Providing biomechanical plaque data for interventional device simulation systems

Abstract

A computer-implemented method for providing biomechanical plaque data for an interventional device simulation system includes extracting plaque data from spectral CT data, where the plaque data represents a spatial distribution of plaque within a vascular region; converting the plaque data into biomechanical plaque data, where the biomechanical plaque data represents a spatial distribution of mechanical constraints to apply to an interventional device of the interventional device simulation system in response to contact between the interventional device and the plaque; and outputting the biomechanical plaque data.

Description

[Technical Field]

[0001] SUMMARY

[0002] The present disclosure relates to providing biomechanical plaque data for interventional device simulation systems. A computer-implemented method, computer program product, and system are disclosed. [Background technology]

[0002] Interventional device simulation systems are becoming increasingly important in familiarizing physicians with the use of interventional devices. Simulation systems provide virtual experiences of interventional procedures, allowing physicians to gain experience with new endovascular procedures in an authentic, low-risk setting. For example, in the vascular field, simulation systems have been developed and integrated into real vascular lab settings. Such systems are also used to plan patient-specific interventions.

[0003] An example of an interventional device simulation system in the vascular field is the Vist G5 endovascular simulator, marketed by Mentis AB, Gothenburg, Sweden. Another example of an interventional device simulation system in the vascular field is the Angio Mentor system by Simbionix Ltd. (Israel), marketed by Surgical Science Sweden AB, Gothenburg, Sweden. Such systems provide both visual and tactile feedback that authentically mimics the look and feel of a real endovascular intervention. Many different types of endovascular procedures can be simulated using such systems. Simulations can be performed with different types of interventional devices and in different types of patient anatomies. For example, navigational procedures such as catheter insertion can be simulated. Interventional procedures such as atherectomy procedures can also be simulated. Summary of the Invention [Problem to be solved by the invention]

[0004] A key factor in providing authentic simulations of the vascular system is accurately representing vascular plaque. Vascular plaque is formed from materials such as fat, cholesterol, calcium, fibrin, and other substances that build up in the walls of arteries. Plaque hardens and narrows arteries, restricting blood flow and oxygen delivery to vital organs and increasing the risk of blood clots that can potentially block blood flow to the heart or brain. Consequently, the presence of vascular plaque also affects interventional device navigation, vessel deformation, and treatment decisions. However, correlation between simulated and actual procedures, and any benefits derived therefrom, can only be realized if plaque characteristics are accurately represented in the simulation. [Means for solving the problem]

[0005] According to one aspect of the present disclosure, a computer-implemented method for providing biomechanical plaque data for an interventional device simulation system is provided, the method comprising: receiving spectral CT data representing a vascular region, the spectral CT data defining x-ray attenuation within the vascular region at a plurality of different energy intervals; extracting plaque data from the spectral CT data, the plaque data representing a spatial distribution of plaque within the vascular region; converting the plaque data into biomechanical plaque data, the biomechanical plaque data representing a spatial distribution of mechanical constraints for application to an interventional device in the interventional device simulation system in response to contact between the interventional device and the plaque; outputting the biomechanical plaque data; It has.

[0006] Spectral CT data defines X-ray attenuation at multiple different energy intervals. Processing data from multiple different energy intervals allows for differentiation between media that have similar X-ray attenuation values ​​when measured within a single energy interval and are indistinguishable using conventional X-ray attenuation data. Because plaque data is extracted from the spectral CT data in the above-described method, the plaque data provides a more accurate representation of the spatial distribution of plaque. Furthermore, the plaque data is converted into biomechanical plaque data that represents the spatial distribution of mechanical constraints to be applied to an interventional device in an interventional device simulation system in response to contact between the interventional device and plaque, providing a more accurate simulation of the interaction between the interventional device and plaque.

[0007] Further aspects, features, and advantages of the present disclosure will become apparent from the following description of exemplary embodiments that proceeds with reference to the accompanying drawings. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is an example of a graphical representation of a vascular region 130, according to some embodiments of the present disclosure. [Figure 2] FIG. 1 is a schematic diagram illustrating an example of the spatial distribution of plaque 140 within a vascular region, according to some embodiments of the present disclosure. [Figure 3] 1 is a flowchart illustrating an example of a computer-implemented method for providing biomechanical plaque data for an interventional device simulation system, according to some aspects of the present disclosure. [Figure 4] FIG. 2 is a schematic diagram illustrating an example of a system 200 for providing biomechanical plaque data for an interventional device simulation system, according to some aspects of the present disclosure. [Figure 5] FIG. 1 is a schematic diagram illustrating an example of an interventional device simulation system 100 according to some aspects of the present disclosure. [Figure 6]1 is a flowchart illustrating an example of a computer-implemented method for simulating the navigation of an interventional device within a vascular region, according to some aspects of the present disclosure. [Figure 7] 1 is a flowchart illustrating an example of a computer-implemented method for providing guidance for a current interventional procedure involving navigation of an interventional device within a vascular region, according to some aspects of the present disclosure. [Figure 8] 1 is a flowchart illustrating an example of a computer-implemented method for identifying the type of interventional device to be used in a current intervention procedure, according to some aspects of the present disclosure. [Figure 9] 1 is a flowchart illustrating an example of a computer-implemented method for providing a predictive model for controlling a robotic interventional device manipulator to navigate an interventional device within a vascular region, according to some aspects of the present disclosure. [Figure 10] 1 is a flowchart illustrating an example of a computer-implemented method for simulating robotic navigation of an interventional device within a vascular region during a current interventional device navigation procedure using an interventional device simulation system, according to some aspects of the present disclosure. [Figure 11] 1 is a flowchart illustrating an example of a computer-implemented method for determining differences between a simulated robotic interventional device navigation procedure and a current interventional device navigation procedure, according to some aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] Examples of the present disclosure are provided with reference to the following description and drawings. In this description, for purposes of explanation, many specific details of several examples are set forth. Reference herein to an "example," "implementation," or similar language means that a feature, structure, or characteristic described in connection with an example is included in at least one of the examples. It should also be appreciated that features described in connection with one example may also be used in other examples, and that for purposes of brevity, not all features are necessarily replicated in each example. For example, features described in connection with a computer-implemented method may be implemented in a corresponding manner in a computer program product and in a system.

[0010] In the following description, reference is made to a computer-implemented method that includes providing biomechanical plaque data for an interventional device simulation system. Reference is made to an example in which biomechanical plaque data is provided for a vascular region within the heart. However, it should be understood that a vascular region may generally be located anywhere within the body. For example, a vascular region may be located in the brain, arm, leg, etc. In some examples, reference is made to providing biomechanical plaque data for an artery. However, it should be noted that biomechanical plaque data may generally be provided for any blood vessel within the vascular system. For example, biomechanical plaque data may alternatively be provided for a vein. In some examples, reference is made to providing biomechanical plaque data for mature plaque. However, it should be understood that this type of plaque serves as an example only, and that the methods disclosed herein may be used to provide biomechanical plaque data for plaques at different stages of maturation. For example, the plaque may be a so-called fatty streak or a ruptured plaque. Accordingly, the methods disclosed herein may also be used to provide biomechanical plaque data for plaques having a composition different from that of mature plaque.

[0011] It should be noted that the computer-implemented methods disclosed herein may be provided as a non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by at least one processor, cause the at least one processor to perform the method. In other words, the computer-implemented methods may be implemented in a computer program product. The computer program product may be provided by dedicated hardware or hardware capable of executing software in association with appropriate software. When provided by a processor, the functionality of the method features may be provided by a single dedicated processor, by a single shared processor, or by multiple individual processors, some of which may be shared. One or more functions of the method features may be provided by a processor shared within a networked processing architecture, such as, for example, a client / server architecture, a peer-to-peer architecture, the Internet, or the cloud.

[0012] Explicit use of the terms "processor" or "controller" should not be construed as exclusively referring to hardware capable of executing software, and can implicitly include, but is not limited to, digital signal processor "DSP" hardware, read-only memory "ROM" for storing software, random access memory "RAM," non-volatile storage, and the like. Furthermore, examples of the present disclosure can take the form of a computer-usable storage medium, or a computer program product accessible from a computer-readable storage medium, the computer program product providing program code for use by or in connection with a computer or any instruction execution system. For purposes of this description, a computer-usable storage medium or computer-readable storage medium can be any apparatus that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or device or propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer diskettes, random access memory "RAM," read-only memory "ROM," rigid magnetic disks, and optical disks. Current examples of optical disks include compact disk-read only memory "CD-ROM", compact disk-read / write "CD-R / W", Blu-Ray™, and DVD.

[0013] As mentioned above, the correlation between the simulated and actual procedures, and any benefits derived therefrom, can only be realized if the properties of the plaque are accurately represented in the simulation.

[0014] FIG. 1 is an example of a graphical representation of a vascular region 130 according to some embodiments of the present disclosure. The vascular region 130 shown in FIG. 1 is a portion of the heart, and the vascular region includes a coronary artery. A lumen 160 of the coronary artery is shown in FIG. 1. FIG. 2 is a schematic diagram illustrating an example of the spatial distribution of plaque 140 within a vascular region according to some embodiments of the present disclosure. The spatial distribution of plaque 140 shown in FIG. 2 represents plaque in the portion of the coronary artery shown in FIG. 1, as indicated by the line connecting FIG. 1 and FIG. 2. The plaque 140 shown in FIG. 2 is a so-called mature plaque and includes a fibrous cap. The fibrous cap is formed from intimal smooth muscle cells and connective tissue. The fibrous cap separates the lumen 160 of the coronary artery from the thrombus-forming core of the plaque and, as such, is the final barrier against thrombus formation. The plaque core shown in FIG. 2 includes foam cells, cholesterol, and other lipids.

[0015] Over time, the composition and distribution of plaque within a blood vessel can also change. For example, a plaque can begin as a so-called fatty streak and then evolve into a mature plaque, as shown in FIG. 1. The mature plaque can then progress further by rupture, resulting in a thrombus. During its evolution, vascular calcification can also occur within the blood vessel 130. Vascular calcification is defined as the deposition of minerals in the vasculature in the form of calcium-phosphate complexes. Vascular calcification can occur in the intimal and medial layers.

[0016] Therefore, the spatial distribution of plaque and its composition depend on its stage of evolution, which in turn influences its biomechanical properties, i.e., the mechanical properties that govern its response to the application of external forces. Therefore, to accurately represent the interaction between interventional devices and plaque, it is important to accurately represent the spatial distribution of plaque's biomechanical properties.

[0017] FIG. 3 is a flowchart illustrating an example of a computer-implemented method for providing biomechanical plaque data for an interventional device simulation system according to some aspects of the present disclosure. FIG. 4 is a schematic diagram illustrating an example of a system 200 for providing biomechanical plaque data for an interventional device simulation system according to some aspects of the present disclosure. The system 200 includes one or more processors 210. It should be noted that the operations described in connection with the method illustrated in FIG. 3 may also be performed by one or more processors 210 of the system 200 illustrated in FIG. 4. Similarly, the operations described in connection with one or more processors 210 of the system 200 may also be performed in the method described with reference to FIG. 3. Referring to FIG. 3, the computer-implemented method for providing biomechanical plaque data 110 for the interventional device simulation system 100 includes: A step S110 of receiving spectral CT data 120 representing a vascular region 130, said spectral CT data being divided into a plurality of different energy intervals DE 1..m defining X-ray attenuation within the vascular region in extracting plaque data from the spectral CT data, the plaque data representing a spatial distribution of plaque within the vascular region; converting S130 the plaque data into biomechanical plaque data 110, the biomechanical plaque data representing a spatial distribution of mechanical constraints for application to the interventional device 150 of the interventional device simulation system 100 in response to contact between the interventional device 150 and the plaque 140; Step S140 of outputting the biomechanical plaque data 110; Includes.

[0018] Spectral CT data defines X-ray attenuation at multiple different energy intervals. Processing data from multiple different energy intervals allows for differentiation between media that have similar X-ray attenuation values ​​when measured within a single energy interval and are indistinguishable using conventional X-ray attenuation data. Because plaque data is extracted from the spectral CT data in the above-described method, the plaque data provides a more accurate representation of the spatial distribution of plaque. Furthermore, the plaque data is converted into biomechanical plaque data that represents the spatial distribution of mechanical constraints to be applied to an interventional device in an interventional device simulation system in response to contact between the interventional device and plaque, providing a more accurate simulation of the interaction between the interventional device and plaque.

[0019] 3, in act S110, spectral CT data 120 is received. The spectral CT data represents a vascular region 130 and includes a plurality of different energy intervals DE 1..m Define the X-ray attenuation within the blood vessel region.

[0020] In general, the spectral CT data 120 received in act S110 may be raw data, i.e., data that has not yet been reconstructed into a volumetric or 3D image, or may be image data, i.e., data that has already been reconstructed into a volumetric image. In general, there may be more than one energy interval, i.e., m is an integer and m≧2.

[0021] The spectral CT data 120 received in act S110 may be generated by a spectral CT imaging system, or alternatively, may be generated by rotating or stepping an X-ray source and an X-ray detector of a spectral X-ray projection imaging system around the vascular region, as described in more detail below. More generally, the spectral CT data 120 received in act S110 may be generated by a spectral X-ray imaging system.

[0022] A spectral CT imaging system generates spectral CT data by rotating or stepping an X-ray source-detector device around an object to obtain X-ray attenuation data of the object from multiple rotation angles relative to the object. The spectral CT data can then be reconstructed into a 3D image of the object. Examples of spectral CT imaging systems that can be used to generate the spectral CT data 120 include a cone-beam spectral CT imaging system, a photon-counting spectral CT imaging system, a dark-field spectral CT imaging system, and a phase-contrast spectral CT imaging system. One example of a spectral CT imaging system that can be used to generate the spectral CT data 120 received in operation S110 is the Spectral CT 7500 sold by Philips Healthcare of Best, The Netherlands.

[0023] As described above, the spectral CT data 120 received in operation S110 can alternatively be generated by rotating or stepping the X-ray source and X-ray detector of a spectral X-ray projection imaging system around the vascular region. A spectral X-ray projection imaging system typically includes a support arm, such as a so-called “C-arm,” that supports the X-ray source and X-ray detector. A spectral X-ray projection imaging system may alternatively include a support arm having a different shape from this example, such as an O-arm. In contrast to a spectral CT imaging system, a spectral X-ray projection imaging system generates X-ray attenuation data for an object in which the X-ray source and X-ray detector are stationary relative to the object. X-ray attenuation data is sometimes referred to as projection data, in contrast to volumetric data generated by a spectral CT imaging system. The X-ray attenuation data generated by a spectral X-ray projection imaging system is typically used to generate a 2D image of the object. However, a spectral X-ray projection imaging system can generate spectral CT data, i.e., volumetric data, by rotating or stepping its X-ray source and X-ray detector around the object and acquiring projection data of the object from multiple rotation angles relative to the object. Image reconstruction techniques can then be used to reconstruct the projection data acquired from the multiple rotation angles into a volumetric image in a manner similar to the reconstruction of a volumetric image using X-ray attenuation data acquired from a spectral CT imaging system. Thus, the spectral CT data 120 received in operation S110 may be generated by a spectral CT imaging system, or alternatively, may be generated by a spectral X-ray projection imaging system.

[0024] X-ray attenuation data is collected over a number of different energy intervals. 1..m The ability to generate X-ray attenuation data distinguishes spectral X-ray imaging systems from conventional X-ray imaging systems. By processing data from multiple different energy intervals, it is possible to distinguish between media that have similar X-ray attenuation values ​​when measured within a single energy interval, and that would be indistinguishable using conventional X-ray attenuation data.

[0025] In general, the spectral CT data 120 can be generated by a variety of different configurations of a spectral X-ray imaging system, including an X-ray source and an X-ray detector. The X-ray source of the spectral X-ray imaging system can include multiple monochromatic sources or one or more polychromatic sources, and the X-ray detector of the spectral X-ray imaging system can include a common detector for detecting multiple different X-ray energy intervals, or multiple detectors, each detector detecting a different X-ray energy interval DE. 1..m The detector may include a multi-layer detector in which X-rays having energies within different X-ray energy intervals are detected by corresponding layers, or a photon-counting detector that classifies detected X-ray photons into one of a plurality of energy intervals based on their individual energies. In a photon-counting detector, the associated energy interval can be determined for each received X-ray photon by detecting the pulse height induced by the electron-hole pairs generated in response to absorption of the X-ray photon in the direct conversion material.

[0026] Using the various configurations of X-ray source and detector described above, various X-ray energy intervals DE 1..m Typically, discrimination between different X-ray energy intervals can be provided at the source by switching the X-ray tube potential of a single X-ray source in time, i.e., by "rapid kVp switching," or by switching or filtering the X-ray emission from multiple X-ray sources in time. The time switch allows multiple different X-ray energy intervals DE to be detected within a rotation of the X-ray source detector device. 1..mAlternatively, X-ray attenuation data for an energy interval may be acquired for a specified number of gantry rotations before switching to another energy interval, and X-ray attenuation data for that energy interval may be acquired in a similar manner. In such a setup, a common X-ray detector may be used to detect X-rays across multiple different energy intervals, and X-ray attenuation data for each energy interval may be generated in a time series. Alternatively, a multi-layer detector or photon-counting detector may be used to provide the detector with the ability to distinguish between different X-ray energy intervals. Such a detector may be used to detect X-rays across multiple X-ray energy intervals. 1..m X-rays from the source can be detected almost simultaneously, and therefore no time switching in the source is required. In this way, a multi-layer detector, or a photon-counting detector, can be used in conjunction with a polychromatic source to detect X-rays from various X-ray energy intervals DE. 1..m X-ray attenuation data can be generated.

[0027] Other combinations of the aforementioned X-ray sources and detectors may also be used to provide the spectral CT data 120. For example, in a further configuration, the need to sequentially switch between different X-ray sources emitting X-rays at different energy intervals may be avoided by mounting the X-ray source-detector pairs on the gantry at rotationally offset positions about the axis of rotation. In this configuration, each source-detector pair operates independently and emits X-rays at different energy intervals DE 1..m The separation between the spectral CT data is facilitated by the rotational offset of the source-detector pair. In this setup, the energy interval DE is obtained by applying an energy selection filter to the X-ray detector to reduce the influence of X-ray scattering. 1..m Improved separation between the spectral CT data can be achieved.

[0028] In general, the spectral CT data 120 received in act S110 may be received via any form of data communication, including wired communication, optical communication, and wireless communication. As some examples, when wired or optical communication is used, the communication may be via signals transmitted over electrical or optical cables, and when wireless communication is used, the communication may be via, for example, RF or optical signals. The spectral CT data 120 received in act S110 may be received from a variety of sources. For example, the spectral CT data 120 may be received from a spectral X-ray imaging system, such as one of the spectral X-ray imaging systems described above. Alternatively, the spectral CT data 120 may be received from another source, such as, for example, a computer-readable storage medium, the internet, or the cloud.

[0029] 3, in S120, plaque data is extracted from the spectral CT data 120. The plaque data represents the spatial distribution of plaque 140 within the vascular region 130.

[0030] In operation S120, various material decomposition techniques can be used to extract the plaque data. These include the use of various material decomposition algorithms and the so-called "Z" which represents the effective atomic number of the medium. eff This involves the generation of an "image" from which various materials can be identified.

[0031] An example of a material decomposition technique that can be used to extract plaque data in act S120 is disclosed in Brendel, B. et al., "Empirical, projection-based basis—component decomposition method," Medical Imaging 2009, Physics of Medical Imaging, edited by Ehsan Samei and Jiang Hsieh, Proc. of SPIE Vol. 7258, 72583Y. Another example of a material decomposition algorithm that can be used is disclosed in Roessl, E. and Proksa, R., "K-edge imaging in X-ray computed tomography using multi-bin photon counting detectors," Phys Med Biol. 2007 Aug 7, 52(15), 4679-96. Another example of a material decomposition algorithm that can be used is disclosed in published PCT patent application WO / 2007 / 034359 A2. These and other material decomposition algorithms can be used to extract plaque data for different types of plaque, such as soft plaque, calcified plaque, etc., from the spectral CT data 120 .

[0032] As mentioned above, in another approach, in operation S120, a so-called "Z" representing the effective atomic number medium is used. eff The plaque data can be extracted using the image. eff A technique for generating images is disclosed, for example, in Saito, M. et al., "A simple formulation for deriving effective atomic numbers via electron density calibration from dual-energy CT data in the human body" (Medical Physics, Vol. 44, Issue 6, June 2017, pages 2293-2303). effAfter generating the image, data for the desired material can be extracted from the image by selecting data within the range of effective atomic numbers corresponding to the material. For example, plaque data for calcified plaque or soft plaque can be extracted by selecting Z 1 within the relevant range of effective atomic numbers corresponding to calcified plaque or soft plaque, respectively. eff By selecting data from the images, it can be extracted from the spectral CT data.

[0033] 3 , in operation S130, the plaque data is converted into biomechanical plaque data 110. The biomechanical plaque data represents a spatial distribution of mechanical constraints to apply to the interventional device 150 of the interventional device simulation system 100 in response to contact between the interventional device 150 and the plaque 140.

[0034] FIG. 5 is a schematic diagram illustrating an example of an interventional device simulation system 100 according to some embodiments of the present disclosure. The interventional device simulation system 100 shown in FIG. 5 includes a user interface device 170 and one or more processors 180. The user interface device 170 generates user input data for guiding the interventional device 150 within the vascular region 130 in response to received user input. The one or more processors 180 determine a virtual position of the interventional device 150 within the vascular region based on the user input data generated by the user interface device 170. The vascular region is defined by a 3D mathematical model, and as a result, the position of the interventional device within the vascular region is a "virtual" position. The virtual position of the interventional device 150 within the vascular region is then displayed on a display device, such as on a monitor 190 shown in FIG. 5. By facilitating the user's navigation of the interventional device within the vasculature, the user can gain experience with various interventional procedures using the interventional device simulation system 100 shown in FIG. 5.

[0035] During navigation of an interventional device in a vascular region, the interventional device often comes into contact with plaque. The contact is "virtual" in the sense that the position of the interventional device in the vascular region is virtual, and therefore the contact with the plaque is also virtual. As mentioned above, the spatial distribution of plaque and its composition depend on its stage of evolution. This affects its biomechanical properties, i.e., the mechanical properties that govern its response to the application of external forces. Therefore, to accurately represent the interaction between the interventional device and plaque, it is important to accurately represent the spatial distribution of plaque's biomechanical properties.

[0036] In act S130, the plaque data is converted into biomechanical plaque data 110. The biomechanical plaque data may represent the spatial distribution of one or more mechanical constraints to apply to the interventional device 150 of the interventional device simulation system 100 in response to contact between the interventional device 150 and the plaque 140.

[0037] An example of a mechanical constraint that may be applied to the interventional device 150 in act S130 is a spatial limit. In this example, the spatial limit is applied to the position of the interventional device 150 in response to contact between the interventional device 150 and the plaque 140. The spatial limit may define that the contact portion of the interventional device cannot extend beyond the surface of the plaque. Thus, the spatial limit affects the freedom of movement of the interventional device. The spatial limit may also change in response to the amount of force applied to the plaque by the interventional device. This may be used to model the deformation of the plaque surface in response to the force, whereupon a new spatial limit is defined based on the deformed surface of the plaque. The deformation of the plaque surface may also be modeled to represent plaque rupture in response to excessive force, whereupon a further spatial limit is defined based on the expected shape of the ruptured plaque. If the plaque data extracted from the spectral CT data 120 in act S110 distinguishes between different types of plaque, the amount of deformation may be calculated for each type of plaque.

[0038] In general, the deformation of the surface of a plaque can be modeled using the mechanical properties of the plaque. In this regard, the deformation of the surface of a plaque can be modeled using the mechanical properties of the plaque, such as its elasticity, plasticity, ductility, malleability, hardness, toughness, brittleness, toughness, fatigue, fatigue resistance, impact resistance, strength, strain energy, elasticity, elasticity, modulus of elasticity, creep, fracture, and toughness modulus. One way to express the mechanical properties of a plaque is through its stress-strain curve. The stress-strain curve of a material governs the amount of deformation of the material when an external force is applied. Therefore, the stress-strain curve of the plaque can be used to model the deformation of the surface of the plaque in response to a force applied to the plaque by an interventional device at the contact point. The modeled deformation of the plaque surface then presents new spatial constraints that apply to the location of the interventional device 150 in response to contact between the interventional device 150 and the plaque 140. If the fracture point on the stress-strain curve is exceeded by applying excessive force, the plaque can be expected to fracture. This effect can also be modeled using the stress-strain curve by applying additional spatial constraints defined based on the expected shape of the ruptured plaque if the failure point on the stress-strain curve is exceeded. In the simulation, this event is recorded, possibly as a key performance indicator leading to a visual notification.

[0039] Another example of a mechanical constraint that may be applied to the interventional device 150 in operation S130 is friction. In this example, an amount of friction is applied to the interventional device 150 in response to contact between the interventional device 150 and the plaque 140. The friction reduces the mobility of the interventional device where it contacts the plaque. In turn, the friction may cause the interventional device to flex, which in turn affects its movement in the vascular region. The amount of friction can be calculated, for example, using the friction coefficient of the plaque.

[0040] Another example of a mechanical constraint that may be applied to the interventional device 150 in operation S130 is the amount of force feedback applied to a force feedback user interface device of the simulation system in response to contact between the interventional device 150 and the plaque 140. Some user interface devices of the simulation system include so-called force feedback, in which the user experiences opposing forces in response to contact between the interventional device 150 and media in the vasculature. In such a user interface, the user may experience opposing forces in response to, for example, an attempt to press a guidewire against an obstacle in the vasculature. Force feedback improves the authenticity of the user's training experience. With such a user interface, the amount of force feedback applied is determined by modeling the mechanical properties of the interventional device. In this example, the applied mechanical constraint, i.e., the amount of force feedback, is determined by further considering the spatial constraints described above. In other words, the spatial constraints provided by the plaque data, within which plaque deformation and plaque rupture can be modeled, are used to determine the amount of force feedback applied to the force feedback user interface device. As a result, so-called soft plaque may feel softer to the user of the user interface device than hard plaque. Additionally, the rupture of the plaque may be experienced by the user of the force feedback user interface device as a feedback change in the amount of force feedback.

[0041] In general, the operation S130 of converting the plaque data into biomechanical plaque data 110 may be performed using a functional relationship between the plaque data and the mechanical constraints. The functional relationship may be provided, for example, by a graph or a look-up table.

[0042] As an example, the plaque data extracted from the spectral CT data 120 may represent the spatial distribution of plaque in two categories: hard plaque and soft plaque. A functional relationship in the form of a graph representing the relationship between stress and strain may be provided for soft plaque, and a separate graph may be provided for hard plaque. Upon contact between an interventional device and plaque, the stress-strain graph for the associated soft / hard plaque is queried based on the amount of force exerted on the plaque by the interventional device to model the amount of deformation induced in the plaque. This deformation is then used to modify the spatial constraints applied to the position of the interventional device by modifying the plaque data so that the shape of the plaque represented by the plaque data is deformed according to the deformation. If, based on the stress-strain graph, the amount of force exerted on the plaque by the interventional device is sufficient to cause the plaque to fracture, this effect may be represented in the modified plaque data by deforming the plaque such that the modified plaque data represents the estimated shape of the ruptured plaque.

[0043] As another example, a lookup table can be used to store values ​​for the coefficient of friction for each plaque category, i.e., soft plaque and hard plaque. Depending on the contact between the interventional device and the plaque, the lookup table for the associated soft / hard plaque is queried to determine the coefficient of friction to use. The coefficient of friction is then used to calculate the amount of friction to apply as a constraint to the interventional device depending on the contact between the interventional device 150 and the plaque 140.

[0044] Returning to the method shown in FIG. 3 , in operation S140, the biomechanical plaque data 110 is output. The biomechanical plaque data 110 can be output in various ways. In one example, the biomechanical plaque data 110 is output to a computer-readable storage medium. In another example, a graphical representation of the biomechanical plaque data 110 is output to a display device. For example, the graphical representation of the biomechanical plaque data 110 may be output to a monitor or another type of display device, such as a virtual reality headset or an augmented reality headset. The graphical representation of the biomechanical plaque data 110 may be output to the display device as a 3D image or as a 2D image. The 2D image may be generated by projecting a 3D image. The graphical representation of the biomechanical plaque data 110 may include color coding, shading, or another type of formatting to show the plaque, and if extracted from spectral CT data, the biomechanical plaque data 110 for different types of plaque, such as soft / hard plaque, may also be depicted. In another example, the biomechanical plaque data 110 is output to the internet or the cloud. In another example, the biomechanical plaque data 110 is output to a printer.

[0045] In one example, a graphical representation of the vascular region 130 is output, where the graphical representation of the vascular region 130 includes a graphical representation of the plaque data and / or a graphical representation of the output biomechanical plaque data 110. In this example, the graphical representation of the vascular region 130 represents blood vessels within the vascular region. The graphical representation of the vascular region 130 may be generated from lumen data extracted from the spectral CT data 120 received in act S110. For example, a material decomposition algorithm, or Z-parameter algorithm, may be used to extract the lumen data from the spectral CT data. effThe image can be used to generate an image of the blood vessels in the vascular region from the lumen data. This image can then be overlaid with the graphical representation of the plaque data and / or the output graphical representation of the biomechanical plaque data 110. In a related example, the graphical representation can include a virtual angiographic projection of the vascular region 130. Such an image can be used to facilitate clinical diagnosis of the vascular region by a physician.

[0046] In another example, lumen data for a vascular region is output. The lumen data may be used in combination with biomechanical plaque data in a simulation performed using the interventional device simulation system 100 shown in FIG. 5. For example, the lumen data may be used to generate a 3D mathematical model representing the vascular region. A simulation may be performed using the lumen data in combination with the biomechanical plaque data, where both the lumen data and the biomechanical plaque data provide spatial constraints that apply to the position of an interventional device as it navigates through the lumen within the vascular region. In this example, the method described with reference to FIG. extracting lumen data from the spectral CT data 120, the lumen data representing the three-dimensional shape of one or more lumens 160 within the vascular region 130; outputting the lumen data; Includes.

[0047] Lumen data can be extracted from the spectral CT data in a similar manner to plaque data, i.e., by using material decomposition techniques. Lumen data can be output in a similar manner to biomechanical plaque data 110. In one example, the lumen data and plaque data are output as graphical representations. For example, a 3D image may be generated from the lumen data, and the 3D image may be overlaid with a graphical representation of the biomechanical plaque data 110. By providing both the lumen data and the biomechanical plaque data 110 from the spectral CT data, personalized simulation data for a vascular region can be provided, where the two data sets are essentially registered to each other.

[0048] In another example, the method described with reference to FIG. 3 includes calculating a spatial distribution of risk of plaque rupture from plaque data and outputting a graphical representation of the risk of plaque rupture.

[0049] Because the plaque data is extracted from the spectral CT data, the plaque data provides a more accurate depiction of the plaque than if the plaque data were extracted from conventional CT data. For example, the use of spectral CT data facilitates the determination of the type or plaque and its composition, as described above. A high risk of rupture is associated with factors such as the presence of a fibrous cap on the plaque, as shown in FIG. 1. Therefore, in this example, the risk of plaque rupture can be calculated by detecting such features in the plaque data. The graphical representation of the risk of plaque rupture can be output in various ways. For example, it may be output as an overlay image on the biomechanical plaque data, where color coding, shading, or another type of formatting is used in the overlay to depict the risk of plaque rupture. The risk of plaque rupture can be used in addition to the biomechanical plaque data in simulations performed using the interventional device simulation system 100 shown in FIG. 5. For example, the risk of plaque rupture can be used to simulate areas of plaque that will rupture as a result of contact between an interventional device and the plaque.

[0050] In another example, a computer-implemented method for providing biomechanical plaque data 110 for an interventional device simulation system 100 is performed by a system 200. The system 200 for providing biomechanical plaque data 110 for an interventional device simulation system 100 includes: receiving spectral CT data 120 representing a vascular region 130, the spectral CT data being spaced apart from a plurality of different energy intervals DE 1..m Step S110: defining X-ray attenuation in a blood vessel region in Step S120: extracting plaque data from the spectral CT data 120, the plaque data representing the spatial distribution of plaque 140 within the vascular region 130; Step S130: converting the plaque data into biomechanical plaque data 110, the biomechanical plaque data representing a spatial distribution of mechanical constraints for application to the interventional device 150 of the interventional device simulation system 100 in response to contact between the interventional device 150 and the plaque 140; Step S140 of outputting biomechanical plaque data 110; The system includes one or more processors 210 configured to execute the

[0051] The system 200 may also include a spectral CT imaging system 220 for providing the spectral CT data 120. The system 200 may also include a display, such as a monitor 230 shown in FIG. 4. The display may be used to display graphical representations of the spectral CT data, plaque data, biomechanical plaque data 110, etc. The system 200 may also include a patient bed 240.

[0052] In another example, an interventional device simulation system 100 is provided for simulating the navigation of an interventional device 150 within a vascular region 130. The simulation system includes: a user interface device 170; and one or more processors 180 Including, the user interface device 170 is configured to generate user input data for guiding the interventional device 150 within the vascular region 130 in response to the received user input; One or more processors 180 receiving lumen data and biomechanical plaque data 110;

[0053] determining a virtual position of an interventional device 150 within one or more lumens 160 represented by the lumen data based on the user input data generated by the user interface device 170; configured to run

[0054] The step of determining the virtual position of the interventional device 150 includes applying mechanical constraints represented by the biomechanical plaque data 110 to the interventional device 150 in response to contact between the interventional device 150 and the plaque 140.

[0055] An example of the system 100 is shown in FIG. 5. The user interface 170 can be provided by various types of devices. For example, the user interface 170 can include one or more input devices, such as a joystick, a mouse, a touchpad, a keyboard, a rotary knob, a switch, a slider control, a gesture control device, etc. Such a user interface generates user input data in the form of electrical, RF, or optical signals using various sensors coupled to the user input devices. The sensors can be provided by various types of sensors, including one or more switches, potentiometers, strain gauges, optical sensors, (depth) cameras, etc. In one example, described below, an actual interventional device forms part of the user interface device. In this example, various sensors, such as those described above, can be used to generate user input data in response to user manipulation of the actual interface device. In another example, the user interface is provided by an actual controller of the interventional device. An example of such a controller is a catheter “handle” used in an actual procedure. This example is shown within the dashed outline in FIG. 5. The catheter handle includes controls in the form of switches, knobs, dials, etc., used to advance, retract, and rotate the catheter, sometimes via one or more motors or actuators. Sensors can be used to monitor the state of the controls, thereby providing user input data. The use of an actual controller, such as a catheter handle, provides the user with a more realistic experience of the interventional procedure. In another example, the user interface device 170 applies force feedback to the user of the user interface device, such that the user may experience, among other things, opposing forces in response to contact between the interventional device 150 and media within the vasculature.

[0056] Continuing with reference to FIG. 5 , one or more processors 180 receive the lumen data and biomechanical plaque data 110 described above. The one or more processors may receive the lumen data, for example, from a computer-readable storage medium or from the internet or the cloud. In one example, system 100 is provided in the form of a training console used to deliver training experiences to users, as shown in FIG. 5 . In this example, one or more processors 180 may form part of the training console. In another example, one or more processors 180 are the same one or more processors 210 used to provide biomechanical plaque data for an interventional device simulation system, as described with reference to FIG. 4 . Thus, in this example, one or more processors 180 may also perform the operations of receiving S110 spectral CT data 120, extracting S120 plaque data from the spectral CT data 120, converting the plaque data to biomechanical plaque data 110, and outputting S140 biomechanical plaque data 110, as described with reference to FIG. 3 .

[0057] Continuing to refer to FIG. 5, one or more processors 180 determine a virtual position of the interventional device 150 within one or more lumens 160 represented by the lumen data based on user input data generated by the user interface device 170.

[0058] In this operation, the lumen data extracted from the spectral CT data 120 is used to generate a 3D mathematical model of one or more lumens 160, and a virtual position of the interventional device 150 is determined in the 3D mathematical model. Because both lumen data and biomechanical plaque data are extracted from the spectral CT data 120, a personalized model of the vascular region is provided in which the two data sets are essentially registered to each other. By way of example, the 3D mathematical model may be generated from the spectral CT data using techniques similar to those disclosed for generating models from conventional CT data in document US 2012 / 197619 A1.

[0059] The position of the interventional device 150 within the one or more lumens 160 determined in this operation is a “virtual” position because the position is determined in a 3D mathematical model. The interventional device 150 is also virtual and is provided by a 3D mathematical model that models the shape of the interventional device as it is navigated in the 3D mathematical model of one or more lumens. The 3D mathematical model of the interventional device may be based on the mechanical properties of an actual interventional device. For example, the stiffness of the actual interventional device may be modeled to provide the user with a more authentic experience of the interventional procedure. The virtual position of the interventional device 150 is determined by using user input data to replicate the user's manipulation of the interventional device in the 3D mathematical model of the interventional device and constraining the position and shape of the interventional device using the 3D mathematical model of the one or more lumens. Thus, the user interface device 170 is used to navigate the virtual interventional device within the one or more lumens based on the user input data generated by the user interface device 170. Note that while the interventional device is virtual in the sense that it is provided by a 3D mathematical model, the user interface device may include a “real” interventional device, as described above. For example, an actual catheter may be provided for manipulation by a user, where the various sensors described above are used to monitor the movement of the catheter, thereby generating user input data that is used to replicate the movement of the actual catheter in a 3D mathematical model of the catheter.

[0060] In general, interventional device 150 may be any type of interventional device suitable for navigation within the vasculature. For example, depending on the type of interventional procedure being simulated, the interventional device may be a guidewire, a catheter, an intravascular ultrasound "IVUS" imager, an optical coherence tomography "OCT" imager, a blood flow sensing device, a blood pressure monitoring device, a balloon catheter, a stent, an atherectomy device, etc.

[0061] 5 , when there is contact between the interventional device 150 and the plaque 140, the one or more processors 180 determine a virtual position of the interventional device 150 within one or more lumens represented by the lumen data by applying the mechanical constraints represented by the biomechanical plaque data 110 to the interventional device 150. In this operation, the above-described mechanical constraints are applied to the interventional device. In other words, spatial restrictions may be applied to the position of the interventional device 150 in response to the contact between the interventional device 150 and the plaque 140. Alternatively or additionally, an amount of friction may be applied to the interventional device 150 in response to the contact between the interventional device 150 and the plaque 140. Alternatively or additionally, an amount of force feedback may be applied to a force feedback user interface device of the simulation system in response to the contact between the interventional device 150 and the plaque 140.

[0062] Continuing with reference to Figure 5, a graphical representation of the vascular region, including the virtual location of the interventional device 150 within one or more lumens 160, may then be displayed on a display such as monitor 190 shown in Figure 5. The graphical representation may alternatively be displayed on other types of display devices, such as, for example, a virtual reality headset or an augmented reality headset. By facilitating the user to navigate the interventional device within the vasculature, the user may gain experience with various interventional procedures using the interventional device simulation system 100 shown in Figure 5.

[0063] 5 applies force feedback to a user of a force feedback user interface device. As discussed above, the use of force feedback facilitates a more authentic training experience. In this example, the user interface device 170 comprises a force feedback interface device, the force feedback interface device is configured to receive a control signal for applying force feedback to a user of the force feedback user interface device, and the mechanical constraint comprises an amount of force feedback to apply to the force feedback user interface device in response to contact between the interventional device 150 and the plaque 140. In this example, one or more processors 180: configured to generate a control signal for applying force feedback to a user of the user input device 170 based on the virtual position of the interventional device 150 within the one or more lumens 160; The control signals generated by one or more processors 180 in response to contact between the interventional device 150 and the plaque 140 within one or more lumens 160 are generated based on the biomechanical plaque data 110 .

[0064] Thus, in this example, virtual contact between the interventional device and the plaque results in force feedback being applied to the force feedback interface device, which can be used to provide a realistic experience when the interventional device contacts the plaque, further deforms the plaque, or ruptures the plaque.

[0065] 5 outputs a graphical representation of the lumen data and the biomechanical plaque data 110. In this example, one or more processors 180 are configured to output the graphical representation of the lumen data and the biomechanical plaque data 110. The graphical representation may be provided, for example, as an overlay image.

[0066] 5 is used to modify the biomechanical plaque data 110. In this example, the one or more processors 180 are further configured to receive inputs defining modifications to the biomechanical plaque data 110, and the control signals to be generated in response to contact between the interventional device 150 and the plaque 140 in the one or more lumens 160 are determined based on the modified biomechanical plaque data 110.

[0067] In this example, modifications to the biomechanical plaque data may be provided via the user interface device 170. The modifications to the biomechanical plaque data may define, for example, modifications to its mechanical properties or its shape. The input may be used, for example, to modify the baseline biomechanical plaque data to provide a more complex or simpler simulation experience to the user. The input may be received, for example, via a menu where the user selects the level of complexity of the simulated procedure.

[0068] In another example, the lumen data is modified using the system described with reference to Figure 5. In this example, one or more processors 180: receiving input defining modifications to the lumen data; modifying the lumen data based on the received input; configured to run Determining a virtual position of the interventional device 150 within one or more lumens 160 represented by the lumen data is performed using the modified lumen data.

[0069] In this example, modifications to the lumen data may be provided via user interface device 170. The modifications to the lumen data may define modifications to the shape of the lumen. For example, the lumen area or lumen curvature may be changed to increase the level of complexity of the simulated procedure.

[0070] In a related example, modifications to the lumen data represent a virtual implanted device inserted into one or more lumens 160. In this example, determining the virtual position of the interventional device 150 includes applying mechanical constraints to the interventional device 150 based on mechanical properties of the virtual implanted device in response to contact between the interventional device 150 and the virtual implanted device. In this example, the implanted device may be, for example, a stent. The mechanical properties of the stent may be, for example, its stiffness or its coefficient of friction. The stiffness of the stent affects the deformation of the lumen in response to contact from the interventional device. The coefficient of friction affects how the interventional device moves during contact with the stent. Thus, this example may be used to provide a more authentic simulation of the navigation of an interventional device through a stent.

[0071] In another example, a computer-implemented method is provided for simulating the navigation of an interventional device 150 within a vascular region 130 using the interventional device simulation system 100 described with reference to FIG. receiving lumen data and biomechanical plaque data 110; a step S220 of receiving user input data generated by the user interface device 170; determining S230 a virtual position of the interventional device 150 within one or more lumens 160 represented by the lumen data based on user input data generated by the user interface device 170; Including, Determining the virtual position of the interventional device 150 includes applying mechanical constraints represented by the biomechanical plaque data 110 to the interventional device 150 in response to contact between the interventional device 150 and the plaque 140 .

[0072] This method is illustrated in Figure 6, a flowchart illustrating an example of a computer-implemented method for simulating the navigation of an interventional device 150 within a vascular region 130, according to some aspects of the present disclosure. Note that the operations described in connection with the system of Figure 5 above may also be performed in the method illustrated in Figure 6.

[0073] In another example, the system described with reference to Figure 5 is used to generate a database of procedure simulation data. In this regard, a computer-implemented method for generating a database of procedure simulation data using the interventional device simulation system 100 is provided. The method includes: recording procedural simulation data, the procedural simulation data comprising, for each simulated procedure: user input data generated by the user interface device 170; and position data representing a virtual position of the interventional device 150 at each of a plurality of time points; biomechanical plaque data 110 used in the simulated procedure; device data representing the type of interventional device 150 used in the simulated procedure; The results data of the simulated procedure and and storing the procedure simulation data in a database; Includes.

[0074] In this example, procedure simulation data may be recorded using and stored on a computer-readable storage medium. User input data represents input provided by a user and may be recorded, for example, by storing signals generated by a user interface device. The signals may represent, for example, the movement of a joystick as shown in FIG. 5. Position data may be recorded by storing a virtual position of an interventional device in a 3D mathematical model of one or more lumens. Device data may indicate that the interventional device 150 is, for example, a catheter, guidewire, or another type of interventional device. Outcome data may represent various factors, such as one or more of the duration of the simulated procedure, a success metric for the procedure, and the location within one or more lumens 160 of one or more confounding factors encountered during the procedure.

[0075] The database of procedural simulation data generated using the system described with reference to Figure 5 can be used for a variety of purposes. In one example, the database of procedural simulation data is used to provide guidance for a current interventional procedure. In another example, the database of procedural simulation data is used to identify the type of interventional device 150 to use in the current interventional procedure. In another example, the database of procedural simulation data is used to train a predictive model for controlling a robotic interventional device manipulator.

[0076] 7 is a flowchart illustrating an example of a computer-implemented method for providing guidance for a current interventional procedure involving navigation of an interventional device within a vascular region 130, according to some aspects of the present disclosure. Referring to FIG. 7, a method for providing guidance for a current interventional procedure involving navigation of an interventional device 150 within a vascular region 130 includes: a step S310 of receiving a database of procedure simulation data; receiving spectral CT data 120 representing a vascular region 130 for the current interventional procedure; Step S330: extracting plaque data for the current interventional procedure from the spectral CT data 120, the plaque data representing a spatial distribution of plaque within a vascular region 130 for the current interventional procedure; a step S340 of identifying one or more similar simulated procedures from the database based on similarities between plaque data for the current interventional procedure and plaque data for the simulated procedures; predicting outcome metrics for the current interventional procedure based on outcome data for the identified one or more similar simulated procedures S350; Step S360 of outputting the predicted outcome metrics to provide guidance for the current intervention procedure; Includes.

[0077] In this example, the operations of identifying S340, predicting S350, and outputting S360 can be repeated during the current intervention procedure to provide updated predicted outcome metrics corresponding to the current time in the current intervention procedure.

[0078] In this example, the operations of receiving S320 spectral CT data 120 and extracting S330 plaque data for the current interventional procedure are performed in the same manner as described above with reference to FIG. 3. Similarity between the plaque data for the current interventional procedure and the plaque data for the simulated procedure may be calculated based on factors such as the type of plaque identified in the procedure and the distribution of the plaque identified in the procedure. Similarity between images may be calculated using metrics such as Dice coefficient, local root-mean-square error, and mutual information. Similarity between types or classes of plaque may be calculated by applying a weighted distance metric to the (multidimensional) class space. An outcome metric is predicted from the outcome data, as described above, which may represent one or more of the duration of the simulated procedure, a procedure success metric, and the location within the lumen 160 of one or more confounding factors encountered during the procedure. Predicting the outcome metric may include, for example, selecting the outcome metric of the most similar simulated procedure from a database or calculating the outcome metric as an average of multiple similar simulated procedures from a database. By providing guidance for current interventional procedures according to this example, physicians may be assisted to perform more successful procedures.

[0079] As mentioned above, in another example, a database of procedure simulation data is used to identify the type of interventional device 150 to use in the current interventional procedure. This example is described with reference to FIG. 8, which is a flowchart illustrating an example of a computer-implemented method for identifying the type of interventional device 150 to use in the current interventional procedure, according to some aspects of the present disclosure. The computer-implemented method for identifying the type of interventional device 150 to use in the current interventional procedure includes: a step S410 of receiving a database of procedure simulation data; receiving spectral CT data 120 representing a vascular region 130 for a current interventional procedure;

[0080] Step S430: extracting plaque data for the current interventional procedure from the spectral CT data 120, the plaque data representing a spatial distribution of plaque 140 within the vascular region 130 for the current interventional procedure; a step S440 of identifying one or more similar simulated procedures from the database based on similarities between the plaque data for the current intervention procedure and the plaque data for the simulated procedures; a step S450 of identifying the type of interventional device 150 to use in the current interventional procedure based on the device data for the identified one or more similar simulated procedures; Step S460 of outputting an indication of the identified type of interventional device 150; Includes.

[0081] In this example, the operations of receiving S420 spectral CT data 120 and extracting S430 plaque data for the current interventional procedure are performed in the same manner as described above with reference to FIG. 3. Similarity between the plaque data for the current interventional procedure and the plaque data for the simulated procedure may be calculated as described above with reference to FIG. 6. The device data represents the type of interventional device 150. The device data may indicate that the interventional device 150 is, for example, a catheter, a guidewire, or another type of interventional device. In this example, the operation of outputting an indication of the identified type of interventional device 150 in S460 may include, for example, outputting the device data for the most similar simulated procedure from a database, or determining the most common device data for multiple similar simulated procedures from a database and outputting the most common device data.

[0082] As mentioned above, in another example, a database of procedure simulation data is used to train a predictive model for controlling a robotic interventional device manipulator. This example is described with reference to FIG. 9, which is a flowchart illustrating an example of a computer-implemented method for providing a predictive model for controlling a robotic interventional device manipulator to navigate an interventional device within a vascular region, according to some aspects of the present disclosure. With reference to FIG. 9, the computer-implemented method for providing a predictive model for controlling a robotic interventional device manipulator to navigate an interventional device 150 within a vascular region 130 includes: a step S510 of receiving a database of procedure simulation data; training the predictive model to control a robotic interventional device manipulator to navigate the interventional device 150 within the vascular region 130 to replicate position data representing a virtual position of the interventional device 150; Includes.

[0083] In this example, the robotic interventional device manipulator may be a catheter handle as described above. Alternatively, the robotic interventional device manipulator may be another type of manipulator, such as the manipulator of the Co-Indus Corpus GRX system sold by Siemens Healthineer of Erlangen, Germany, or the R-One robotic-assisted platform sold by Robocarch (Rouen, France). The latter two devices use manipulators that can be used to navigate elongated interventional devices within the vasculature.

[0084] In this example, the database of procedure simulation data may include procedures for a variety of different spatial distributions of plaque and various compositions of plaque. The predictive model in this example may be provided by a machine learning model or by a neural network. Examples of suitable machine learning models include shallow predictors such as random forests and support vector machines. Examples of suitable neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers.

[0085] In this example, the act of training the predictive model can include controlling a user interface device 170 of the interventional device simulation system 100 via a robotic device manipulator, where the control is based on the output of the predictive model. Thus, in this example, the robotic interventional device manipulator controls the user interface device 170 of the system 100 to provide a virtual position of the interventional device 150. The difference between the virtual position of the interventional device and the position of the interventional device in the procedure simulation data is used to train the predictive model.

[0086] In one example, the predictive model receiving procedural simulation data; inputting the procedural simulation data into the predictive model; For each of a plurality of simulated procedures and for each of a plurality of time points, inputting position data representing a virtual position of the interventional device 150 at a point in time from the simulated procedure; predicting a position of the interventional device 150 at a point in time using the predictive model; adjusting parameters of the predictive model based on a difference between a predicted position of the interventional device 150 at a time point and a virtual position of the interventional device 150 at a time point; repeating the predicting and adjusting steps until a stopping criterion is met; By performing the procedure simulation, the robotic interventional device manipulator can be trained to control the interventional device 150 within the vascular region 130 using the procedure simulation data.

[0087] In another example, neural networks can be trained in an unsupervised manner. Reinforcement learning on simulated procedures allows AI to train based on one or more success measures, independent of labeled ground truth data. Biomechanical plaque data used during simulated training defines the specific clinical scenarios in which the AI ​​is trained. CT-based plaque data from clinical cases is used to inform whether such trained AI is suitable for a particular clinical case.

[0088] Generally, training a neural network involves inputting a training dataset into the neural network and iteratively adjusting the neural network's parameters until the trained neural network provides accurate outputs. Training is often performed using dedicated neural processors such as graphics processing units (GPUs), neural processing units (NPUs), or tensor processing units (TPUs). Training often employs a centralized approach, in which cloud- or mainframe-based neural processors are used to train the neural network. Following its training with the training dataset, the trained neural network can be deployed to a device for analyzing new input data during inference. Processing requirements during inference are significantly lower than those needed during training, allowing neural networks to be deployed to a variety of systems, such as laptop computers, tablets, and mobile phones. Inference can be performed, for example, on a central processing unit (CPU), GPU, NPU, TPU, server, or in the cloud.

[0089] Therefore, the process of training the above-mentioned neural network involves adjusting its parameters. The parameters, more specifically, weights and biases, control the behavior of the activation function in the neural network. In supervised learning, the training process automatically adjusts the weights and biases so that when input data is presented, the neural network accurately provides the corresponding expected output data. To do this, a loss function or error value is calculated based on the difference between the predicted output data and the expected output data. The value of the loss function may be calculated using functions such as negative log-likelihood loss, mean absolute error (or L1 norm), mean squared error, root mean squared error (or L2 norm), Huber loss, or (binary) cross-entropy loss. During training, the value of the loss function is typically minimized, and training is terminated when the value of the loss function meets a stopping criterion. In some cases, training is terminated when the value of the loss function meets one or more of several criteria.

[0090] Various methods are known for solving loss minimization problems, such as gradient descent and quasi-Newton methods. Various algorithms have been developed to implement these methods and their variations, including, but not limited to, stochastic gradient descent (SGD), batch gradient descent, mini-batch gradient descent, Gauss-Newton, Levenberg-Marquardt, Momentum, Adam, Nadam, Adagrad, Adadelta, RMSProp, and Adamax optimizers. This process is called backpropagation because derivatives are calculated starting from the last layer, or output layer, moving toward the first layer, or input layer. These derivatives inform the algorithm how to adjust the model parameters to minimize the error function. That is, adjustments to the model parameters are made starting from the output layer and working backward through the network until the input layer is reached. In the first training iteration, the initial weights and biases are often randomized. The neural network then predicts output data, which is also random. Backpropagation is then used to adjust the weights and biases. The training process is performed iteratively, adjusting the weights and biases at each iteration. Training is terminated when the error or difference between the predicted output data and the expected output data is within an acceptable range for the training data or some validation data. The neural network can then be deployed, and the trained neural network will make predictions for new input data using the trained values ​​of its parameters. If the training process is successful, the trained neural network will accurately predict the expected output data from the new input data.

[0091] In another example, the procedure simulation data is grouped based on plaque type, and separate predictive models are provided for different types of plaque. In this example, the method described with reference to FIG. grouping the procedure simulation data based on the plaque type represented by the biomechanical plaque data 110; The method includes providing a separate predictive model for controlling a robotic interventional device manipulator for each of a plurality of different types of plaque, each predictive model being trained using procedure simulation data for the corresponding type of plaque.

[0092] Therefore, different predictive models can be provided for different types of plaque.

[0093] In another example, a computer-implemented method is provided for controlling a robotic interventional device manipulator to navigate an interventional device 150 within a vascular region 130 during a current interventional device navigation procedure. The method includes: receiving a plurality of predictive models; receiving spectral received CT data 120 representing a vascular region 130 for a current interventional device navigation procedure; extracting plaque data for a current interventional device navigation procedure from the spectral CT data, the plaque data representing a spatial distribution of plaque within a vascular region for the current interventional device navigation procedure; selecting, from the predictive models, a predictive model based on similarity between the plaque data for the current interventional device navigation procedure and the plaque data used to train the predictive model; controlling a robotic interventional device manipulator to navigate an interventional device 150 within the vascular region 130 during a current interventional device navigation procedure using the selected predictive model; Includes.

[0094] In this example, the received plurality of predictive models includes a separate predictive model for controlling a robotic interventional device manipulator for each of a plurality of different types of plaque. Selecting a predictive model is based on the similarity between the plaque data for the current interventional device navigation procedure and the plaque data used to train the predictive model. The similarity between the plaque data for the current interventional procedure and the plaque data used to train the predictive model may be calculated as described above with reference to FIG. 6. This example provides a predictive model for controlling a robotic interventional device manipulator to navigate an interventional device 150 where the predictive model is suitable for the plaque in the current interventional procedure.

[0095] In another example, a computer-implemented method for controlling a robotic interventional device manipulator to navigate an interventional device 150 within a vascular region 130 during a current interventional device navigation procedure is performed by a system. Thus, a system for controlling a robotic interventional device manipulator to navigate an interventional device 150 within a vascular region 130 during a current interventional device navigation procedure is provided. The system includes: receiving a plurality of predictive models; receiving spectral CT data 120 representing a vascular region 130 for a current interventional device navigation procedure; extracting plaque data for a current interventional device navigation procedure from the spectral CT data, the plaque data representing a spatial distribution of plaque within a vascular region for the current interventional device navigation procedure; selecting, from the predictive models, a predictive model based on similarity between the plaque data for the current interventional device navigation procedure and the plaque data used to train the predictive model; controlling a robotic interventional device manipulator to navigate an interventional device 150 within the vascular region 130 during a current interventional device navigation procedure using the selected predictive model; The system includes one or more processors configured to execute the

[0096] In another example, a computer-implemented method is provided for simulating robotic navigation of an interventional device 150 within a vascular region 130 during a current interventional device navigation procedure using an interventional device simulation system 100. This example is described with reference to FIG. 10 , which is a flowchart illustrating an example of a computer-implemented method for simulating robotic navigation of an interventional device within a vascular region 130 during a current interventional device navigation procedure using an interventional device simulation system, according to some aspects of the present disclosure. In this example, the method includes: a step S610 of receiving a plurality of predictive models; receiving spectral CT data 120 representing a vascular region 130 for a current interventional device navigation procedure; extracting plaque data for a current interventional device navigation procedure from the spectral CT data S630, the plaque data representing a spatial distribution of plaque 140 within the vascular region 130 for the current interventional device navigation procedure; selecting 640 from the predictive models based on similarity between the plaque data for the current interventional device navigation procedure and the plaque data used to train the predictive model; a step S650 of robotically controlling the user interface device 170 of the interventional device simulation system 100 to navigate the interventional device 150 within the vascular region 130 during the current interventional device navigation procedure using the selected predictive model; Includes.

[0097] In this example, the multiple predictive models received in operation S610 include a separate predictive model for controlling the robotic interventional device manipulator for each of multiple different types of plaque. The robot control S650 controls the user interface device 170 of the interventional device simulation system 100 using the selected predictive model, thereby providing a more optimal simulation because the selected model is the appropriate model for the type of plaque in the current interventional device navigation procedure. The simulation can be used to demonstrate to a physician how to perform the interventional procedure.

[0098] In another example, differences between a simulated robotic interventional device navigation procedure and a current interventional device navigation procedure are determined. This can be used during the navigation procedure to determine when the current interventional device navigation deviates from the expected procedure. This example is described with reference to FIG. 11, which is a flowchart illustrating an example of a computer-implemented method for determining differences between a simulated robotic interventional device navigation procedure and a current interventional device navigation procedure, according to some aspects of the present disclosure. In this example, the method includes:

[0099] a step S710 of simulating robotic navigation of the interventional device 150 within the vascular region 130 during a current interventional device navigation procedure;

[0100] a step S720 of controlling the robotic interventional device manipulator to navigate the interventional device 150 within the vascular region 130 during the current interventional device navigation procedure; a step S730 of determining a difference between the simulated robotic navigation of the interventional device 150 and the navigation of the interventional device 150 performed by controlling the robotic interventional device manipulator to navigate the interventional device 150 within the vascular region 130 during a current interventional device navigation procedure; Includes.

[0101] In this example, the operations simulating robot navigation S710 are performed according to the method shown in Figure 10. The difference may be output in a variety of ways. For example, the difference may be output on a display device or audibly. In one example, the user is alerted via a message if the magnitude of the difference exceeds a threshold.

[0102] In another example, a computer-implemented method for determining differences between a simulated robotic interventional device navigation procedure and a current interventional device navigation procedure is performed by a system. Thus, a system for determining differences between a simulated robotic interventional device navigation procedure and a current interventional device navigation procedure is provided. The system comprises: a step S710 of simulating robotic navigation of the interventional device 150 within the vascular region 130 during a current interventional device navigation procedure; a step S720 of controlling the robotic interventional device manipulator to navigate the interventional device 150 within the vascular region 130 during the current interventional device navigation procedure; a step S730 of determining a difference between the simulated robotic navigation of the interventional device 150 and the navigation of the interventional device 150 performed by controlling the robotic interventional device manipulator to navigate the interventional device 150 within the vascular region 130 during a current interventional device navigation procedure; The system includes one or more processors configured to execute the

[0103] The above examples should be understood as illustrating, not limiting, the present disclosure. Further examples are contemplated. For example, examples described in connection with a computer-implemented method may also be provided in a corresponding manner by a computer program product, a computer-readable storage medium, or a system. It should be understood that features described with respect to any one embodiment may be used alone or in combination with other described features, or in combination with one or more other features of the embodiment or with combinations of other embodiments. Furthermore, equivalents and modifications not described above may also be used without departing from the scope of the present invention as defined in the appended claims. In the claims, the word "comprising" does not exclude other elements or operations, and the indefinite articles "a" or "an" do not exclude a plurality. The mere fact that certain features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be advantageously used. Any reference signs in the claims should not be construed as limiting their scope.

Claims

1. 1. A computer-implemented method for providing biomechanical plaque data to an interventional device simulation system, the method comprising: receiving spectral CT data representing a vascular region, the spectral CT data defining x-ray attenuation within the vascular region at a plurality of different energy intervals; extracting plaque data from the spectral CT data, the plaque data representing a spatial distribution of plaque within the vascular region; converting the plaque data into biomechanical plaque data, the biomechanical plaque data representing a spatial distribution of mechanical constraints for application to an interventional device in the interventional device simulation system in response to contact between the interventional device and the plaque; outputting the biomechanical plaque data; A method comprising:

2. 2. The computer-implemented method of claim 1, wherein the mechanical constraints comprise one or more of: a spatial restriction to apply to a position of the interventional device in response to contact between the interventional device and the plaque; and / or an amount of friction to apply to the interventional device in response to contact between the interventional device and the plaque; and / or an amount of force feedback to apply to a force feedback user interface device of the simulation system in response to contact between the interventional device and the plaque.

3. outputting a graphical representation of the vascular region. and the graphical representation of the vascular region comprises a graphical representation of the plaque data and / or a graphical representation of the output biomechanical plaque data.

3. The computer-implemented method of claim 1 or claim 2.

4. The method further comprises: extracting lumen data from the spectral CT data, the lumen data representing a three-dimensional shape of one or more lumens within the vascular region; outputting the lumen data; 4. The computer-implemented method of claim 1, further comprising:

5. The method comprises: calculating a spatial distribution of risk of plaque rupture from the plaque data; outputting a graphical representation of the risk of plaque rupture; 5. The computer-implemented method of claim 1, further comprising:

6. 1. An interventional device simulation system for simulating navigation of an interventional device within the vascular region, the simulation system comprising: a user interface device; one or more processors and and the user interface device is configured to generate user input data for guiding the interventional device within the vascular region in response to the received user input; One or more processors receiving the lumen data and the biomechanical plaque data output according to the method of claim 4; determining a virtual position of an interventional device within one or more lumens represented by the lumen data based on the user input data generated by the user interface device; configured to run 11. An interventional device simulation system, wherein determining the virtual position of the interventional device comprises applying mechanical constraints represented by the biomechanical plaque data to the interventional device in response to contact between the interventional device and the plaque.

7. the user interface device comprises a force feedback interface device, the force feedback interface device further configured to receive a control signal for applying force feedback to a user of the force feedback user interface device, the mechanical constraint comprising an amount of force feedback to apply to the force feedback user interface device in response to contact between the interventional device and the plaque; The one or more processors: generating a control signal for applying the force feedback to a user of the user input device based on a virtual position of an interventional device within the one or more lumens; further configured as follows: the control signal generated by the one or more processors in response to contact between the plaque in the one or more lumens and the interventional device is generated based on the biomechanical plaque data.

7. The interventional device simulation system of claim 6.

8. 10. A computer-implemented method for simulating navigation of an interventional device within a vascular region using the interventional device simulation system of claim 6, comprising: receiving the lumen data and the biomechanical plaque data output according to the method of claim 4; receiving user input data generated by the user interface device; determining a virtual position of an interventional device within one or more lumens represented by the lumen data based on user input data generated by the user interface device; and determining a virtual position of the interventional device comprises applying mechanical constraints represented by the biomechanical plaque data to the interventional device in response to contact between the interventional device and the plaque; Computer-implemented methods.

9. 10. A computer-implemented method for generating a database of procedure simulation data using the interventional device simulation system of claim 6, comprising: recording procedural simulation data, the procedural simulation data comprising, for each simulated procedure: user input data generated by the user interface device; position data representing a virtual position of the interventional device at each of a plurality of time points; a biomechanical plaque used in the simulated procedure; and device data representing the type of interventional device used in the simulated procedure; outcome data for said simulated procedure; and storing the procedural simulation data in a database; 10. A computer-implemented method comprising:

10. 1. A computer-implemented method for providing guidance for a current interventional procedure, including navigation of an interventional device within a vascular region, the method comprising: receiving a database of procedural simulation data generated according to the method of claim 9; receiving spectral CT data representative of a vascular region for the current interventional procedure; extracting plaque data for the current interventional procedure from the spectral CT data, the plaque data representing a spatial distribution of plaque within a vascular region for the current interventional procedure; identifying one or more similar simulated procedures from the database based on similarities between plaque data for the current interventional procedure and plaque data for the simulated procedures; predicting outcome metrics for the current interventional procedure based on outcome data for the identified one or more similar simulated procedures; outputting the predicted outcome metrics to provide guidance for the current intervention procedure; 10. A computer-implemented method comprising:

11. 1. A computer-implemented method for providing a predictive model for controlling a robotic interventional device manipulator to navigate an interventional device within a vascular region, the method comprising: receiving a database of procedural simulation data generated according to the method of claim 9; training the predictive model to control the robotic interventional device manipulator to navigate the interventional device within the vascular region to replicate position data representing a virtual position of the interventional device; 10. A computer-implemented method comprising:

12. The method comprises: grouping the procedure simulation data based on the plaque type represented by the biomechanical plaque data; and the method comprising providing a separate predictive model for controlling the robotic interventional device manipulator for each of a plurality of different types of plaque, each predictive model being trained using the procedure simulation data for a corresponding type of plaque; 12. The computer-implemented method of claim 11.

13. 1. A computer-implemented method for controlling a robotic interventional device manipulator to navigate an interventional device within a vascular region during a current interventional device navigation procedure, the method comprising: receiving a plurality of predictive models provided in accordance with the method of claim 12; receiving spectral CT data representative of a vascular region for the current interventional device navigation procedure; extracting plaque data for the current interventional device navigation procedure from the spectral CT data, the plaque data representing a spatial distribution of plaque within a vascular region for the current interventional device navigation procedure; selecting a predictive model from the plurality of predictive models based on similarity between plaque data for the current interventional device navigation procedure and plaque data used to train the predictive model; controlling the robotic interventional device manipulator to navigate the interventional device within the vascular region during the current interventional device navigation procedure using the selected predictive model; 10. A computer-implemented method comprising:

14. 10. A computer-implemented method for simulating robotic navigation of an interventional device within a vascular region during a current interventional device navigation procedure using an interventional device simulation system according to claim 4, the method comprising: receiving a plurality of predictive models provided in accordance with the method of claim 12; receiving spectral CT data representative of a vascular region for the current interventional device navigation procedure; extracting plaque data for the current interventional device navigation procedure from the spectral CT data, the plaque data representing a spatial distribution of plaque within a vascular region for the current interventional device navigation procedure; selecting a predictive model from the plurality of predictive models based on similarity between plaque data for the current interventional device navigation procedure and plaque data used to train the predictive model; robotically controlling a user interface device of the interventional device simulation system to navigate the interventional device within the vascular region during the current interventional device navigation procedure using the selected predictive model; 10. A computer-implemented method comprising:

15. 1. A computer-implemented method for determining differences between a simulated robotic interventional device navigation procedure and a current interventional device navigation procedure, the method comprising: simulating robotic navigation of an interventional device within a vascular region during a current interventional device navigation procedure according to the method of claim 14; 14. The method of claim 13, wherein the method comprises controlling a robotic interventional device manipulator to navigate an interventional device within a vascular region during a current interventional device navigation procedure; determining a difference between the simulated robotic navigation of the interventional device and the navigation of the interventional device performed by controlling a robotic interventional device manipulator to navigate the interventional device within the vascular region during the current interventional device navigation procedure; 10. A computer-implemented method comprising:

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