiratory-sensitive signal and the cardiac-sensitive signal.

[0056] PCA was performed on the Z-Score normalized results of the respiratory-sensitive signal and the Z-Score normalized results of the cardiac-sensitive signal to obtain the first principal component signal for each.

[0057] In one embodiment, the filtering rules include:

[0058] Sort the respiratory-to-cardiopulmonary ratios at each projection point of all coils from largest to smallest, and select the projection points of the coils with the highest respiratory-to-cardiopulmonary ratios as the signals at the projection points of the respiratory-sensitive coils, and select the respiratory-to-cardiopulmonary ratios at the projection points of the coils with the lowest respiratory-to-cardiopulmonary ratios as the signals at the projection points of the cardiopulmonary-sensitive coils.

[0059] For example, the respiratory-cardiac ratios at each projection point of all coils are sorted from largest to smallest, and the points with large RCRs are selected as respiratory-sensitive signals. That is, the signals at the top 10% of the projection points are selected as the signals at the projection points of the respiratory-sensitive coils, and the signals at the bottom 0.5% of the projection points are selected as the signals at the projection points of the cardiac-sensitive coils.

[0060] In one embodiment, the method for calculating the signal values ​​of the respiratory signal at each frequency below the set sampling frequency and the signal values ​​of the cardiac signal at each frequency below the set sampling frequency includes:

[0061] Based on the magnetic resonance signal data collected by each coil along the center line of space k, Fourier transform is performed to obtain the signal values ​​corresponding to each frequency below the set sampling frequency at each projection position point, as well as the initial signal values ​​of the respiratory signal and cardiac signal at each frequency below the set sampling frequency. The respiratory-cardioresonance ratio at each projection position point of each coil is calculated. Among them, the initial signal values ​​of the respiratory signal and cardiac signal at each frequency below the set sampling frequency can be obtained in advance from the signal of the centroid for analysis, and the frequency distribution of respiratory and cardiac signals can be directly set to a constant 1. The respiratory signal and cardiac signal calculated by steps S1 and S2 are used as iterations to replace s in formula (2). r (f) and s r (f) Repeat steps S1 and S2 to obtain the final respiratory and cardiac signals; in addition, the method of obtaining the signal values ​​of each projection position point at each frequency below the set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil through the center line of k space can be achieved by obtaining the signal values ​​of each projection position point at each frequency below the set sampling frequency in step S2 mentioned in the above embodiment, and the method of calculating the respiratory-cardiac ratio at each projection position point of each coil mentioned here can also be achieved by calculating the respiratory-cardiac ratio at each projection position point of each coil mentioned in step S2 mentioned in the above embodiment.

[0062] Based on the respiratory-cardiopulmonary ratio at all coil projection points, the projections at the projection points of each respiratory-sensitive coil and each cardiac-sensitive coil are selected separately, and PCA is calculated separately. The signal values ​​of the first principal component signals obtained are used as the signal values ​​corresponding to each frequency below the set sampling frequency, and the signal values ​​corresponding to each frequency of the cardiac signal below the set sampling frequency. The method of calculating PCA based on the signals at the projection points of each respiratory-sensitive coil and each cardiac-sensitive coil selected from the respiratory-cardiopulmonary ratio at all coil projection points can be implemented by the method in step S2 mentioned in the above embodiment, which calculates PCA based on the signals at the projection points of each respiratory-sensitive coil and each cardiac-sensitive coil selected from the respiratory-cardiopulmonary ratio at all coil projection points.

[0063] To better describe the complete method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, the following is combined with... Figure 2 Please provide an explanation.

[0064] like Figure 2 As shown, the method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging includes:

[0065] The respiratory-cardiopulmonary ratio at each projection point of each coil is calculated based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the initial signal values ​​of the respiratory signal and cardiac signal at each frequency below the set sampling frequency.

[0066] Based on the respiratory-cardiac ratio at all coil projection locations, the projections at the projection locations of each respiratory-sensitive coil and each cardiac-sensitive coil are selected separately, and PCA is calculated separately. The signal values ​​of the first principal component signals obtained are used as the signal values ​​corresponding to each frequency below the set sampling frequency and the signal values ​​corresponding to each frequency of the cardiac signal below the set sampling frequency.

[0067] The respiratory-cardiorespiratory ratio of each projection point of each coil is calculated based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the respiratory signal, and the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal.

[0068] PCA is calculated based on the signals at the projection points of multiple respiratory-sensitive coils and multiple cardiac-sensitive coils, which are selected from the respiratory-cardioresonance ratio at each projection point of all coils. The first principal component signal obtained is then used as the final extracted respiratory signal and cardiac signal.

[0069] like Figure 3 This demonstrates how to filter respiratory and cardiac signals from data collected from different coils. The filtered image clearly shows respiratory and cardiac signals. This method, because it does not filter the original SI projection data, obtains respiratory and cardiac signals by calculating and filtering the PCA of the original data. The cardiac signals retain complete cardiac phase information, which has potential value for reconstructing cardiac data from patients with irregular heartbeats.

[0070] To better illustrate the above-mentioned method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, the present invention provides the following specific embodiments.

[0071] Example 1: A method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging.

[0072] like Figure 4 The demonstration uses a 3D Cartesian rock method to periodically acquire data along a line from the center of k-space; alternatively, a 3D radial method, as used by Di Sopra, is also possible. Then, 2D Cartesian variable density data acquisition is used, scanning the same line in k-space at intervals. Data collected under free breathing conditions, passing through the center of k-space, is transformed to image space using FFT, and the sum of squares of all signals acquired by the coils is calculated. Respiratory-related points, filtered using RCR, are normalized along the time direction; heart rate-related points, also filtered using RCR, are normalized along the time direction; such as... Figure 5 Comparing the cardiac signals calculated using selective PCA with ECG R-peaks, in A), B), and C), there were no false triggers in the ECG, so the results corresponded well. However, in D), the R-peak monitoring was incorrect, while the R-peaks calculated using self-gating technology did not have false triggers. Figure 6 As shown, experiments were conducted on different subjects to compare the accuracy of ECG triggering and self-gating time. Statistical results for all results showed a time accuracy of 11 ms. Respiratory and cardiac gating was implemented using respiratory and cardiac signals calculated by selective PCA, and cardiac images of different cardiac phases were obtained using FFT and compressed sensing methods, respectively.

[0073] Similar to the above embodiments, the present invention provides a system for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging.

[0074] The following specific embodiments are provided in conjunction with the accompanying drawings:

[0075] like Figure 7 This invention presents a schematic diagram of a projection-selection-based respiratory and cardiac signal extraction system in magnetic resonance imaging, according to an embodiment of the present invention.

[0076] The system includes:

[0077] The respiratory-to-cardioresonance ratio calculation module 1 is used to calculate the respiratory-to-cardioresonance ratio of each projection position point of each coil based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the respiratory signal, and the signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal.

[0078] The respiratory signal and cardiac signal extraction module 2 is connected to the respiratory-cardiac ratio calculation module 1. It is used to calculate PCA based on the signals at the projection positions of multiple respiratory-sensitive coils and multiple cardiac-sensitive coils selected from the respiratory-cardiac ratios at each projection position point of all coils, and to use the first principal component signal obtained as the final extracted respiratory signal and cardiac signal.

[0079] It should be noted that, as should be understood Figure 7 The division of modules in the system embodiment is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these units can be implemented entirely in software through processing element calls; they can be implemented entirely in hardware; or some units can be implemented by processing element calls to software, while others are implemented in hardware.

[0080] Since the implementation principle of the projection-selective respiratory and cardiac signal extraction system in this magnetic resonance imaging has been described in the foregoing embodiments, it will not be repeated here.

[0081] In one embodiment, the method for calculating the signal values ​​of the respiratory signal and the cardiac signal at each frequency below the set sampling frequency includes: calculating the respiratory-cardiopulmonary ratio at each projection position point of each coil based on the signal values ​​of each projection position point at each frequency below the set sampling frequency obtained by performing Fourier transform on the magnetic resonance signal data collected by each coil through the k-space center line, and the initial signal values ​​of the respiratory signal and cardiac signal at each frequency below the set sampling frequency; based on the respiratory-cardiopulmonary ratio at all coil projection position points, filtering the projections at the projection position points of each respiratory-sensitive coil and the projections at the projection position points of each cardiac-sensitive coil, calculating PCA respectively, and using the signal values ​​of the first principal component signals obtained respectively as the signal values ​​corresponding to each frequency below the set sampling frequency and the signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal.

[0082] In one embodiment, the method of obtaining the signal values ​​corresponding to each projection position point below a set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil along the k-space center line includes: performing Fourier transform on the magnetic resonance signal data collected by each coil along the k-space center line at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scanning area; and performing a time-based Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal values ​​corresponding to each projection position point of each coil below the set sampling frequency.

[0083] In one embodiment, the method for calculating the respiratory-cardiac ratio at each coil position of each coil includes: calculating the integral of the signal value at each position of each coil in a set respiratory frequency range and the integral of the signal value at each position of each coil in a set cardiac frequency range based on the signal value at each frequency of each projected position of each coil, and the ratio of the two integrals is used as the respiratory-cardiac ratio for each coil and each coil position.

[0084] In one embodiment, calculating PCA based on signals at projection points of multiple respiratory-sensitive coils and signals at projection points of multiple cardiac-sensitive coils, selected from the respiratory-cardioresonance ratios at each projection point of all coils, includes: selecting signals at projection points of multiple respiratory-sensitive coils and signals at projection points of multiple cardiac-sensitive coils based on screening rules and according to the respiratory-cardioresonance ratios at each projection point of all coils; performing Z-Score normalization on the selected signals at projection points of each respiratory-sensitive coil and performing Z-Score normalization on the selected signals at projection points of each cardiac-sensitive coil to obtain Z-Score normalization results for respiratory-sensitive signals and Z-Score normalization results for cardiac-sensitive signals; and performing PCA calculation on the Z-Score normalization results for respiratory-sensitive signals and Z-Score normalization results for cardiac-sensitive signals to obtain the corresponding first principal component signals.

[0085] In one embodiment, the screening rule includes: sorting the respiratory-to-cardiopulmonary ratio at each projection point of all coils in descending order, selecting the projections of the projection points of the coils with the highest respiratory-to-cardiopulmonary ratio as the signal at the projection point of the respiratory-sensitive coil, and selecting the respiratory-to-cardiopulmonary ratio at the projection points of the coils with the lowest respiratory-to-cardiopulmonary ratio as the signal at the projection point of the cardiopulmonary-sensitive coil.

[0086] In one embodiment, the respiratory-cardiac ratio at each projection location point of each coil is calculated based on the respiratory-cardiac ratio calculation formula;

[0087] The formula for calculating the respiratory-to-cardiac ratio includes:

[0088]

[0089] And among them, n c n is the coil's serial number. x For n c The projection position point R is obtained by performing a Fourier transform on the magnetic resonance signal data collected along the center line of space k. u and R l C represents the upper and lower limits of the effective frequency band for the preset respiratory rate. u and C l The upper and lower limits of the effective frequency band for the pre-set cardiac rate, where f is the frequency, and s(n c n x f) is the coil n c The projection position point n x The signal value at frequency f, s r (f) represents the respiratory signal value at frequency f, s c(f) represents the cardiac signal value at frequency f.

[0090] like Figure 8 A schematic diagram of the structure of the projection-selective respiratory and cardiac signal extraction terminal 10 in magnetic resonance imaging according to an embodiment of the present invention is shown.

[0091] The projection-selective respiratory and cardiac signal extraction terminal 80 in the magnetic resonance imaging includes: a memory 81 and a processor 82. The memory 81 stores computer programs; the processor 82 runs the computer programs to implement, for example... Figure 1 The method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging.

[0092] Optionally, the number of memories 81 can be one or more, and the number of processors 82 can be one or more. Figure 8 Each example is taken as an instance.

[0093] Optionally, the processor 82 in the projection-selected respiratory and cardiac signal extraction terminal 80 of the magnetic resonance imaging will follow the procedure as follows: Figure 1 The steps described involve loading one or more instructions corresponding to the process of an application into memory 81, and then having the processor 82 run the application stored in the first memory 81, thereby achieving the following: Figure 1 Various functions in the projection-selective respiratory and cardiac signal extraction method in magnetic resonance imaging.

[0094] Optionally, the memory 81 may include, but is not limited to, high-speed random access memory and non-volatile memory. For example, one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices; the processor 82 may include, but is not limited to, a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0095] Optionally, the processor 82 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0096] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed, implements as follows: Figure 1 The illustrated method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging. The computer-readable storage medium may include, but is not limited to, floppy disks, optical disks, CD-ROMs (Read-Only Optical Disk Memory), magneto-optical disks, ROMs (Read-Only Memory), RAMs (Random Access Memory), EPROMs (Erasable Programmable Read-Only Memory), EEPROMs (Electrically Erasable Programmable Read-Only Memory), magnetic cards or optical cards, flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions. The computer-readable storage medium may be a product not connected to a computer device or a component used with a computer device.

[0097] This patent has the following advantages compared to existing technologies:

[0098] 1. The cardiac signal calculated by this invention has higher accuracy than that of the prior art.

[0099] 2. The frequency band used in this invention is independent of the individual being scanned, and there is no need to find a suitable frequency band for different patients, whereas the Liu and Di Sopra methods require finding a frequency band corresponding to the individual.

[0100] 3. This invention uses PCA calculation, which requires less computation.

[0101] 4. The present invention does not require screening the corresponding respiratory and cardiac signals from multiple components of the PCA obtained; the largest first principal component is the required respiratory and cardiac signal.

[0102] 5. Because this invention does not filter the raw data (filtering would result in signal loss), the obtained respiratory and cardiac signals retain complete respiratory and cardiac phase information. In particular, the complete cardiac phase information can guide cardiac phase segmentation in data scanned from patients with irregular heartbeats.

[0103] 6. The PCA method of the present invention can be applied not only to signals in the SI direction, but also to 2D and 3D signals in the RO (readout) direction, such as AP (front and back), LR (left and right), short axis, and long axis.

[0104] In summary, the projection-selective respiratory and cardiac signal extraction system for magnetic resonance imaging of this invention calculates the respiratory-cardiopulmonary ratio (RCR) at each projection point of each coil by performing Fourier transform on data collected from each coil along the k-space centerline. This is done using the signal values ​​at each frequency below a set sampling frequency at each projection point, along with the calculated respiratory and cardiac signals at the same frequencies below the set sampling frequency. Furthermore, PCA is calculated on signals at each selected respiratory-sensitive and cardiac-sensitive projection point, and the first principal component signal obtained is used as the final extracted respiratory and cardiac signals. The respiratory and cardiac signals calculated by this invention have high accuracy, and their frequency bands are independent of the individual being scanned. Moreover, the computational load of PCA calculation is small, and it eliminates the need to filter corresponding respiratory and cardiac signals from multiple components of the PCA. The respiratory and cardiac signals obtained by this invention retain complete respiratory and cardiac phase information, which can guide cardiac phase segmentation in data scanned from patients with irregular heartbeats. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0105] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, characterized in that, The method includes: The respiratory-cardiorespiratory ratio of each projection point of each coil is calculated based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the respiratory signal, and the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal. PCA is calculated based on the signals at the projection points of multiple respiratory-sensitive coils and multiple cardiac-sensitive coils, which are selected from the respiratory-cardioresonance ratio at each projection point of all coils, and the first principal component signal obtained is used as the final extracted respiratory signal and cardiac signal. Specifically, the respiratory-cardiotonic ratio at each projection position point of each coil is calculated based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the initial signal value of the respiratory signal below the set sampling frequency, and the initial signal value of the cardiac signal. The method for obtaining the signal values ​​corresponding to each projection position point below the set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space includes: performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scanning area; performing a time-based Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal values ​​corresponding to each projection position point of each coil below the set sampling frequency; The method for obtaining the signal values ​​corresponding to each projection position point below the set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space includes: performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scanning area; performing a time-based Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal values ​​corresponding to each projection position point of each coil below the set sampling frequency; The method for calculating the respiratory-cardiac ratio at each coil position of each coil includes: calculating the integral of the signal value at each position of each coil in a set respiratory frequency range and the integral of the signal value at each position of each coil in a set cardiac frequency range based on the signal value at each projected position of each coil at each set frequency range, and the ratio of the two integrals is used as the respiratory-cardiac ratio for each coil and each coil position; The selection rules include: sorting the respiratory-to-cardiopulmonary ratio (RPPR) at each projection point of all coils from largest to smallest, selecting the projections of the coils with the highest RPR as the signals at the projection points of the respiratory-sensitive coils, and selecting the RPRs of the coils with the lowest RPR as the signals at the projection points of the cardiac-sensitive coils.

2. The method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging according to claim 1, characterized in that, PCA is calculated based on signals from multiple respiratory-sensitive coil projection points selected from the respiratory-cardioresonance ratio at each projection point of all coils, and signals from multiple cardiac-sensitive coil projection points, including: Based on the screening rules, signals from multiple respiratory-sensitive coils and multiple cardiac-sensitive coils are screened according to the respiratory-cardiac ratio at all coil projection locations. The signals at the projection points of each selected respiratory-sensitive coil and the signals at the projection points of each selected cardiac-sensitive coil are Z-Score standardized to obtain the Z-Score standardized results of the respiratory-sensitive signal and the cardiac-sensitive signal. PCA was performed on the Z-Score normalized results of the respiratory-sensitive signal and the Z-Score normalized results of the cardiac-sensitive signal to obtain the first principal component signal for each.

3. The method for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging according to claim 1, characterized in that, The respiratory-cardiac ratio at each projection point of each coil is calculated based on the respiratory-cardiac ratio calculation formula; The formula for calculating the respiratory-to-cardiac ratio includes: ； And among them, This is the coil's serial number. For the reason The projection position points are obtained by performing Fourier transform on the magnetic resonance signal data collected along the center line of k-space. and The upper and lower limits of the effective frequency band for the preset respiratory rate. and The upper and lower limits of the effective frequency band for the heart rate are preset. For frequency, coil Projection position point In frequency The signal value below, For respiratory signals at frequency The signal value below, For cardiac signals at frequency The signal value below.

4. A system for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, characterized in that, The system includes: The respiratory-to-cardioresonance ratio calculation module is used to calculate the respiratory-to-cardioresonance ratio at each projection position point of each coil based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the calculated signal values ​​corresponding to each frequency below the set sampling frequency of the respiratory signal, and the signal values ​​corresponding to each frequency below the set sampling frequency of the cardiac signal. The respiratory signal and cardiac signal extraction module is connected to the respiratory-cardiac ratio calculation module. It is used to calculate PCA based on the signals at the projection points of multiple respiratory-sensitive coils and the signals at the projection points of multiple cardiac-sensitive coils, which are selected from the respiratory-cardiac ratios at each projection point of all coils respectively. The first principal component signal obtained is used as the final extracted respiratory signal and cardiac signal. Specifically, the respiratory-cardiotonic ratio at each projection position point of each coil is calculated based on the signal values ​​corresponding to each frequency below the set sampling frequency obtained by Fourier transform of the magnetic resonance signal data collected by each coil through the center line of k space, the initial signal value of the respiratory signal below the set sampling frequency, and the initial signal value of the cardiac signal. The method for obtaining the signal values ​​corresponding to each projection position point below the set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space includes: performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scanning area; performing a time-based Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal values ​​corresponding to each projection position point of each coil below the set sampling frequency; The method for obtaining the signal values ​​corresponding to each projection position point below the set sampling frequency by performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space includes: performing Fourier transform on the magnetic resonance signal data collected by each coil along the center line of k space at set time intervals to obtain one-dimensional projection data of the image space of the corresponding scanning area; performing a time-based Fourier transform on the time data of each projection position point in the one-dimensional projection data of the image space of each coil to obtain the signal values ​​corresponding to each projection position point of each coil below the set sampling frequency; The method for calculating the respiratory-cardiac ratio at each coil position of each coil includes: calculating the integral of the signal value at each position of each coil in a set respiratory frequency range and the integral of the signal value at each position of each coil in a set cardiac frequency range based on the signal value at each projected position of each coil at each set frequency range, and the ratio of the two integrals is used as the respiratory-cardiac ratio for each coil and each coil position; The selection rules include: sorting the respiratory-to-cardiopulmonary ratio (RPPR) at each projection point of all coils from largest to smallest, selecting the projections of the coils with the highest RPR as the signals at the projection points of the respiratory-sensitive coils, and selecting the RPRs of the coils with the lowest RPR as the signals at the projection points of the cardiac-sensitive coils.

5. A terminal for extracting respiratory and cardiac signals based on projection selection in magnetic resonance imaging, characterized in that, include: One or more memories and one or more processors; The one or more memories are used to store computer programs; The one or more processors are connected to the memory and are used to run the computer program to perform the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by one or more processors, performs the method as described in any one of claims 1 to 3.

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