Multi-modal elastography system and method

Abstract

The invention discloses a multi-mode elastic imaging system and method. The system comprises an ultrasonic probe, a transmitting / receiving sequence controller, a processor and an output device. The processor is used for determining elastic parameters of the target tissue in various elastic imaging modes according to the first ultrasonic echo signal under each elastic detection; fusing the elastic parameters of each elastic detection under the plurality of elastic imaging modes to obtain tissue risk parameters of the target tissue under each elastic detection; determining the risk change rate of the target tissue based on the tissue risk parameters under the at least two times of elastic detection; determining the elastic change rate of the target tissue based on the elastic parameter of the target tissue in at least one elastic imaging mode under the at least two times of elastic detection; and at least fusing the risk change rate and the elastic change rate to obtain a comprehensive time risk parameter of the target tissue. The system can perform fusion analysis on various elastic imaging results and multiple detection results of the target tissue.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic imaging technology, and particularly relates to a multi-modal elastography system and method. Background Art

[0002] As a gradually mature technology, ultrasonic elastography has been widely used in clinical diagnosis and research. It mainly reflects the elasticity or softness of tissues, and can effectively assist in the diagnosis and evaluation of cancer lesions, the benign and malignant nature of tumors, and postoperative recovery. Currently, there have emerged many different types of elastography, such as strain elastography based on quasi-static compression, shear wave elastography based on acoustic radiation force to generate shear waves, and transient elastography based on external vibration to generate shear waves.

[0003] Different elastographies have their own advantages in different clinical scenarios according to their own characteristics. For example, strain elastography based on quasi-static compression determines the relative hardness relationship between lesions and tissues by comparing the deformation degrees of tissues and lesions before and after pressing, and shear wave elastography provides quantitative parameters such as the elasticity ratio between lesions and normal tissues by measuring the shear wave propagation speed in tissues.

[0004] Although the elastography systems adopted in existing clinics can provide multiple elastography indexes, such as strain value, strain ratio, elastography score, shear modulus, Young's modulus, etc., the multiple elastography indexes provided by them are all independent. Providing multiple independent elastography indexes to clinicians at the same time cannot highlight the key points and is likely to cause trouble to the doctors' decision-making. Summary of the Invention

[0005] The embodiments of the present application provide a multi-modal elastography system and method, which can perform fusion analysis on multiple elastography results and multiple detection results of a target tissue, enabling users to intuitively observe the risk degree of the target tissue and the development trend of the target tissue over time.

[0006] The first aspect of the embodiments of the present application provides a multi-modal elastography system. The multi-modal elastography system has multiple elastography modes and can perform at least two elastography detections on the same target tissue of the same patient at different times. The multi-modal elastography system includes:

[0007] An ultrasonic probe;

[0008] A transmit / receive sequence controller, configured to stimulate the ultrasonic probe to transmit a first ultrasonic wave to the target tissue in multiple elastography modes during each elastography detection process, receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtain a first ultrasonic echo signal;

[0009] A processor, configured to:

[0010] Determine the elastic parameters of the target tissue in multiple elastography modes based on the first ultrasonic echo signal under each elastic detection;

[0011] Fuse the elastic parameters of each elastic detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elastic detection, and the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy;

[0012] Determine the risk change rate of the target tissue based on the tissue risk parameters under at least two elastic detections, and the risk change rate is used to characterize the degree of change of the tissue risk parameter over time;

[0013] Based on the elastic parameters of the target tissue in at least one elastography mode under at least two elastic detections, determine the elastic change rate of the target tissue in at least one elastography mode, and the elastic change rate is used to characterize the degree of change of the elastic parameters in at least one elastography mode over time;

[0014] Fuse at least the risk change rate and the elastic change rate to obtain the comprehensive time risk parameter of the target tissue, and the comprehensive time risk parameter is used to characterize the development trend of the target tissue over time;

[0015] An output device for outputting the comprehensive time risk parameter.

[0016] The second aspect of the embodiments of the present application provides a multi-modal elastography system. The multi-modal elastography system has multiple elastography modes and can perform at least two elastic detections on the same target tissue of the same patient at different times. The multi-modal elastography system includes:

[0017] An ultrasonic probe;

[0018] A transmit / receive sequence controller for exciting the ultrasonic probe to transmit the first ultrasonic wave to the target tissue in multiple elastography modes during each elastic detection, receiving the first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtaining the first ultrasonic echo signal;

[0019] A processor for:

[0020] Determine the elastic parameters of the target tissue in multiple elastography modes based on the first ultrasonic echo signal under each elastic detection;

[0021] Fuse the elastic parameters of each elastic detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elastic detection, and the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy;

[0022] Determine a risk trend graph of the target tissue based on tissue risk parameters under at least two elasticity detections, where the risk trend graph is used to characterize the change trend of the tissue risk parameters over time;

[0023] Based on the elasticity parameters of the target tissue in at least one elasticity imaging mode under at least two elasticity detections, determine an elasticity trend graph of the target tissue in at least one elasticity imaging mode, where the elasticity trend graph is used to characterize the change trend of the elasticity parameters in at least one elasticity imaging mode over time;

[0024] An output device for outputting the risk trend graph and the elasticity trend graph.

[0025] A third aspect of the embodiments of the present application provides a multimodal elasticity imaging system. The multimodal elasticity imaging system has multiple elasticity imaging modes, and the multimodal elasticity imaging system includes:

[0026] An ultrasonic probe;

[0027] A transmit / receive sequence controller for exciting the ultrasonic probe to transmit a first ultrasonic wave to the target tissue, receiving a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtaining a first ultrasonic echo signal;

[0028] A processor for:

[0029] Determine a scanning area of the target tissue based on an interactive operation on the tissue image of the target tissue;

[0030] Enter multiple elasticity imaging modes based on interactive operations of multiple elasticity imaging modes;

[0031] Determine the elasticity parameters of the target tissue in multiple elasticity imaging modes according to the first ultrasonic echo signal;

[0032] Fuse the elasticity parameters in multiple elasticity imaging modes to obtain tissue risk parameters of the target tissue, where the tissue risk parameters are used to characterize the risk degree of the target tissue showing malignancy;

[0033] An output device for outputting the tissue risk parameters.

[0034] A fourth aspect of the embodiments of the present application provides a multimodal elasticity imaging system. The multimodal elasticity imaging system has multiple elasticity imaging modes and can perform at least two elasticity detections on the same target tissue of the same patient at different times. The multimodal elasticity imaging system includes:

[0035] An ultrasonic probe;

[0036] A transmit / receive sequence controller for exciting an ultrasonic probe to transmit a first ultrasonic wave to a target tissue in multiple elastography modes during each elastography examination, receiving a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtaining a first ultrasonic echo signal;

[0037] A processor for:

[0038] Determining elastography parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal in each elastography examination;

[0039] Based on the elastography parameters of the target tissue in multiple elastography modes in at least two elastography examinations, determining an elastography change rate of the target tissue in various elastography modes, where the elastography change rate is used to characterize the degree of change of the elastography parameters with time in various elastography modes;

[0040] Fusing the elastography change rates in various elastography modes to obtain a tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the degree of risk that the target tissue exhibits malignancy;

[0041] An output device for outputting the tissue risk parameter.

[0042] A fifth aspect of the embodiments of the present application provides a multi-modal elastography system, where the multi-modal elastography system includes:

[0043] A processor for:

[0044] Obtaining elastography parameters of the target tissue in multiple elastography modes in at least two elastography examinations;

[0045] Fusing the elastography parameters in multiple elastography modes for each elastography examination to obtain a tissue risk parameter of the target tissue for each elastography examination, where the tissue risk parameter is used to characterize the degree of risk that the target tissue exhibits malignancy;

[0046] Determining a risk change rate of the target tissue based on the tissue risk parameters in at least two elastography examinations, where the risk change rate is used to characterize the degree of change of the tissue risk parameter with time;

[0047] Based on the elastography parameters of the target tissue in at least one elastography mode in at least two elastography examinations, determining an elastography change rate of the target tissue in at least one elastography mode, where the elastography change rate is used to characterize the degree of change of the elastography parameters with time in at least one elastography mode;

[0048] Fusing at least the risk change rate and the elastography change rate to obtain a comprehensive time risk parameter of the target tissue, where the comprehensive time risk parameter is used to characterize the development trend of the target tissue with time;

[0049] An output device for outputting a comprehensive time risk parameter.

[0050] The sixth aspect of the embodiments of the present application provides a multi-modal elastography system, and the multi-modal elastography system includes:

[0051] A processor for:

[0052] Obtaining elastic parameters of a target tissue in multiple elastography modes under at least two elastic detections;

[0053] Based on the elastic parameters of the target tissue in multiple elastography modes under at least two elastic detections, determining the elastic change rate of the target tissue in various elastography modes, where the elastic change rate is used to characterize the degree of change of the elastic parameters in various elastography modes over time;

[0054] Fusing the elastic change rates in various elastography modes to obtain a tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy;

[0055] An output device for outputting the tissue risk parameter.

[0056] The seventh aspect of the embodiments of the present application provides a multi-modal elastography method, and the multi-modal elastography method is a multi-modal elastography method executed by the multi-modal elastography system according to any one of the first to sixth aspects above.

[0057] The multi-modal elastography system and method provided by the embodiments of the present application perform at least two elastic detections on the same target tissue of the same patient at different times, fuse the elastic parameters of each elastic detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elastic detection, determine the risk change rate of the target tissue based on the tissue risk parameters under at least two elastic detections, determine the elastic change rate of the target tissue in at least one elastography mode based on the elastic parameters of the target tissue in at least one elastography mode under at least two elastic detections, and fuse at least the risk change rate and the elastic change rate to obtain the comprehensive time risk parameter of the target tissue, which can perform fusion analysis on multiple elastography results and multiple detection results of the target tissue, enabling the user to intuitively observe the risk degree of the target tissue and the development trend of the target tissue over time, and assisting the user to more comprehensively evaluate the development of the tissue. Description of the Drawings

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0059] Figure 1 Schematic block diagram of a multimodal elastography system provided by an embodiment of the present application;

[0060] Figure 2 Schematic diagram for determining a comprehensive time risk parameter based on the results of three elastography detections provided by an embodiment of the present application;

[0061] Figure 3 Schematic diagram for determining a comprehensive time risk parameter based on the results of five elastography detections provided by an embodiment of the present application;

[0062] Figure 4 Schematic diagram for determining a change rate based on the results of multiple elastography detections provided by an embodiment of the present application;

[0063] Figure 5 Schematic diagram of a fused risk distribution map provided by an embodiment of the present application;

[0064] Figure 6 Schematic diagram for selecting a region of interest on the fused risk distribution map provided by an embodiment of the present application;

[0065] Figure 7 Schematic block diagram of a multimodal elastography system provided by another embodiment of the present application;

[0066] Figure 8 Schematic flowchart of a multimodal elastography method provided by an embodiment of the present application;

[0067] Figure 9 Schematic flowchart of a multimodal elastography method provided by another embodiment of the present application;

[0068] Figure 10 Schematic flowchart of a multimodal elastography method provided by yet another embodiment of the present application;

[0069] Figure 11 Schematic flowchart of a multimodal elastography method provided by yet another embodiment of the present application. Detailed implementation manners

[0070] In order to make the objectives, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. Based on the embodiments of the present application described herein, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0071] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. However, it will be apparent to one of ordinary skill in the art that the present application may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the present application.

[0072] It should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0073] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, specify the presence of the 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. As used herein, the term "and / or" includes any and all combinations of the associated listed items.

[0074] For a thorough understanding of the present application, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present application. The alternative embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may have other implementation manners.

[0075] Specifically, the multi-modal elastography system and analysis method of the present application will be described in detail below with reference to the accompanying drawings. Without conflict, the features in the following embodiments and implementation manners may be combined with each other.

[0076] First, Figure 1 A schematic block diagram of a multi-modal elastography system in an embodiment of the present application is shown. As Figure 1 shown, the multi-modal elastography system 100 may include: an ultrasonic probe 110, a transmit / receive selection switch 120, a transmit / receive sequence controller 130, a processor 140, an output device 150, and a memory 160. The multi-modal elastography system 100 has multiple elastography modes, for example, strain elastography mode, shear wave elastography mode, viscoelastic imaging mode, transient elastography mode, etc.

[0077] Furthermore, the multimodal elastography system 100 can perform at least two elastography detections on the same target tissue of the same patient at different times. The at least two elastography detections refer to at least two elastography examinations performed on the same target tissue of the same patient at different time periods according to a preset time period, and the time period can be set according to clinical needs. For example, the time period includes, but is not limited to, monthly, semi-annual, annual, etc. For example, the at least two elastography detections include three elastography examinations performed on the same target tissue of the same patient in October, November, and December respectively.

[0078] In this article, the target tissue can be any tissue that needs to be detected by the multimodal elastography system, such as the thyroid gland, breast, blood vessels, musculoskeletal, uterus, prostate, etc. The target tissue can be the tissue of any human or animal, where the animal can be a cat, dog, rabbit, etc., and no specific limitation is made here.

[0079] Specifically, the transmit / receive sequence controller 130 is configured to stimulate the ultrasonic probe 110 to emit a first ultrasonic wave to the target tissue in multiple elastography modes during each elastography detection, and receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue to obtain a first ultrasonic echo signal. For example, during the first elastography detection, the transmit / receive sequence controller 130 stimulates the ultrasonic probe 110 to emit a first ultrasonic wave to the target tissue in multiple elastography modes, and receives a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue to obtain a first ultrasonic echo signal under the first elastography detection; during the second elastography detection, the transmit / receive sequence controller 130 stimulates the ultrasonic probe 110 to emit a first ultrasonic wave to the target tissue in multiple elastography modes, and receives a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue to obtain a first ultrasonic echo signal under the second elastography detection; …; during the nth elastography detection, the transmit / receive sequence controller 130 stimulates the ultrasonic probe 110 to emit a first ultrasonic wave to the target tissue in multiple elastography modes, and receives a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue to obtain a first ultrasonic echo signal under the nth elastography detection.

[0080] The ultrasonic probe 110 generally includes an array of multiple elements. When emitting the first ultrasonic wave each time, all elements or a part of all elements of the ultrasonic probe 110 participate in the emission of the first ultrasonic wave. At this time, each element or each part of the elements participating in the emission of the first ultrasonic wave is respectively excited by a transmit pulse and emits the first ultrasonic wave respectively. The first ultrasonic waves emitted by these elements are superimposed during propagation to form a synthetic ultrasonic beam emitted to the scanning target, and the direction of the synthetic ultrasonic beam is the ultrasonic propagation direction.

[0081] The processor 140 is configured to determine the elasticity parameters of the target tissue in multiple elastography modes based on the first ultrasonic echo signal under each elasticity detection; fuse the elasticity parameters of each elasticity detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elasticity detection, and the tissue risk parameter is used to characterize the risk degree of the target tissue presenting as malignant; determine the risk change rate of the target tissue based on the tissue risk parameters under at least two elasticity detections, and the risk change rate is used to characterize the degree of change of the tissue risk parameter over time; determine the elasticity change rate of the target tissue based on the elasticity parameters of the target tissue in at least one elastography mode under at least two elasticity detections, and the elasticity change rate is used to characterize the degree of change of the elasticity parameter in at least one elastography mode over time; at least fuse the risk change rate and the elasticity change rate to obtain the comprehensive time risk parameter of the target tissue, and the comprehensive time risk parameter is used to characterize the development trend of the target tissue over time.

[0082] In one example, the processor 140 can be implemented by software, hardware, firmware, or a combination thereof, and can use circuits, single or multiple application specific integrated circuits (ASICs), single or multiple general integrated circuits, single or multiple microprocessors, single or multiple programmable logic devices, or a combination of the foregoing circuits or devices, or other suitable circuits or devices, so that the processor 140 can execute the functions that need to be implemented by it and / or other desired functions.

[0083] The output device 150 is configured to output the comprehensive time risk parameter. The output device 150 can include one or more of a display, a printer, a speaker, etc. In one example, the output device 150 includes a display, and the output device 150 can display the comprehensive time risk parameter in a preset display manner through the display interface of the display. In other examples, the output device 150 includes a printer, and the output device 150 can output the comprehensive time risk parameter through the printer, that is, print out the comprehensive time risk parameter for clinicians to view.

[0084] The preset display manner can be any display method. For example, the preset display manner includes at least one of the following manners: graphics, text, voice, and color. As Figure 2 and Figure 3 shown, the comprehensive time risk parameter is displayed in text. For example, in the current 3rd examination, the comprehensive time risk index S = 1.2; in the current 5th examination, the comprehensive time risk index S = 1.9.

[0085] In one example, the display of the multimodal elastography system can be a touch display screen, a liquid crystal display screen, etc., or can be an independent liquid crystal display, a television, etc., which are independent display devices outside the multimodal elastography system, or can also be a display screen on electronic devices such as mobile phones and tablet computers. The display can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the multimodal elastography system, and these graphical user interfaces can be composed of graphics, text, icons, videos, and any combination thereof.

[0086] In one example, the memory 160 of the multimodal elastography system may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 140 may run the program instructions to implement the functions (implemented by the processor 140) in the embodiments of the present application and / or other desired functions. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs, etc.

[0087] In one example, the multimodal elastography system may further include other human-machine interaction devices other than the display, which are connected to the processor 140. For example, the processor 140 may be connected to the human-machine interaction device through an external input / output port, and the external input / output port may be a wireless communication module, a wired communication module, or a combination of both. The external input / output port may also be implemented based on USB, bus protocols such as CAN, and / or wired network protocols, etc.

[0088] Among them, the human-machine interaction device may include an input device for detecting the input information of the user. The input information may be, for example, a control instruction for the ultrasonic emission / reception timing, an operation input instruction for drawing points, lines, or frames on the fusion risk distribution map, or may also include other instruction types. The input device may include one or a combination of a keyboard, a mouse, a roller, a trackball, a mobile input device (such as a mobile device with a touch display screen, a mobile phone, etc.), a multi-functional knob, etc.

[0089] It should be understood that Figure 1 The components included in the illustrated multimodal elastography system are only illustrative, and it may include more or fewer components. The present invention is not limited thereto.

[0090] In one example, the processor 140 may fuse the elasticity parameters in multiple elastography modes for each elastic detection using a preset first fusion strategy to obtain the tissue risk parameter of the target tissue for each elastic detection. The preset first fusion strategy includes, but is not limited to, linear, non-linear, weighted summation, etc.

[0091] In one example, if the preset first fusion strategy is weighted summation, then fusing the elasticity parameters in multiple elastography modes for each elastic detection to obtain the tissue risk parameter of the target tissue for each elastic detection includes: obtaining the first fusion weights of the elasticity parameters in multiple elastography modes; and fusing the elasticity parameters in multiple elastography modes for each elastic detection based on the first fusion weights to obtain the tissue risk parameter of the target tissue for each elastic detection. For example, the elasticity parameters of the nth elastic detection in multiple elastography modes include parameter A, parameter B, and parameter C. Obtain the first fusion weights of parameter A, parameter B, and parameter C, and fuse parameter A, parameter B, and parameter C based on the first fusion weights to obtain the tissue risk parameter of the target tissue for the nth elastic detection.

[0092] In one example, the elasticity parameters of each elastic detection in multiple elastography modes include strain parameters and Young's modulus, and the first fusion weights include the strain fusion weights corresponding to the strain parameters and the modulus fusion weights corresponding to Young's modulus. Then, fusing the elasticity parameters of each elastic detection in multiple elastography modes based on the first fusion weights to obtain the tissue risk parameter of the target tissue for each elastic detection includes: fusing the strain parameters and Young's modulus based on the modulus fusion weights and the strain fusion weights to obtain the tissue risk parameter of the target tissue for each elastic detection. The process of fusing the strain parameters and Young's modulus can be expressed as: where A represents Young's modulus, B represents the strain parameter, represents the mean strain, c1 represents the modulus fusion weight, and c2 represents the strain fusion weight.

[0093] In one example, after the processor 140 determines the tissue risk parameter of the target tissue for each elastic detection, it may determine the risk trend graph of the target tissue based on the tissue risk parameters of at least two elastic detections, and determine the risk change rate of the target tissue based on the risk trend graph. The risk trend graph may characterize the change trend of the tissue risk parameter over time. As Figure 4 shown, the risk trend graph includes a risk change curve. The abscissa of the risk change curve is the time of elastic detection, and the ordinate of the risk change curve is the tissue risk parameter. For example, Figure 4 is the risk trend graph obtained from five elastic detections. The abscissa of the risk change curve is the time of the five elastic detections, and the ordinate of the risk change curve is the tissue risk parameters obtained from the five elastic detections.

[0094] When determining the risk change rate of the target tissue based on the risk trend graph, the change rate of the risk change curve in the risk trend graph can be calculated, and then the change rate of the risk change curve is determined as the risk change rate of the target tissue. Among them, existing methods such as the difference method or fitting first and then differentiating can be used to calculate the change rate of the risk change curve, which will not be elaborated in this application.

[0095] In one example, after the processor 140 determines the elasticity parameters of the target tissue in multiple elastography modes, based on the elasticity parameters of the target tissue in at least one elastography mode under at least two elasticity detections, an elasticity trend graph of the target tissue in at least one elastography mode can be determined, and the elasticity change rate of the target tissue in at least one elastography mode is determined based on the elasticity trend graph. For example, the processor 140 determines the strain trend graph of the target tissue based on the strain parameters of the target tissue under at least two elasticity detections, and determines the strain change rate of the target tissue based on the strain trend graph.

[0096] Among them, the elasticity trend graph can characterize the degree of change of the elasticity parameters in at least one elastography mode over time. Continuing to refer to Figure 4 as shown, the elasticity trend graph includes an elasticity change curve. The abscissa of the elasticity change curve is the time of elasticity detection, and the ordinate of the elasticity change curve is the elasticity parameter. For example, Figure 4 is the elasticity trend graph obtained from five elasticity detections. The abscissa of the elasticity change curve is the time of the five elasticity detections, and the ordinate of the elasticity change curve is the elasticity parameters obtained from the five elasticity detections.

[0097] Similar to the determination method of the risk change rate, when determining the elasticity change rate of the target tissue in at least one elastography mode based on the elasticity trend graph, the change rate of the elasticity change curve in the elasticity trend graph can be calculated, and then the change rate of the elasticity change curve is determined as the elasticity change rate of the target tissue in at least one elastography mode. Among them, existing methods such as the difference method or fitting first and then differentiating can be used to calculate the change rate of the elasticity change curve in the elasticity trend graph, which will not be elaborated in this application.

[0098] In one example, at least the risk change rate and the elasticity change rate are fused to obtain the comprehensive time risk parameter of the target tissue, including: fusing the risk change rate and the elasticity change rate to obtain the comprehensive time risk parameter of the target tissue, and / or fusing the elasticity change rate, the risk change rate and the feature change rate to obtain the comprehensive time risk parameter of the target tissue.

[0099] In one example, before fusing the elasticity change rate, the risk change rate, and the feature change rate to obtain the comprehensive time risk parameter of the target tissue, the processor 140 first obtains at least one feature parameter of the target tissue in at least one elastography mode under at least two elasticity detections, and then determines the feature change rate of the target tissue based on the at least one feature parameter. Herein, the feature parameter is other characterization parameters of the target tissue except for the elasticity parameter and the tissue risk parameter. For example, the at least one feature parameter includes the lesion geometric area, or the at least one feature parameter includes the blood flow change, or the at least one feature parameter includes the lesion geometric area and the blood flow change.

[0100] Further, the feature change rate is used to characterize the change degree of at least one feature parameter over time. When the processor 140 determines the feature change rate of the target tissue based on the at least one feature parameter, it first determines the feature trend graph of the target tissue based on the at least one feature parameter, and then determines the feature change rate of the target tissue based on the feature trend graph of the target tissue. Herein, the feature trend graph can characterize the change trend of at least one feature parameter over time. Continuing to refer to Figure 4 as shown, the feature trend graph includes a feature change curve. The abscissa of the feature change curve is the time of the elasticity detection, and the ordinate of the feature change curve is the feature parameter. For example, Figure 4 it is the lesion area trend graph obtained by performing five elasticity detections. The abscissa of the lesion area change curve is the time of the five elasticity detections, and the ordinate of the lesion area change curve is the lesion geometric area obtained by the five elasticity detections.

[0101] Similar to the determination method of the elasticity change rate, when determining the feature change rate of the target tissue based on the feature trend graph of the target tissue, the change rate of the feature change curve in the feature trend graph can be calculated, and then the change rate of the feature change curve is determined as the feature change rate of the target tissue. Herein, the existing methods such as the difference method or fitting first and then differentiating can be used to calculate the change rate of the feature change curve in the feature trend graph, which will not be elaborated herein.

[0102] In one example, the processor 140 fuses the risk change rate and the elasticity change rate by using a preset second fusion strategy to obtain the comprehensive time risk parameter of the target tissue, and / or fuses the elasticity change rate, the risk change rate, and the feature change rate by using a preset third fusion strategy to obtain the comprehensive time risk parameter of the target tissue. Herein, the preset second fusion strategy includes, but is not limited to, linear, non-linear, weighted summation, etc. The preset third fusion strategy includes, but is not limited to, linear, non-linear, weighted summation, etc. The preset second fusion strategy and the preset third fusion strategy can be the same or different.

[0103] In one example, the preset second fusion strategy is weighted summation. Then, the preset second fusion strategy is used to fuse the risk change rate and the elasticity change rate to obtain the comprehensive time risk parameter of the target tissue, including: obtaining the second fusion weights corresponding to the risk change rate and the elasticity change rate respectively; and fusing the risk change rate and the elasticity change rate based on the second fusion weights to obtain the comprehensive time risk parameter of the target tissue.

[0104] In one example, the preset third fusion strategy is weighted summation. Then, the elasticity change rate, the risk change rate, and the feature change rate are fused to obtain the comprehensive time risk parameter of the target tissue, including: obtaining the third fusion weights corresponding to the elasticity change rate, the risk change rate, and the feature change rate respectively; and fusing the elasticity change rate, the risk change rate, and the feature change rate based on the third fusion weights to obtain the comprehensive time risk parameter of the target tissue.

[0105] Among them, the calculation formula of the comprehensive time risk parameter can be expressed as: Ki represents the i-th type of change rate, a i represents the fusion weight corresponding to the i-th type of change rate, and n represents the number of types of change rates. For example, if the change rates include the strain change rate, the risk change rate, the lesion area change rate, and the blood flow change rate, the calculation formula of the comprehensive time risk parameter can be expressed as: K1 represents the strain change rate, K2 represents the risk change rate, K3 represents the lesion area change rate, K4 represents the blood flow change rate, a1 represents the fusion weight corresponding to the strain change rate, a2 represents the fusion weight corresponding to the risk change rate, a3 represents the fusion weight corresponding to the lesion area change rate, and a4 represents the fusion weight corresponding to the blood flow change rate.

[0106] In one example, after the processor 140 obtains the comprehensive time risk parameter of the target tissue, it can also obtain a preset reference threshold, and determine the development state of the target tissue based on the comprehensive time risk parameter and the reference threshold. Among them, the reference threshold is a reference value preset for measuring the development state of the target tissue, and it can be set as needed.

[0107] In one example, the reference threshold includes a first threshold and a second threshold. When the processor 140 determines the development state of the target tissue based on the comprehensive time risk parameter and the reference threshold, it first compares the comprehensive time risk parameter with the first threshold and the second threshold; when the comprehensive time risk parameter is less than or equal to the first threshold, it determines that the development state of the target tissue is the first state; when the comprehensive time risk parameter is greater than the first threshold and less than the second threshold, it determines that the development state of the target tissue is the second state; when the comprehensive time risk parameter is greater than or equal to the second threshold, it determines that the development state of the target tissue is the third state.

[0108] Among them, the first state, the second state, and the third state are different development states. Specifically, the first state indicates that the target tissue tends to improve, the second state indicates that the target tissue develops in a serious direction, but the development speed is relatively gentle, and the third state indicates that the target tissue develops in a serious direction, and the development speed is relatively rapid. For example, the first threshold can be set to 1, the second threshold can be set to 1.5, the comprehensive time risk parameter is denoted as S. When S ≤ 1, it is determined that the development state of the target tissue is the first state (that is, the target tissue tends to improve); when 1 < S < 1.5, it is determined that the development state of the target tissue is the second state (that is, the target tissue develops in a serious direction, but the development speed is relatively gentle); when S ≥ 1.5, it is determined that the development state of the target tissue is the third state (the target tissue develops in a serious direction, and the development speed is relatively rapid), so as to assist clinicians in judging the development state of the target tissue.

[0109] In another embodiment of the present application, a multi-modal elastography system is further provided. Referring to Figure 1 As shown, the multi-modal elastography system 100 may include: an ultrasonic probe 110, a transmit / receive selection switch 120, a transmit / receive sequence controller 130, a processor 140, an output device 150, and a memory 160. The multi-modal elastography system 100 has multiple elastography modes. For example, strain elastography mode, shear wave elastography mode, viscoelastic imaging mode, transient elastography mode, etc.

[0110] Furthermore, the multi-modal elastography system 100 can perform at least two elastography detections on the same target tissue of the same patient at different times. The at least two elastography detections refer to at least two elastography examinations performed on the same target tissue of the same patient at different time periods according to a preset time period. The time period can be set according to clinical needs. For example, the time period includes but is not limited to monthly, semi-annual, annual, etc. For example, the at least two elastography detections include three elastography examinations performed on the same target tissue of the same patient in October, November, and December respectively.

[0111] Specifically, the transmit / receive sequence controller 130 is configured to stimulate the ultrasonic probe 110 to transmit a first ultrasonic wave to the target tissue in a plurality of elastography modes during each elastography detection process, receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtain a first ultrasonic echo signal; the processor 140 is configured to: determine the elastic parameters of the target tissue in a plurality of elastography modes according to the first ultrasonic echo signal in each elastography detection; fuse the elastic parameters in each elastography detection in a plurality of elastography modes to obtain the tissue risk parameter of the target tissue in each elastography detection, where the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy; determine a risk trend graph of the target tissue based on the tissue risk parameters in at least two elastography detections, where the risk trend graph is used to characterize the change trend of the tissue risk parameter over time; determine an elasticity trend graph of the target tissue in at least one elastography mode based on the elastic parameters of the target tissue in at least one elastography mode in at least two elastography detections, where the elasticity trend graph is used to characterize the change trend of the elastic parameters in at least one elastography mode over time; the output device 150 is configured to output the risk trend graph and the elasticity trend graph.

[0112] In one example, after the processor 140 determines the risk trend graph and the elasticity trend graph, it can determine the risk change rate of the target tissue based on the risk trend graph, and determine the elasticity change rate of the target tissue in at least one elastography mode based on the elasticity trend graph; the output device 150 is further configured to output the risk change rate and the elasticity change rate. Wherein, the risk change rate is used to characterize the degree of change of the tissue risk parameter over time, and the elasticity change rate is used to characterize the degree of change of the elastic parameters in at least one elastography mode over time. The specific process of determining the risk change rate of the target tissue based on the risk trend graph and determining the elasticity change rate of the target tissue in at least one elastography mode based on the elasticity trend graph can refer to the description of the multi-modal elastography system above and will not be repeated here.

[0113] In one example, the processor 140 can also obtain at least one characteristic parameter of the target tissue in at least one elastography mode in at least two elastography detections; determine a characteristic trend graph of the target tissue based on the at least one characteristic parameter; the output device 150 is further configured to output the characteristic trend graph. Wherein, the characteristic parameter is other characterization parameters of the target tissue except for the elastic parameter and the tissue risk parameter. For example, the at least one characteristic parameter includes the lesion geometric area, or the at least one characteristic parameter includes the blood flow change, or the at least one characteristic parameter includes the lesion geometric area and the blood flow change.

[0114] Another embodiment of the present application further provides a multi-modal elastography system, refer to Figure 1As shown, the multimodal elastography system 100 may include: an ultrasonic probe 110, a transmit / receive selection switch 120, a transmit / receive sequence controller 130, a processor 140, an output device 150, and a memory 160. The multimodal elastography system 100 has multiple elastography modes, for example, strain elastography mode, shear wave elastography mode, viscoelastic imaging mode, transient elastography mode, etc.

[0115] Specifically, the transmit / receive sequence controller 130 is configured to excite the ultrasonic probe to transmit a first ultrasonic wave to the target tissue, receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtain a first ultrasonic echo signal; the processor is configured to: determine a scanning area of the target tissue based on an interactive operation on the tissue image of the target tissue; enter multiple elastography modes based on interactive operations of multiple elastography modes; determine elastic parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal; fuse the elastic parameters in multiple elastography modes to obtain a tissue risk parameter of the target tissue, and the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy; the output device 150 is configured to output the tissue risk parameter.

[0116] The multimodal elastography system 100 in this embodiment may be an imaging system that supports the elastic dual / multiplex real-time elastography mode or an imaging system that does not support the elastic dual / multiplex real-time elastography mode. When the multimodal elastography imaging system 100 is an imaging system that supports the pop-up dual / multiplex real-time elastography mode, based on interactive operations of multiple elastography modes, entering multiple elastography modes includes: entering multiple elastography modes simultaneously based on one interactive operation, so that the user only needs to perform one interactive operation to achieve multimodal elastography, thereby improving the efficiency of multimodal elastography.

[0117] When the multimodal elastography system 100 is an imaging system that does not support the pop-up dual / multiplex real-time elastography mode, based on interactive operations of multiple elastography modes, entering multiple elastography modes includes: entering multiple elastography modes sequentially based on multiple interactive operations. For example, entering the strain elastography mode, shear wave elastography mode, viscoelastic imaging mode, and transient elastography mode respectively based on four interactive operations.

[0118] The processor 140 can automatically fuse the elasticity parameters in multiple elastography modes to obtain the tissue risk parameter of the target tissue, or fuse the elasticity parameters in multiple elastography modes based on the interaction operation of the elasticity parameters in multiple elastography modes to obtain the tissue risk parameter of the target tissue. For example, the user selects the strain parameter and Young's modulus on the display interface of the display via the mouse, and clicks on the "Fusion" identifier on the display interface, and the processor 140 can then fuse the strain parameter and Young's modulus to obtain the tissue risk parameter of the target tissue.

[0119] In one example, when multiple elastography modes are entered simultaneously, the processor 140 can simultaneously determine the elasticity parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal. For example, the processor 140 simultaneously determines the strain parameter and Young's modulus of the target tissue according to the first ultrasonic echo signal.

[0120] In one example, the processor 140 can automatically switch between multiple elastography modes based on a mode switching operation, thereby facilitating switching between different elastography modes during multimodal elastography. For example, based on the mode switching operation, switching from the strain elastography mode to the shear wave elastography mode, based on the mode switching operation, switching from the shear wave elastography mode to the viscoelastic imaging mode, based on the mode switching operation, switching from the viscoelastic imaging mode to the transient elastography mode, etc.

[0121] In one embodiment, the multiple elastography modes include a first elastography mode and a second elastography mode. For example, the first elastography mode can be the strain elastography mode, and the second elastography mode can be the shear wave elastography mode, or the first elastography mode can be the shear wave elastography mode, and the second elastography mode can be the viscoelastic imaging mode, or the first elastography mode can be the viscoelastic imaging mode, and the second elastography mode can be the transient elastography mode. When the processor 140 automatically switches between multiple elastography modes, it can obtain the first scanning area in the first elastography mode and the second scanning area in the second elastography mode, and determine the matching degree between the first scanning area and the second scanning area; the output device 150 is further configured to output the matching degree, so as to ensure multimodal elastography of exactly the same or similar scanning sections.

[0122] The tissue image includes, but is not limited to, the B image of the target tissue. Before the processor 140 determines the scanning area of the target tissue based on the interaction operation on the tissue image of the target tissue, it is necessary to obtain the tissue image of the target tissue. Specifically, the transmit / receive sequence controller 130 is further configured to stimulate the ultrasonic probe 110 to transmit a second ultrasonic wave to the target tissue, receive a second ultrasonic echo based on the second ultrasonic wave returned from the target tissue, and obtain a second ultrasonic echo signal; the processor 140 is further configured to determine the tissue image of the target tissue according to the second ultrasonic echo signal.

[0123] After obtaining the tissue image, the processor 140 can display the tissue image through the display interface of the display, and receive the interaction operation of the user on the tissue image, and determine the scanning area of the target tissue based on this interaction operation. Among them, the interaction operation on the tissue image of the target tissue includes, but is not limited to, the box selection operation on the tissue image of the target tissue.

[0124] Of course, the processor 140 can also automatically determine the scanning area of the target tissue on the tissue image based on relevant machine recognition algorithms, or can also obtain the scanning area of the target tissue through a semi-automatic detection method. For example, first automatically detect the scanning area on the tissue image based on the machine recognition algorithm, and then the user further modifies or corrects it to obtain a more accurate scanning area.

[0125] To avoid repetition, the specific details of the multi-modal elastography system in the embodiments of the present application can refer to the description of the multi-modal elastography system above, and will not be repeated here.

[0126] In another embodiment of the present application, a multi-modal elastography system is further provided. Referring to Figure 1 As shown, the multi-modal elastography system 100 may include: an ultrasonic probe 110, a transmit / receive selection switch 120, a transmit / receive sequence controller 130, a processor 140, an output device 150, and a memory 160. The multi-modal elastography system 100 has a variety of elastography modes, for example, strain elastography mode, shear wave elastography mode, viscoelastic imaging mode, and transient elastography mode, etc.

[0127] Furthermore, the multi-modal elastography system 100 can perform at least two elastography detections on the same target tissue of the same patient at different times. The at least two elastography detections refer to at least two elastography examinations performed on the same target tissue of the same patient at different time periods according to a preset time period. The time period can be set according to clinical needs. For example, the time period includes, but is not limited to, monthly, semi-annual, annual, etc. For example, the at least two elastography detections include three elastography examinations performed on the same target tissue of the same patient in October, November, and December respectively.

[0128] Specifically, the transmit / receive sequence controller 130 is configured to stimulate the ultrasonic probe 110 to transmit a first ultrasonic wave to the target tissue in a plurality of elastography modes during each elastography examination, receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtain a first ultrasonic echo signal; the processor 140 is configured to: determine the elastography parameters of the target tissue in a plurality of elastography modes according to the first ultrasonic echo signal in each elastography examination; determine the elastography change rate of the target tissue in various elastography modes based on the elastography parameters of the target tissue in a plurality of elastography modes during at least two elastography examinations, where the elastography change rate is used to characterize the degree of change of the elastography parameters with time in various elastography modes; fuse the elastography change rates in various elastography modes to obtain the tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the risk degree of the target tissue being malignant; and the output device 150 is configured to output the tissue risk parameter.

[0129] In one example, the processor 140 fuses the elastography change rates in various elastography modes according to a preset fourth fusion strategy to obtain the tissue risk parameter of the target tissue. Wherein, the preset fourth fusion strategy includes, but is not limited to, operations such as superposition and weighted summation. For example, determine the strain change rate and Young's modulus change rate of the target tissue, and perform a weighted summation on the strain change rate and Young's modulus change rate to obtain the tissue risk parameter of the target tissue.

[0130] In one example, when the processor 140 determines the elastography change rate of the target tissue in various elastography modes based on the elastography parameters of the target tissue in a plurality of elastography modes during at least two elastography examinations, first, based on the elastography parameters of the target tissue in a plurality of elastography modes during at least two elastography examinations, determine the elastography trend graph of the target tissue in various elastography modes, and then determine the elastography change rate of the target tissue in various elastography modes based on the elastography trend graph. Wherein, the elastography trend graph is used to characterize the change trend of the elastography parameters with time in various elastography modes, and the specific process of determining the elastography change rate of the target tissue in various elastography modes based on the elastography trend graph can refer to the description of the multimodal elastography system above and will not be repeated here.

[0131] In this article, after the processor 140 determines the tissue risk parameter of the target tissue, it can perform normalization processing on the tissue risk parameter to obtain the normalized tissue risk parameter, and then map the normalized tissue risk parameter to the tissue image of the target tissue to obtain a fusion risk distribution map; the output device 150 is further configured to output the fusion risk distribution map. As Figure 5As shown, the processor 140 fuses the strain parameter and Young's modulus to obtain the tissue risk parameter of the target tissue, then normalizes the tissue risk parameter, and maps the normalized tissue risk parameter to the tissue image of the target tissue to obtain the fusion risk distribution map.

[0132] Before the processor 140 maps the normalized tissue risk parameter to the tissue image of the target tissue, it is necessary to obtain the tissue image of the target tissue. Among them, the tissue image includes but is not limited to the B image of the target tissue. Specifically, the transmit / receive sequence controller 130 is further configured to stimulate the ultrasonic probe 110 to transmit a second ultrasonic wave to the target tissue, receive the second ultrasonic echo based on the second ultrasonic wave returned from the target tissue, and obtain a second ultrasonic echo signal; the processor is further configured to determine the tissue image of the target tissue according to the second ultrasonic echo signal.

[0133] In one example, after the output device 150 outputs the fusion risk distribution map, the processor 140 can further determine the tissue risk parameter and / or the normalized tissue risk parameter of the target area based on the interaction operation of the user on the target area on the fusion risk distribution map; the output device 150 is further configured to output the tissue risk parameter and / or the normalized tissue risk parameter of the target area. Among them, the interaction operation includes but is not limited to the box selection operation for the target area, and the box selection operation can be a rectangular box selection operation, a circular box selection operation, a manual tracing box selection operation, etc. For example, as Figure 6 shown, the processor 140 determines that the tissue risk parameter of the target area corresponding to the rectangular box selected by the user is 91%, and the output device 150 displays "P = 91%".

[0134] In another embodiment of the present application, a multi-modal elastography system is further provided. Refer to Figure 7As shown, the multimodal elastography system 200 includes: a processor 210, an output device 220, and a memory 230. The processor 210 is configured to: obtain elastic parameters of a target tissue under at least two elastic detections in multiple elastography modes; fuse the elastic parameters of each elastic detection in multiple elastography modes to obtain a tissue risk parameter of the target tissue for each elastic detection, where the tissue risk parameter is used to characterize the risk degree of the target tissue presenting as malignant; determine a risk change rate of the target tissue based on the tissue risk parameters under at least two elastic detections, where the risk change rate is used to characterize the degree of change of the tissue risk parameter over time; determine an elastic change rate of the target tissue based on the elastic parameters of the target tissue under at least one elastography mode in at least two elastic detections, where the elastic change rate is used to characterize the degree of change of the elastic parameters in at least one elastography mode over time; fuse at least the risk change rate and the elastic change rate to obtain a comprehensive time risk parameter of the target tissue, where the comprehensive time risk parameter is used to characterize the development trend of the target tissue over time; and an output device 220 for outputting the comprehensive time risk parameter.

[0135] Different from the multimodal elastography system described above, the multimodal elastography system 200 provided in this embodiment does not include an ultrasonic probe, a transmit / receive selection switch, and a transmit / receive sequence controller. In this embodiment, the multimodal elastography system 200 can be connected to other elastography devices. After other elastography devices obtain the elastic parameters of the target tissue under at least two elastic detections in multiple elastography modes, the elastic parameters of the target tissue under at least two elastic detections in multiple elastography modes are transmitted to the processor 210 of the multimodal elastography system 200 in this embodiment to implement the functions (implemented by the processor 210) in the embodiments of the present application and / or other desired functions.

[0136] In one example, fusing at least the risk change rate and the elastic change rate to obtain a comprehensive time risk parameter of the target tissue includes: fusing the risk change rate and the elastic change rate to obtain a comprehensive time risk parameter of the target tissue, and / or fusing the elastic change rate, the risk change rate, and a feature change rate to obtain a comprehensive time risk parameter of the target tissue.

[0137] In one example, before fusing the elasticity change rate, the risk change rate, and the feature change rate to obtain the comprehensive time risk parameter of the target tissue, the processor 210 may acquire at least one feature parameter of the target tissue in at least one elastography mode under at least two elasticity detections, and determine the feature change rate of the target tissue based on the at least one feature parameter. The feature parameter is other characterization parameters of the target tissue except for the elasticity parameter and the tissue risk parameter. For example, the at least one feature parameter includes the lesion geometric area, or the at least one feature parameter includes blood flow changes, or the at least one feature parameter includes the lesion geometric area and blood flow changes.

[0138] To avoid repetition, the specific details of the multimodal elastography system in the embodiments of the present application may refer to the description of the multimodal elastography system above, and will not be repeated here.

[0139] In another embodiment of the present application, a multimodal elastography system is further provided. Referring to Figure 7 As shown, the multimodal elastography system 200 includes: a processor 210, an output device 220, and a memory 230. The processor 210 is configured to: acquire elasticity parameters of the target tissue in multiple elastography modes under at least two elasticity detections; determine the elasticity change rate of the target tissue in various elastography modes based on the elasticity parameters in multiple elastography modes under at least two elasticity detections, where the elasticity change rate is used to characterize the degree of change of the elasticity parameters in various elastography modes over time; fuse the elasticity change rates in various elastography modes to obtain a tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the risk degree of the target tissue presenting as malignant; and an output device 220 for outputting the tissue risk parameter.

[0140] Different from the multimodal elastography system described above, the multimodal elastography system 200 provided in this embodiment does not include an ultrasound probe, a transmit / receive selection switch, and a transmit / receive sequence controller. In this embodiment, the multimodal elastography system 200 may be connected to other elastography devices. After other elastography devices acquire the elasticity parameters of the target tissue in multiple elastography modes under at least two elasticity detections, the elasticity parameters of the target tissue in multiple elastography modes under at least two elasticity detections are transmitted to the processor 210 of the multimodal elastography system 200 in this embodiment, so as to implement the functions (implemented by the processor 210) in the embodiments of the present application and / or other desired functions.

[0141] To avoid repetition, the specific details of the multimodal elastography system in the embodiments of the present application may refer to the description of the multimodal elastography system above, and will not be repeated here.

[0142] In addition, the present application also provides a multi-modal elastography method implemented based on the multi-modal elastography system described above.

[0143] In one embodiment, referring to Figure 8 A multi-modal elastography method in an embodiment of the present application is described. This multi-modal elastography method can be implemented based on the multi-modal elastography system described above, and some detailed descriptions of this multi-modal elastography method can refer to the above.

[0144] As an example, as Figure 8 shown, the multi-modal elastography method may include the following steps 301 to 306, specifically as follows:

[0145] 301. Determine the elastic parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal under each elastic detection.

[0146] Among them, the multiple elastography modes include at least one of a strain elastography mode, a shear wave elastography mode, a viscoelasticity imaging mode, and an instantaneous elastography mode.

[0147] 302. Fuse the elastic parameters under each elastic detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elastic detection; wherein, the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy.

[0148] In an example, fusing the elastic parameters under each elastic detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elastic detection includes: obtaining the first fusion weight of the elastic parameters in multiple elastography modes; fusing the elastic parameters under each elastic detection in multiple elastography modes based on the first fusion weight to obtain the tissue risk parameter of the target tissue under each elastic detection.

[0149] In an example, the elastic parameters under each elastic detection in multiple elastography modes include a strain parameter and a Young's modulus, and the first fusion weight includes a strain fusion weight corresponding to the strain parameter and a modulus fusion weight corresponding to the Young's modulus. Fusing the elastic parameters under each elastic detection in multiple elastography modes based on the first fusion weight to obtain the tissue risk parameter of the target tissue under each elastic detection includes: fusing the strain parameter and the Young's modulus based on the modulus fusion weight and the strain fusion weight to obtain the tissue risk parameter of the target tissue under each elastic detection.

[0150] 303. Determine the risk change rate of the target tissue based on the tissue risk parameters under at least two elastic detections; wherein, the risk change rate is used to characterize the degree of change of the tissue risk parameter over time.

[0151] In one example, determining a risk change rate of a target tissue based on tissue risk parameters under at least two elasticity detections includes: determining a risk trend graph of the target tissue based on the tissue risk parameters under at least two elasticity detections, where the risk trend graph is used to characterize the change trend of the tissue risk parameters over time; determining the risk change rate of the target tissue based on the risk trend graph.

[0152] 304. Determine an elasticity change rate of the target tissue in at least one elasticity imaging mode based on elasticity parameters of the target tissue in at least one elasticity imaging mode under at least two elasticity detections; where the elasticity change rate is used to characterize the degree of change of the elasticity parameters in at least one elasticity imaging mode over time.

[0153] In one example, determining an elasticity change rate of the target tissue in at least one elasticity imaging mode based on elasticity parameters of the target tissue in at least one elasticity imaging mode under at least two elasticity detections includes: determining an elasticity trend graph of the target tissue in at least one elasticity imaging mode based on the elasticity parameters of the target tissue in at least one elasticity imaging mode under at least two elasticity detections, where the elasticity trend graph is used to characterize the change trend of the elasticity parameters in at least one elasticity imaging mode over time; determining the elasticity change rate of the target tissue in at least one elasticity imaging mode based on the elasticity trend graph.

[0154] 305. At least fuse the risk change rate and the elasticity change rate to obtain a comprehensive time risk parameter of the target tissue; where the comprehensive time risk parameter is used to characterize the development trend of the target tissue over time.

[0155] In one example, at least fusing the risk change rate and the elasticity change rate to obtain a comprehensive time risk parameter of the target tissue includes: fusing the elasticity change rate, the risk change rate, and a feature change rate to obtain a comprehensive time risk parameter of the target tissue. Where the feature parameter is other characterization parameters of the target tissue except for the elasticity parameter and the tissue risk parameter. For example, at least one feature parameter includes the geometric area of the lesion, or at least one feature parameter includes blood flow change, or at least one feature parameter includes the geometric area of the lesion and blood flow change.

[0156] In one example, before fusing the elasticity change rate, the risk change rate, and the feature change rate to obtain a comprehensive time risk parameter of the target tissue, it includes: obtaining at least one feature parameter of the target tissue in at least one elasticity imaging mode under at least two elasticity detections, where the feature parameter is other characterization parameters of the target tissue except for the elasticity parameter and the tissue risk parameter; determining the feature change rate of the target tissue based on at least one feature parameter.

[0157] In one example, after at least fusing the risk change rate and the elasticity change rate to obtain the comprehensive time risk parameter of the target tissue, it includes: obtaining a preset reference threshold; determining the development state of the target tissue based on the comprehensive time risk parameter and the reference threshold.

[0158] In one example, the reference threshold includes a first threshold and a second threshold. Determining the development state of the target tissue based on the comprehensive time risk parameter and the reference threshold includes: comparing the comprehensive time risk parameter with the first threshold and the second threshold; when the comprehensive time risk parameter is less than or equal to the first threshold, determining that the development state of the target tissue is the first state; when the comprehensive time risk parameter is greater than the first threshold and less than the second threshold, determining that the development state of the target tissue is the second state; when the comprehensive time risk parameter is greater than or equal to the second threshold, determining that the development state of the target tissue is the third state; wherein, the first state, the second state, and the third state are different development states.

[0159] 306. Output the comprehensive time risk parameter.

[0160] The above process of obtaining the comprehensive time risk parameter is automatically calculated by the multimodal elastography system. After obtaining the above comprehensive time risk parameter, in order to facilitate the user to view the comprehensive time risk parameter, the processor can also be used to control the output device to output the comprehensive time risk parameter, such as controlling the display to display the comprehensive time risk parameter on the display interface.

[0161] In one example, outputting the comprehensive time risk parameter includes: displaying the comprehensive time risk parameter on the display interface of the display in a preset display manner. In other examples, the output device can also be a printer, and the comprehensive time risk parameter can be output through the printer, that is, the comprehensive time risk parameter is printed out for the clinician to view.

[0162] The preset display manner can be any display method. For example, the preset display manner includes at least one of the following manners: graphics, text, voice, and color. As Figure 2 and Figure 3 shown, the comprehensive time risk parameter is displayed in text. For example, in the current 3rd examination, the comprehensive time risk index S = 1.2; in the current 5th examination, the comprehensive time risk index S = 1.9.

[0163] In another embodiment, refer to Figure 9 Describe another embodiment of the multimodal elastography method of the present application. This method can be executed based on the foregoing multimodal elastography system, or it can also be part or all of a computer device that can implement the multimodal elastography method through software, hardware, or a combination of software and hardware.

[0164] AsFigure 9 As shown in the figure, in the embodiment of the present application, the multi-modal elastography method may include the following steps 401 to 405, specifically as follows:

[0165] 401. Determine the elastic parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal under each elastic detection;

[0166] 402. Fuse the elastic parameters in multiple elastography modes for each elastic detection to obtain the tissue risk parameter of the target tissue under each elastic detection; wherein, the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy;

[0167] 403. Determine the risk trend graph of the target tissue based on the tissue risk parameters under at least two elastic detections; wherein, the risk trend graph is used to characterize the change trend of the tissue risk parameter over time;

[0168] 404. Determine the elastic trend graph of the target tissue in at least one elastography mode based on the elastic parameters of the target tissue in at least one elastography mode under at least two elastic detections; wherein, the elastic trend graph is used to characterize the change trend of the elastic parameters in at least one elastography mode over time;

[0169] 405. Output the risk trend graph and the elastic trend graph.

[0170] To avoid repetition, the specific details of each step in the embodiment of the present application can refer to the description of the multi-modal elastography system in the previous text, or can also refer to the relevant description of the method shown in the previous text Figure 8 shown.

[0171] In another embodiment, refer to Figure 10 A description is made of the multi-modal elastography method of another embodiment of the present application. This method can be executed based on the multi-modal elastography system in the previous text, or it can also be part or all of a computer device that can implement the multi-modal elastography method through software, hardware, or a combination of software and hardware.

[0172] As Figure 10 shown, in the embodiment of the present application, the multi-modal elastography method may include the following steps 501 to 505, specifically as follows:

[0173] 501. Determine the scanning area of the target tissue based on the interaction operation on the tissue image of the target tissue;

[0174] 502. Enter multiple elastography modes based on the interaction operations of multiple elastography modes;

[0175] 503. Determine the elastic parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal;

[0176] 504. Fuse the elastic parameters under multiple elastography modes to obtain the tissue risk parameter of the target tissue; wherein, the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy.

[0177] 505. Output the tissue risk parameter.

[0178] To avoid repetition, the specific details of each step in the embodiments of the present application can refer to the description of the multimodal elastography system above.

[0179] In another embodiment, refer to Figure 11 Describe the multimodal elastography method of another embodiment of the present application. This method can be executed based on the multimodal elastography system above, or it can also be part or all of a computer device that can implement the multimodal elastography method in a software, hardware, or software-hardware combination manner.

[0180] As Figure 11 shown, in the embodiments of the present application, the multimodal elastography method may include the following steps 601 to 604, specifically as follows:

[0181] 601. Determine the elastic parameters of the target tissue under multiple elastography modes according to the first ultrasonic echo signal in each elastic detection.

[0182] 602. Based on the elastic parameters of the target tissue under multiple elastography modes in at least two elastic detections, determine the elastic change rate of the target tissue under various elastography modes. The elastic change rate is used to characterize the degree of change of the elastic parameters under various elastography modes over time.

[0183] 603. Fuse the elastic change rates under various elastography modes to obtain the tissue risk parameter of the target tissue. The tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy.

[0184] 604. Output the tissue risk parameter.

[0185] To avoid repetition, the specific details of each step in the embodiments of the present application can refer to the description of the multimodal elastography system above.

[0186] It is worth mentioning that, on the premise of being reasonable, the order of each step shown in this article can also be adjusted. Figures 8 to 11 Figures 8 to 11 ​At least some of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least some of the sub-steps or stages of other steps or other steps.

[0187] In addition, an embodiment of the present application also provides a computer storage medium, on which a computer program is stored. One or more computer program instructions can be stored on the computer-readable storage medium, and the processor can run the program instructions stored in the storage device to implement the functions (implemented by the processor) in the embodiments of the present application described herein and / or other desired functions, such as to execute the corresponding steps of the multi-modal elastography method of the embodiments of the present application. Various application programs and various data can also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs, etc.

[0188] For example, the computer storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media.

[0189] The above has introduced in detail a multi-modal elastography system and method provided by an embodiment of the present application. Specific examples are used herein to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A multimodal elastography system, characterized in that, The multi-modal elastography system has multiple elastography modes and can perform at least two elastography detections on the same target tissue of the same patient at different times. The multi-modal elastography system includes: An ultrasound probe; A transmit / receive sequence controller for exciting the ultrasound probe to transmit a first ultrasonic wave to the target tissue in multiple elastography modes during each elastography detection, receiving a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtaining a first ultrasonic echo signal; A processor for: Determining the elastic parameters of the target tissue in multiple elastography modes according to the first ultrasonic echo signal under each elastography detection; Fusing the elastic parameters under each elastography detection in multiple elastography modes to obtain the tissue risk parameter of the target tissue under each elastography detection, where the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy; Determining the risk change rate of the target tissue based on the tissue risk parameters under at least two elastography detections, where the risk change rate is used to characterize the change degree of the tissue risk parameter over time; Determining the elastic change rate of the target tissue based on the elastic parameters of the target tissue in at least one elastography mode under at least two elastography detections, where the elastic change rate is used to characterize the change degree of the elastic parameters in at least one elastography mode over time; Fusing at least the risk change rate and the elastic change rate to obtain the comprehensive time risk parameter of the target tissue, where the comprehensive time risk parameter is used to characterize the development trend of the target tissue over time; An output device for outputting the comprehensive time risk parameter.

2. The multimodal elastography system according to claim 1, wherein, The processor is further used for: Obtaining at least one characteristic parameter of the target tissue in at least one elastography mode under at least two elastography detections, where the characteristic parameter is other characterization parameters of the target tissue except the elastic parameter and the tissue risk parameter; Determining the characteristic change rate of the target tissue based on at least one characteristic parameter, where the characteristic change rate is used to characterize the change degree of at least one characteristic parameter over time; The fusing at least the risk change rate and the elastic change rate to obtain the comprehensive time risk parameter of the target tissue includes: Fusing the elastic change rate, the risk change rate, and the characteristic change rate to obtain the comprehensive time risk parameter of the target tissue.

3. The multimodal elastography system according to claim 2, wherein At least one of the characteristic parameters includes at least one of the lesion geometric area and the blood flow change.

4. The multimodal elastography system according to claim 1, characterized in that The processor is further used for: Obtaining the first fusion weight of the elastic parameters in multiple elastography modes; Fusing the elastic parameters under each elastography detection in multiple elastography modes based on the first fusion weight to obtain the tissue risk parameter of the target tissue under each elastography detection.

5. The multimodal elastography system according to claim 4, wherein Each of the elastic parameters in multiple elastic imaging modes for each elastic detection includes a strain parameter and a Young's modulus, and the first fusion weight includes a strain fusion weight corresponding to the strain parameter and a modulus fusion weight corresponding to the Young's modulus; The processor is further configured to: Fuse the strain parameter and the Young's modulus based on the modulus fusion weight and the strain fusion weight to obtain a tissue risk parameter of the target tissue for each elastic detection.

6. The multimodal elastography system according to claim 1, wherein The processor is further configured to: Determine a risk trend graph of the target tissue based on the tissue risk parameters under at least two elastic detections, where the risk trend graph is used to characterize the change trend of the tissue risk parameter over time; Determine a risk change rate of the target tissue based on the risk trend graph.

7. The multimodal elastography system according to claim 1, wherein The processor is further configured to: Determine an elasticity trend graph of the target tissue based on the elastic parameters of the target tissue in at least one elastic imaging mode under at least two elastic detections, where the elasticity trend graph is used to characterize the change trend of the elastic parameters in at least one elastic imaging mode over time; Determine an elasticity change rate of the target tissue based on the elasticity trend graph in at least one elastic imaging mode.

8. The multimodal elastography system according to claim 1, wherein After at least fusing the risk change rate and the elasticity change rate to obtain a comprehensive time risk parameter of the target tissue, the processor is further configured to: Obtain a preset reference threshold; Determine the development state of the target tissue based on the comprehensive time risk parameter and the reference threshold.

9. The multi-modal elastography system according to claim 8, characterized in that, The reference threshold includes: a first threshold and a second threshold; The processor is further configured to: Compare the comprehensive time risk parameter with the first threshold and the second threshold; When the comprehensive time risk parameter is less than or equal to the first threshold, determine that the development state of the target tissue is the first state; When the comprehensive time risk parameter is greater than the first threshold and less than the second threshold, determine that the development state of the target tissue is the second state; When the comprehensive time risk parameter is greater than or equal to the second threshold, determine that the development state of the target tissue is the third state; where the first state, the second state, and the third state are different development states.

10. A multimodal elastography system, characterized in that, The multi-modal elastography system has multiple elastic imaging modes and can perform at least two elastic detections on the same target tissue of the same patient at different times. The multi-modal elastography system includes: An ultrasound probe; A transmit / receive sequence controller, configured to excite the ultrasound probe to transmit a first ultrasonic wave to the target tissue in multiple elastic imaging modes during each elastic detection process, receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtain a first ultrasonic echo signal; A processor, configured to: Determine the elastic parameters of the target tissue in multiple elastic imaging modes according to the first ultrasonic echo signal for each elastic detection; Fuse the elastic parameters of each elastic detection in multiple elastic imaging modes to obtain the tissue risk parameter of the target tissue under each elastic detection, where the tissue risk parameter is used to characterize the risk degree of the target tissue presenting as malignant; Determine the risk trend graph of the target tissue based on the tissue risk parameters under at least two elastic detections, where the risk trend graph is used to characterize the change trend of the tissue risk parameter over time; Determine the elastic trend graph of the target tissue in at least one elastic imaging mode based on the elastic parameters of the target tissue in at least one elastic imaging mode under at least two elastic detections, where the elastic trend graph is used to characterize the change trend of the elastic parameters in at least one elastic imaging mode over time; An output device for outputting the risk trend graph and the elastic trend graph.

11. The multimodal elastography system according to claim 10, wherein, The processor is further configured to: Determine the risk change rate of the target tissue based on the risk trend graph, where the risk change rate is used to characterize the degree of change of the tissue risk parameter over time; Determine the elastic change rate of the target tissue in at least one elastic imaging mode based on the elastic trend graph, where the elastic change rate is used to characterize the degree of change of the elastic parameters in at least one elastic imaging mode over time; The output device is further configured to output the risk change rate and the elastic change rate.

12. The multimodal elastography system according to claim 10, wherein The processor is further configured to: Obtain at least one characteristic parameter of the target tissue in at least one elastic imaging mode under at least two elastic detections, where the characteristic parameter is other characterization parameters of the target tissue except the elastic parameter and the tissue risk parameter; Determine the characteristic trend graph of the target tissue based on at least one characteristic parameter, where the characteristic trend graph is used to characterize the change trend of at least one characteristic parameter over time; The output device is further configured to output the characteristic trend graph.

13. The multimodal elastography system according to claim 12, wherein At least one of the characteristic parameters includes at least one of the lesion geometric area and the blood flow change.

14. A multimodal elastography system, characterized in that, The multi-modal elastography system has multiple elastic imaging modes, and the multi-modal elastography system includes: An ultrasonic probe; A transmit / receive sequence controller for exciting the ultrasonic probe to transmit a first ultrasonic wave to the target tissue, receiving a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtaining a first ultrasonic echo signal; A processor for: Determine the scanning area of the target tissue based on the interactive operation of the tissue image of the target tissue; Enter multiple elastic imaging modes based on the interactive operation of multiple elastic imaging modes; Determine the elastic parameters of the target tissue in multiple elastic imaging modes according to the first ultrasonic echo signal; Fuse the elastic parameters in multiple elastic imaging modes to obtain the tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the risk degree of the target tissue presenting as malignant; An output device for outputting the tissue risk parameter.

15. The multi-modal elastography system according to claim 14, wherein The fusing the elastic parameters in multiple elastic imaging modes to obtain the tissue risk parameter of the target tissue includes: Automatically fuse the elastic parameters under multiple elastic imaging modes to obtain the tissue risk parameter of the target tissue, or, based on the interaction operation of the elastic parameters under multiple elastic imaging modes, fuse the elastic parameters under multiple elastic imaging modes to obtain the tissue risk parameter of the target tissue.

16. The multi-modal elastography system according to claim 14, wherein The entering of the multiple elastic imaging modes based on the interaction operation of multiple elastic imaging modes includes: Enter multiple elastic imaging modes simultaneously based on one interaction operation; Or, Enter multiple elastic imaging modes sequentially based on multiple interaction operations.

17. The multi-modal elastography system according to claim 16, wherein, When entering multiple elastic imaging modes simultaneously, the processor is further configured to: Simultaneously determine the elastic parameters of the target tissue under multiple elastic imaging modes according to the first ultrasonic echo signal.

18. The multimodal elastography system according to claim 16, wherein, The processor is further configured to: Automatically switch between multiple elastic imaging modes based on a mode switching operation.

19. The multimodal elastography system according to claim 18, wherein The multiple elastic imaging modes include a first elastic imaging mode and a second elastic imaging mode. When automatically switching between multiple elastic imaging modes, the processor is further configured to: Obtain a first scanning area under the first elastic imaging mode and a second scanning area under the second elastic imaging mode; Determine the matching degree between the first scanning area and the second scanning area; The output device is further configured to output the matching degree.

20. A multimodal elastography system, characterized in that, The multimodal elastic imaging system has multiple elastic imaging modes and can perform at least two elastic detections on the same target tissue of the same patient at different times. The multimodal elastic imaging system includes: An ultrasonic probe; A transmit / receive sequence controller, configured to excite the ultrasonic probe to transmit a first ultrasonic wave to the target tissue under multiple elastic imaging modes during each elastic detection process, receive a first ultrasonic echo based on the first ultrasonic wave returned from the target tissue, and obtain a first ultrasonic echo signal; A processor, configured to: Determine the elastic parameters of the target tissue under multiple elastic imaging modes according to the first ultrasonic echo signal in each elastic detection; Based on the elastic parameters of the target tissue under multiple elastic imaging modes in at least two elastic detections, determine the elastic change rate of the target tissue under various elastic imaging modes, where the elastic change rate is used to characterize the degree of change of the elastic parameters with time under various elastic imaging modes; Fuse the elastic change rates under various elastic imaging modes to obtain the tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the risk degree of the target tissue presenting as malignant; An output device, configured to output the tissue risk parameter.

21. The multimodal elastography system according to claim 20, wherein, The processor is configured to: Based on the elastic parameters of the target tissue under multiple elastic imaging modes in at least two elastic detections, determine the elastic trend graph of the target tissue under various elastic imaging modes, where the elastic trend graph is used to characterize the change trend of the elastic parameters with time under various elastic imaging modes; Based on the elastic trend graph, determine the elastic change rate of the target tissue under various elastic imaging modes.

22. The multimodal elastography system according to claim 20 or 21, characterized in that, The processor is further configured to: normalize the tissue risk parameter to obtain a normalized tissue risk parameter; map the normalized tissue risk parameter to the tissue image of the target tissue to obtain a fused risk distribution map; The output device is further configured to output the fused risk distribution map.

23. The multimodal elastography system according to claim 22, wherein The processor is further configured to: determine the tissue risk parameter and / or the normalized tissue risk parameter of the target region based on an interaction operation on the target region in the fused risk distribution map; The output device is further configured to output the tissue risk parameter and / or the normalized tissue risk parameter of the target region.

24. The multimodal elastography system according to any one of claims 20 to 23, characterized in that, The elastography mode includes at least one of a strain elastography mode, a shear wave elastography mode, a viscoelastic imaging mode, and an instantaneous elastography mode.

25. The multimodal elastography system according to any one of claims 20 to 23, characterized in that, The transmit / receive sequence controller is further configured to excite the ultrasonic probe to transmit a second ultrasonic wave to the target tissue, and receive a second ultrasonic echo based on the second ultrasonic wave returned from the target tissue to obtain a second ultrasonic echo signal; The processor is further configured to: determine the tissue image of the target tissue according to the second ultrasonic echo signal.

26. A multimodal elastography system, characterized in that, The multimodal elastography system includes: a processor, configured to: acquire elastic parameters of a target tissue in a plurality of elastography modes under at least two elastic detections; fuse the elastic parameters in the plurality of elastography modes for each elastic detection to obtain a tissue risk parameter of the target tissue for each elastic detection, where the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy; determine a risk change rate of the target tissue based on the tissue risk parameters under at least two elastic detections, where the risk change rate is used to characterize the change degree of the tissue risk parameter over time; determine an elastic change rate of the target tissue in at least one of the elastography modes based on the elastic parameters of the target tissue in at least one of the elastography modes under at least two elastic detections, where the elastic change rate is used to characterize the change degree of the elastic parameters in at least one elastography mode over time; fuse at least the risk change rate and the elastic change rate to obtain a comprehensive time risk parameter of the target tissue, where the comprehensive time risk parameter is used to characterize the development trend of the target tissue over time; an output device, configured to output the comprehensive time risk parameter.

27. A multimodal elastography system, characterized in that, The multimodal elastography system includes: a processor, configured to: acquire elastic parameters of a target tissue in a plurality of elastography modes under at least two elastic detections; determine an elastic change rate of the target tissue in each of the elastography modes based on the elastic parameters in the plurality of elastography modes under at least two elastic detections, where the elastic change rate is used to characterize the change degree of the elastic parameters in each elastography mode over time; fuse the elastic change rates in each of the elastography modes to obtain a tissue risk parameter of the target tissue, where the tissue risk parameter is used to characterize the risk degree of the target tissue showing malignancy; an output device, configured to output the tissue risk parameter.

28. The multimodal elastography system according to claim 26 or 27, characterized in that, The elastography modes include at least one of a strain elastography mode, a shear wave elastography mode, a viscoelasticity elastography mode, and an instantaneous elastography mode.

29. The multimodal elastography system according to claim 26 or 27, wherein The processor is further configured to: acquire at least one characteristic parameter of the target tissue in at least one of the elastography modes under at least two elastic detections, where the characteristic parameter is another characterization parameter of the target tissue other than the elastic parameter and the tissue risk parameter; determine a characteristic change rate of the target tissue based on at least one of the characteristic parameters, where the characteristic change rate is used to characterize the change degree of at least one characteristic parameter over time; fuse at least the risk change rate and the elastic change rate to obtain a comprehensive time risk parameter of the target tissue, including: fuse the elastic change rate, the risk change rate, and the characteristic change rate to obtain a comprehensive time risk parameter of the target tissue.

30. The multi-modal elastography system according to claim 29, wherein, At least one of the characteristic parameters includes at least one of a lesion geometric area and a blood flow change.

31. A multimodal elastography method, characterized in that, The method is a multi-modal elastography method performed by the multi-modal elastography system according to any one of claims 1 to 30.

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