CN120284294A

CN120284294A - Method, device and apparatus for simulating myocardial blood flow

Info

Publication number: CN120284294A
Application number: CN202510546723.XA
Authority: CN
Inventors: 赵喜
Original assignee: Siemens Digital Medical Technology Shanghai Co ltd
Current assignee: Siemens Digital Medical Technology Shanghai Co ltd
Priority date: 2025-04-27
Filing date: 2025-04-27
Publication date: 2025-07-11

Abstract

The present disclosure relates to a method, device and apparatus for simulating myocardial blood flow. The method comprises: obtaining an angiographic image including a coronary artery and a left ventricle adjacent to the coronary artery, the angiographic image comprising at least one first region and at least one second region, wherein at least one first region is located in the myocardial region of the left ventricle, and at least one second region is located in the lumen region of the coronary artery; obtaining a first time-density curve for at least one first region to obtain the maximum slope of the first time-density curve; obtaining a second time-density curve for at least one second region to obtain the maximum enhancement value of the second time-density curve; and determining the myocardial blood flow according to the maximum slope and the maximum enhancement value. The present disclosure helps to reduce the health risks of patients, reduce medical costs and improve the efficiency of obtaining myocardial blood flow.

Description

Method, device and equipment for simulating myocardial blood flow

Technical Field

The present disclosure relates to the field of CT imaging technologies, and in particular, to a method for simulating myocardial blood flow, a device for simulating myocardial blood flow, and an electronic apparatus.

Background

Coronary heart disease is one of the diseases with the highest global mortality rate. With the continuing rise in coronary heart disease morbidity and mortality in recent years, cardiovascular disease has become a significant public health problem. Coronary CT angiography (coronary tomography angiography, CTA) has been widely used as a non-invasive imaging means for assessing the extent and efficacy of coronary stenosis in patients with coronary heart disease, but it does not provide accurate quantification of coronary hemodynamic and myocardial blood flow (myocardial blood flow, MBF) information. Thus, there remains a need for imaging (myocardial CT perfusion, CTP) such as CT myocardial perfusion to measure myocardial blood flow and myocardial activity, etc.

However, CTPs use larger doses of contrast agent and radiate more strongly, thereby posing greater health risks. And, CTP inspection takes a long time.

Thus, there is a need for a method of simulating myocardial blood flow that reduces the health risk of the patient, reduces the cost of medical treatment, and increases the efficiency of obtaining myocardial blood flow.

Disclosure of Invention

In view of this, according to one aspect of the present disclosure, there is provided a method of simulating myocardial blood flow, comprising acquiring an angiographic image comprising a coronary artery and a left ventricle adjacent to the coronary artery, the angiographic image comprising at least one first region and at least one second region, wherein the at least one first region is located in a myocardial region of the left ventricle and the at least one second region is located in a luminal region of the coronary artery, acquiring a first time density curve for the at least one first region to obtain a maximum slope of the first time density curve, acquiring a second time density curve for the at least one second region to obtain a maximum enhancement value of the second time density curve, and determining myocardial blood flow based on the maximum slope and the maximum enhancement value.

According to yet another aspect of the present disclosure, there is provided an apparatus for simulating myocardial blood flow, the apparatus comprising a first unit configured to acquire an angiographic image comprising a coronary artery and a left ventricle adjacent to the coronary artery, the angiographic image comprising at least one first region and at least one second region, wherein the at least one first region is located in a myocardial region of the left ventricle and the at least one second region is located in a luminal region of the coronary artery, a second unit configured to acquire a first time density curve for the at least one first region to obtain a maximum slope of the first time density curve, a third unit configured to acquire a second time density curve for the at least one second region to obtain a maximum enhancement value of the second time density curve, and a fourth unit configured to determine myocardial blood flow based on the maximum slope and the maximum enhancement value.

According to yet another aspect of the present disclosure, there is provided an electronic device comprising at least one processor, and at least one memory communicatively coupled to the at least one processor, wherein the at least one memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform a method of simulating myocardial blood flow.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.

The above and other features and advantages of the present disclosure will become more apparent to those of ordinary skill in the art by describing in detail embodiments thereof with reference to the attached drawings, in which:

FIG. 1 illustrates a flow chart of a method of simulating myocardial blood flow in accordance with an embodiment of the present disclosure;

FIG. 2 shows a flow chart of a portion of the process of the method of simulating myocardial blood flow in FIG. 1;

FIG. 3 shows a flow chart of a portion of the process of the method of simulating myocardial blood flow in FIG. 1;

FIG. 4 illustrates a flow chart of a treatment plan recommendation method according to an embodiment of the present disclosure;

FIG. 5 illustrates a schematic diagram of generating an angiographic image using a method of simulating myocardial blood flow in accordance with an embodiment of the present disclosure;

FIGS. 6A-6B illustrate schematic diagrams of acquiring myocardial blood flow perfusion images using a method of simulating myocardial blood flow in accordance with embodiments of the present disclosure;

FIG. 7 shows a block diagram of an apparatus for simulating myocardial blood flow in accordance with an embodiment of the present disclosure, and

Fig. 8 illustrates a block diagram of an exemplary electronic device, according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.

The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.

As described above, a practitioner may utilize invasive coronary angiography or CT myocardial perfusion imaging to obtain myocardial blood flow information of a patient to assess the blood flow function of the patient. In some scenarios, the patient is not conditioned to perform both of the above-described examinations, e.g., the patient's physical condition does not allow for injection of large doses of contrast agent or to perform invasive examinations, the patient's condition is urgent and cannot wait for the results of perfusion imaging. In addition, the above two examination methods cannot accurately reflect early ischemia or microvascular ischemia.

Based on this, the present disclosure provides a method of simulating myocardial blood flow, a treatment plan recommendation method, an apparatus for simulating myocardial blood flow, and an electronic device.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 shows a flowchart of a method 100 of simulating myocardial blood flow in accordance with an embodiment of the present disclosure.

Referring to fig. 1, a method 100 of simulating myocardial blood flow includes:

Step S110, acquiring an angiography image comprising a coronary artery and a left ventricle adjacent to the coronary artery, wherein the angiography image comprises at least one first area and at least one second area, the at least one first area is positioned in a myocardial area of the left ventricle, and the at least one second area is positioned in a lumen area of the coronary artery;

Step S130, a first time density curve is obtained aiming at least one first area so as to obtain the maximum slope of the first time density curve;

Step S140, for at least one second region, obtaining a second time density curve to obtain a maximum enhancement value of the second time density curve, and

And step S150, determining myocardial blood flow according to the maximum slope and the maximum enhancement value.

The method 100 provides a method of simulating myocardial blood flow, and in particular, the method 100 acquires a time density curve from a particular region of a coronary CT angiography image to simulate myocardial blood flow.

A time-density curve (TDC) is a graph reflecting the change in CT value of a specific region (e.g., a blood vessel or tissue) of a patient over time, and is generally represented by time on the horizontal axis and CT value on the vertical axis, for reflecting the change in density of the specific region at different points in time. For example, in CT myocardial perfusion imaging, discrete CT value data at a plurality of time points are acquired and fit to a time density curve. Still taking CT myocardial perfusion imaging as an example, to further calculate myocardial blood flow, it is necessary to measure the time density curve of the coronary artery lumen region of the patient and the time density curve of the left ventricular myocardial region of the patient simultaneously, both of which need to completely contain the whole process of increasing the CT value to the peak value and then decreasing to the point of tending to stabilize. Further, myocardial blood flow can be calculated from the maximum slope of the time density curve of the left ventricular myocardial region and the maximum enhancement of the time density curve of the coronary artery lumen region.

In the example of step S110, similar to the CT myocardial perfusion imaging described above, the method 100 also entails acquiring angiographic images including at least one myocardial region located in the left ventricle and at least one luminal region located in the coronary arteries simultaneously.

Fig. 5 shows a schematic diagram of generating an angiographic image using a method of simulating myocardial blood flow in accordance with an embodiment of the present disclosure. As shown in fig. 5, the angiographic image acquired by the method 100 may be a three-dimensional reconstructed image of a myocardial region of the left ventricle and a luminal region of the coronary arteries.

According to some embodiments of the present disclosure, after step S110, step S120 of acquiring at least one first region of interest and at least one second region of interest based on the angiographic image may be further included.

Fig. 3 shows a flow chart of a part of the procedure of the method of simulating myocardial blood flow in fig. 1. As shown in fig. 3, step S120 further includes:

Step S310, preprocessing an angiography image to obtain a preprocessed angiography image;

step S320, dividing the preprocessed angiography image into a myocardial region of a left ventricle and a lumen region of a coronary artery;

Step S330, extracting at least one first region of interest as at least one first region in the myocardial region of the left ventricle, and

Step S340, extracting at least one second region of interest as at least one second region in the lumen region of the coronary artery.

In the example of step S310, preprocessing of the angiographic image includes noise reduction such as gaussian filtering, median filtering, image registration, resampling, normalization processing, and the like, by which the image quality of the angiographic image can be improved, providing a reliable basis for subsequent calculation of myocardial blood flow.

Based on this, in the example of step S320, the preprocessed angiographic image is segmented into the myocardial region of the left ventricle and the lumen region of the coronary artery, so as to obtain a time density curve of the lumen region of the coronary artery of the patient and a time density curve of the myocardial region of the left ventricle of the patient, respectively.

Further, in the examples of steps S330 and S340, the first region and the second region are determined by extracting a region of interest (region of interest, ROI) for the lumen region of the coronary artery and the myocardial region of the left ventricle, respectively. Wherein the region of interest may be identified automatically or defined by a professional based on clinical experience and related knowledge. The goal of this is firstly to reduce unnecessary computations, to increase the computational efficiency, especially when processing large amounts of data, to save time and computational resources, secondly to exclude disturbances of irrelevant areas, to increase the accuracy of the diagnosis, thirdly to help standardize the analysis process, to ensure comparability of different time points or different patients, and finally to track changes of specific areas, such as treatment effects or disease progression, in dynamic monitoring.

The angiographic image acquired through the step S120, i.e. steps S310-S340, is preprocessed to facilitate accurate simulation of myocardial blood flow, and the generation of a subsequent time-density curve and the simplified calculation of myocardial blood flow are facilitated by extracting the region of interest and taking the region of interest as the first and second regions.

With continued reference to fig. 1, in the example of step S130, a first time density curve of the first region is generated and a maximum slope of the first time density curve is calculated, and in the example of step S140, a second time density curve of the second region is generated and a maximum enhancement value of the second time density curve is calculated.

As mentioned above, the time density curves in CT myocardial perfusion imaging all need to fully contain the whole process of CT value rising to peak and then falling to tend to stabilize, however this will lead to a larger radiation dose and longer data acquisition time. To address this problem, the time density curves in embodiments of the present disclosure are sparsely sampled, that is, in step S130, only data for a specific point in time or period of time is acquired for the first region and the second region, thereby forming two "incomplete" time density curves.

In some embodiments of the present disclosure, the time at which the time density curve starts to collect may be set by setting a trigger value, in particular, the first time density curve starts to collect from a first trigger time, the first trigger time being a time at which a first monitored CT value of at least one first region first exceeds a preset first trigger value, and the second time density curve starts to collect from a second trigger time, the second trigger time being a time at which a second monitored CT value of at least one second region first exceeds a preset second trigger value. The time at which the acquisition is started may be a time interval after the injection of the contrast medium.

Similarly, the time at which the time density curve stops collecting may be set by setting a threshold value, in particular, the first time density curve stops collecting at a first cut-off time configured as a time at which the first monitored CT value of the at least one first region exceeds a preset first threshold value for the first time, and the second time density curve stops collecting from a second cut-off time configured as a time at which the second monitored CT value of the at least one second region exceeds a preset second threshold value for the first time. The time at which the acquisition is stopped may be set to a time interval after the start of the acquisition.

Fig. 2 shows a flow chart of a part of the procedure of the method of simulating myocardial blood flow in fig. 1. As shown in fig. 2, in step S130, the maximum slope of the first time-density curve is determined by:

S210, calculating a first slope of each first sub-line segment for a plurality of first sub-line segments of the first time density curve, and

S220, determining the maximum slope calculated in the plurality of first sub-line segments as the maximum slope, wherein the plurality of first sub-line segments are composed of connecting lines between the first monitoring CT values of every two adjacent times of the plurality of first monitoring CT values of at least one first area in the angiography process.

In the example of step S210, it can be seen that the first time-density curve is composed of a plurality of first sub-line segments between adjacent data points, and compared with the case that tens of data points are adopted to fit into one time-density curve in the conventional CT myocardial perfusion imaging, the first time-density curve of the embodiment of the present disclosure is directly composed of a plurality of first sub-line segments, so that the first slope of each first sub-line segment can be simply and quickly calculated, which is beneficial to improving the efficiency of simulating myocardial blood flow.

In the example of step S220, the maximum value of the first slopes of the plurality of first sub-line segments is directly taken as the maximum slope, so as to quickly obtain the maximum slope of the first time density curve.

Further, for step S140, the maximum enhancement value may be simply calculated by calculating a difference between a maximum value and a minimum value among a plurality of second monitor CT values of at least one second region in the angiography process as the maximum enhancement value.

Based on this, in the example of step S150, the myocardial blood flow of the coronary artery is determined from the maximum slope described above and the maximum enhancement value described above.

Since the method of embodiments of the present disclosure simulates myocardial blood flow of a coronary artery by CT angiography data of the coronary artery and the left ventricle adjacent to the coronary artery, i.e., angiographic image, first time density curve, and second time density curve, the patient does not need to be subjected to invasive examination nor injected with a large dose of contrast agent, thereby reducing the associated health risks and complications. The method of the embodiment of the disclosure directly uses the maximum slope and the maximum enhancement value of the time density curve, thereby being capable of providing the estimated value of the myocardial blood flow in an express way, being suitable for real-time clinical decision, and being particularly beneficial to the preliminary evaluation of the situation of patients suffering from acute coronary heart disease or myocardial ischemia in emergency. In addition, since coronary artery CT angiography has been widely used in clinical practice for coronary artery disease assessment, the methods of embodiments of the present disclosure are easily combined with existing clinical workflows, thereby reducing application costs and difficulty in popularization.

In some embodiments of the present disclosure, myocardial blood flow is calculated by a weighted linear combination of the maximum slope and the maximum enhancement value. Compared with the method for calculating the myocardial blood flow by adopting a complex hemodynamic model in CT myocardial perfusion imaging, the method of the embodiment of the disclosure greatly simplifies the calculation process by a linear formula containing the maximum slope and the maximum enhancement value and provides relatively accurate preliminary estimation of the myocardial blood flow.

Specifically, the myocardial blood flow may be calculated using the following empirical formula:

MBF = (k₁×S+k₂×E) + b

wherein MBF is myocardial blood flow, S is the maximum slope of the first time density curve, and E is the maximum enhancement of the second time density curve. k ₁、k₂, b are coefficients determined from clinical data or experiments, respectively, and k ₁、k₂, b may be constants.

The linear empirical formula described above does not require significant computational resources or detailed anatomical and physiological information and provides an objective and repeatable assessment of myocardial blood flow, thereby improving accuracy and consistency of disease diagnosis relying on myocardial blood flow and reducing uncontrolled changes associated with subjective interpretation in clinical practice.

In some embodiments of the present disclosure, the area of myocardial ischemia may be determined from the simulated myocardial blood flow. For example, the myocardial ischemia region may be displayed in the acquired angiographic image using a different color or a different CT value from the normal myocardial region, or the myocardial ischemia region may be marked in the angiographic image by a special marking symbol. The content of the myocardial ischemia area, the degree and proportion of myocardial ischemia and the like can also be described through words.

Fig. 6A-6B illustrate schematic diagrams of acquiring myocardial blood flow perfusion images using a method of simulating myocardial blood flow in accordance with embodiments of the present disclosure. 6A-6B, in some embodiments of the present disclosure, a visualization map of the coronary blood flow distribution may be generated based on the simulated myocardial blood flow, and/or an angiographic image may be image fused with myocardial blood flow to generate a myocardial perfusion image. Specifically, as shown in fig. 6A, a visual map of coronary blood flow distribution may be presented in the form of a bullseye chart. In the bullseye chart, the wall thickness change percentage, the myocardial perfusion condition, the speed, displacement, strain rate, rotation angle and other dynamic behaviors of the myocardial tissue motion and the like of the heart in different systole and diastole are represented by different colors or gray levels, so that the severity degree and the lesion range of myocardial damage in different sections of the heart are intuitively, rapidly and accurately judged, and the whole and partial systolic and diastolic function changes of the heart are comprehensively evaluated.

The angiographic image may be image fused with myocardial blood flow as shown in fig. 6B, the fused image of fig. 6B being based on a tomographic two-dimensional image of the angiographic image, and myocardial blood flow at that location. The three-dimensional reconstructed angiographic image as described above may also be used to fuse with myocardial blood flow to obtain myocardial perfusion dynamic images.

According to another aspect of the present disclosure, a treatment plan recommendation method is provided.

Fig. 4 illustrates a flow chart of a treatment plan recommendation method 400 according to an embodiment of the present disclosure. As shown in fig. 4, the treatment plan recommendation method 400 includes:

Step S410, obtaining myocardial blood flow of a target object simulated by a method for simulating myocardial blood flow;

step S420, generating data output for assisting in treatment planning based on myocardial blood flow and the personalized information of the target object.

In the example of step S410, myocardial blood flow of the target object is acquired using the method 100 described in fig. 1. Thus, the operations, features, and advantages described above with respect to method 100 apply equally to method 400. For brevity, certain operations, features and advantages are not described in detail herein.

In the example of step S420, the individuation information of the target object may include basic information of the target object, such as information of height, weight, blood type, family history, allergy history, etc., pathological information of the target object, such as electrocardiogram information, chest X-ray examination, etc., and subjective information of the target object, such as treatment mode preference, treatment compliance, psychological state, etc.

Based on the above-described individualization information of the target object and the myocardial blood flow, a preliminary treatment plan may be automatically generated. The treatment plan may include advice on the selection of the treatment modality, such as what medications are to be used, whether surgical or interventional therapy is to be used, and other adjunctive treatments, lifestyle interventions, such as dietary adjustments, exercise plans, smoking cessation, alcohol withdrawal, etc., and psychological interventions to help the target subject address psychological stress caused by the disease.

The above description is not related to the limitation of step S420, and the appropriate form and content may be selected to generate the data output of the treatment plan, as desired.

According to another aspect of the present disclosure, an apparatus for simulating myocardial blood flow is provided.

Fig. 7 shows a block diagram of a device 700 for simulating myocardial blood flow in accordance with an embodiment of the present disclosure. As shown in fig. 7, an apparatus 700 for simulating myocardial blood flow includes:

A first unit 710 configured to acquire an angiographic image comprising a coronary artery and a left ventricle adjacent to the coronary artery, the angiographic image comprising at least one first region and at least one second region, wherein the at least one first region is located in a myocardial region of the left ventricle and the at least one second region is located in a luminal region of the coronary artery;

A second unit 720 configured to obtain a first time density curve for at least one first region, to obtain a maximum slope of the first time density curve;

A third unit 730 configured to obtain a second time density curve for at least one second region to obtain a maximum enhancement value of the second time density curve, and

A fourth unit 740 configured to determine myocardial blood flow based on the maximum slope and the maximum enhancement value.

It should be appreciated that the various elements of the apparatus 700 shown in fig. 7 may correspond to steps S110 and S130-S150 in the method 100 described with reference to fig. 1. Thus, the operations, features and advantages described above with respect to method 100 apply equally to apparatus 700 and the units it comprises. For brevity, certain operations, features and advantages are not described in detail herein.

Although specific functions are discussed above with reference to specific units, it should be noted that the functions of the various units discussed herein may be divided into multiple units and/or at least some of the functions of the multiple units may be combined into a single unit. The particular unit performing the action discussed herein includes the particular unit itself performing the action, or alternatively the particular unit invoking or otherwise accessing another component or unit performing the action (or performing the action in conjunction with the particular unit). Thus, a particular element performing an action may include the particular element performing the action itself and/or another element performing the action that the particular element invokes or otherwise accesses.

It should also be appreciated that various techniques may be described herein in the general context of software hardware elements or program elements. The various elements described above with respect to fig. 7 may be implemented in hardware or in hardware in combination with software and/or firmware. For example, the units may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these units may be implemented as hardware logic/circuitry. For example, in some embodiments, one or more of the first unit 710, the second unit 720, the third unit 730, and the fourth unit 740 may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip including one or more components of a Processor (e.g., a central processing unit (Central Processing Unit, CPU), microcontroller, microprocessor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), etc.), memory, one or more communication interfaces, and/or other circuitry, and may optionally execute received program code and/or include embedded firmware to perform functions.

According to another aspect of the present disclosure, there is provided an electronic device comprising at least one processor, and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the above-described method of simulating myocardial blood flow.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing a computer program for causing a computer to perform the above-described method of simulating myocardial blood flow.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the above-described method of simulating myocardial blood flow.

Fig. 8 is a block diagram illustrating an example of an electronic device 800 according to an example embodiment of the present disclosure. It should be noted that the structure shown in fig. 8 is only an example, and the electronic device of the present disclosure may include only one or more of the components shown in fig. 8 according to a specific implementation.

The electronic device 800 may be, for example, a general-purpose computer (e.g., a laptop computer, a tablet computer, etc., various computers), a mobile phone, a personal digital assistant, and the like. According to some embodiments, the electronic device 800 may be a cloud computing device and a smart device. According to some embodiments, the electronic device 800 may be an X-ray imaging device, such as a computed tomography CT device.

According to some embodiments, the electronic device 800 may be configured to process at least one of an image, text, and audio, and transmit the processing results to an output device for provision to a user. The output device may be, for example, a display screen, a device including a display screen, or a sound output device such as a headphone, a speaker, or an oscillator. For example, the electronic device 800 may be configured to perform object detection on an image, transmit the object detection result to a display device for display, and the electronic device 800 may be further configured to perform enhancement processing on the image and transmit the enhancement result to the display device for display. The electronic device 800 may also be configured to recognize text in an image and transmit the recognition result to a display device for display and/or convert the recognition result to sound data and transmit to a sound output device for playback. The electronic device 800 may also be configured to recognize and process audio and transmit the recognition results to a display device for display and/or convert the processing results to sound data and transmit to a sound output device for playback.

The electronic device 800 may include an image processing circuit 803, and the image processing circuit 803 may be configured to perform various image processes on an image. The image processing circuitry 803 may be configured to at least one of noise-reduce the image, normalize the image, register the image, geometrically correct the image, extract features of the image, detect and/or identify objects in the image, enhance the image, detect and/or identify text contained in the image, and so forth, for example.

The electronic device 800 may also include a text recognition circuit 804, the text recognition circuit 804 configured to perform text detection and/or recognition (e.g., OCR processing) of text regions in an image to obtain text data. The word recognition circuit 804 may be implemented, for example, by a dedicated chip. The electronic device 800 may further comprise a sound conversion circuit 805, said sound conversion circuit 805 being configured to convert said text data into sound data. The sound conversion circuit 805 may be implemented by a dedicated chip, for example.

The electronic device 800 may also include an audio processing circuit 806, the audio processing circuit 806 being configured to convert the audio to text, thereby obtaining audio corresponding text data. The audio processing circuitry 806 may also be configured to process the audio-corresponding text data, which may include keyword extraction, intent recognition, intelligent recommendation, intelligent question-answering, and the like, for example. The audio processing circuit 806 may be implemented, for example, by a dedicated chip. The sound conversion circuit 805 may also be configured to convert the audio processing results into sound data for application scenarios such as voice assistants or virtual customer service.

One or more of the various circuits described above (e.g., image processing circuitry 803, text recognition circuitry 804, sound conversion circuitry 805, audio processing circuitry 806) may be implemented using custom hardware, and/or may be implemented in hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof, e.g., one or more of the various circuits described above may be implemented by programming hardware, e.g., programmable logic circuits including Field Programmable Gate Arrays (FPGAs) and/or Programmable Logic Arrays (PLAs), in an assembly language or hardware programming language, such as VERILOG, VHDL, c++, using logic and algorithms according to the present disclosure.

According to some embodiments, electronic device 800 may also include an output device 807, which output device 807 may be any type of device for presenting information including, but not limited to, a display screen, a terminal having display capabilities, headphones, speakers, vibrators, and/or printers, among others.

According to some embodiments, electronic device 800 may also include an input device 808, which input device 808 may be any type of device for inputting information to electronic device 800, and may include, but is not limited to, various sensors, mice, keyboards, touch screens, buttons, levers, microphones, and/or remote controls, and the like.

According to some embodiments, electronic device 800 may also include a communication device 809, which communication device 809 may be any type of device or system that enables communication with external devices and/or with a network, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication devices, and/or chipsets, such as bluetooth devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

According to some implementations, the electronic device 800 may also include a processor 801. The processor 801 may be any type of processor and may include, but is not limited to, one or more general purpose processors and/or one or more special purpose processors (e.g., special processing chips). The processor 801 may be, for example, but is not limited to, a central processing unit CPU, a graphics processor GPU, or various dedicated Artificial Intelligence (AI) computing chips, or the like.

The electronic device 800 may also include a working memory 802 and a storage device 811. The processor 801 may be configured to obtain and execute computer readable instructions stored in the working memory 802, storage 811, or other computer readable medium, such as program code of the operating system 802a, program code of the application programs 802b, and the like. Working memory 802 and storage 811 are examples of computer-readable storage media for storing instructions that can be executed by processor 801 to implement the various functions described previously. Working memory 802 may include both volatile memory and nonvolatile memory (e.g., RAM, ROM, etc.). Storage 811 may include hard disk drives, solid state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network attached storage, storage area networks, and the like. Working memory 802 and storage 811 may both be referred to herein collectively as memory or computer-readable storage medium, and may be non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code that may be executed by processor 801 as a particular machine configured to implement the operations and functions described in the examples herein.

According to some embodiments, the processor 801 may control and schedule at least one of the image processing circuitry 803, the text recognition circuitry 804, the sound conversion circuitry 805, the audio processing circuitry 806, and other various devices and circuits included in the electronic apparatus 800. According to some embodiments, at least some of the various components described in fig. 8 may be interconnected and/or communicate by a bus 810.

Software elements (programs) may reside in the working memory 802 including, but not limited to, an operating system 802a, one or more application programs 802b, drivers, and/or other data and code.

According to some embodiments, instructions for performing the aforementioned control and scheduling may be included in the operating system 802a or one or more application programs 802 b.

According to some embodiments, instructions to perform the method steps described in the present disclosure may be included in one or more applications 802b, and the various modules of the electronic device 800 described above may be implemented by the instructions of one or more applications 802b being read and executed by the processor 801. In other words, electronic device 800 may include a processor 801 and memory (e.g., working memory 802 and/or storage 811) storing a program including instructions that, when executed by the processor 801, cause the processor 801 to perform methods as described in various embodiments of the disclosure.

According to some embodiments, some or all of the operations performed by at least one of the image processing circuit 803, the text recognition circuit 804, the sound conversion circuit 805, the audio processing circuit 807 may be implemented by the processor 801 reading and executing instructions of one or more applications 802 b.

Executable code or source code of instructions of software elements (programs) may be stored in a non-transitory computer readable storage medium (such as the storage device 811) and may be stored in the working memory 802 (possibly compiled and/or installed) when executed. Accordingly, the present disclosure provides a computer readable storage medium storing a program comprising instructions that, when executed by a processor of an electronic device, cause the electronic device to perform a method as described in various embodiments of the present disclosure. According to another embodiment, executable code or source code of instructions of the software elements (programs) may also be downloaded from a remote location.

It should also be understood that various modifications may be made according to specific requirements. For example, custom hardware may also be used, and/or individual circuits, units, modules or elements may be implemented in hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. For example, some or all of the circuits, units, modules, or elements contained in the disclosed methods and apparatus may be implemented by programming hardware (e.g., programmable logic circuits including Field Programmable Gate Arrays (FPGAs) and/or Programmable Logic Arrays (PLAs)) in an assembly language or hardware programming language such as VERILOG, VHDL, c++ using logic and algorithms according to the present disclosure.

According to some implementations, the processor 801 in the electronic device 800 may be distributed over a network. For example, some processes may be performed using one processor while other processes may be performed by another processor remote from the one processor. Other modules of the electronic device 800 may also be similarly distributed. As such, the electronic device 800 may be interpreted as a distributed computing system that performs processing in multiple locations. The processor 801 of the electronic device 800 may also be a processor of a cloud computing system or a processor that incorporates a blockchain.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.

Claims

1. A method of simulating myocardial blood flow, comprising:

Acquiring an angiographic image comprising a coronary artery and a left ventricle adjacent to the coronary artery, the angiographic image comprising at least one first region and at least one second region, wherein the at least one first region is located in a myocardial region of the left ventricle and the at least one second region is located in a luminal region of the coronary artery;

acquiring a first time density curve aiming at the at least one first area so as to obtain the maximum slope of the first time density curve;

Acquiring a second time density curve for the at least one second region to obtain a maximum enhancement value of the second time density curve, and

Determining the myocardial blood flow from the maximum slope and the maximum enhancement value.

2. The method of claim 1, wherein the maximum slope is determined by:

calculating a first slope of each first sub-segment for a plurality of first sub-segments of the first time density curve, and

Determining a calculated maximum slope among the plurality of first sub-line segments as the maximum slope,

The first sub-line segments are composed of connecting lines between the first monitoring CT values of every two adjacent times of the first monitoring CT values of the at least one first area in the angiography process.

3. The method of claim 1, wherein the maximum enhancement value is determined by:

and calculating the difference value between the maximum value and the minimum value of a plurality of second monitoring CT values of the at least one second region in the angiography process as the maximum enhancement value.

4. The method of claim 1, wherein determining the myocardial blood flow from the maximum slope and the maximum enhancement value comprises:

the myocardial blood flow is calculated by a weighted linear combination of the maximum slope and the maximum enhancement value.

5. The method of claim 1, wherein acquiring an angiographic image comprising a coronary artery and a left ventricle adjacent to the coronary artery further comprises:

preprocessing the angiographic image to obtain a preprocessed angiographic image;

Segmenting the preprocessed angiographic image into a myocardial region of the left ventricle and a luminal region of the coronary artery;

Extracting at least one first region of interest as said at least one first region in a myocardial region of said left ventricle, and

At least one second region of interest is extracted as the at least one second region in a luminal region of the coronary artery.

6. The method according to claim 1, wherein:

The first time density curve is acquired from a first trigger time, wherein the first trigger time is the time when a first monitoring CT value of the at least one first area exceeds a preset first trigger value for the first time, and

The second time density curve is acquired from a second trigger time, wherein the second trigger time is the time when a second monitoring CT value of the at least one second area exceeds a preset second trigger value for the first time.

7. The method according to claim 6, wherein:

Stopping acquisition of the first time density curve at a first cutoff time configured to be a time when a first monitored CT value of the at least one first region first exceeds a preset first threshold;

the second time density profile is acquired from a second cutoff time configured to be a time when a second monitored CT value of the at least one second region first exceeds a preset second threshold.

8. A device for simulating myocardial blood flow, the device comprising:

A first unit configured to acquire an angiographic image comprising a coronary artery and a left ventricle adjacent to the coronary artery, the angiographic image comprising at least one first region and at least one second region, wherein the at least one first region is located in a myocardial region of the left ventricle and the at least one second region is located in a luminal region of the coronary artery;

A second unit configured to acquire a first time density curve for the at least one first region, to obtain a maximum slope of the first time density curve;

A third unit configured to acquire a second time density curve for the at least one second region to obtain a maximum enhancement value of the second time density curve, and

A fourth unit configured to determine the myocardial blood flow from the maximum slope and the maximum enhancement value.

9. An electronic device, comprising:

at least one processor, and

At least one memory communicatively coupled to the at least one processor, wherein

The at least one memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.