WO2022121493A1

WO2022121493A1 - Systems and methods for blood vessel assessment

Info

Publication number: WO2022121493A1
Application number: PCT/CN2021/123346
Authority: WO
Inventors: Peiming QIN; Jian Guo; Xiaodong Wang
Original assignee: Shanghai United Imaging Healthcare Co., Ltd.
Priority date: 2020-12-07
Filing date: 2021-10-12
Publication date: 2022-06-16
Also published as: CN112711831B; CN112711831A

Abstract

Systems and methods for determining characteristic parameters of a blood vessel is provided. The methods may include obtaining an image of a vessel tree acquired by an imaging device. The vessel tree may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. The target blood vessel may include an inlet and an outlet. The methods may further include determining, based on the image of the vessel tree, at least part of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels; determining a flow rate boundary condition based on the at least part of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient model; and determining characteristic parameters of the target blood vessel based at least on the flow rate boundary condition.

Description

SYSTEMS AND METHODS FOR BLOOD VESSEL ASSESSMENT

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No. 202011419172.4, filed on December 7, 2020, the entire contents of which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to medical technology, and more particularly, relates to systems and methods for non-invasive functional assessment of a blood vessel based on medical image data and a blood flow simulation.

BACKGROUND

Cardiovascular diseases (CVDs) include a series of ubiquitous diseases that seriously threaten human health, especially the health of people over 50 years old. Among various CVDs, coronary artery disease (CAD) accounts for a relatively high proportion. In general, measuring a fractional flow reserve (FFR) by inserting a pressure wire into a target blood vessel (e.g., a stenosed blood vessel) has been shown to be a good option for the assessment of the CAD. At present, due to the development of medical imaging devices, various applications of calculating FFR through medical image post-processing using a computational fluid dynamics (CFD) approach have become more and more widely used. However, an average blood flow rate of the target blood vessel is always used directly as a flow rate boundary condition during the CFD approach, often leading to inaccurate simulation results. Therefore, it is desirable to develop systems and methods for improving an accuracy and/or efficiency of blood vessel simulation.

SUMMARY

According to an aspect of the present disclosure, a system is provided. The system may include at least one storage device including a set of instructions for determining one or more characteristic parameters of a target blood vessel; and at least one processor in communication with the at least one storage device. When executing the set of instructions, the at least one processor is configured to cause the system to perform operations. The operations may include obtaining an image of a vessel tree acquired by an imaging device. The vessel tree may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. The target blood vessel may include an inlet and an outlet. The operations may further include determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels. The operations may further include determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model. The operations may further include determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.

In some embodiments, to determine each feature value of a blood vessel (e.g., the target blood vessel, each branch vessel) , the at least one processor is configured to cause the system to perform operations· The operations may include sampling at least two sample points on the blood vessel in the image to obtain locations and pixel values corresponding to the at least two sample points, wherein a distance between each of the at least two sample points and a center line of the blood vessel is within a distance threshold; and determining, based on the locations and the pixel values corresponding to the at least two sample points, the feature value of the blood vessel.

In some embodiments, the determining, based on the locations and the pixel values corresponding to the at least two sample points, the feature value of the blood vessel may include determining a streamline distance between each of the at least two sample points and the inlet; performing a linear fitting operation with the at least two streamline distances as abscissa and the pixels values of the at least two pixels as ordinates to determine a fitted line; and designating a slope of the fitted line as the feature value of the blood vessel.

In some embodiments, to obtain the average blood flow rate, the operations may further comprise obtaining a plurality of image frames of a region of interest including the target blood vessel, the plurality of image frames including a first set of first image frames acquired by the imaging device at a first view angle during a first contrast filling process and a second set of second image frames acquired by the imaging device at a second view angle during a second contrast filling process, the first view angle being different from the second view angle; determining at least one first template image frame from the first set of first image frames and at least one second template image frame from the second set of second image frames satisfying a reconstruction condition to generate a 3D model for the target blood vessel; determining a start image frame and an end image frame associated with the target blood vessel in the plurality of image frames, the start image frame and the end image frame being associated with a same view angle; determining a time interval between the start image frame and the end image frame; and determining, based on a volume of the 3D model and the time interval, the average blood flow rate of the target blood vessel.

In some embodiments, the reconstruction condition may include that the target blood vessel is fully filled with a contrast agent, a vascular overlap rate of the target blood vessel is smaller than an overlap threshold, or vessel boundaries of the target blood vessel are visible.

In some embodiments, the determining a start image frame and an end image frame associated with the target blood vessel in the plurality of image frames may include for any view angle of the first view angle or the second view angle, projecting the 3D model to each image frame of the corresponding set of image frames of the view angle to determine a front location of the inlet and an end location of the outlet; for each image frame of the view angle, determining, based on pixel values in the image frame, a pixel gradient change location of the image frame; and determining an image frame whose pixel gradient change location is consistent with the front location of the inlet as the start image frame and an image frame whose pixel gradient change location is consistent with the end location of the outlet as the end image frame.

In some embodiments, the determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model may include inputting the at least part of the plurality of feature values and the average blood flow rate into the TAG model; and determining an output of the TAG model as the flow rate boundary condition.

In some embodiments, the determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model may include obtaining a second average blood flow rate of any one of the plurality of branch vessels; inputting the at least part of the plurality of feature values and the second average blood flow rate into the TAG model to determine a total blood flow rate of the inlet; and determining, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition.

In some embodiments, the determining, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition may include determining an average value of the average blood flow rate and the total blood flow rate as the flow rate boundary condition.

In some embodiments, the determining, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition may include determining, based on the total blood flow rate of the inlet, an outlet blood flow rate of the outlet; and designating the outlet blood flow rate of the outlet as the flow rate boundary condition.

In some embodiments, the determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel may include dividing a 3D model of the target blood vessel into a plurality of grids to generate a gridded 3D model; performing, based on the gridded 3D model and boundary conditions, a simulation operation of blood flow according to a simulation model, the boundary conditions including the flow rate boundary condition and a corresponding pressure boundary condition; and determining, based on a blood flow simulation result, the one or more characteristic parameters of the target blood vessel, the blood simulation result including at least pressure distribution information of the target blood vessel.

In some embodiments, the dividing the 3D model into a plurality of grids to generate a gridded 3D model may include determining multiple center points on a target center line of the target blood vessel, each of the multiple center points corresponding to a radial section; for each radial section corresponding to each of the multiple center points, determining a diameter of the radial section; determining, based on the diameter and a regularization term, a grid size associated with the radial section; and dividing, based on the multiple grid sizes, the 3D model of the target blood vessel into the plurality of grids to generate a gridded 3D model. The regularization term may be configured to stabilize a calculation precision and a calculation speed associated with the simulation operation.

In some embodiments, the one or more characteristic parameters may include a fractional flow reserve (FFR) of a reference location on the target blood vessel. The determining, based on a blood flow simulation result, the one or more characteristic parameters of the target blood vessel may include obtaining, based on the pressure distribution information of the target blood vessel, a front pressure of the inlet and a reference pressure of the reference location on the target blood vessel; and determining, based on the front pressure and the reference pressure, the FFR of the reference location.

In some embodiments, the at least one processor may be configured to cause the system to perform operations including determining whether the FFR is less than an FFR threshold; and in response to a determination that the FFR is less than the FFR threshold, determining that a blood vessel segment between the inlet and the reference location of the target blood vessel has a blockage.

In some embodiments, the imaging device may include a digital subtraction angiography (DSA) device.

According to another aspect of the present disclosure, a method for determining one or more characteristic parameters of a target blood vessel is provided. The method may be implemented on a computing device having at least one processor and at least one storage device. The method may include obtaining an image of a vessel tree acquired by an imaging device. The vessel tree may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. The target blood vessel may include an inlet and an outlet. The method may further include determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels. The method may further include determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model. The method may further include determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.

According to yet another aspect of the present disclosure, a non-transitory computer readable medium is provided, comprising at least one set of instructions, wherein when executed by at least one processor of a computing device, the at least one set of instructions direct the at least one processor to perform a method. The method may include obtaining an image of a vessel tree acquired by an imaging device. The vessel tree may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. The target blood vessel may include an inlet and an outlet. The method may further include determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels. The method may further include determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model. The method may further include determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.

According to yet another aspect of the present disclosure, a method is provided. The method may be implemented on a computing device having at least one processor and at least one storage device. The method may include obtaining at least two 2D image sequences of a target blood vessel to generate a 3D model of the target blood vessel; obtaining cross-section positions of the 3D model, the cross-section positions including a position of an inlet of the target blood vessel and a position of an outlet of the target blood vessel; calculating an average blood flow rate of blood in the target blood vessel based on values of gray gradient of the cross-section positions and target image frames corresponding to the cross-section positions; and performing a simulation operation to obtain a simulation result of the target blood vessel based on the 3D model of the target blood vessel, a preset boundary condition, and a preset simulation model. The preset boundary condition may include a flow rate boundary value and a pressure boundary value. The flow rate boundary value may be the average blood flow rate of blood in the target blood vessel.

In some embodiments, the method may further include obtaining multiple branch vessels of the target blood vessel; determining a first outlet blood flow rate of the target blood vessel by inputting multiple feature values (or slope information) of the branch vessels and the average blood flow rate of the target blood vessel into a TAG model; and performing a correction operation on the average blood flow rate to obtain a corrected average blood flow rate based on the first outlet blood flow rate of the outlet of the target blood vessel. The performing the simulation operation to obtain the simulation result of the target blood vessel based on the 3D model of the target blood vessel, the average blood flow rate, the preset boundary condition, and a preset iteration equation may include performing the simulation operation to obtain the simulation result of the target blood vessel based on the 3D model of the target blood vessel, the corrected average blood flow rate, the preset boundary condition, and the preset iteration equation.

In some embodiments, the calculating an average blood flow rate of blood in the target blood vessel based on values of gray gradient of the cross-section positions and target image frames corresponding to the cross-section positions may include determining current positions as the cross-section positions in response to determining that the values of gray gradient of the 3D model is greater than a preset threshold and obtaining the target image frames corresponding to the cross-section positions; determining a time interval between the cross-section positions based on the target image frames corresponding to the cross-section positions; and calculating the average blood flow rate of blood in the target blood vessel based on the time interval and a volume of the target blood vessel.

In some embodiments, the method may further include dividing the 3D model of the target blood vessel into a plurality of grids to generate a gridded 3D model. The performing the simulation operation to obtain the simulation result of the target blood vessel based on the 3D model of the target blood vessel, the average blood flow rate, the preset boundary condition, and the preset iteration equation may include performing the simulation operation to obtain the simulation result of the target blood vessel based on the gridded 3D model of the target blood vessel, the average blood flow rate, the preset boundary condition, and the preset iteration equation.

In some embodiments, the method may further include obtaining multiple initial 2D image sequences of a target region (e.g., an ROI) of an object using a planar contrast imaging technology, the target region including the target blood vessel; and selecting the at least two 2D image sequences from the multiple initial 2D image sequences based on view angles of the multiple initial 2D image sequences.

In some embodiments, after performing the simulation operation to obtain the simulation result of the target blood vessel based on the 3D model of the target blood vessel, the average blood flow rate, the preset boundary condition, and the preset iteration equation, the method may further include calculating one or more FFR values associated with the target blood vessel based on pressure distribution information in the simulation result and a pressure of the aorta.

In some embodiments, the method may may further include determining a status of the target blood vessel (e.g., whether the target blood vessel has a blockage) based on the one or more FFR values and a preset FFR threshold.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities, and combinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. The drawings are not to scale. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary medical system according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure;

FIG. 4A is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure;

FIG. 4B is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determining one or more characteristic parameters of a target blood vessel according to some embodiments of the present disclosure;

FIG. 6 is a schematic flowchart illustrating an exemplary process for determining an average blood flow rate of a blood vessel according to some embodiments of the present disclosure;

FIG. 7 is a scheme diagram illustrating an exemplary vessel tree according to some embodiments of the present disclosure;

FIG. 8 is a scheme diagram illustrating an exemplary rendered 3D model of a target blood vessel according to some embodiments of the present disclosure;

FIG. 9 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure; and

FIG. 10 is a schematic diagram illustrating an exemplary process for perform a simulation operation to obtain a simulation result of a target blood vessel according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well-known methods, procedures, systems, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown, but to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms “a, ” “an, ” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise, ” “comprises, ” and/or “comprising, ” “include, ” “includes, ” and/or “including, ” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It will be understood that the term “system, ” “engine, ” “unit, ” “module, ” and/or “block” used herein are one method to distinguish different components, elements, parts, sections, or assembly of different levels in ascending order. However, the terms may be displaced by another expression if they achieve the same purpose.

Generally, the word “module, ” “unit, ” or “block, ” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions. A module, a unit, or a block described herein may be implemented as software and/or hardware and may be stored in any type of non-transitory computer-readable medium or another storage device. In some embodiments, a software module/unit/block may be compiled and linked into an executable program. It will be appreciated that software modules can be callable from other modules/units/blocks or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules/units/blocks configured for execution on computing devices (e.g., processor 210 as illustrated in FIG. 2) may be provided on a computer-readable medium, such as a compact disc, a digital video disc, a flash drive, a magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that needs installation, decompression, or decryption prior to execution) . Such software code may be stored, partially or fully, on a storage device of the executing computing device, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules/units/blocks may be included in connected logic components, such as gates and flip-flops, and/or can be included in programmable units, such as programmable gate arrays or processors. The modules/units/blocks or computing device functionality described herein may be implemented as software modules/units/blocks but may be represented in hardware or firmware. In general, the modules/units/blocks described herein refer to logical modules/units/blocks that may be combined with other modules/units/blocks or divided into sub-modules/sub-units/sub-blocks despite their physical organization or storage. The description may be applicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module, or block is referred to as being “on, ” “connected to, ” or “coupled to, ” another unit, engine, module, or block, it may be directly on, connected or coupled to, or communicate with the other unit, engine, module, or block, or an intervening unit, engine, module, or block may be present unless the context clearly indicates otherwise. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

These and other features, and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

The term “image” in the present disclosure is used to collectively refer to image data (e.g., scan data, projection data) and/or images of various forms, including a two-dimensional (2D) image, a three-dimensional (3D) image, a four-dimensional (4D) , etc. The term “pixel” and “voxel” in the present disclosure are used interchangeably to refer to an element of an image. The term “region, ” “location, ” and "area" in the present disclosure may refer to a location of an anatomical structure shown in the image or an actual location of the anatomical structure existing in or on a target subject’s body, since the image may indicate the actual location of a certain anatomical structure existing in or on the target subject's body. In some embodiments, an image of a subject may be referred to as the subject for brevity. Segmentation of an image of a subject may be referred to as segmentation of the subject. For example, segmentation of an organ refers to segmentation of a region corresponding to the organ in an image.

The present disclosure provides mechanisms (which can include methods, systems, computer-readable media, etc. ) for determining one or more characteristic parameters of a target blood vessel. The methods provided in the present disclosure may include obtaining an image of a vessel tree acquired by an imaging device. The vessel tree may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. The target blood vessel may include an inlet and an outlet. The methods may further include determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels. The methods may further include determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model. The methods may further include determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.

Compared with conventional characteristic parameter determination techniques which directly use an average blood flow rate of a target blood vessel as a flow rate boundary condition during a computational fluid dynamics (CFD) approach, the systems and methods of the present disclosure optimizes the flow rate boundary condition using the TAG model based on the average blood flow rate of the target blood vessel, thereby improving an accuracy of the computational fluid dynamics. In addition, during the CFD approach, a 3D model of the target blood vessel may be adaptively divided into a plurality of grids based on diameters of radial sections of the target blood vessel, which can balance a calculation precision and a calculation speed associated with the simulation operation, thereby increasing the calculation speed while ensuring that the calculation precision meets a requirement.

FIG. 1 is a schematic diagram illustrating an exemplary medical system according to some embodiments of the present disclosure. As shown in FIG. 1, a medical system 100 may include an imaging device 110, a network 120, a terminal device 130, a processing device 140, and a storage device 150. In some embodiments, two or more components of the medical system 100 may be connected to and/or communicate with each other via a wireless connection (e.g., the network 120) , a wired connection, or a combination thereof. The connection between the components of the medical system 100 may be variable. Mere by way of example, as illustrated in FIG. 1, the processing device 140 may be connected to the imaging device 110 through the network 120. As another example, the processing device 140 may be connected to the imaging device 110 directly (as indicated by the bi-directional arrow in dotted lines linking the processing device 140 and the imaging device 110) . As a further example, the storage device 150 may be connected to the imaging device 110 directly or through the network 120. As still a further example, a terminal device (e.g., 131, 132, 133, etc. ) may be connected to the processing device 140 directly (as indicated by the bi-directional arrow in dotted lines linking the processing device 140 and the terminal device 130) or through the network 120.

The imaging device 110 may be configured to acquire scan data relating to at least part of a subject including a blood vessel. The subject may be biological or non-biological. For example, the subject may include a patient, an animal, a man-made subject, etc. As another example, the subject may include a specific portion, organ, and/or tissue of the patient. For example, the subject may include the head, the chest, the neck, the thorax, the heart, the stomach, an arm, a palm, a blood vessel, soft tissue, a tumor, nodules, or the like, or any combination thereof. In some embodiments, the imaging device 110 may include a digital subtraction angiography (DSA) device, a computed tomography (CT) device, a magnetic resonance angiography (MRA) device, or the like, or any combination thereof.

The network 120 may include any suitable network that can facilitate the exchange of information and/or data for the medical system 100. In some embodiments, one or more components of the medical system 100 (e.g., the imaging device 110, the processing device 140, the storage device 150, the terminal device 130) may communicate information and/or data with one or more other components of the medical system 100 via the network 120. For example, the processing device 140 may obtain image data from the imaging device 110 via the network 120. As another example, the processing device 140 may obtain user instruction (s) from the terminal device 130 via the network 120. The network 120 may be or include a public network (e.g., the Internet) , a private network (e.g., a local area network (LAN) ) , a wired network, a wireless network (e.g., an 802.11 network, a Wi-Fi network) , a frame relay network, a virtual private network (VPN) , a satellite network, a telephone network, routers, hubs, switches, server computers, and/or any combination thereof. For example, the network 120 may include a cable network, a wireline network, a fiber-optic network, a telecommunications network, an intranet, a wireless local area network (WLAN) , a metropolitan area network (MAN) , a public telephone switched network (PSTN) , a Bluetooth ^TM network, a ZigBee ^TM network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include wired and/or wireless network access points such as base stations and/or internet exchange points through which one or more components of the medical system 100 may be connected to the network 120 to exchange data and/or information.

The terminal device 130 may be connected to and/or communicate with the imaging device 110, the processing device 140, and/or the storage device 150. For example, the terminal device 130 may enable user interactions between a user and the medical system 100. In some embodiments, the terminal device 130 may include a mobile device 131, a tablet computer 132, a laptop computer 133, or the like, or any combination thereof. For example, the mobile device 131 may include a mobile phone, a personal digital assistant (PDA) , a gaming device, a navigation device, a point of sale (POS) device, a laptop, a tablet computer, a desktop, or the like, or any combination thereof. In some embodiments, the terminal device 130 may include an input device, an output device, etc. The input device may include alphanumeric and other keys that may be input via a keyboard, a touch screen (for example, with haptics or tactile feedback) , a speech input, an eye-tracking input, a brain monitoring system, or any other comparable input mechanism. The input information received through the input device may be transmitted to the processing device 140 via, for example, a bus, for further processing. Other types of input device may include a cursor control device, such as a mouse, a trackball, or cursor direction keys, etc. The output device may include a display, a speaker, a printer, or the like, or a combination thereof. In some embodiments, the terminal device 130 may be part of the processing device 140.

The processing device 140 may process data and/or information obtained from the imaging device 110, the storage device 150, the terminal device 130, or other components of the medical system 100. For example, the processing device 140 may optimize a flow rate boundary condition associated with a simulation model based on an average blood flow rate of a target blood vessel. The processing device 140 may determine one or more characteristic parameters of the target blood vessel based on the optimized flow rate boundary condition. As another example, the processing device 140 may determine the average blood flow rate of the target blood vessel based on a plurality of image frames acquired by the imaging device 110 at different view angle in different contrast filling process. In some embodiments, the processing device 140 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 140 may be local to or remote from the medical system 100. For example, the processing device 140 may access information and/or data from the imaging device 110, the storage device 150, and/or the terminal device 130 via the network 120. As another example, the processing device 140 may be directly connected to the imaging device 110, the terminal device 130, and/or the storage device 150 to access information and/or data. In some embodiments, the processing device 140 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or a combination thereof. In some embodiments, the processing device 140 may be implemented by a computing device 200 having one or more components as described in connection with FIG. 2.

The storage device 150 may store data, instructions, and/or any other information. In some embodiments, the storage device 150 may store data obtained from the processing device 140, the terminal device 130, and/or the storage device 150. In some embodiments, the storage device 150 may store data and/or instructions that the processing device 140 may execute or use to perform exemplary methods described in the present disclosure. In some embodiments, the storage device 150 may include a mass storage device, a removable storage device, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof. Exemplary mass storage devices may include a magnetic disk, an optical disk, a solid-state drive, etc. Exemplary removable storage devices may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM) . Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc. Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc. In some embodiments, the storage device 150 may be implemented on a cloud platform as described elsewhere in the disclosure.

In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more other components of the medical system 100 (e.g., the processing device 140, the terminal device 130) . One or more components of the medical system 100 may access the data or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be part of the processing device 140.

This description is intended to be illustrative, and not to limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. However, those variations and modifications do not depart from the scope of the present disclosure. For example, the assembly and/or function of the medical system 100 may be varied or changed according to specific implementation scenarios. In some embodiments, the medical system 100 may include one or more additional components, and/or one or more components of the medical system 100 described above may be omitted. Additionally or alternatively, two or more components of the medical system 100 may be integrated into a single component. A component of the medical system 100 may be implemented on two or more sub-components.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device on which the processing device 140 may be implemented according to some embodiments of the present disclosure. As illustrated in FIG. 2, a computing device 200 may include a processor 210, a storage 220, an input/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code) and perform functions of the processing device 140 in accordance with techniques described herein. The computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions described herein. For example, the processor 210 may process image data obtained from the imaging device 110, the terminal device 130, the storage device 150, and/or any other component of the medical system 100. In some embodiments, the processor 210 may include one or more hardware processors, such as a microcontroller, a microprocessor, a reduced instruction set computer (RISC) , an application-specific integrated circuits (ASICs) , an application-specific instruction-set processor (ASIP) , a central processing unit (CPU) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a microcontroller unit, a digital signal processor (DSP) , a field-programmable gate array (FPGA) , an advanced RISC machine (ARM) , a programmable logic device (PLD) , any circuit or processor capable of executing one or more functions, or the like, or any combinations thereof.

Merely for illustration, only one processor is described in the computing device 200. However, it should be noted that the computing device 200 in the present disclosure may also include multiple processors, and thus operations and/or method operations that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, if in the present disclosure the processor of the computing device 200 executes both operation A and operation B, it should be understood that operation A and operation B may also be performed by two or more different processors jointly or separately in the computing device 200 (e.g., a first processor executes operation A and a second processor executes operation B, or the first and second processors jointly execute operations A and B) .

The storage 220 may store data/information obtained from the imaging device 110, the terminal device 130, the storage device 150, and/or any other component of the medical system 100. In some embodiments, the storage 220 may include a mass storage device, a removable storage device, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof. In some embodiments, the storage 220 may store one or more programs and/or instructions to perform exemplary methods described in the present disclosure. For example, the storage 220 may store a program for the processing device 140 for determining a flow rate boundary condition associated with a simulation model.

The I/O 230 may input and/or output signals, data, information, etc. In some embodiments, the I/O 230 may enable a user interaction with the processing device 140. In some embodiments, the I/O 230 may include an input device and an output device. Exemplary input devices may include a keyboard, a mouse, a touch screen, a microphone, or the like, or a combination thereof. Exemplary output devices may include a display device, a loudspeaker, a printer, a projector, or the like, or a combination thereof. Exemplary display devices may include a liquid crystal display (LCD) , a light-emitting diode (LED) -based display, a flat panel display, a curved screen, a television device, a cathode ray tube (CRT) , a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., the network 120) to facilitate data communications. The communication port 240 may establish connections between the processing device 140 and the imaging device 110, the terminal device 130, and/or the storage device 150. The connection may be a wired connection, a wireless connection, any other communication connection that can enable data transmission and/or reception, and/or any combination of these connections. The wired connection may include, for example, an electrical cable, an optical cable, a telephone wire, or the like, or any combination thereof. The wireless connection may include, for example, a Bluetooth ^TM link, a Wi-Fi ^TM link, a WiMax ^TM link, a WLAN link, a ZigBee ^TM link, a mobile network link (e.g., 3G, 4G, 5G) , or the like, or a combination thereof. In some embodiments, the communication port 240 may be and/or include a standardized communication port, such as RS232, RS485, etc. In some embodiments, the communication port 240 may be a specially designed communication port. For example, the communication port 240 may be designed in accordance with the digital imaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure. In some embodiments, one or more components (e.g., the terminal device 130 and/or the processing device 140) of the medical system 100 may be implemented on the mobile device 300.

As illustrated in FIG. 3, the mobile device 300 may include a communication platform 310, a display 320, a graphic processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, and a storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300. In some embodiments, a mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM) and one or more applications 380 may be loaded into the memory 360 from the storage 390 in order to be executed by the CPU 340. The applications 380 may include a browser or any other suitable mobile apps for receiving and rendering information relating to image processing or other information from the processing device 140. User interactions with the information stream may be achieved via the I/O 350 and provided to the processing device 140 and/or other components of the medical system 100 via the network 120.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform (s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

FIGs. 4A and 4B are block diagrams illustrating exemplary processing devices according to some embodiments of the present disclosure. The

processing devices

140A and 140B may be exemplary processing devices 140 as described in connection with FIG. 1. In some embodiments, the processing device 140A may be configured to determine one or more characteristic parameters of a target blood vessel. The processing device 140B may be configured to generate an average blood flow rate of a blood vessel. In some embodiments, the

processing devices

140A and 140B may be respectively implemented on a processing unit (e.g., the processor 210 illustrated in FIG. 2 or the CPU 340 illustrated in FIG. 3) . Merely by way of example, the processing device 140A may be implemented on a CPU 340 of a terminal device, and the processing device 140B may be implemented on a computing device 200. Alternatively, the

processing devices

140A and 140B may be implemented on a same computing device 200 or a same CPU 340. For example, the

processing devices

140A and 140B may be implemented on a same computing device 200.

As illustrated in FIG. 4A, the processing device 140A may include an obtaining module 410, a feature value determination module 420, a flow rate boundary condition determination module 430, and a characteristic parameter determination module 440.

The obtaining module 410 may be configured to obtain an image of a vessel tree acquired by an imaging device. The vessel tree may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. The target blood vessel may be a trunk of the vessel tree. The target blood vessel may include an inlet and an outlet. Blood can flow into the inlet, to the outlet, and through each of the plurality of branch vessels.

The feature value determination module 420 may be configured to determine at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels based on the image of the vessel tree. For a specific vessel segment (e.g., the target blood vessel, each branch vessel) , the feature value determination module 420 may sample two or more sample points on the specific vessel segment in the image. Each sample point may include information associated with a location and a pixel value of the sample point. The feature value determination module 420 may determine the specific feature value of the specific vessel segment based on the locations and the pixel values of the sample points. For example, the feature value determination module 420 may determine a streamline distance between each sample point and a location of the inlet of the target blood vessel. The feature value determination module 420 may determine a fitted line by performing a linear fitting operation with the streamline distances of the sample points as abscissa and the pixel values of pixels corresponding to the sample points as ordinates. The feature value determination module 420 may designate a slope of the fitted line as the specific feature value of the specific vessel segment.

The flow rate boundary condition determination module 430 may be configured to determine a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model. In some embodiments, the flow rate boundary condition determination module 430 may input the at least part of the plurality of feature values and the average blood flow rate into the TAG model and directly determine an output of the TAG model as the flow rate boundary condition. In some embodiments, the flow rate boundary condition determination module 430 may obtain a second average blood flow rate of any one of the plurality of branch vessels. The flow rate boundary condition determination module 430 may input the at least part of the plurality of feature values and the second average blood flow rate into the TAG model to determine a total blood flow rate of the inlet and determine the flow rate boundary condition based on the average blood flow rate and the total blood flow rate.

The characteristic parameter determination module 440 may be configured to determine one or more characteristic parameters of the target blood vessel based at least on the flow rate boundary condition. For example, the characteristic parameter determination module 440 may perform a simulation operation of blood flow based at least on the flow rate boundary condition according to a simulation model. The characteristic parameter determination module 440 may determine the characteristic parameter (s) based on a blood flow simulation result.

As illustrated in FIG. 4B, the processing device 140B may include an obtaining module 450, a 3D model determination module 460, a time interval determination module 470, and an average blood flow rate determination module 480.

The obtaining module 450 may be configured to obtain a plurality of image frames of a region of interest (ROI) including a blood vessel. The plurality of image frames may include a first set of first image frames acquired by an imaging device at a first view angle during a first contrast filling process and a second set of second image frames acquired by the imaging device at a second view angle during a second contrast filling process.

The 3D model determination module 460 may be configured to determine at least one first template image frame from the first set of first image frames and at least one second template image frame from the second set of second image frames satisfying a reconstruction condition to generate a 3D model for the blood vessel. For example, the 3D model determination module 460 may reconstruct the 3D model based on a contour of the blood vessel, a center line of the blood vessel, and/or the view angles of the template image frame (s) .

The time interval determination module 470 may be configured to determine a start image frame and an end image frame associated with the blood vessel in the plurality of image frames and determine a time interval between the start image frame and the end image frame. For example, for a specific view angle, the time interval determination module 470 may project the 3D model to a plane where each specific image frame of a specific set of specific image frames of the specific view angle is located. The time interval determination module 470 may determine a front location of the inlet and an end location of the outlet of the blood vessel on each specific image frame. For each specific image frame of the specific view angle, the time interval determination module 470 may determine a pixel gradient change location of the specific image frame based on pixel values in the specific image frame. The time interval determination module 470 may determine a specific image frame whose pixel gradient change location is consistent with the front location of the inlet as the start image frame. The time interval determination module 470 may determine another specific image frame whose pixel gradient change location is consistent with the end location of the outlet as the end image frame. In some embodiments, the time interval determination module 470 may determine a specific image frame whose pixel gradient change location is closest to the front location of the inlet as the start image frame and an image frame whose pixel gradient change location is closest to the end location of the outlet as the end image frame. The time interval determination module 470 may count image frames between the stand image frame and the end image frame. The time interval determination module 470 may determine the time interval based on a count or number of image frames between the start image frame and the end image frame.

The average blood flow rate determination module 480 may be configured to determine an average blood flow rate of the blood vessel based on a volume of the 3D model and the time interval.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, the processing device 140A and/or the processing device 140B may share two or more of the modules, and any one of the modules may be divided into two or more units. For instance, the

processing devices

140A and 140B may share a same obtaining module, that is, the obtaining module 410 and the obtaining module 450 are a same module. In some embodiments, the processing device 140A and/or the processing device 140B may include one or more additional modules, such as a storage module (not shown) for storing data. In some embodiments, the processing device 140A and the processing device 140B may be integrated into one processing device 140.

FIG. 5 is a flowchart illustrating an exemplary process for determining one or more characteristic parameters of a target blood vessel according to some embodiments of the present disclosure. In some embodiments, a process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage device 150, storage 220, or storage 390. The processing device 140A, the processor 210, and/or the CPU 340 may execute the set of instructions, and when executing the instructions, the processing device 140A, the processor 210, and/or the CPU 340 may be configured to perform the process 500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order of the operations of the process 500 illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing device 140A (e.g., the obtaining module 410) may obtain an image of a vessel tree acquired by an imaging device. In some embodiments, the image may be obtained from the imaging device (e.g., the imaging device 110) , the storage device 150, or any other storage device. The imaging device may include a DSA device, a CT device, etc., as described elsewhere in the present disclosure (e.g., FIG. 1 and the descriptions thereof) .

In some embodiments, the image of the vessel tree may be generated based on imaging data (e.g., projection data) acquired by the imaging device (e.g., a DSA device) after a contrast agent (or tracer) is injected into a subject including the vessel tree. The imaging device may acquire the imaging data after the contrast agent reaches any outlet of the vessel tree, i.e., after the vessel tree is fully filled with the contrast agent. In some embodiments, the subject may include the neck, the heart, the head, the abdomen, a lung, or the like, or any combination thereof. The vessel tree may include a vessel tree of the heart, the neck, the head, the abdomen, etc. For example, the vessel tree may include a coronary artery (or vein) , a carotid artery (or vein) , a cerebral artery (or vein) , an abdominal aorta (or vein) , a hepatic artery (or vein) , a splenic artery (or vein) , a renal artery (or vein) , etc., or a portion thereof.

The vessel tree (e.g., a vessel tree 700 illustrated in FIG. 7) may include a target blood vessel and a plurality of branch vessels connected to the target blood vessel. In some embodiments, the vessel tree may include a single target blood vessel. In some embodiments, the vessel tree may include two or more target blood vessels. The target blood vessel may be a trunk of the vessel tree. The target blood vessel may include an inlet and an outlet. Blood can flow into the inlet, to the outlet, and through each of the plurality of branch vessels. For example, as illustrated in FIG. 7, for the vessel tree 700, blood can flow through the inlet 712 to the outlet 714 and the first branch vessel 720, the second branch vessel 730, and the third branch vessel 740 (as indicated by dotted arrows) . In some embodiments, the processing device 140A may select the plurality of branch vessels from a plurality of initial branch vessels satisfying a compliance condition. In some embodiments, the compliance condition may include that an average diameter of the initial branch vessel is greater than a diameter threshold, a contour of the initial branch vessel is visible, a location of the initial branch vessel is within a distance from a lesion, etc.

The image of the vessel tree may include a plurality of pixels with pixel values or characteristics (e.g., luminance values, gray values, colors (or RGB values) , saturation values, etc. ) associated with a concentration of the contrast agent. Each pixel in the image may represent a concentration (i.e., a radioactivity) of the contrast agent. In some embodiments, the target blood vessel or each branch vessel may also be referred to as a vessel segment of the vessel tree. For example, as illustrated in FIG. 7, the vessel tree 700 may include four vessel segments including the target blood vessel 710, the first branch vessel 720, the second branch vessel 730, and the third branch vessel 740. Different vessel segments may correspond to different concentrations of the contrast agent. In some embodiments, the image of the vessel tree may be a two-dimensional (2D) image, a three-dimensional (3D) image, a four-dimensional (4D) image (e.g., a temporal series of 3D images) , etc.

In 520, the processing device 140A (e.g., the feature value determination module 420) may determine at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels based on the image of the vessel tree.

A feature value of a vessel segment (e.g., the target blood vessel or each vessel branch) may be associated with a location of the vessel segment and/or a concentration of the contrast agent in the vessel segment. In some embodiments, for a specific vessel segment, the processing device 140A may determine the specific feature value of the specific vessel segment by sampling (or selecting) two or more sample points (or pixels) on the specific vessel segment in the image. For example, the processing device 140A may determine each sample point at any region on the specific vessel segment. Each sample point may include information associated with a location and a pixel value of the sample point. The processing device 140A may determine the specific feature value of the specific vessel segment based on the locations and the pixel values of the sample points. In some embodiments, a distance between each sample point and a center line of the specific vessel segment may be within a distance threshold. For example, the sample point may be close to or locate at the center line of the specific vessel segment.

In some embodiments, the processing device 140A may determine a streamline distance between each sample point and a location of the inlet of the target blood vessel. As used herein, a location of the inlet of the target blood vessel may refer to a location of a center point corresponding to the inlet. A streamline distance between two points on a vessel segment (or two vessel segments) may refer to a distance traveled by blood from one of the two points of the vessel segment to the other point. For example, as illustrated in FIG. 7, a streamline distance between a point A on the target blood vessel (e.g., the location of the inlet) and a point B on the second branch vessel 730 may be a length of a center line between the point A and the point B. The processing device 140A may determine a fitted line by performing a linear fitting operation with the streamline distances of the sample points as abscissa and the pixel values of pixels corresponding to the sample points as ordinates. The processing device 140A may designate a slope of the fitted line as the specific feature value of the specific vessel segment. Specifically, the processing device 140A may draw a scatter diagram of the pixel values versus the streamline distances. The processing device 140A may perform the linear fitting operation based on the scatter diagram. In some embodiments, a fitting technique used in the linear fitting operation may include using an ordinary least square technique, a linear interpolation technique, a line regression technique, or the like, or any combination thereof. In some embodiments, a feature value of a specific blood segment (e.g., a branch vessel) may also be referred to as slope information of the specific blood segment.

In some embodiments, the processing device 140A may determine a streamline distance between each sample point and an inlet of the specific vessel segment. The processing device 140A may determine the feature value of the specific vessel segment based on the streamline distances and the pixel values of the sample points. For example, for each branch vessel, the processing device 140A may determine a streamline distance between each sample point on the branch vessel and an inlet (also referred to as a branch inlet) of the branch vessel. The processing device 140A may determine a feature value of the branch vessel based on the streamline distances and the pixel values of the sample points. Meanwhile, for the target blood vessel, the processing device 140A may determine a streamline distance between each sample point and the inlet of the target blood vessel. The processing device 140A may determine a feature value of the target blood vessel based on the streamline distances and the pixel values of the sample points.

In 530, the processing device 140A (e.g., the flow rate boundary condition determination module 430) may determine a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model.

In some embodiments, the processing device 140A may obtain the average blood flow rate of the target blood vessel from the storage device 150 or any other external storage device. In some embodiments, the average blood flow rate of the target blood vessel may be determined based on a 3D model of the target blood vessel. For example, a time interval for blood to flow from the inlet to the outlet of the target blood vessel may be determined. The average blood flow rate may be determined based on a volume of the 3D model and the time interval. More descriptions regarding the determination of the average blood flow rate may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .

The TAG model may be associated with a process of gradual fading of a contrast agent in a blood vessel (e.g., the vessel tree) . The TAG model may be configured to determine a total blood flow rate of the inlet of the target blood vessel (or the vessel tree) based on the at least part of the plurality of feature values and a known blood flow rate of an outlet of a known vessel segment (i.e., the target blood vessel or each branch vessel) in the vessel tree. In some embodiments, the TAG model may include a first constraint function and a second constraint function. The first constraint function may represent a mapping relationship between blood flow rates of outlets (also referred to as outlet blood flow rates) of the vessel segments (including the target blood vessel and the plurality of the branch vessels) and the feature values of the vessel segments. The second constraint function may represent a mapping relationship among the outlet blood flow rates and the total blood flow rate. The total blood flow rate may be associated with a total volume of the vessel segments (or the vessel tree) and a total time interval for blood (or the contrast agent) to flow through the vessel tree. In some embodiments, the first constraint function may be denoted as Equation (1) as follows:

where u ₁ denotes a known outlet blood flow rate of the known vessel segment; u _x denotes an x-th outlet blood flow rate of an x-th vessel segment other than the known vessel segment; k ₁ denotes a feature value of the known vessel segment; k _x denotes an x-th feature value of the x-th vessel segment, and n denotes a count or number of the vessel segments, wherein n is an integer greater than 2. The second constraint function may be denoted as Equation (2) as follows:

where u _total denotes the total blood flow rate; V _total denotes the total volume of the vessel tree; and Δt denotes the total time interval for blood to flow through the vessel tree.

In some embodiments, the known vessel segment may be the target blood vessel. The processing device 140A may designate the average blood flow rate of the target blood vessel as the known outlet blood flow rate. The processing device 140A may input the at least part of the plurality of feature values (e.g., a feature value of the target blood vessel and a feature value of at least one of the branch vessels) and the average blood flow rate into the TAG model. The processing device 140A may directly determine an output of the TAG model as the flow rate boundary condition. That is, the processing device 140A may determine the total blood flow rate of the inlet of the target blood vessel as the flow rate boundary condition. In some embodiments, the processing device 140A may determine an average value of the average blood flow rate of the target blood vessel and the total blood flow rate as the flow rate boundary condition. In some embodiments, the processing device 140A may correct the total blood flow rate and determine the corrected total blood flow rate as the flow rate boundary condition. For example, the processing device 140A may determine a correction deviation value being set according to a default setting of the medical system 100 or preset by a user or operator via the terminal device 130. The processing device 140A may correct the total blood flow rate based on the correction deviation value.

In some embodiments, the known vessel segment may be any one of the plurality of branch vessels. The processing device 140A may obtain an average blood flow rate (also be referred to as a second average blood flow rate) of the known branch vessel. The determination of the second average blood flow rate of the known branch vessel may be similar to the determination of the average blood flow rate of the target blood vessel. The processing device 140A may input the at least part of the plurality of feature values and the second average blood flow rate into the TAG model to determine a total blood flow rate of the inlet. In some embodiments, the processing device 140A may determine the total blood flow rate of the inlet as the flow rate boundary condition. In some embodiments, the processing device 140A may determine, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition. In some embodiments, the processing device 140A may determine an average value of the average blood flow rate of the target blood vessel and the total blood flow rate as the flow rate boundary condition. In some embodiments, the processing device 140A may determine, based on the total blood flow rate of the inlet, an outlet blood flow rate (also referred to as a target outlet blood flow rate) of the outlet of the target blood vessel. In some embodiments, the processing device 140A may determine the target outlet blood flow rate according to Equation (3) as follows:

where u _outlet denotes the target outlet blood flow rate of the target blood vessel and k _outlet denotes a feature value of the target blood vessel, wherein k _outlet may be included in k ₂, …, k _x, …, k _n. The processing device 140A may designate the target outlet blood flow rate as the flow rate boundary condition or determine an average value of the average blood flow rate of the target blood vessel and the target outlet blood flow rate as the flow rate boundary condition.

In 540, the processing device 140A (e.g., the characteristic parameter determination module 440) may determine one or more characteristic parameters of the target blood vessel based at least on the flow rate boundary condition. In some embodiments, the one or more characteristic parameters may include a flow rate (or flow rate distribution information) , a pressure (or pressure distribution information) , one or more fractional flow reserve (FFR) values, a force (or force distribution information) , or the like, or any combination thereof. In some embodiments, the one or more characteristic parameters may also be referred to as a simulation result.

In some embodiments, the processing device 140A may perform a simulation operation of blood flow based at least on the flow rate boundary condition according to a simulation model. The processing device 140A may determine the characteristic parameter (s) based on a blood flow simulation result. For example, the processing device 140A may determine target flow rate distribution information and target pressure distribution information by performing the simulation operation according to the simulation model. The processing device 140A may determine the one or more characteristic parameters based on the target flow rate distribution information and/or the target pressure distribution information. In some embodiments, the simulation model may include a machine learning model reconstructed based on a neural network model. Exemplary neural network models may include a convolutional neural network model, a recurrent neural network model, a reinforcement learning neural model, a transfer learning network model, etc.

In some embodiments, the simulation operation performed on the simulation model may be associated with the flow rate boundary condition and a corresponding pressure boundary condition. For example, if the flow rate boundary condition is the total blood flow rate of the inlet of the target blood vessel, the corresponding pressure boundary condition may be a pressure of the outlet of the target blood vessel. As another example, if the flow rate boundary condition is the outlet blood flow rate of the outlet of the target blood vessel, the corresponding pressure boundary condition may be a pressure of the inlet of the target blood vessel. In some embodiments, for a person, if the pressure boundary condition is associated with the inlet of the target blood vessel, the processing device 140A may determine a pressure of the aorta of the heart as the pressure boundary condition. The pressure of the aorta of the heart may be acquired by a sphygmomanometer. In some embodiments, if the pressure boundary condition is associated with the outlet of the target blood vessel, the pressure boundary condition may be determined as a constant of 0.

In some embodiments, the simulation operation may include a plurality of iterations. The processing device 140A may perform the plurality of iterations based on a 3D model of the target blood vessel. In some embodiments, the processing device 140A may divide the 3D model of the target blood vessel into a plurality of grids to generate a gridded 3D model. The processing device 140A may perform the simulation operation based on the gridded 3D model, the flow rate boundary condition, and the corresponding pressure boundary condition. The processing device 140A may determine a blood flow simulation result including at least one of the target flow rate distribution information or the target pressure distribution information.

As used herein, a grid may refer to a tetrahedral grid. Each grid may have a grid size. Each grid may include information of a flow rate and a pressure. In some embodiments, a grid size of a grid may be a maximum size of a tetrahedral grid calculated based on the 3D model. The processing device 140A may generate the gridded 3D model based on the grid sizes of the plurality of grids. A density or precision of the grids may be determined by adjusting the grid sizes. The density or precision of the grids may be associated with a calculation precision of the simulation operation. In some embodiments, different locations on the target blood vessel may correspond to different grid sizes. For example, grid sizes of a lesion with blood vessel stenosis or the outlet of the target blood vessel may be greater than that of other portions of the target blood vessel. In some embodiments, the grid sizes may be a default setting of the medical system 100 or preset by a user via a terminal device. In some embodiments, the processing device 140A may adaptively determine the plurality of grids (or grid sizes) . For example, the processing device 140A may determine multiple center points on a target center line of the target blood vessel. Each center point may correspond to a radial section. The processing device 140A may determine a diameter of each radial section. The processing device 140A may determine a grid size associated with each radial section based on the diameter and a regulation term. The regulation term may be configured to stabilize a calculation precision and a calculation speed associated with the simulation operation. For a same calculation speed, the greater the diameter of the radial section is, the greater the grid size associated with the radial section may be. For a same diameter, the greater the grid size is, the faster the calculation speed may be while the worse the calculation precision may be. In some embodiments, the multiple center points may be a default setting of the medical system 100 or preset by a user via a terminal device. In some embodiments, the multiple center points may be determined based on a detection model. For example, the processing device 140A may determine center points corresponding to locations connected to branch vessels as the multiple center points. Exemplary detection model may include a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a deep neural network (DNN) model, a feedback neural network, a back propagation (BP) neural network, a dyadic wavelet transform algorithm, etc.

It should be noted that during the simulation operation, due to the 3D model of the target blood vessel is adaptively divided into the plurality of grids based on diameters of radial sections of the target blood vessel, which can balance a calculation precision and a calculation speed associated with the simulation operation, thereby increasing the calculation speed while ensuring that the calculation precision meets a requirement and ensuring the accuracy of the blood flow simulation result.

In some embodiments, during the simulation operation, one or more parameter values of the simulation model may be iteratively updated. Before the plurality of iterations start, the parameter values of the simulation model may be initialized. The processing device 140A may input the gridded 3D model, the flow rate boundary conduction, and the pressure boundary condition into the simulation model to perform the plurality of iterations. Specifically, the processing device 140A may perform the plurality of iterations according to a mass conservation equation, a momentum conservation equation, an energy conservation equation, a Navier-Stokes equation, or the like, or any combination. For an i-th iteration, the simulation model may output an i-th iteration result including intermediate flow rate distribution information and intermediate pressure distribution information of the target blood vessel (or associated with the plurality of grids) . The intermediate flow rate distribution information may include an intermediate flow rate corresponding to the flow rate boundary condition and the intermediate pressure distribution information may include an intermediate pressure corresponding to the pressure boundary condition. For example, if the flow rate boundary condition is associated with the inlet of the target blood vessel and the pressure boundary condition is associated with the outlet of the target blood vessel, the intermediate flow rate may be a flow rate of the inlet and the intermediate pressure may be a pressure of the outlet. The intermediate flow rate may then be compared with the flow rate boundary condition and the intermediate pressure may then be compared with the pressure boundary condition. If a first difference between the intermediate flow rate and the flow rate boundary condition exceeds a first threshold in a current iteration, and a second difference between the intermediate pressure and the pressure boundary condition exceeds a second threshold in the current iteration, parameter values of the simulation model may be adjusted and/or updated in order to decrease the first difference to be less than the first threshold and the second difference to be less than the second threshold. Accordingly, in the next iteration, the processing device 140A may determine an updated intermediate flow rate and an updated intermediate pressure of the target blood vessel according to the mass conservation equation, the momentum conservation equation, the energy conservation equation, the Navier-Stokes equation, etc., as described above.

The plurality of iterations may be performed to update the parameter values of the simulation model until a termination condition is satisfied. The termination condition may relate to the first difference and the second difference, or an iteration count of the simulation operation. For example, the termination condition may be satisfied if the first difference is less than the first threshold and the second difference is less than the second threshold. As another example, the termination condition may be satisfied when a specified number (or count) of iterations are performed during the execution of the simulation operation. The simulation model may output the blood flow simulation result including target flow rate distribution information and target pressure distribution information based on the updated parameter values.

In some embodiments, the simulation model may output an (i+1) -th iteration result. The processing device 140A may determine a residual between the i-th iteration result and the (i+1) -th iteration result. The processing device 140A may determine that the termination condition is satisfied if the residual is less than a threshold. The processing device 140A may determine the (i+1) -th iteration result as the target flow rate distribution information and the target pressure distribution information.

In some embodiments, for an FFR of a reference location on the target blood vessel, the processing device 140A may determine a first pressure of the reference location and a second pressure of the inlet of the target blood vessel based on the target pressure distribution information. The processing device 140A may determine the FFR of the reference location corresponding to the inlet of the target blood vessel based on the first pressure and the second pressure. For example, the processing device 140A may determine the FFR according to Equation (4) as follows:

where P _first denotes the first pressure of the reference location and P _second denotes the second pressure of the inlet of the target blood vessel. In some embodiments, the processing device 140A may further determine whether the FFR is less than an FFR threshold (e.g., 75%, 80%, etc. ) . In response to a determination that the FFR is less than the FFR threshold, the processing device 140A may determine that a blood vessel portion between the inlet of the target blood vessel and the reference location of the target blood vessel has a blockage and a function of the target blood vessel may be abnormal.

In some embodiments, if the vessel tree is the coronary artery or a portion thereof, the processing device 140A may determine a pressure of the aorta (e.g., measured by a sphygmomanometer) as the first pressure of the inlet. The processing device 140A may determine the FFR of the reference location based on the pressure of the aorta and the second pressure of the reference location.

In some embodiments, the processing device 140A may generate a streamline distribution result of the target blood vessel based on target flow rate distribution information. As used herein, the streamline distribution result may refer to a performance of magnitudes and directions of blood flow rates of blood in the target blood vessel at a certain time point.

In some embodiments, the processing device 140A may determine a force distribution result based on the target flow rate distribution information, the 3D model of the target blood vessel, and a blood viscosity of blood in the target blood vessel. The blood viscosity may be preset by a user or an operator. In some embodiments, the blood viscosity may be a parameter during the simulation operation. The processing device 140A may determine forces of blood in the target blood vessel at different locations on a blood vessel wall.

It should be noted that the above description regarding the process 500 is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, one or more operations may be omitted and/or one or more additional operations may be added. For example, the process 500 may further include an operation to render the 3D model of the target blood vessel based on the one or more characteristic parameters. The process 500 may further include transmitting the one or more characteristic parameters and/or the rendered 3D model (e.g., the rendered 3D model 800) to a terminal device (e.g., the terminal device 130) of a user. The user may view one or more characteristic parameters and/or the rendered 3D model via the terminal device to diagnose the subject.

FIG. 6 is a schematic flowchart illustrating an exemplary process for determining an average blood flow rate of a blood vessel according to some embodiments of the present disclosure. In some embodiments, a process 600 may be implemented as a set of instructions (e.g., an application) stored in the storage device 150, storage 220, or storage 390. The processing device 140B, the processor 210, and/or the CPU 340 may execute the set of instructions, and when executing the instructions, the processing device 140B, the processor 210, and/or the CPU 340 may be configured to perform the process 600. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order of the operations of process 600 illustrated in FIG. 6 and described below is not intended to be limiting. In some embodiments, the average blood flow rate of the target blood vessel or the second average blood flow rate of the known branch vessel described elsewhere in the present disclosure (e.g., operation 530 illustrated in FIG. 5) may be obtained according to the process 600. In some embodiments, the process 600 may be performed by another device or system other than the medical system 100, e.g., a device or system of a vendor of a manufacturer. For illustration purposes, the implementation of the process 600 by the processing device 140B is described as an example.

In 610, the processing device 140B (e.g., the obtaining module 450) may obtain a plurality of image frames of a region of interest (ROI) including a blood vessel. In some embodiments, each image frame may also be referred to as a 2D image. The plurality of image frames may include a first set of first image frames acquired by an imaging device at a first view angle during a first contrast filling process and a second set of second image frames acquired by the imaging device at a second view angle during a second contrast filling process. The first view angle may be different from the second view angle. In some embodiments, an angle difference between the first view angle and the second view angle may be greater than or equal to an angle threshold, for example, 10°, 15°, 20°, 30°, 50°, 60°, etc.

As used herein, a contrast filling process may refer to a process after a contrast agent being injected into the ROI including the blood vessel. In other words, the first set of first image frames and the second set of second image frames may be acquired by the imaging device after the contrast agent is injected into the ROI. That is, for a specific set of specific image frames acquired during a specific contrast filling process, the specific image frames may be acquired at different time points. Each specific image frame may correspond to a unique time point which indicates a unique position of the contrast agent in the blood vessel. As used herein, a position of the contrast agent may refer to a foremost position of the contrast agent in a blood flow direction. In some embodiments, a position of the contrast agent in an image frame may also be referred to as a pixel gradient change location of the image frame. Any two image frames acquired in a same contrast filling process may be configured to determine a time interval for blood to flow from one location corresponding to one image frame acquired at an earlier time point to another location corresponding to another image frame acquired at a later time point. In some embodiments, each set of image frames may be acquired according to a desired time sequence, for example, a uniform time sequence, a default setting time sequence, etc. For example, for a uniform time sequence, the imaging device may acquire 30 image frames in one minute. Each two adjacent image frames may be separated by 2 seconds.

In some embodiments, the ROI may include the heart, the neck, the head, the abdomen, a lung, or the like, or any combination thereof. In some embodiments, the processing device 140B may select the blood vessel from the ROI based on a location of the blood vessel, a length of the blood vessel, or the like, or any combination thereof. For example, the processing device 140B may select the blood vessel to have a blockage or be connected with a branch vessel. In some embodiments, the blood vessel may include a coronary artery (or vein) , a carotid artery (or vein) , a cerebral artery (or vein) , an abdominal aorta (or vein) , a hepatic artery (or vein) , a splenic artery (or vein) , a renal artery (or vein) , etc., or a portion thereof. The blood vessel may include an inlet and an outlet. Blood can flow through the inlet to the outlet of the blood vessel.

Each image frame (e.g., each first image frame or each second image frame) may include a plurality of pixels with pixel values or characteristics (e.g., luminance values, gray values, color information (e.g., RGB values) , saturation values, etc. ) associated with a concentration of the contrast agent. Each pixel in each image frame may represent a concentration (i.e., a radioactivity) of the contrast agent. Different image frames acquired in a same contrast filling process may include different representations of different portions of the blood vessel. For example, one image frame may include a representation of half of the blood vessel, while another image frame may include a complete representation of the blood vessel.

In 620, the processing device 140B (e.g., the 3D model determination module 460) may determine at least one first template image frame from the first set of first image frames and at least one second template image frame from the second set of second image frames satisfying a reconstruction condition to generate a 3D model for the blood vessel.

In some embodiments, the reconstruction condition may include that the blood vessel is fully filled with the contrast agent, a vascular overlap rate of the blood vessel is smaller than an overlap threshold, a boundary (also referred to as a vessel boundary) of the blood vessel is visible, a table of the imaging device does not move when acquiring image frames during a contrast filling process, or the like, or any combination thereof. As used herein, the blood vessel being fully filled with the contrast agent may indicate that the template image frame (s) have a complete representation of the blood vessel. A vascular overlap rate may refer to a ratio of an area covered by any other blood vessels to an area of the blood vessel. The smaller the vascular overlap rate of the blood vessel is, the more accurate the reconstruction of the 3D model may be. In some embodiments, the overlap threshold may be set according to a default setting of the medical system 100 or preset by a user or operator via the terminal device 130. For example, the overlap threshold may be 1%, 3%, 5%, 10%, 15%, etc.

In some embodiments, the first template image frame (s) and/or the second template image frame (s) may be determined automatically by the processing device 140B and/or manually by a user through a terminal device. For example, the processing device 140B may select the first template image frame (s) and/or the second template image frame (s) from the plurality of image frames according to a request triggered by a user or operator via a terminal device (e.g., the terminal device 130) . As another example, the processing device 140B may determine the first template image frame (s) and/or the second template image frame (s) according to a default setting of the medical system 100, for example, the processing device 140B may determine an image frame corresponding to a time point that after the blood vessel is fully filled with the contrast agent. As a further example, the user may adjust the first template image frame (s) and/or the second template image frame (s) determined by the processing device 140B via the terminal device.

The 3D model may be reconstructed based on the first template image frame (s) and/or the second template image frame (s) according to a 3D reconstruction technique. In some embodiments, the 3D reconstruction technique may include a surface reconstruction technique, a volume reconstruction technique, or the like, or a combination thereof. In some embodiments, the 3D model may be reconstructed based on a contour of the blood vessel, a center line of the blood vessel, and/or the view angles of the template image frame (s) . In some embodiments, the contour of the blood vessel in each of the first template image frame (s) and the second template image frame (s) may be determined based on a pixel value gradient, a pixel value threshold, etc. In some embodiments, the contour of the blood vessel may be determined using an image segmentation algorithm. Exemplary image segmentation algorithms may include a threshold-based segmentation algorithm, a compression-based algorithm, an edge detection algorithm, a machine learning-based segmentation algorithm, etc. The processing device 140B may identify the center line of the blood vessel based on the contour of the blood vessel. For example, the processing device 140B may determine a plurality of center points on a plurality of radial sections of the blood vessel. The processing device 140B may connect the plurality of center points one by one to generate the center line. In some embodiments, to obtain a more accurate center line and contour, the processing device 140B may further perform a post-processing operation on the contour and/or the center line of the blood vessel. The processing device 140B may reconstruct the 3D model based on the processed contour of the blood vessel, the processed center line of the blood vessel, and/or the view angles of the template image frame (s) . In some embodiments, the post-processing operations may include a smoothing operation, a resampling operation, a denoising operation, or the like, or any combination thereof. As used herein, the smoothing operation may refer to perform a smoothing calculation based on an average of two adjacent points of the center line and/or the contour. The resampling operation may refer to redetermine information of points of the center line and/or the contour.

It should be noted that in the present disclosure, since the first template image frame (s) and/or the second template image frame (s) are determined based on the reconstruction condition, the 3D model of the blood vessel may have a relatively high definition, thereby improving the efficiency and accuracy of subsequent calculations (e.g., simulation calculations of the processing device 140A) .

In 630, the processing device 140B (e.g., the time interval determination module 470) may determine a start image frame and an end image frame associated with the blood vessel in the plurality of image frames. As used herein, the start image frame may refer to an image frame in which a position of the contrast agent is at the inlet of the blood vessel. The end frame may refer to an image frame in which a position of the contrast agent is at the outlet of the blood vessel. In some embodiments, the position of the contrast agent at the inlet of the blood vessel may also be referred to as a cross-section position of the 3D model. The position of the contrast agent at the outlet of the blood vessel may also be referred to as another cross-section position of the 3D model. The start image frame and the end image frame may be associated with a same view angle. In other words, the start image frame and the end image frame may be determined from the first set of first image frames or the second set of second image frames. In some embodiments, the imaging device may acquire a third set of third image frames at a view angle other than the first view angle and the second view angle during a third contrast filling process. The start image frame and the end image frame may be determined from the third set of third image frames.

For a specific view angle, the processing device 140B may project the 3D model to a plane where each specific image frame of a specific set of specific image frames of the specific view angle is located. The processing device 140B may determine a front location of the inlet and an end location of the outlet of the blood vessel on each specific image frame. For each specific image frame of the specific view angle, the processing device 140B may determine a pixel gradient change location of the specific image frame based on pixel values in the specific image frame. The processing device 140B may determine a specific image frame whose pixel gradient change location is consistent with the front location of the inlet as the start image frame. The processing device 140B may determine another specific image frame whose pixel gradient change location is consistent with the end location of the outlet as the end image frame. In some embodiments, the processing device 140B may determine a specific image frame whose pixel gradient change location is closest to the front location of the inlet as the start image frame and an image frame whose pixel gradient change location is closest to the end location of the outlet as the end image frame. As used herein, a pixel gradient change location of the specific image frame may refer to a foremost position of the contrast agent in a blood flow direction in the specific image frame. In some embodiments, the processing device 140B may determine the pixel gradient change location according to a Gaussian filtering algorithm, a mean filtering algorithm, or the like, or a combination thereof. In some embodiments, the processing device 140B may determine a location where a pixel gradient value exceeds a pixel gradient threshold as the pixel gradient change location.

In some embodiments, the start image frame and the end image frame may be selected by a user or operator via a terminal device.

In 640, the processing device 140B (e.g., the time interval determination module 470) may determine a time interval between the start image frame and the end image frame. The time interval between the start image frame and the end image frame may refer to a time for the contrast agent to flow from the inlet to the outlet of the blood vessel.

In some embodiments, the processing device 140B may count image frames between the stand image frame and the end image frame. The processing device 140B may determine the time interval based on a count or number of image frames between the start image frame and the end image frame. For example, for a set of image frames acquired at equal intervals, the processing device 140B may determine the time interval based on the count image frames between the stand image frame and the end image frame and a time interval between each two adjacent image frames.

In 650, the processing device 140B (e.g., the average flow rate determination module 480) may determine an average blood flow rate of the blood vessel based on a volume of the 3D model and the time interval.

In some embodiments, the volume of the 3D model of the blood vessel may refer to a volume between the start frame and the end frame within the 3D model. The volume of the 3D model may be obtained using a divergence theorem and/or using an integral of the radius and length of the center line of the blood vessel.

The processing device 140B may determine the average blood flow rate based on Equation (5) as follows:

where u denotes the average blood flow rate of the blood vessel; V denotes the volume of the 3D model of the blood vessel; and Δt denotes the time interval between the start image frame and the end image frame.

It should be noted that the above description regarding the process 600 is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure.

FIG. 7 is a schematic diagram illustrating an exemplary vessel tree according to some embodiments of the present disclosure. As illustrated in FIG. 7, a vessel tree 700 may include a target blood vessel 710 and a plurality of branch vessels (e.g., a first branch vessel 720, a second branch vessel 730, a third branch vessel 740, etc. ) connected to the target blood vessel 710. The target blood vessel 710 is a trunk of the vessel tree 700. The target blood vessel 710 includes an inlet 712 and an outlet 714. Blood can flow through the inlet 712 to the outlet 714 and the plurality of branch vessels (as indicated by dotted arrows) . In some embodiments, each of the target blood vessel 710 and the plurality of branch vessels may be referred as a vessel segment. In other words, the target blood vessel 710 may be referred to as a vessel segment. The first branch vessel 720, the second branch vessel 730, or the third branch vessel 740 may also be referred to as a vessel segment. For each branch vessel, an end of the branch vessel that connects to the target blood vessel 710 is a corresponding inlet of the branch vessel. For example, the part indicated by arrow 742 may be the inlet of the third branch vessel 740. Each branch vessel has a corresponding inlet and a corresponding outlet.

FIG. 8 is a schematic diagram illustrating an exemplary rendered 3D model of a target blood vessel according to some embodiments of the present disclosure. As illustrated in FIG. 8, a rendered 3D model 800 may be generated based on a plurality of FFRs (i.e., characteristic parameters) using different voxels with different voxel values (or different colors) . Each voxel value (or color) of one voxel may indicate an FFR value of a location of the voxel. A user (e.g., a doctor) can directly diagnose a subject (e.g., a patient) including the target blood vessel based on the voxel values (or the colors) . For example, a color of blue may indicate an FFR value smaller than 75% (e.g., a conventional parameter used to judge whether a blood vessel is blocked) , and a color of red may indicate an FFR value greater than 75%. The rendered 3D model 800 may be transmitted to a terminal device for display. A user can directly diagnose the subject according to the colors.

FIG. 9 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure. As illustrated in FIG. 9, the processing device 140 may include a constructing module 910, a determination module 920, a calculation module 930, and a simulation module 940.

The constructing module 910 may be configured to construct a 3D model of a target blood vessel based on at least two 2D image sequences of the target blood vessel.

The determination module 920 may be configured to obtain cross-section positions of the 3D model. The cross-section positions may include a position of an inlet of the target blood vessel and a position of an outlet of the target blood vessel.

The calculation module 930 may be configured to calculate an average blood flow rate of blood in the target blood vessel based on target image frames corresponding to the cross-section positions.

The simulation module 940 may be configured to perform a simulation operation to obtain a simulation result of the target blood vessel based on the 3D model of the target blood vessel, a preset boundary condition, and a preset simulation model. The preset boundary condition may include a flow rate boundary value and a pressure boundary value. The flow rate boundary value may be the average blood flow rate of blood in the target blood vessel.

FIG. 10 is a schematic diagram illustrating an exemplary process for perform a simulation operation to obtain a simulation result of a target blood vessel according to some embodiments of the present disclosure. In some embodiments, a process 1000 may be implemented as a set of instructions (e.g., an application) stored in the storage device 150, storage 220, or storage 390. The processing device 140, the processor 210, and/or the CPU 340 may execute the set of instructions, and when executing the instructions, the processing device 140, the processor 210, and/or the CPU 340 may be configured to perform the process 1000. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 1000 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order of the operations of the process 1000 illustrated in FIG. 10 and described below is not intended to be limiting.

In 1010, the processing device 140 may obtain at least two 2D image sequences of a target blood vessel to generate a 3D model of the target blood vessel. In some embodiments, the processing device 140 may obtain multiple initial 2D image sequences of a target region (e.g., an ROI) of an object. The multiple initial 2D image sequences may be captured by an imaging device at different view angles. The target region may include the target blood vessel. The processing device 140 may select the at least two 2D image sequences from the multiple initial 2D image sequences based on the view angles. In some embodiments, the at least two 2D image sequences may be captured based on a planar contrast imaging technology.

In 1020, the processing device 140 may obtain cross-section positions of the 3D model. The cross-section positions may include a position of an inlet of the target blood vessel and a position of an outlet of the target blood vessel.

In 1030, the processing device 140 may calculate an average blood flow rate of blood in the target blood vessel based on target image frames corresponding to the cross-section positions. For example, the processing device 140 may project the 3D model to a plane where each image frame captured during a contrast filling process is located. The processing device 140 may determine the corresponding position in the image frame of the position of the inlet of the target blood vessel. The processing device 140 may determine a gray gradient (or a change rate of gray value) of the position of the inlet in each image frame. As used herein, a gray gradient of a position may be a gray gradient of at least one position pixel (corresponding the position) in a direction directing from a vacant pixel to the at least one position pixel. The processing device 140 may identify an image frame with a gray gradient greater than a first preset threshold as the target image frame corresponding to the inlet of the target blood vessel. Similarity, the processing device 140 may determine the corresponding position in the image frame of the position of the outlet of the target blood vessel. The processing device 140 may determine a gray gradient of the position of the outlet in each image frame. The processing device 140 may identify an image frame with a gray gradient greater than a second preset threshold as the target image frame corresponding to the outlet of the target blood vessel. The processing device 140 may determine a time interval between the cross-section positions of the inlet and outlet of the target blood vessel based on the image frames corresponding to the cross-section positions. The processing device 140 may calculate the average blood flow rate of blood in the target blood vessel based on the time interval and a volume of the target blood vessel.

In some embodiments, the processing device 140 may obtain multiple branch vessels of the target blood vessel. The processing device 140 may determine a first outlet blood flow rate by inputting multiple feature values (or slope information) of the branch vessels and the average blood flow rate of the target blood vessel into a TAG model. The processing device 140 may perform a correction operation on the average blood flow rate to obtain a corrected average blood flow rate based on the first outlet blood flow rate of the outlet of the target blood vessel.

In 1040, the processing device 140 may perform a simulation operation to obtain a simulation result of the target blood vessel based on the 3D model of the target blood vessel, a preset boundary condition, and a preset simulation model. The preset boundary condition may include a flow rate boundary value and a pressure boundary value. The flow rate boundary value may be the average blood flow rate of blood in the target blood vessel.

In some embodiments, the processing device 140 may perform the simulation operation to obtain the simulation result of the target blood vessel based on the 3D model of the target blood vessel, the corrected average blood flow rate, the pressure boundary value, and the preset simulation model (e.g., a mass conservation equation, a momentum conservation equation, an energy conservation equation, a Navier-Stokes equation, etc. ) . In some embodiments, the processing device 140 may divide the 3D model of the target blood vessel into a plurality of grids to generate a gridded 3D model. The processing device 140 may perform the simulation operation to obtain the simulation result of the target blood vessel based on the gridded 3D model of the target blood vessel, the (corrected) average blood flow rate, the pressure boundary value, and the preset simulation model.

In some embodiments, the simulation result may include a pressure distribution information. The processing device 140 may calculate one or more FFR values associated with the target blood vessel based on the pressure distribution information and a preset pressure value (e.g., a pressure value of the aorta) . In some embodiments, the processing device 140 may determine a status of the target blood vessel (e.g., whether the target blood vessel has a blockage) based on the one or more FFR values and a preset FFR threshold.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure and are within the spirit and scope of the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments of the present disclosure. For example, the terms “one embodiment, ” “an embodiment, ” and “some embodiments” mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, it is emphasized and should be appreciated that two or more references to “an embodiment” or “one embodiment” or “an alternative embodiment” in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined as suitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “module, ” “unit, ” “component, ” “device, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon.

A computer-readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. Although the above disclosure discusses through various examples what is currently considered to be a variety of useful embodiments of the disclosure, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the disclosed embodiments. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the various embodiments. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, claim subject matter lies in less than all features of a single foregoing disclosed embodiment.

Claims

A system, comprising:

at least one storage device storing executable instructions; and

at least one processor in communication with the at least one storage device, wherein when executing the executable instructions, the at least one processor is configured to cause the system to perform operations including:

obtaining an image of a vessel tree acquired by an imaging device, the vessel tree including a target blood vessel and a plurality of branch vessels connected to the target blood vessel, the target blood vessel including an inlet and an outlet;

determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels;

determining a flow rate boundary condition based on at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model; and

determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.
The system of claim 1, wherein the determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels includes:

for each of the at least part of the plurality of feature values of a blood vessel, wherein the blood vessel is the target blood vessel or each of the plurality of branch vessels,

sampling at least two sample points on the blood vessel in the image to obtain locations and pixel values corresponding to the at least two sample points, wherein a distance between each of the at least two sample points and a center line of the blood vessel is within a distance threshold; and

determining, based on the locations and the pixel values corresponding to the at least two sample points, the feature value of the blood vessel.
The system of claim 2, wherein the determining, based on the locations and the pixel values corresponding to the at least two sample points, the feature value of the blood vessel includes:

determining a streamline distance between each of the at least two sample points and the inlet;

performing a linear fitting operation with the at least two streamline distances as abscissa and the pixels values of the at least two pixels as ordinates to determine a fitted line; and

designating a slope of the fitted line as the feature value of the blood vessel.
The system of claim 1, wherein to obtain the average blood flow rate, the operations further comprise:

obtaining a plurality of image frames of a region of interest including the target blood vessel, the plurality of image frames including a first set of first image frames acquired by the imaging device at a first view angle during a first contrast filling process and a second set of second image frames acquired by the imaging device at a second view angle during a second contrast filling process, the first view angle being different from the second view angle;

determining at least one first template image frame from the first set of first image frames and at least one second template image frame from the second set of second image frames satisfying a reconstruction condition to generate a 3D model for the target blood vessel;

determining a start image frame and an end image frame associated with the target blood vessel in the plurality of image frames, the start image frame and the end image frame being associated with a same view angle;

determining a time interval between the start image frame and the end image frame; and

determining, based on a volume of the 3D model and the time interval, the average blood flow rate of the target blood vessel.
The system of claim 4, wherein the reconstruction condition includes that the target blood vessel is fully filled with a contrast agent, a vascular overlap rate of the target blood vessel is smaller than an overlap threshold, or vessel boundaries of the target blood vessel are visible.
The system of claim 4, wherein the determining a start image frame and an end image frame associated with the target blood vessel in the plurality of image frames includes:

for any view angle of the first view angle or the second view angle,

projecting the 3D model to each image frame of the corresponding set of image frames of the view angle to determine a front location of the inlet and an end location of the outlet;

for each image frame of the view angle, determining, based on pixel values in the image frame, a pixel gradient change location of the image frame; and

determining an image frame whose pixel gradient change location is consistent with the front location of the inlet as the start image frame and an image frame whose pixel gradient change location is consistent with the end location of the outlet as the end image frame.
The system of claim 1, wherein the determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model includes:

inputting the at least part of the plurality of feature values and the average blood flow rate into the TAG model; and

determining an output of the TAG model as the flow rate boundary condition.
The system of claim 1, wherein the determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model includes:

obtaining a second average blood flow rate of any one of the plurality of branch vessels;

inputting the at least part of the plurality of feature values and the second average blood flow rate into the TAG model to determine a total blood flow rate of the inlet; and

determining, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition.
The system of claim 8, wherein the determining, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition includes:

determining an average value of the average blood flow rate and the total blood flow rate as the flow rate boundary condition.
The system of claim 8, wherein the determining, based on the average blood flow rate and the total blood flow rate, the flow rate boundary condition includes:

determining, based on the total blood flow rate of the inlet, an outlet blood flow rate of the outlet; and

designating the outlet blood flow rate of the outlet as the flow rate boundary condition.
The system of claim 1, wherein the determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel includes:

dividing a 3D model of the target blood vessel into a plurality of grids to generate a gridded 3D model;

performing, based on the gridded 3D model and boundary conditions, a simulation operation of blood flow according to a simulation model, the boundary conditions including the flow rate boundary condition and a corresponding pressure boundary condition; and

determining, based on a blood flow simulation result, the one or more characteristic parameters of the target blood vessel, the blood simulation result including at least pressure distribution information of the target blood vessel.
The system of claim 11, wherein the dividing the 3D model into a plurality of grids to generate a gridded 3D model includes:

determining multiple center points on a target center line of the target blood vessel, each of the multiple center points corresponding to a radial section;

for each radial section corresponding to each of the multiple center points,

determining a diameter of the radial section;

determining, based on the diameter and a regularization term, a grid size associated with the radial section, the regularization term being configured to stabilize a calculation precision and a calculation speed associated with the simulation operation; and

dividing, based on the multiple grid sizes, the 3D model of the target blood vessel into the plurality of grids to generate a gridded 3D model.
The system of claim 11, wherein the one or more characteristic parameters includes a fractional flow reserve (FFR) of a reference location on the target blood vessel, the determining, based on a blood flow simulation result, the one or more characteristic parameters of the target blood vessel includes:

obtaining, based on the pressure distribution information of the target blood vessel, a front pressure of the inlet and a reference pressure of the reference location on the target blood vessel; and

determining, based on the front pressure and the reference pressure, the FFR of the reference location.
The system of claim 13, wherein the at least one processor is configured to cause the system to perform operations including:

determining whether the FFR is less than an FFR threshold; and

in response to a determination that the FFR is less than the FFR threshold,

determining that a blood vessel segment between the inlet and the reference location of the target blood vessel has a blockage.
The system of claim 1, wherein the imaging device includes a digital subtraction angiography (DSA) device.
A method for determining one or more characteristic parameters of a target blood vessel, implemented on a computing device having at least one processor and at least one storage device, the method comprising:

obtaining an image of a vessel tree acquired by an imaging device, the vessel tree including a target blood vessel and a plurality of branch vessels connected to the target blood vessel, the target blood vessel including an inlet and an outlet;

determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels;

determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model; and

determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.
The method of claim 16, wherein to obtain the average blood flow rate, the method further comprising:

obtaining a plurality of image frames of a region of interest including the target blood vessel, the plurality of image frames including a first set of first image frames acquired by the imaging device at a first view angle during a first contrast filling process and a second set of second image frames acquired by the imaging device at a second view angle during a second contrast filling process, the first view angle being different from the second view angle;

determining at least one first template image frame from the first set of first image frames and at least one second template image frame from the second set of second image frames satisfying a reconstruction condition to generate a 3D model for the target blood vessel;

determining a start image frame and an end image frame associated with the target blood vessel in the plurality of image frames, the start image frame and the end image frame being associated with a same view angle;

determining a time interval between the start image frame and the end image frame; and

determining, based on a volume of the 3D model and the time interval, the average blood flow rate of the target blood vessel.
The method of claim 16, wherein the determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel includes:

dividing a 3D model of the target blood vessel into a plurality of grids to generate a gridded 3D model;

performing, based on the gridded 3D model and boundary conditions, a simulation operation of blood flow according to a simulation model, the boundary conditions including the flow rate boundary condition and a corresponding pressure boundary condition; and

determining, based on a blood flow simulation result, the one or more characteristic parameters of the target blood vessel, the blood simulation result including at least pressure distribution information of the target blood vessel.
The method of claim 18, wherein the dividing the 3D model into a plurality of grids to generate a gridded 3D model includes:

determining multiple center points on a target center line of the target blood vessel, each of the multiple center points corresponding to a radial section;

for each radial section corresponding to each of the multiple center points,

determining a diameter of the radial section;

determining, based on the diameter and a regularization term, a grid size associated with the radial section, the regularization term being configured to stabilize a calculation precision and a calculation speed associated with the simulation operation; and

dividing, based on the multiple grid sizes, the 3D model of the target blood vessel into the plurality of grids to generate a gridded 3D model.
A non-transitory computer readable medium, comprising at least one set of instructions for determining one or more characteristic parameters of a target blood vessel, wherein when executed by at least one processor of a computing device, the at least one set of instructions direct the at least one processor to perform operations including:

obtaining an image of a vessel tree acquired by an imaging device, the vessel tree including a target blood vessel and a plurality of branch vessels connected to the target blood vessel, the target blood vessel including an inlet and an outlet;

determining, based on the image of the vessel tree, at least part of a plurality of feature values each of which respectively corresponds to the target blood vessel and each of the plurality of branch vessels;

determining a flow rate boundary condition based on the at least part of the plurality of feature values, an average blood flow rate of blood in the target blood vessel, and a transluminal attenuation gradient (TAG) model; and

determining, based at least on the flow rate boundary condition, one or more characteristic parameters of the target blood vessel.