Pulmonary artery embolism imaging method and device, electronic equipment and storage medium

Abstract

The invention provides a pulmonary artery embolism imaging method, and relates to the field of magnetic resonance imaging. According to the specific implementation scheme, an acquisition window is determined according to electrocardio data and diaphragm navigation data of an adaptive patient; according to the collection window, original frequency domain data are collected in a Cartesian filling mode; performing phase compensation on the original frequency domain data by utilizing pulmonary artery pulse vector field modeling and a dynamic compensation algorithm to obtain proportionality coefficient spatial data; and performing compressed sensing reconstruction on the proportionality coefficient spatial data to generate a three-dimensional pulmonary artery image. The scheme is used for detecting pulmonary artery embolism in the free breathing state, no contrast agent is needed, radiation is avoided, and the universality of applicable people and the inspection safety are improved. According to the technology, by reducing artifact interference and optimizing image quality and scanning efficiency, rapid and accurate diagnosis is achieved, the comfort level of a patient and clinical diagnosis efficiency are remarkably improved, and the technology has important application value and social benefits.

Description

Technical Field

[0001] This disclosure relates to the field of magnetic resonance imaging technology, specifically to a method, apparatus, electronic device, and storage medium for pulmonary embolism imaging. Background Technology

[0002] Pulmonary embolism is the third leading cause of acute cardiovascular and cerebrovascular diseases, after myocardial infarction and stroke. Acute pulmonary embolism has a high mortality rate; without timely diagnosis and treatment, the mortality rate can reach as high as 30%. Currently, the diagnosis of pulmonary embolism mainly relies on contrast-enhanced computed tomography (CT), which is fast and provides clear imaging results, and has become the gold standard for clinical diagnosis. However, contrast-enhanced CT has certain limitations, especially for patients with contrast agent allergies, renal insufficiency who cannot use contrast agents, and patients with chronic pulmonary embolism who require multiple follow-up examinations. The cumulative radiation dose of contrast-enhanced CT and the risks associated with contrast agent use limit its widespread application. Therefore, developing a contrast agent-free and radiation-free imaging technique is of significant clinical importance. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and storage medium for pulmonary embolism imaging.

[0004] According to a first aspect of this disclosure, a method for pulmonary embolism imaging is provided, comprising: determining an acquisition window based on electrocardiogram data and diaphragmatic navigation data of an adapted patient; acquiring raw frequency domain data using a Cartesian filling method based on the acquisition window; performing phase compensation on the raw frequency domain data using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain scale coefficient spatial data; and performing compressed sensing reconstruction on the scale coefficient spatial data to generate a three-dimensional pulmonary artery image.

[0005] Optionally, a suitable patient is determined by: acquiring the patient's electrocardiogram (ECG) data and respiratory monitoring data; determining a first degree of fit based on the ECG data and a preset heart rate threshold; determining a second degree of fit based on the respiratory monitoring data and a preset respiratory rate threshold; and determining a suitable patient based on the first and second degrees of fit.

[0006] Optionally, the acquisition window is determined based on the patient's electrocardiogram (ECG) data and diaphragmatic navigation data, including: determining the cardiac motion phase based on the patient's ECG data; determining the respiratory phase based on the patient's diaphragmatic navigation data; and determining the acquisition window based on a preset respiratory phase threshold, according to the cardiac motion phase and respiratory phase.

[0007] Optionally, the raw frequency domain data is acquired by a superconducting magnetic resonance scanning system;

[0008] Optionally, the superconducting magnetic resonance imaging system is used to: set the longest trigger delay according to vector electrocardiogram gating technology so that data acquisition is located in the mid-to-late diastolic phase of the heart, and simultaneously track the displacement of the right diaphragm top in real time through the diaphragm navigation bar to achieve respiratory motion monitoring.

[0009] Optionally, the phase compensation of the original frequency domain data is performed using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain the proportional coefficient spatial data. This includes: establishing a fluid motion mathematical model containing ventricular ejection pulse terms based on the hemodynamic characteristics of the pulmonary artery; using numerical calculation methods to solve the fluid velocity field and pressure field based on the fluid motion mathematical model to construct the pulmonary artery pulsation vector field; and using an acceleration weighting algorithm to dynamically compensate for the phase shift of the original frequency domain data based on the pulmonary artery pulsation vector field to obtain the proportional coefficient spatial data.

[0010] Optionally, the original frequency domain data is phase-compensated using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain proportional coefficient spatial data. This also includes repeatedly performing displacement vector field modeling and phase compensation until the residual of the proportional coefficient spatial data meets the preset conditions.

[0011] According to a second aspect of this disclosure, a pulmonary embolism imaging device is provided, comprising: a window determination module for determining an acquisition window based on electrocardiogram data and diaphragmatic navigation data of an adapted patient; a data acquisition module for acquiring raw frequency domain data using a Cartesian filling method according to the acquisition window; a data processing module for performing phase compensation on the raw frequency domain data using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain scale coefficient spatial data; and an image generation module for performing compressed sensing reconstruction on the scale coefficient spatial data to generate a three-dimensional pulmonary artery image.

[0012] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods described in the embodiments of this disclosure.

[0013] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform any of the methods according to embodiments of this disclosure.

[0014] This disclosed approach, combining free-breathing imaging with advanced image processing technology, avoids the risks of radiation exposure and contrast agents associated with traditional enhanced CT and MRI, making it particularly suitable for pregnant women, patients with renal insufficiency, and specific populations requiring multiple follow-up examinations. This approach improves patient comfort and compliance while effectively reducing motion and magnetic susceptibility artifacts, ensuring high-quality image generation. Its accelerated scanning and real-time reconstruction capabilities significantly enhance diagnostic efficiency. This technology provides a novel solution for the safe and efficient diagnosis of pulmonary embolism, possessing significant clinical and social value.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0016] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0017] Figure 1 This is a schematic flowchart of a pulmonary embolism imaging method according to an embodiment of the present disclosure;

[0018] Figure 2 This is a first imaging comparison image of pulmonary embolism using computed tomography (CT) pulmonary angiography and MRI according to embodiments of the present disclosure;

[0019] Figure 3 This is a second imaging comparison image of pulmonary embolism using computed tomography (CT) and MRI according to embodiments of the present disclosure.

[0020] Figure 4 This is a third imaging comparison of pulmonary embolism using computed tomography (CT) and MRI according to embodiments of the present disclosure.

[0021] Figure 5 This is a comparison of three-dimensional high-resolution black blood sequences of T2-weighted imaging and T1-weighted imaging for pulmonary embolism according to embodiments of this disclosure.

[0022] Figure 6 This is a comparison of three-dimensional high-resolution black blood sequences of T2-weighted imaging with fat suppression and T1-weighted imaging for pulmonary embolism according to embodiments of the present disclosure.

[0023] Figure 7 This is a comparison image of pulmonary embolism using steady-state precession bright blood and T1-weighted imaging three-dimensional high-resolution dark blood sequences according to embodiments of the present disclosure.

[0024] Figure 8 This is a comparison of pulmonary embolism imaging using T1-weighted fat suppression and T1-weighted three-dimensional high-resolution black blood sequences according to embodiments of this disclosure.

[0025] Figure 9 This is a first imaging comparison of pulmonary embolism using T1-weighted imaging enhanced fat suppression and T1-weighted imaging three-dimensional high-resolution black blood sequences according to embodiments of this disclosure;

[0026] Figure 10 This is a second imaging comparison of pulmonary embolism using T1-weighted imaging with enhanced fat suppression and T1-weighted imaging in a three-dimensional high-resolution black blood sequence, according to an embodiment of this disclosure.

[0027] Figure 11 This is a schematic diagram of the pulmonary embolism imaging device according to an embodiment of the present disclosure;

[0028] Figure 12 This is a structural diagram of an electronic device used to implement the pulmonary embolism imaging method of the present disclosure embodiments. Detailed Implementation

[0029] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0030] In this document, the term "and / or" merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The term "at least one" in this document indicates any combination of at least two of a plurality of elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C. The terms "first" and "second" in this document refer to and distinguish between multiple similar technical terms, not to restrict the order or to limit there to only two. For example, "first feature" and "second feature" refer to two categories / two features; the first feature can be one or more, and the second feature can also be one or more.

[0031] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0032] Before introducing the technical solutions of the embodiments of this disclosure, the technical terms that may be used in this disclosure will be further explained:

[0033] Pulmonary embolism imaging is a medical imaging technique used to detect the presence of blood clots in the pulmonary arteries, a critical condition caused by blood clots blocking the pulmonary artery or its branches. Its primary goal is to visually assess the location, extent, and blood flow obstruction of the embolism within the pulmonary artery, providing crucial information for early diagnosis and treatment.

[0034] Pulmonary embolism is the third leading cause of acute cardiovascular and cerebrovascular diseases, after myocardial infarction and stroke. Acute pulmonary embolism has a high mortality rate; without timely diagnosis and treatment, the mortality rate can reach as high as 30%. Currently, the diagnosis of pulmonary embolism mainly relies on contrast-enhanced CT, which is fast and provides clear imaging results, and has become the gold standard for clinical diagnosis. However, contrast-enhanced CT has certain limitations, especially for patients allergic to contrast agents, those with renal insufficiency who cannot use contrast agents, and patients with chronic pulmonary embolism who require multiple follow-up examinations. The cumulative radiation dose of contrast-enhanced CT and the risks associated with contrast agent use limit its widespread application. Currently, the MRI diagnosis of pulmonary embolism still mainly relies on contrast-enhanced imaging, and the application of non-contrast MRI techniques in the diagnosis of pulmonary embolism has not been fully studied. In particular, free-breathing black-blood MRI, as a contrast agent-free and radiation-free imaging method, has potential clinical application value, but its application in the diagnosis of pulmonary embolism has not been reported.

[0035] Currently, enhanced CT pulmonary angiography is the primary clinical method due to its high efficiency, non-invasiveness, and ability to clearly visualize the location and extent of pulmonary embolism, making it the preferred examination method. Computed tomographic pulmonary angiography (CTPA) not only provides high-quality images but also effectively reduces radiation dose and contrast agent usage, leading to its widespread clinical application. However, enhanced CT still has certain limitations, particularly for patients allergic to contrast agents, those with renal insufficiency who cannot use contrast agents, and patients with chronic pulmonary embolism requiring multiple follow-up examinations. The cumulative radiation dose and risks associated with contrast agent use limit its widespread application. To address these issues, the following solutions are proposed.

[0036] This disclosure proposes a pulmonary embolism imaging method to at least partially address one or more of the aforementioned problems and other potential issues, aiming to provide patients with a safe, efficient, and non-invasive diagnostic tool. Compared to traditional enhanced CT or enhanced MRI, this technique avoids radiation exposure and the use of contrast agents, overcoming the limitations of these techniques in specific patient groups (such as pregnant women, patients with renal insufficiency, and those allergic to contrast agents). By employing a free breathing technique, this approach allows patients to complete the examination while breathing spontaneously, significantly improving comfort and compliance, especially suitable for patients who cannot hold their breath or have low cooperation levels. Simultaneously, the use of black-blood sequence imaging effectively reduces interference from magnetic susceptibility artifacts, significantly improving image clarity and diagnostic accuracy. Furthermore, this technique excels in optimizing the scanning process, shortening examination time by increasing data acquisition and processing speed, and enabling rapid diagnosis and result uploading by combining it with immediate post-scan 3D image reconstruction, greatly improving medical efficiency. Clinical trials show that this approach achieves a detection rate of embolism in the pulmonary trunk and branches comparable to traditional CTPA, and can effectively identify false positive artifacts, avoiding misdiagnosis and missed diagnosis. As a non-invasive, safe, and efficient medical technology, this solution not only provides a reliable alternative for patients who cannot undergo traditional examinations, but also significantly reduces radiation risks and medical resource consumption, meeting the current medical demand for efficient and accurate diagnosis. Overall, this technology provides crucial support for the early diagnosis and treatment of pulmonary embolism, demonstrating significant clinical value and promising prospects for widespread application.

[0037] This disclosure provides a method for pulmonary embolism imaging. Figure 1 This is a schematic flowchart of a pulmonary embolism imaging method according to an embodiment of the present disclosure. This pulmonary embolism imaging method can be applied to a pulmonary embolism imaging device. The pulmonary embolism imaging device is located in an electronic device. The electronic device includes, but is not limited to, fixed devices and / or mobile devices. For example, fixed devices include, but are not limited to, servers, which can be cloud servers or ordinary servers. Mobile devices include, but are not limited to, mobile phones, tablets, and vehicle-mounted terminals. In some possible implementations, the pulmonary embolism imaging method can also be implemented by a processor calling computer-readable instructions stored in memory. Figure 1 As shown, this pulmonary embolism imaging method includes:

[0038] S101. Determine the acquisition window based on the patient's ECG data and diaphragmatic navigation data.

[0039] S102. Based on the acquisition window, acquire the original frequency domain data using the Cartesian fill method.

[0040] S103. Phase compensation is performed on the original frequency domain data using pulmonary artery pulsation vector field modeling and dynamic compensation algorithms to obtain the scale coefficient spatial data.

[0041] S104. Compressed sensing reconstruction is performed on the scale coefficient spatial data to generate a three-dimensional pulmonary artery image.

[0042] Among them, electrocardiogram (ECG) data is obtained by recording the electrical activity of the heart during the cardiac cycle using an electrocardiogram. In this embodiment of the disclosure, ECG data is used to determine the optimal scanning time point for the pulmonary artery, avoiding image blurring caused by heartbeats.

[0043] Among them, diaphragmatic navigation data is obtained by real-time monitoring of respiratory status through navigation sequences, ensuring that the diaphragm is in a stable state when data is collected, and reducing artifacts caused by respiratory movements.

[0044] The acquisition window refers to the stable time period for locating the cardiac and respiratory cycles after combining electrocardiogram data and diaphragmatic navigation data. This time period can be used as the time window for acquiring imaging data.

[0045] In this embodiment, an electrocardiogram (ECG) monitoring device can first be used to record the patient's ECG signal in real time to obtain electrical signals reflecting the dynamic changes of the cardiac cycle. Exemplarily, the dynamic changes of the cardiac cycle can be the dynamic changes during the systolic and diastolic phases. Then, an ECG analysis algorithm is used to identify key nodes in the ECG cycle and determine the stable phase of the heart, reducing artifacts caused by heartbeats. Exemplarily, based on the patient's ECG data, R-wave trigger points can be used to identify the periodic activity of the heart and locate the stable phase of the heart, which is typically diastole. Secondly, diaphragm navigation technology can be used to monitor diaphragm movement and capture the dynamic position information of the diaphragm in real time. By analyzing the diaphragm movement trend, the resting point in the patient's respiratory cycle, i.e., the end of expiration, is identified, avoiding image blurring caused by diaphragm movement. Exemplarily, diaphragm navigation technology based on position-sensitive MRI signals can be used to monitor diaphragm movement. Finally, the ECG data and diaphragm navigation data are analyzed synchronously, combining the cardiac diastolic phase and the respiratory resting phase, to calculate the optimal acquisition window. By dynamically adjusting the acquisition time point using automated algorithms, data acquisition is concentrated within the window of least motion interference, accurately locating the optimal imaging time, reducing artifacts caused by heart or diaphragm movement, and improving image quality. For example, automated algorithms for dynamically adjusting the acquisition time point can employ Kalman filters, Bayesian inference algorithms, or dynamic time warping algorithms. The above are merely illustrative examples and do not constitute a limitation on all possible scenarios for determining the acquisition window; they are simply not exhaustive.

[0046] Cartesian filling is a data acquisition method that collects frequency domain data according to a Cartesian coordinate grid to ensure the uniformity and spatial distribution regularity of sampling points.

[0047] The raw frequency domain data consists of unprocessed frequency information acquired through a magnetic resonance imaging system, including raw signal data from the pulmonary artery region.

[0048] In this embodiment, the determined acquisition window can be first input into the MRI system, ensuring that data acquisition is strictly limited to the diastolic and respiratory quiescence periods before starting the magnetic resonance scanning device. Then, the MRI system acquires data in the frequency domain according to the Cartesian coordinate grid rules using a Cartesian fill method. Specifically, the Cartesian fill method employs regular horizontal and vertical sampling to ensure uniform distribution of frequency domain data and avoid reconstruction artifacts caused by uneven sampling points. Finally, after data acquisition is complete, the frequency domain information acquired by the MRI device is output as raw frequency domain data for subsequent processing. The above is merely an illustrative example and does not limit the scope to all possible scenarios for acquiring scale factor spatial data; it is simply not exhaustive.

[0049] Among them, the pulmonary artery pulsation vector field represents the motion change field generated by the pulmonary artery as the heart beats, and captures the dynamic characteristics of the pulmonary artery in vector form.

[0050] Among them, the dynamic compensation algorithm refers to establishing a model based on the pulmonary artery pulsation vector field, performing phase correction on the frequency domain data, and compensating for signal distortion caused by dynamic motion.

[0051] Among them, the scale coefficient spatial data is the data after phase compensation processing, which includes frequency domain information and the scale relationship of the compensated motion vector, and is suitable for subsequent image reconstruction.

[0052] In this embodiment, the motion change information of the pulmonary artery can first be captured based on electrocardiogram (ECG) data and magnetic resonance imaging (MRI) sequences. A vector field is then used to represent the dynamic motion of the pulmonary artery at each stage of the cardiac cycle, constructing a pulmonary artery pulsation vector field model. Exemplarily, the pulmonary artery pulsation vector field is a mathematical expression of the motion characteristics of the pulmonary artery during the cardiac cycle. By combining ECG data and MRI acquisition sequences, the motion trajectory of the pulmonary artery at each time point is captured, and its dynamic changes are modeled. Dynamic changes may include changes in position, velocity, and direction. Then, a dynamic compensation model can be constructed based on the pulmonary artery pulsation vector field to predict the phase changes in frequency domain data caused by pulmonary artery motion. A dynamic compensation algorithm is used to perform phase correction on the original frequency domain data to compensate for data offset and distortion caused by pulmonary artery motion. Exemplarily, the dynamic compensation model, based on the pulmonary artery pulsation vector field, uses motion correction algorithms, such as phase correction algorithms or motion prediction models, to correct the phase offset in the original frequency domain data caused by pulmonary artery motion in real time, thereby compensating for motion artifacts. Finally, the original frequency domain data after dynamic compensation is converted into scale factor space data. The resulting scale factor space data contains correction information and spatial distribution relationships of the pulmonary artery region signal. For example, the original frequency domain data after dynamic compensation has eliminated motion artifacts, but its information is still expressed in the frequency domain. The original frequency domain data can be converted from frequency space to scale factor space using inverse Fourier transform or other mathematical mapping methods to generate scale factor space data containing the pulmonary artery region signal intensity, location distribution, and dynamic correction relationships. This scale factor space data is the foundation of image reconstruction and can accurately express the spatial distribution characteristics of the pulmonary artery. The above is only an illustrative example and does not represent all possible cases for obtaining scale factor space data; it is simply not exhaustive.

[0053] Compressed sensing reconstruction is an image reconstruction algorithm that reduces the number of sampling points required by utilizing the sparsity of the original frequency domain data and the relationship between sampling points, while obtaining a high-quality image. In this embodiment, the compressed sensing algorithm can reconstruct a high-quality image with a relatively small number of sampling points.

[0054] Among them, the three-dimensional pulmonary artery map is a three-dimensional structural image generated by reconstruction, which shows the spatial distribution and morphology of the pulmonary artery, facilitating diagnosis and analysis.

[0055] In this embodiment, the scale coefficient spatial data after dynamic compensation processing can be input into a compressed sensing reconstruction algorithm. Utilizing the sparsity constraint of the compressed sensing algorithm, valuable signal features are extracted from the data, and irrelevant noise is reduced. Then, frequency domain data is reconstructed through an iterative optimization algorithm, reconstructing the complete three-dimensional spatial information of the pulmonary artery from the acquired sparse data. Exemplarily, regularization techniques and gradient projection algorithms can be used to reconstruct the complete three-dimensional spatial information of the pulmonary artery from the acquired sparse data. Finally, after reconstruction, a three-dimensional spatial structure image of the pulmonary artery is output, displaying its morphology and distribution for clinical diagnosis and analysis. Exemplarily, the entire reconstruction process includes signal decoding, data interpolation, and spatial structure construction, ultimately generating a high-resolution three-dimensional pulmonary artery image that realistically presents the morphology and distribution of the pulmonary artery, providing clear and reliable image support for clinical diagnosis. The above is merely an illustrative example and is not intended to limit all possible scenarios for generating a three-dimensional pulmonary artery image; it is simply not exhaustive.

[0056] The technical solution of this disclosure integrates electrocardiogram data and diaphragmatic navigation data, dynamically selects the acquisition window, uniformly acquires frequency domain data using Cartesian fill, corrects motion artifacts through pulmonary artery pulsation vector field modeling and dynamic compensation algorithms, and finally generates high-resolution three-dimensional pulmonary artery images using compressed sensing reconstruction. This overall process optimizes the stability and efficiency of data acquisition, significantly improves image quality, reduces scanning time and artifact interference, provides a reliable imaging solution for clinical diagnosis, and improves patient experience.

[0057] In some embodiments, a suitable patient is determined by: acquiring the patient's electrocardiogram (ECG) data and respiratory monitoring data; determining a first fit based on the ECG data and a preset heart rate threshold; determining a second fit based on the respiratory monitoring data and a preset respiratory rate threshold; and determining a suitable patient based on the first fit and the second fit.

[0058] The respiratory monitoring data refers to the patient's respiratory information collected through respiratory monitoring equipment, including dynamic data such as respiratory rate, respiratory intensity, and diaphragmatic movement trajectory, used to analyze the patient's respiratory patterns and stability. In this embodiment, respiratory monitoring data can be collected using devices such as diaphragmatic navigation devices or respiratory belt recorders.

[0059] In this embodiment, an electrocardiogram (ECG) monitoring device can first be used to collect the patient's ECG signals in real time to obtain periodic information about cardiac activity, such as heart rate and ECG waveform. Then, a respiratory monitoring device, such as a diaphragm navigation system, breathing belt, or gas flow meter, can be used to acquire the patient's respiratory rate, respiratory depth, and diaphragm movement trajectory data. Finally, the data acquisition device records the ECG and respiratory data via a synchronization signal for subsequent analysis. The above is merely an illustrative example and does not constitute a limitation on all possible scenarios for acquiring the patient's ECG and respiratory monitoring data; it is simply not exhaustive.

[0060] Among them, the preset heart rate threshold refers to the heart rate range determined according to medical standards or clinical experience, which is used to determine whether the patient's heart rate is within the normal range or suitable for imaging conditions.

[0061] The first fit refers to the fit score obtained through mathematical calculation or statistical analysis based on the relationship between electrocardiogram data and preset heart rate threshold, which is used to evaluate the degree of matching between the patient's heart rate and imaging conditions.

[0062] In this embodiment, the collected electrocardiogram (ECG) data can first be compared with a preset heart rate threshold to calculate whether the heart rate is within a preset range. Then, a scoring algorithm is used to quantify the degree to which the heart rate deviates from the threshold, generating a first fit. Exemplarily, the scoring algorithm can employ linear weighted scoring or a logistic regression model. Finally, if the heart rate is within the preset range, the fit score is higher. If the heart rate deviates from the range, the score is lower. The above is merely an illustrative example and is not intended to limit all possible scenarios for determining the first fit; it is simply not exhaustive.

[0063] Among them, the preset respiratory rate threshold refers to the ideal respiratory rate range set according to medical standards or clinical experience, which is used to determine whether the patient's respiratory status is stable or suitable for imaging.

[0064] The second fit refers to a score calculated based on the relationship between respiratory monitoring data and a preset respiratory rate threshold, which is used to assess the degree of matching between the patient's respiratory rate stability and imaging needs.

[0065] In this embodiment, the collected respiratory monitoring data can first be compared with a preset respiratory rate threshold. Then, respiratory rate correlation analysis or a dynamic scoring model is used to determine the stability of the patient's respiratory status and generate a second fit. When the respiratory rate is stable and within the ideal range, the fit score is high. When the respiratory rate deviates or is irregular, the score is low. The above is only an illustrative example and is not intended to limit all possible situations for determining the second fit; it is simply not exhaustive.

[0066] In this embodiment, the first and second fit scores can be combined to generate a final fit score. For example, weighted average, logical induction, or fuzzy decision algorithms can be used for this combined calculation. Further, the final fit score is used to determine whether the patient is suitable for pulmonary artery imaging or other medical imaging needs. If the patient's fit is high, they are marked as a suitable patient and proceed to the subsequent imaging process; if the fit is low, the patient is prompted to adjust ECG or respiratory conditions and recalculate; if the requirements are still not met, the patient is marked as an unsuitable patient and other detection methods need to be selected.

[0067] Preferably, the method for determining a suitable patient can further include first comparing the collected patient's electrocardiogram (ECG) data with a preset heart rate threshold to determine whether the heart rate is within a preset range. For example, based on medical standards and clinical experience, the heart rate threshold can be preset to ≤100 beats / min. When the heart rate is within the preset range, a first fit score of 1 is assigned; otherwise, a score of 0 is assigned. Next, the collected patient's respiratory monitoring data is compared with a preset respiratory rate threshold to determine whether the respiratory rate is within a preset range. For example, based on medical standards and clinical experience, the respiratory rate threshold can be preset to 12-18 breaths / min. If the respiratory rate is within the preset range, a second fit score of 1 is assigned; otherwise, a score of 0 is assigned. Finally, if and only if both the first and second fit scores are 1, the patient is determined to be a suitable patient and can proceed to the subsequent imaging process.

[0068] In particular, methods for identifying suitable patients may include rigorously assessing the patient's indications before the examination, especially excluding the following contraindications: implanted pacemakers, neurostimulators, or metal implants (non-MRI compatible); early pregnancy (<12 weeks); claustrophobia (requiring prior psychological intervention); cooperative patients (unable to maintain voluntary resting breathing).

[0069] The above is merely an illustrative example and is not intended to limit the determination of all possible situations for a suitable patient; it is simply not an exhaustive list.

[0070] In this way, by acquiring and analyzing ECG and respiratory monitoring data in real time, combined with preset heart rate and respiratory rate thresholds, patient suitability can be scientifically assessed. This comprehensive assessment ensures a high degree of match between the patient's dynamic conditions and equipment requirements during imaging, effectively reducing artifacts caused by cardiac or respiratory movements and improving pulmonary artery imaging quality. Simultaneously, this process helps select suitable patients for imaging, optimizes the utilization of medical resources, and improves imaging efficiency and diagnostic accuracy.

[0071] In some embodiments, determining the acquisition window based on the patient's electrocardiogram (ECG) data and diaphragmatic navigation data includes: determining the cardiac motion phase based on the patient's ECG data; determining the respiratory phase based on the patient's diaphragmatic navigation data; and determining the acquisition window based on a preset respiratory phase threshold, according to the cardiac motion phase and respiratory phase.

[0072] The cardiac motion phase refers to the specific stage of cardiac motion within a complete beating cycle (cardiac cycle), including systole and diastole. In this embodiment, the cardiac motion phase can be defined and determined by key feature points in an electrocardiogram (ECG).

[0073] In this embodiment, the ECG data of the patient can be acquired in real time and the waveform characteristics of the ECG can be analyzed to extract key points in the cardiac cycle (such as R-wave peak, P-wave onset, T-wave end, etc.). Then, based on the time information of the ECG waveform, the cardiac cycle is divided into multiple motion phases (such as systole and diastole). Finally, algorithms (such as peak detection, time difference analysis, or template matching) can be used to determine the cardiac motion phase corresponding to the current ECG signal in real time and mark whether it is in a suitable acquisition stage. In particular, the suitable acquisition stage is usually the mid-to-late diastole of the heart, that is, the steady period of pulmonary artery blood flow. The above is only an illustrative example and is not intended to limit all possible situations for determining the cardiac motion phase; it is simply not exhaustive.

[0074] The respiratory phase refers to a specific movement stage in the respiratory cycle, such as inhalation, exhalation, or end-expiration (resting phase). In this embodiment, the respiratory phase can be determined and divided by detecting the movement trajectory of the diaphragm using diaphragm navigation data or respiratory monitoring data.

[0075] In this embodiment, a diaphragm navigation device can be used to collect diaphragm motion data, such as diaphragm displacement, velocity, or acceleration. Then, the diaphragm navigation data is analyzed to divide the respiratory cycle into different phases, such as the inspiratory phase, expiratory phase, and end-expiratory resting phase. Finally, the current respiratory phase can be determined by feature point extraction (such as extreme points or stationary intervals of the displacement curve) or phase decomposition algorithms (such as Fourier analysis), and it can be marked whether it is in a stable end-expiratory state. The above is merely an illustrative example and is not intended to limit all possible cases for determining the respiratory phase; it is simply not exhaustive.

[0076] The respiratory phase threshold refers to a pre-defined range of specific respiratory stages. In this embodiment, the respiratory phase threshold is used to define which respiratory phases are suitable for imaging acquisition, in order to avoid artifacts caused by respiratory motion.

[0077] In this embodiment, a preset respiratory phase threshold can be used to select only specific resting phases within the respiratory cycle, avoiding artifacts caused by respiratory motion. Preferably, based on medical standards and clinical experience, the respiratory phase threshold can be set to accept only respiratory phase data with diaphragmatic displacement <3mm, minimizing artifacts caused by respiratory motion. Then, a time period in which both cardiac motion and respiration are in a stable state can be determined using logical judgments (such as Boolean operations) or cross-window algorithms (such as time intersection calculations), serving as the data acquisition window. Finally, the time range of the acquisition window is output to the magnetic resonance imaging device to trigger imaging acquisition. The above is merely an illustrative example and is not intended to limit all possible situations in determining the acquisition window; it is simply not exhaustive.

[0078] Thus, by combining electrocardiogram (ECG) data and diaphragmatic navigation data, the cardiac motion phase and respiratory phase are identified separately, and the acquisition window is dynamically determined based on a preset respiratory phase threshold. This method enables data acquisition when both the heart and respiration are in a stable state. This multi-signal joint analysis effectively reduces artifacts caused by cardiac and respiratory motions, ensuring the clarity and accuracy of magnetic resonance imaging (MRI), while improving imaging efficiency and providing a scientific guarantee for high-quality imaging of the pulmonary artery or other delicate structures.

[0079] In some embodiments, the raw frequency domain data is acquired by a superconducting magnetic resonance scanning system.

[0080] Among them, the superconducting magnetic resonance imaging (MRI) system is a high-precision medical imaging device based on the principle of nuclear magnetic resonance. By working in conjunction with radio frequency coils and gradient coils, the superconducting MRI system can efficiently acquire frequency domain signals from human tissues and convert them into scale factor spatial data for further image reconstruction.

[0081] In this embodiment, the superconducting magnetic resonance imaging (MRI) system generates a high-intensity, stable, and uniform magnetic field through a superconducting magnet, polarizing the hydrogen nuclei in the patient's body and causing magnetic resonance. Furthermore, a radio frequency (RF) coil emits pulses to excite the hydrogen nuclei, generating electromagnetic signals of a specific frequency. Simultaneously, a gradient coil applies a spatially positioned gradient magnetic field, giving the signals spatial resolution. A receiving coil captures these signals and records them as raw frequency domain data. This data is digitized, stored, and used for subsequent image reconstruction and analysis. Preferably, based on medical standards and clinical experience, the "Philips Ingenia CX 3.0T superconducting MRI system" can be selected as the superconducting MRI scanning system. Alternatively, an integrated multi-channel abdominal phased array coil (32 channels or more) can be used as the RF coil to achieve optimal scanning results. The above is merely an illustrative example and does not limit the possibilities for acquiring raw frequency domain data; it is simply not exhaustive.

[0082] Thus, through the synergistic effect of the high field strength, uniform magnetic field, and high-sensitivity radio frequency coil of the superconducting magnetic resonance imaging (MRI) system, high-quality raw frequency domain data acquisition was achieved. This method ensures a high signal-to-noise ratio and stability of the signal, providing a reliable foundation for subsequent dynamic compensation, image reconstruction, and data analysis. The powerful performance of the superconducting MRI system also supports precise localization and high-resolution imaging of complex human structures (such as the pulmonary artery), aiding in early disease diagnosis and pathological research, while improving imaging efficiency and patient experience.

[0083] In some embodiments, the superconducting magnetic resonance scanning system is used to: set the longest trigger delay according to vector electrocardiogram gating technology so that data acquisition is located in the mid-to-late diastolic phase of the heart, and simultaneously monitor respiratory motion by tracking the displacement of the right diaphragm top in real time through a diaphragm navigation bar.

[0084] Vector ECG gating technology is a timing control method based on electrocardiogram (ECG) signals. By comprehensively analyzing the waveform characteristics and temporal relationships of the ECG, it accurately locates the target motion phase in the cardiac cycle. In this embodiment, vector ECG gating technology uses multiple electrodes to record the three-dimensional vector information of the ECG signal, and combines algorithms to perform real-time dynamic monitoring and gating triggering of cardiac activity.

[0085] The longest trigger delay refers to the maximum time delay calculated from a certain trigger point in the cardiac cycle. It is used to control the start time of data acquisition and ensure that the acquisition window is in a specific cardiac cycle phase to reduce cardiac motion artifacts.

[0086] The mid-to-late diastolic phase is one of the active phases of the cardiac cycle, usually occurring after the T wave. At this time, the amplitude of cardiac motion is minimal, making it an ideal time for magnetic resonance imaging (MRI) data acquisition.

[0087] The diaphragm navigation bar is a navigation signal based on diaphragm movement, monitoring respiratory status through the real-time position information of the diaphragm. In this embodiment, the dynamic signal of the diaphragm is acquired by an MRI system and displayed as a bar, which is an important tool for respiratory motion compensation.

[0088] The right diaphragmatic dome displacement refers to the displacement change of the top of the right diaphragm during respiration, and is usually used as a stable respiratory motion indicator. In this embodiment, by monitoring the vertical movement of the right diaphragmatic dome in real time, different phases of the respiratory cycle can be determined.

[0089] Among them, respiratory motion monitoring refers to tracking the respiratory status through the diaphragm navigation bar, detecting and recording the movement trajectory of the patient's diaphragm in real time, ensuring that data collection is carried out during the stable respiratory stage, thereby reducing artifacts caused by respiratory motion.

[0090] In this embodiment, synchronous data acquisition can be performed using a superconducting magnetic resonance imaging (MRI) system combined with dual-gated coordination. Dual-gated coordination refers to the coordinated determination of the acquisition window using vector ECG gating technology and diaphragmatic navigation. First, vector ECG gating technology is used to analyze the ECG signal, accurately locating the mid-to-late diastolic phase of the cardiac cycle, and setting the longest trigger delay to ensure the acquisition time coincides with the phase of minimal cardiac motion. Simultaneously, the displacement changes of the right diaphragm roof are tracked in real time using a diaphragmatic navigation bar to monitor the patient's respiratory status, ensuring the acquisition window is located during a stable respiratory phase. By coordinating the dual gating of cardiac and respiratory functions, the accuracy and stability of the data acquisition process are guaranteed, effectively reducing the image quality degradation caused by motion artifacts.

[0091] Preferably, the process of detecting pulmonary embolism using a superconducting magnetic resonance imaging (MRI) system can also be as follows: First, the patient is required to maintain a standard supine position with their hands placed at their sides for greater comfort and cooperation during the examination. Simultaneously, the patient is required to cooperate with spontaneous, even breathing (12-18 breaths / minute) to avoid image artifacts caused by deep breathing or breath-holding. Then, the bifurcation of the pulmonary artery is set as the anatomical positioning center (approximately at the level of the sternal angle). Further, dual gating is used to collaboratively determine the acquisition window. For vector ECG gating, vector ECG technology can be used for triggering, with the R-wave trigger delay set to "longest" mode to ensure data acquisition occurs in the mid-to-late diastolic phase, i.e., the relatively stable period of pulmonary blood flow. For diaphragmatic navigation, the navigation bar is placed at the level of the right diaphragm top to track diaphragmatic movement in real time, and is set to only accept respiratory phase data with diaphragmatic displacement <3mm to reduce respiratory motion artifacts. Finally, the scanning process was performed using T1-weighted imaging-three-dimensional high-resolution black-blood sequence (T1WI-3D). Simultaneously, the magnetic resonance imaging system used gradient coils to control spatial encoding and progressively acquire signals, following a Cartesian fill method to obtain the raw frequency domain data. Specifically, the scanning parameters could be set as follows: TR / TE = 1000ms / 26ms, slice thickness / slice spacing 1.5mm / 0mm, FOV = 250mm×250mm, reconstruction matrix = 672×672, reconstruction voxels 0.372mm×0.372mm×1.5mm, NSA = 2.

[0092] Specifically, during data acquisition, compressed sensing (CS) imaging can be used to further optimize the acquisition speed. For example, a CS factor of 4 can be set to acquire raw frequency domain data using an undersampling method. In this case, the amount of data acquired is only one-quarter of that acquired using conventional methods, reducing the data acquisition volume and accelerating the scanning speed. After scanning, the system can automatically invoke an instantaneous reconstruction algorithm to restore the acquired sparse data into a complete image through inverse transformation and optimized solution, generating three-dimensional image formats such as Maximum Intensity Projection (MIP) and Multi-Planar Reconstruction (MPR). Finally, the reconstruction results are automatically uploaded to the Picture Archiving & Communication System (PACS) for clinical review and analysis.

[0093] The above is merely an illustrative example and is not intended to limit all possible uses of a superconducting magnetic resonance imaging system; it is simply not an exhaustive list.

[0094] Thus, by using vector ECG gating technology to analyze ECG signals, the mid-to-late diastolic phase of the cardiac cycle is precisely located, and the longest trigger delay is set to ensure that the acquisition time is consistent with the phase of minimal cardiac motion. Simultaneously, the displacement of the right diaphragm top is tracked in real time using a diaphragmatic navigation bar to monitor the patient's respiratory status and ensure that the acquisition window is located during a stable respiratory phase. By coordinating dual gating of cardiac and respiratory functions, the accuracy and stability of the data acquisition process are guaranteed, effectively reducing the image quality degradation caused by motion artifacts.

[0095] In some embodiments, a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm is used to perform phase compensation on the original frequency domain data to obtain proportional coefficient spatial data. This includes: establishing a fluid motion mathematical model containing ventricular ejection pulse terms based on the hemodynamic characteristics of the pulmonary artery; using numerical calculation methods to solve the fluid velocity field and pressure field based on the fluid motion mathematical model to construct the pulmonary artery pulsation vector field; and using an acceleration weighting algorithm to dynamically compensate for the phase shift of the original frequency domain data based on the pulmonary artery pulsation vector field to obtain the proportional coefficient spatial data.

[0096] Pulmonary artery hemodynamics specifically refers to the patterns and characteristics of blood flow within the pulmonary artery, including flow velocity, flow rate, pressure, and pulse rate. These characteristics are influenced by the cardiac cycle and respiratory movements, exhibiting periodic changes.

[0097] Among them, the ventricular ejection pulse term is part of the description of the dynamic characteristics of blood being ejected from the right ventricle into the pulmonary artery, and is usually expressed as pressure pulses and velocity pulses.

[0098] Among them, the fluid motion mathematical model is a mathematical framework based on fluid dynamics theory, used to describe the spatial and temporal changes in blood flow within the pulmonary artery.

[0099] In this embodiment, a suitable fluid dynamics equation can be selected based on the anatomical structure and hemodynamic characteristics of the pulmonary artery to describe the rules governing blood flow within it. Simultaneously, considering the characteristics of blood flow within the pulmonary artery, including pulsatile flow, unsteady flow, and the driving forces of ventricular ejection, the ventricular ejection pulse term can be introduced into the model as a driving condition. Subsequently, the model can be initialized using real physiological parameters of pulmonary artery blood flow (such as flow velocity, pressure, and viscosity) to ensure that the model conforms to actual conditions.

[0100] For example, Navier-Stokes equations can be used to construct a term βδ(t―t) that includes the ventricular ejection pulse. R The mathematical model of fluid motion is given by the following formula:

[0101]

[0102] In the formula, Let t represent the blood flow velocity vector field, and t denote any moment in the cardiac cycle. R The R-wave trigger time is indicated by ρ, blood density, p, blood pressure scalar field, v, viscosity adaptive coefficient, β, and the velocity jump amplitude caused by ventricular ejection. δ(t―t) R ) is the Dirac v function, For Hamiltonian operators, For the Laplace operator.

[0103] Furthermore, the formula for calculating the viscosity adaptive coefficient v in the Navier-Stokes equations is as follows:

[0104]

[0105] In the formula, η is the dynamic viscosity coefficient, k is the turbulence intensity coefficient, and L ref Characteristic length of the pulmonary artery. This represents the maximum magnitude of the velocity gradient.

[0106] The above is merely an illustrative example and is not intended to limit all possible cases for establishing a fluid motion mathematical model that includes ventricular ejection pulse terms; it is simply not exhaustive here.

[0107] The fluid velocity field is a set of variables output from the fluid motion mathematical model, representing the velocity distribution of blood at different locations within the pulmonary artery. In this embodiment, the fluid velocity field is dynamic three-dimensional spatial data used to describe the direction and velocity changes of blood flow.

[0108] Among them, the fluid pressure field is another set of variables in the fluid motion mathematical model, representing the pressure distribution exerted by blood flow on the vessel wall within the pulmonary artery.

[0109] In this embodiment, the equations can be solved using numerical methods based on a fluid motion mathematical model to calculate the flow velocity and pressure of blood at various locations within the pulmonary artery. Exemplarily, the continuous Navier-Stokes equations can be discretized using the finite element method, finite volume method, or finite difference method, transforming the complex partial differential equations into a series of discrete linear algebraic equations. The discretized equations are then iteratively solved using computational fluid dynamics software or numerical calculation tools to obtain the velocity vector (fluid velocity field) and pressure value (pressure field) at each point within the pulmonary artery. Then, the fluid velocity field and pressure field data are combined to form a pulmonary artery pulsation vector field, reflecting the overall dynamic characteristics of blood flow, including blood flow direction, velocity changes, and pulsation intensity. Finally, the pulmonary artery pulsation vector field is transformed into three-dimensional dynamic data, serving as input to a dynamic compensation algorithm. The above is merely an illustrative example and does not constitute a limitation on all possible scenarios for constructing a pulmonary artery pulsation vector field; it is simply not exhaustive.

[0110] The acceleration-weighted algorithm is a weighting method based on the changes in acceleration during fluid motion, used to dynamically compensate for phase shifts in signals. In this embodiment, the algorithm can correct phase errors in the original frequency domain data based on the characteristics of blood pulsation acceleration.

[0111] Phase shift refers to the change in phase of the magnetic resonance signal caused by the pulsation and acceleration of blood flow in the pulmonary artery during magnetic resonance data acquisition. Phase shift can lead to imaging artifacts and data distortion.

[0112] In this embodiment, acceleration information from the pulmonary artery pulsation vector field can be used to analyze the phase shift in the original frequency domain data using an acceleration weighting algorithm. Then, based on the variation of blood flow acceleration, the phase of the frequency domain data is dynamically adjusted to eliminate the shift error. During the compensation process, the algorithm corrects the magnetic resonance signal in real time to ensure the consistency of the acquired data. Finally, after compensation, the frequency domain data is converted into scale factor spatial data to support subsequent image reconstruction and analysis. Specifically, a non-rigid registration algorithm based on navigation signals can also be used to assist in the correction of motion artifacts.

[0113] For example, phase compensation of the original frequency domain data using an acceleration weighting algorithm can be expressed by the following formula:

[0114]

[0115] t c =1 / f heart

[0116] In the formula, S corr For the scale coefficient spatial data, S raw For the original frequency domain data, The frequency vector is the spatial frequency vector, and t is the time variable. Here, γ represents the pulmonary artery pulsation vector field, and γ is the acceleration weighting term. The root mean square velocity vector, i is the imaginary unit, π is pi, and t c f represents the characteristic time of the heartbeat cycle. heart This refers to the heartbeat cycle.

[0117] The formula for calculating the acceleration weight term γ is as follows:

[0118]

[0119] In the formula, Here, Δt represents the acceleration magnitude, and Δt represents the temporal resolution of the MRI sequence. This represents the blood flow velocity modulus.

[0120] Furthermore, the root mean square velocity vector The calculation formula is:

[0121]

[0122] In the formula, T is the duration of a single heartbeat cycle, and τ is the integral time variable.

[0123] The above is merely an illustrative example and is not intended to limit all possible scenarios for obtaining the scale coefficient spatial data; it is simply not an exhaustive list.

[0124] Thus, by establishing a mathematical model of the fluid motion of the pulmonary artery and combining it with the velocity and pressure fields of blood flow, a pulmonary artery pulsation vector field is constructed to dynamically compensate for phase shifts in magnetic resonance imaging (MRI) data. This method effectively reduces artifacts and data distortion caused by blood pulsation, significantly improving the clarity and accuracy of MRI images. The resulting scale factor spatial data provides a reliable foundation for subsequent high-resolution image reconstruction, supporting the accurate diagnosis and pathological analysis of pulmonary artery-related diseases.

[0125] In some embodiments, the original frequency domain data is phase-compensated using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain proportional coefficient spatial data. The method further includes: repeatedly performing pulmonary artery pulsation vector field modeling and phase compensation until the residual of the proportional coefficient spatial data meets a preset condition.

[0126] In this embodiment, pulmonary artery pulsation vector field modeling and phase compensation optimization can be achieved iteratively. First, a pulmonary artery pulsation vector field is constructed based on initial conditions, and phase compensation is performed on the original frequency domain data to generate scale factor spatial data. Then, the compensated residual is calculated. If the residual does not meet preset conditions, the parameters of the vector field model (such as hydrodynamic boundary conditions, acceleration weights, etc.) are adjusted, the model is re-modeled, and phase compensation is performed again. Specifically, a non-rigid registration algorithm based on navigation signals can be used to assist in the correction of motion artifacts. Finally, the residual is re-evaluated after each iteration of optimization until it meets preset accuracy requirements, thereby ensuring the accuracy of the compensation effect and the consistency of the data. Exemplarily, this iterative optimization process can involve repeatedly performing pulmonary artery pulsation vector field modeling and phase compensation until the scale factor spatial data residual before and after correction meets the requirements. Where N is the number of iterations, 10 ―4 The threshold is ‖·‖2, which is the L2 norm. The above is only an illustrative example and does not represent all possible cases where the spatial residuals of the scaling factor satisfy the preset conditions; it is simply not exhaustive here.

[0127] Thus, through precise modeling and iterative compensation algorithms of the pulmonary artery pulsation vector field, the phase shift of the original frequency domain data can be dynamically optimized to generate high-quality scale factor spatial data. The iterative process ensures that the compensation effect reaches the preset accuracy by gradually reducing the residuals, reducing artifacts and errors in the magnetic resonance imaging acquisition process, and improving the clarity and accuracy of the final images, providing reliable data support for the accurate diagnosis and analysis of pulmonary artery-related lesions.

[0128] like Figure 2-10 As shown, Figure 2 Comparison of CTPA (left) and MRI (right), both of which clearly show pulmonary embolism in the right pulmonary artery trunk; Figure 3 Comparison of CTPA (left) and MRI (right), both clearly show pulmonary embolism in the posterior basal segment of the right lower lobe (circled). Figure 4 A comparison of CTPA (left) and MRI (right) clearly shows pulmonary embolism in the left pulmonary artery trunk; Figure 5 Comparison of T2-weighted imaging (T1WI-3D) (left) and T1WI-3D (right); T2WI shows pulmonary embolism less clearly. Figure 6Comparison of fat suppression on T2WI (left) and T1WI-3D (right); T2WI showed poorer pulmonary embolism than T1WI-3D. Figure 7 For the comparison of steady-state progressive bright blood (left) and T1WI-3D (right), the former is affected by magnetic susceptibility artifacts, making the pulmonary embolism less clearly displayed and difficult to distinguish from the artifacts; Figure 8 Comparison of fat suppression in T1-weighted imaging (T1WI) (left) and T1WI-3D (right); pulmonary embolism cannot be detected on T1WI. Figure 9 Comparison of T1WI (left) with enhanced fat suppression and T1WI-3D (right). Both methods can detect pulmonary embolism. The former requires the patient to hold their breath and requires contrast agent, while the latter allows free breathing and does not require contrast agent. Figure 10 Comparison of T1WI (left) with enhanced fat suppression and T1WI-3D (right). Pulmonary artery embolism in the posterior basal segment of the right lower lobe cannot be visualized in the former due to respiratory artifacts, while pulmonary embolism is visible in the latter during free breathing.

[0129] The above images demonstrate that the present invention can meet the testing needs of contraindicated populations without the need for contrast agents and radiation, and has the ability to serve as an alternative or supplement to CTPA.

[0130] This disclosure provides a pulmonary embolism imaging device, such as... Figure 11 As shown, the device may include: a window determination module 1101, used to determine the acquisition window based on the patient's electrocardiogram data and diaphragmatic navigation data; a data acquisition module 1102, used to acquire raw frequency domain data using Cartesian filling based on the acquisition window; a data processing module 1103, used to perform phase compensation on the raw frequency domain data using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain scale coefficient spatial data; and an image generation module 1104, used to perform compressed sensing reconstruction on the scale coefficient spatial data to generate a three-dimensional pulmonary artery image.

[0131] In some embodiments, the window determination module 1101 includes: a patient fitting submodule, configured to acquire the patient's electrocardiogram data and respiratory monitoring data; determine a first fitting degree based on the electrocardiogram data and a preset heart rate threshold; determine a second fitting degree based on the respiratory monitoring data and a preset respiratory rate threshold; and determine a fitted patient based on the first fitting degree and the second fitting degree.

[0132] In some embodiments, the window determination module 1101 further includes: a motion phase submodule, used to determine the cardiac motion phase based on the patient's electrocardiogram data; a respiratory phase submodule, used to determine the respiratory phase based on the patient's diaphragm navigation data; and an acquisition window submodule, used to determine the acquisition window based on a preset respiratory phase threshold and the cardiac motion phase and respiratory phase.

[0133] In some embodiments, in the data acquisition module 1102, the raw frequency domain data is acquired by a superconducting magnetic resonance scanning system.

[0134] In some embodiments, the superconducting magnetic resonance scanning system is used to: set the longest trigger delay according to vector electrocardiogram gating technology so that data acquisition is located in the mid-to-late diastolic phase of the heart, and simultaneously monitor respiratory motion by tracking the displacement of the right diaphragm top in real time through a diaphragm navigation bar.

[0135] In some embodiments, the data processing module 1103 includes: a model building submodule, used to establish a fluid motion mathematical model including ventricular ejection pulse terms based on the hemodynamic characteristics of the pulmonary artery; a vector field building submodule, used to solve the fluid velocity field and pressure field using numerical calculation methods based on the fluid motion mathematical model to construct the pulmonary artery pulsation vector field; and a data acquisition submodule, used to dynamically compensate for the phase shift of the proportional coefficient spatial data using an acceleration weighting algorithm based on the pulmonary artery pulsation vector field to obtain the proportional coefficient spatial data.

[0136] In some embodiments, the data processing module 1103 further includes an iterative generation submodule, used to repeatedly perform pulmonary artery pulsation vector field modeling, displacement vector field modeling, and phase compensation until the scale factor spatial data residuals meet preset conditions.

[0137] The specific functions and examples of each module and submodule of the apparatus in this disclosure can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0138] The pulmonary embolism imaging device in this embodiment integrates electrocardiogram (ECG) data and diaphragmatic navigation data, dynamically selects the acquisition window, uniformly acquires frequency domain data using Cartesian fill, corrects motion artifacts through pulmonary artery pulsation vector field modeling and dynamic compensation algorithms, and finally generates high-resolution three-dimensional pulmonary artery images using compressed sensing reconstruction. The overall process optimizes the stability and efficiency of data acquisition, significantly improves image quality, reduces scanning time and artifact interference, provides a reliable imaging solution for clinical diagnosis, and improves patient experience.

[0139] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0140] Figure 12A schematic block diagram of an example electronic device 1200 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0141] like Figure 12 As shown, device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 1202 or a computer program loaded from storage unit 1208 into random access memory (RAM) 1203. The RAM 1203 may also store various programs and data required for the operation of device 1200. The computing unit 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. An input / output (I / O) interface 1205 is also connected to bus 1204.

[0142] Multiple components in device 1200 are connected to I / O interface 1205, including: input unit 1206, such as keyboard, mouse, etc.; output unit 1207, such as various types of monitors, speakers, etc.; storage unit 1208, such as disk, optical disk, etc.; and communication unit 1209, such as network card, modem, wireless transceiver, etc. Communication unit 1209 allows device 1200 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0143] The computing unit 1201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 performs the various methods and processes described above, such as the pulmonary embolism imaging method. For example, in some embodiments, the pulmonary embolism imaging method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program can be loaded and / or installed on device 1200 via ROM 1202 and / or communication unit 1209. When the computer program is loaded into RAM 1203 and executed by the computing unit 1201, one or more steps of the pulmonary embolism imaging method described above can be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to perform a pulmonary embolism imaging method by any other suitable means (e.g., by means of firmware).

[0144] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0148] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0149] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0150] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0151] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for imaging pulmonary embolism, characterized in that, The method includes: The acquisition window is determined based on the patient's electrocardiogram and diaphragmatic navigation data; According to the acquisition window, the original frequency domain data is acquired using the Cartesian fill method; Phase compensation was performed on the original frequency domain data using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain the scale coefficient spatial data. Compressed sensing reconstruction is performed on the spatial data of the scaling factor to generate a three-dimensional pulmonary artery image.

2. The method according to claim 1, characterized in that, The suitable patient was determined in the following way: Acquire the patient's electrocardiogram and respiratory monitoring data; Based on the electrocardiogram data and the preset heart rate threshold, a first degree of fit is determined; The second fit is determined based on the respiratory monitoring data and the preset respiratory rate threshold. The suitable patient is determined based on the first fit and the second fit.

3. The method according to claim 1, characterized in that, The process of determining the acquisition window based on the patient's electrocardiogram and diaphragmatic navigation data includes: Based on the electrocardiogram data of the matched patient, the cardiac motion phase is determined; The respiratory phase is determined based on the diaphragmatic navigation data of the adapted patient; Based on a preset respiratory phase threshold, the acquisition window is determined according to the cardiac motion phase and the respiratory phase.

4. The method according to claim 1, characterized in that, The raw frequency domain data was acquired by a superconducting magnetic resonance scanning system.

5. The method according to claim 4, characterized in that, The superconducting magnetic resonance scanning system is used for: Based on vector ECG gating technology, the longest trigger delay is set so that data acquisition occurs in the mid-to-late diastolic phase of the heart. Simultaneously, respiratory motion monitoring is achieved by tracking the displacement of the right diaphragm top in real time through the diaphragm navigation bar.

6. The method according to claim 1, characterized in that, The step of using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to perform phase compensation on the original frequency domain data to obtain scale factor spatial data includes: Based on the hemodynamic characteristics of the pulmonary artery, a fluid motion mathematical model including the ventricular ejection pulse term was established; Based on the fluid motion mathematical model, the fluid velocity field and pressure field are solved by numerical calculation method to construct the pulmonary artery pulsation vector field. Based on the pulmonary artery pulsation vector field, the phase shift of the original frequency domain data is dynamically compensated using an acceleration weighting algorithm to obtain the proportional coefficient spatial data.

7. The method according to claim 6, characterized in that, Phase compensation is performed on the original frequency domain data using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain the scale factor spatial data, which also includes: Repeat the pulmonary artery pulsation vector field modeling and phase compensation until the scale factor spatial data residuals meet the preset conditions.

8. A pulmonary embolism imaging device, characterized in that, include: The window determination module is used to determine the acquisition window based on the patient's electrocardiogram data and diaphragmatic navigation data. The data acquisition module is used to acquire raw frequency domain data using the Cartesian fill method according to the acquisition window; The data processing module is used to perform phase compensation on the original frequency domain data using a pulmonary artery pulsation vector field modeling and dynamic compensation algorithm to obtain proportional coefficient spatial data. The image generation module is used to perform compressed sensing reconstruction on the scale coefficient spatial data to generate a three-dimensional pulmonary artery image.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

Images