Providing plaque data on plaque deposits in blood vessels

Abstract

A computer-implemented method for providing plaque data for plaque deposits in a blood vessel is provided, the method including receiving computed tomography (CT) data representing the blood vessel, generating from the CT data a cross-sectional view of the blood vessel at each of a plurality of locations along the blood vessel, extracting from the cross-sectional view plaque data having at least one measurement of the plaque deposits at the plurality of locations along the blood vessel, and outputting a graphical representation of the plaque data.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE A computer-implemented method, computer program product, and system are disclosed that provide plaque data for plaque deposits within blood vessels. [Background technology]

[0002] Atherosclerosis is a leading cause of mortality and morbidity worldwide. According to the WHO report, "Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2016," Geneva, World Health Organization, 2018, cardiovascular disease, primarily atherosclerosis, accounts for approximately 30% of deaths worldwide. Atherosclerosis is the underlying cause of cardiovascular disease (CVD), in which plaques composed of fat, cholesterol, calcium, fibrin, and other substances build up in the walls of arteries. These plaques harden and narrow arteries, restricting blood flow and oxygen delivery to vital organs and increasing the risk of blood clots that can block blood flow to the heart or brain. Over time, plaques can open or rupture, forming thrombi, or blood clots, further restricting blood flow. Blood clots can also detach as emboli. An embolus may lodge elsewhere in the body and form an embolus that blocks an artery as well. Blood clots can occur in various parts of the body, including the heart and brain, and unless treated promptly, their effects can be serious. For example, in the brain, a blood clot or embolus can lead to conditions such as (ischemic) stroke.

[0003] Various endovascular treatment devices are available for treating vascular plaque. These devices include intravascular lithotripsy (IVL) balloons, which deliver shockwave pulses into the blood vessel to fragment the plaque; laser atherectomy catheters, which use laser irradiation to fragment the plaque; orbital atherectomy devices, which perform an orbital sanding action around the vessel axis to fragment the plaque; rotablation atherectomy devices, which perform a rotational sanding or cutting action around the vessel axis to fragment the plaque; and scoring or cutting balloons, which include blades or wires on the outer surface of the balloon that rotate within the vessel to scrape plaque from the vessel wall. An example of a commercially available IVL balloon is the Shockwave C2 Coronary IVL Catheter, marketed by Shockwave Medical, Santa Clara, USA. An example of a commercially available laser atherectomy catheter is the Turbo-Elite laser atherectomy catheter, marketed by Philips Healthcare, Vest, The Netherlands. An example of a commercially available orbital atherectomy device is the Diamondback 360 Coronary orbital atherectomy system marketed by Cardiovascular Systems Inc., Minneapolis, USA. An example of a commercially available rotablator atherectomy device is the Peripheral Rotablator rotational atherectomy system marketed by Boston Scientific, Massachusetts, USA. An example of a commercially available scoring or cutting balloon is the Advance Enforcer 35 Focal-Force PTA Balloon Catheter marketed by Cook Medical, Limerick, Ireland.

[0004] Intravascular ultrasound (IVUS) imaging is currently considered the gold standard for assessing vascular plaque and planning its treatment. Optical coherence tomography (OCT) imaging is also often used. Both IVUS and OCT provide comprehensive information about the distribution of plaque around and along blood vessels. Summary of the Invention [Problem to be solved by the invention]

[0005] However, adoption of these imaging technologies faces challenges due to their size, cost, and steep learning curve for image interpretation.

[0006] Therefore, there remains a need to provide improved techniques for assessing vascular plaque. There also remains a need to provide improved techniques for planning and guiding vascular plaque treatment procedures. This is because, after selecting a treatment device to treat plaque deposits, a challenge is determining the values ​​of parameters to use with the treatment device. After selecting a treatment device, it is typically necessary to determine values ​​for parameters such as the size of the treatment device, the location of the treatment device within the blood vessel, and the amount of treatment to deliver with the treatment device. [Means for solving the problem]

[0007] According to one aspect of the present disclosure, there is provided a computer-implemented method for providing plaque data of plaque deposits in a blood vessel, the method comprising: receiving computed tomography (CT) data representative of a blood vessel; generating a cross-sectional view of the blood vessel at each of a plurality of locations along the blood vessel from the CT data; extracting plaque data from the cross-sectional view, the plaque data comprising at least one measurement of plaque deposits at a plurality of locations along the blood vessel; outputting a graphical representation of the plaque data; Includes.

[0008] Because the plaque data is provided from CT data, this method eliminates the challenges to adopting IVUS, the current gold-standard technology for assessing vascular plaque. Often, CT data is already available for the vasculature of subjects affected by vascular plaque, and thus, using CT data in this method eliminates the need to acquire additional imaging data for the subject. Furthermore, because the plaque data is extracted from a cross-sectional view of the blood vessel in the CT data, this method provides an accurate measurement of plaque deposits along the blood vessel.

[0009] Further aspects, features, and advantages of the present disclosure will become apparent from the following description of examples which refers to the accompanying drawings. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of a blood vessel 130 having a plaque deposit 120, according to some aspects of the present disclosure. [Figure 2] FIG. 2 is a flowchart illustrating an example of a computer-implemented method for providing plaque data of plaque deposits in blood vessels, according to some aspects of the present disclosure. [Figure 3] FIG. 3 is a schematic diagram illustrating an example of a system 200 for providing plaque data of plaque deposits in blood vessels, according to some aspects of the present disclosure. [Figure 4] FIG. 4 illustrates a volumetric image reconstructed from CT data 140 representing a blood vessel 130, according to some aspects of the present disclosure. [Figure 5] FIG. 5 illustrates a cross-sectional view 150 of a blood vessel 130 at a location AA' along the vessel, according to some embodiments of the present disclosure. [Figure 6] FIG. 6 is a schematic diagram illustrating a cross-sectional view 150 of a blood vessel 130 including a first example of a measurement of a plaque deposit 120 in the form of an angular extent φ of the plaque deposit around a centerline 160 of the blood vessel 130, according to some embodiments of the present disclosure. [Figure 7] FIG. 7 is a schematic diagram showing a cross-sectional view 150 of a blood vessel 130 including a second example of a measurement of a plaque deposit 120 in the form of an angular extent φ′ of the plaque deposit around a centerline 160 of the blood vessel 130, according to some embodiments of the present disclosure. [Figure 8] FIG. 8 is a schematic diagram illustrating an example of an endovascular treatment device 180 in the form of an IVL balloon for use in an endovascular plaque treatment procedure, according to some aspects of the present disclosure. [Figure 9] FIG. 9 is a flowchart illustrating an example of a computer-implemented method for planning an intravascular plaque treatment procedure, according to some aspects of the present disclosure. [Figure 10] FIG. 10 is a flowchart illustrating a first example of a computer-implemented method for providing guidance during an intravascular plaque treatment procedure, according to some aspects of the present disclosure. [Figure 11] FIG. 11 is a schematic diagram illustrating an example of a system 300 for providing guidance during an intravascular plaque treatment procedure, according to some aspects of the present disclosure. [Figure 12] FIG. 12 is a flowchart illustrating a second example of a computer-implemented method for providing guidance during an intravascular plaque treatment procedure, according to some aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] 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 particular examples are set forth. Reference herein to an "example," "embodiment," or similar terms means that a feature, structure, or characteristic described in connection with an example is included in at least that example. It should also be understood 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 repeated in each example. For example, features described in connection with a computer-implemented method may be implemented in a computer program product, a system, and a corresponding method.

[0012] In the following description, reference is made to an example of a computer-implemented method for providing plaque data for plaque deposits in blood vessels. Generally, the blood vessels may be in any anatomical region, and the blood vessels may be any type of blood vessel. Thus, the blood vessels may be arteries or veins, and the arteries or veins may be located anywhere in the body, such as, for example, the heart, brain, arms, legs, etc.

[0013] In some examples, reference is made to providing plaque data for plaque deposits in the form of mature plaques. However, it should be understood that this type of plaque serves merely as an example, and that the methods disclosed herein may be used to provide plaque data for plaque deposits at different stages of maturity. For example, the plaque may be a so-called fatty streak or a ruptured plaque. The disclosed methods may also be used to provide plaque data for plaque deposits having a different composition than mature plaques.

[0014] 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 functions of the method features may be provided by a single dedicated processor, a single shared processor, or multiple individual processors, some of which are shared. One or more of the functions of the method features may be provided by a processor shared within a networked processing architecture, such as a client / server architecture, a peer-to-peer architecture, the Internet, or a cloud.

[0015] Explicit use of the terms "processor" or "controller" should not be construed as exclusively referring to hardware capable of executing software, but can implicitly include, but is not limited to, digital signal processor "DSP" hardware, read-only memory "ROM," random access memory "RAM," and non-volatile storage devices for storing software. Furthermore, examples of the present disclosure may take the form of a computer-usable storage medium or 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 discussion, a computer-usable storage medium or computer-readable storage medium may be any apparatus that can have, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or a 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 CD-ROM, CD-R / W, Blu-Ray, and DVD.

[0016] As noted above, there remains a need to provide improved techniques for assessing vascular plaque.

[0017] FIG. 1 is a schematic diagram illustrating an example of a blood vessel 130 having a plaque deposit 120, according to some embodiments of the present disclosure. The plaque deposit 120 shown in FIG. 1 represents 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 thrombogenic core of the plaque from the vessel lumen and is therefore the final barrier against thrombus formation. The core of the plaque shown in FIG. 1 includes foam cells, cholesterol, and other lipids.

[0018] Over time, the composition and distribution of plaque within a blood vessel can also change. For example, plaque begins as a so-called fatty streak and then develops into a mature plaque, as shown in FIG. 1. Mature plaques can then further develop by rupture, resulting in a thrombus. During this development, vascular calcification occurs within the blood vessel 130. As described in the document "Vascular Calcification—New Insights Into Its Mechanism," by Lee, S. et al., Int. J. Mol. Sci. 2020, 21, 2685, pages 1-33, vascular calcification is defined as the deposition of minerals in the form of calcium phosphate complexes in the vascular system. Vascular calcification can be classified into two types depending on where the minerals are deposited: vascular calcification occurs in the intimal layer and the medial layer. Calcified intimal tissue is associated with atherosclerotic plaques and is known to result from lipid accumulation, macrophage infiltration, smooth muscle cell proliferation, and extracellular matrix protein dysfunction in response to chronic arteritis. These unstable and rupturable plaques form on the inner walls of blood vessels, causing occlusive vascular disease. Medial calcification is associated with aging, diabetes, hypertension, osteoporosis, and chronic kidney disease. Medial calcification primarily occurs without vascular stenosis, which causes vascular stiffness, and increases the incidence of cardiovascular complications. The inner layer of the blood vessel wall is composed of smooth muscle cells and an elastin-rich extracellular matrix. In medial calcification, the process of differentiation of smooth muscle cells into osteoblast-like cells resembles bone formation. Therefore, vascular plaque assessment is necessary to assess the risk of medical complications such as those mentioned above.

[0019] FIG. 2 is a flowchart illustrating an example of a computer-implemented method for providing plaque data of a plaque deposit in a blood vessel according to some aspects of the present disclosure. FIG. 3 is a schematic diagram illustrating an example of a system 200 for providing plaque data of a plaque deposit in a blood vessel 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. 2 are also performed by the one or more processors 210 of the system 200 illustrated in FIG. 3. Similarly, the operations described in connection with the one or more processors 210 of the system 200 are also performed in the method described with reference to FIG. 2. Referring to FIG. 2, a computer-implemented method for providing plaque data 110 of a plaque deposit 120 in a blood vessel 130 includes: an operation S110 of receiving computed tomography (CT) data 140 representing a blood vessel 130; an operation S120 of generating, from the CT data 140, a cross-sectional view 150 of the blood vessel 130 at each of a plurality of positions A-A', B-B' along the blood vessel; an operation S130 of extracting plaque data 110 from the cross-sectional view, the plaque data 110 comprising at least one measurement of a plaque deposit 120 at a plurality of locations along the blood vessel; an operation S140 of outputting a graphical representation of said plaque data 110; Includes.

[0020] Because the plaque data is provided from CT data, this method eliminates the challenges to adopting IVUS, the current gold-standard technology for assessing vascular plaque. Often, CT data is already available for the vasculature of subjects affected by vascular plaque, and thus, using CT data in this method eliminates the need to acquire additional imaging data for the subject. Furthermore, because the plaque data is extracted from a cross-sectional view of the blood vessel in the CT data, this method provides an accurate measurement of plaque deposits along the blood vessel.

[0021] Referring to the method shown in FIG. 2, in act S110, CT data 140 representing a blood vessel 130 is received.

[0022] The CT data 140 received in act S110 may generally represent a still image of the blood vessel 130, or may represent a temporal sequence of images of the blood vessel 130. In the latter case, the temporal sequence of images may be generated in substantially real time, and the methods described above may be performed in substantially real time, such that a graphical representation of the plaque data is output in real time.

[0023] In general, the CT data 140 received in act S110 may be raw data, i.e., data that has not yet been reconstructed into a volumetric image, i.e., a 3D image, or may be image data, i.e., data that has already been reconstructed into a volumetric image. CT data is sometimes referred to as volumetric data. The CT data 140 may be generated by a CT imaging system, or may be generated by rotating or stepping an X-ray source and X-ray detector of an X-ray projection imaging system around a blood vessel, as described more fully below.

[0024] CT imaging systems generate CT data by rotating or stepping an X-ray source-detector arrangement around a subject and acquiring X-ray attenuation data of the subject from multiple rotational angles relative to the subject. The CT data is then reconstructed into a 3D image of the subject. Examples of CT imaging systems used to generate CT data 140 include cone-beam CT imaging systems, photon-counting CT imaging systems, dark-field CT imaging systems, and phase-contrast CT imaging systems. An example of a CT imaging system 220 used to generate the CT data 140 received in operation S110 is shown in FIG. 3. As an example, the CT data 140 is generated by a CT 5000 Ingenuity CT scanner commercially available from Philips Healthcare of Vest, The Netherlands.

[0025] As described above, the CT data 140 received in operation S110 may alternatively be generated by rotating or stepping the X-ray source and X-ray detector of an X-ray projection imaging system around the blood vessel. An X-ray projection imaging system typically includes a support arm, known as a "C-arm," that supports the X-ray source and X-ray detector. Alternatively, the X-ray projection imaging system may include a support arm having a different shape, such as an O-arm. In contrast to a CT imaging system, an X-ray projection imaging system generates X-ray attenuation data of a subject when the X-ray source and X-ray detector are stationary relative to the subject. X-ray attenuation data is called projection data, in contrast to volumetric data generated by a CT imaging system. X-ray attenuation data generated by an X-ray projection imaging system is typically used to generate a 2D image of the subject. However, an X-ray projection imaging system can generate CT data, i.e., volumetric data, by rotating or stepping the X-ray source and X-ray detector of the system around the subject to acquire projection data of the subject from multiple rotation angles relative to the subject. Image reconstruction techniques can then be used to reconstruct the projection data acquired from the multiple rotational angles into a volumetric image in a manner similar to the reconstruction of a volumetric image using X-ray attenuation data acquired from a CT imaging system. Thus, the CT data 140 received in operation S110 may be generated by a CT imaging system, or alternatively, may be generated by an X-ray projection imaging system. An example of an X-ray projection imaging system used to generate the CT data 140 is the Azurion 7 X-ray projection imaging system commercially available from Philips Healthcare of Vest, The Netherlands.

[0026] In some examples, the CT data 140 received in act S110 is spectral CT data. The spectral CT data may be divided into a plurality of different energy intervals DE 1..mdefines the X-ray attenuation of the blood vessel in m. Generally, there are two or more energy intervals, i.e., m is an integer, and m≧2. In this regard, the spectral CT data 140 received in act S110 may be generated by a spectral CT imaging system or by a spectral X-ray projection imaging system. In the latter case, the spectral CT data is acquired by rotating or stepping the X-ray source and X-ray detector of the spectral X-ray projection imaging system around the blood vessel, as described above. More generally, the spectral CT data 140 received in act S110 may be generated by a spectral X-ray imaging system.

[0027] Multiple different energy intervals DE 1..m The ability to generate X-ray attenuation data in a range of energy intervals distinguishes spectral X-ray imaging systems from conventional X-ray imaging systems. By processing data from multiple different energy intervals, media having similar X-ray attenuation values ​​when measured within a single energy interval can be distinguished, which is indistinguishable with conventional X-ray attenuation data. Examples of spectral X-ray imaging systems that may be used to generate the spectral CT data 140 received in act S110 include cone-beam spectral X-ray imaging systems, photon-counting spectral X-ray imaging systems, dark-field spectral X-ray imaging systems, and phase-contrast spectral X-ray imaging systems. One example of a spectral CT imaging system that may be used to generate the spectral CT data 140 received in act S110 is the Spectral CT 7500, commercially available from Philips Healthcare of Vest, The Netherlands.

[0028] In general, the spectral CT data 140 is generated by a variety of different configurations of spectral X-ray imaging systems, 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, each detector detecting a different X-ray energy interval DE. 1..m The detector may include multiple detectors that detect X-rays having energies within different X-ray energy intervals, 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 for each received X-ray photon is determined by detecting pulse heights induced by electron-hole pairs generated in response to absorption of the X-ray photons in the direct conversion material.

[0029] 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 at an X-ray source can be provided by temporally switching the X-ray tube potential of a single X-ray source, i.e., "rapid kVp switching," or by temporally switching or filtering the X-ray emissions from multiple X-ray sources. In such configurations, a common X-ray detector can be used to detect X-rays across multiple different energy intervals, and X-ray attenuation data for each energy interval is generated in a time series. Alternatively, discrimination between different X-ray energy intervals at a detector can be provided by using a multi-layer detector or a photon-counting detector. Such detectors can detect X-rays across multiple X-ray energy intervals DE 1..m X-rays from different X-ray energy intervals DE can be detected almost simultaneously, and therefore no time switching in the radiation source is required. In this way, a multi-layer detector or a photon counting detector can be used in combination with a polychromatic source to detect X-rays from different X-ray energy intervals DE.1..m It is possible to generate X-ray attenuation data in

[0030] Other combinations of X-ray sources and detectors described above can also be used to provide the spectral CT data 140. For example, in a further configuration, the need to continuously switch between different X-ray sources emitting X-rays at different energy intervals is eliminated by mounting the X-ray source-detector pairs on the gantry at rotationally offset positions about the axis of rotation. In this configuration, each X-ray source-detector pair operates independently and emits X-rays at different energy intervals DE 1..m The distance between the spectral CT data for each energy interval DE is facilitated by the rotational offset of the X-ray source-detector pair. To reduce the effect of X-ray scattering, an energy-selective filter is applied to the X-ray detector, and in this configuration, the spectral CT data for each energy interval DE 1..m Therefore, an improvement in the distance between the spectral CT data can be achieved.

[0031] In general, the CT data 140 received in act S110 can be received via any form of data communication, including wired, optical, and wireless communication. As some examples, when wired or optical communication is used, the communication occurs via signals transmitted over electrical or optical cables, and when wireless communication is used, the communication occurs via, for example, RF or optical signals. The CT data 140 received in act S110 can be received from a variety of sources. For example, the CT data 140 is received from an imaging system, such as one of the imaging systems described above. Alternatively, the CT data 140 may be received from another source, such as, for example, a computer-readable storage medium, the internet, or the cloud.

[0032] An example of the CT data 140 received in operation S110 will be described with reference to FIG. 4, which shows a volumetric image reconstructed from the CT data 140 representing a blood vessel 130, in accordance with some aspects of the present disclosure. The volumetric image shown in FIG. 4 represents a blood vessel 130 in the form of a coronary artery. It should be noted that the volumetric image appears to be planar due to limitations in its representation.

[0033] Returning to the method shown in FIG. 2 , in act S120, cross-sectional views 150 of the blood vessel 130 at each of a plurality of positions A-A′, B-B′ along the blood vessel are generated from the CT data 140. Each cross-sectional view 150 represents x-ray attenuation in a transverse slice through the vessel axis. The transverse slices may be positioned perpendicular to the vessel axis. The cross-sectional views can be generated from the CT data 140 using known image processing techniques. As an example, FIG. 5 shows a cross-sectional view 150 of the blood vessel 130 at a position A-A′ along the vessel, according to some embodiments of the present disclosure. The cross-sectional view 150 shown in FIG. 5 was generated at position A-A′ in FIG. 4. In act S120, cross-sectional views of the blood vessel 130 at one or more other positions along the vessel are generated. For example, a further cross-sectional view at position B-B′ in FIG. 4 is generated. These positions can be spaced at various intervals along the vessel. The cross-sectional views may be spaced at regular intervals along the vessel 130. For example, the cross-sections may be spaced less than 1 millimeter apart, or more than 5 millimeters apart. For example, cross-sections may be generated at 1 millimeter intervals, 5 millimeter intervals, etc. Alternatively, the locations may be spaced at various intervals. In some instances, the size of the intervals depends on the size of the blood vessel.

[0034] In one example, operation S120 of generating cross-sectional views 150 of blood vessel 130 at each of multiple locations A-A', B-B' along the blood vessel includes identifying a centerline 160 of blood vessel 130 from CT data 140, and cross-sectional views of the blood vessel are generated at multiple locations along centerline 160 of the blood vessel. As an example, centerline 160 of blood vessel 130 is shown in FIG. 4. In this example, each cross-sectional view 150 represents x-ray attenuation in a transverse slice through the centerline of the blood vessel. The transverse slices may be positioned perpendicular to the centerline of the blood vessel.

[0035] In one example, identifying the centerline 160 of the blood vessel 130 includes determining the centerline of the lumen 170 of the blood vessel and defining the centerline 160 of the blood vessel 130 as the centerline of the lumen 170. The lumen can be considered to provide a more accurate location of the centerline of the blood vessel. This operation may be performed by reconstructing a volumetric image from the CT data 140 and segmenting the reconstructed volumetric image to identify the lumen 170 of the blood vessel 140. Various segmentation algorithms are used for this purpose, including model-based segmentation, watershed-based segmentation, region growing, level set, and graph cut. A neural network may also be trained to segment the reconstructed volumetric image to identify the lumen 170 of the blood vessel 140.

[0036] In one embodiment, the CT data 140 received in act S110 is divided into a plurality of different energy intervals DE 1..mThe example includes spectral CT data defining the X-ray attenuation of a blood vessel 130 in a region of the blood vessel 130. In this example, determining the centerline of a lumen 170 of the blood vessel 130 involves applying a material decomposition algorithm to the spectral CT data. In this example, the material decomposition algorithm is applied to the spectral CT data to identify the lumen, then the centerline of the lumen 170 is identified, and the lumen centerline is used as the centerline of the blood vessel. As described above, the use of spectral CT data facilitates distinguishing between media with similar X-ray attenuation values ​​when measured within a single energy interval, which is indistinguishable with conventional X-ray attenuation data. In this example, applying the material decomposition algorithm to the spectral CT data provides improved distinction between a lumen filled with blood or a contrast agent, such as iodine, and surrounding tissue, which is made of a different material. Thus, this example facilitates a more accurate definition of the centerline 160 of the lumen 170.

[0037] This example allows various material decomposition algorithms to be applied to spectral CT data. One example of a material decomposition algorithm that can be used 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 international patent application WO / 2007 / 034359A2.

[0038] 2, in operation S130, plaque data 110 is extracted from the cross-sectional view. The plaque data includes at least one measurement of plaque deposits 120 at multiple locations along the blood vessel.

[0039] Generally, extraction of plaque data from a cross-sectional view can be based on differences in X-ray attenuation in the cross-sectional view. If the CT data 140 is conventional CT data, the plaque data can be extracted using image processing techniques such as segmentation. Plaque deposits also tend to have a different level of X-ray attenuation than the X-ray attenuation of the lumen of a blood vessel. For example, calcium plaque deposits also tend to have a higher Hounsfield unit attenuation than the blood vessel wall, so that segmentation can be used to delineate features in the cross-sectional view. However, if the CT data 140 is divided into multiple different energy intervals DE 1..m With spectral CT data defining the X-ray attenuation of blood vessels 130 in the plaque image, operation S130 of extracting plaque data 110 can be performed by applying a material decomposition algorithm to the spectral CT data. As described above, the use of spectral CT data and a material decomposition algorithm facilitates improved differentiation between plaque data and surrounding tissue, as compared to the use of conventional CT data. To this end, various material decomposition algorithms described above can be used.

[0040] In one example, the operation S130 of extracting plaque data 110 includes applying a material decomposition algorithm to the spectral CT data to identify types of plaque deposits 120 at multiple locations A-A', B-B' along the blood vessel 130. Different types of plaque can be identified based on the presence of materials present in the plaque. Examples of plaque types identified by this example include soft plaque, mixed plaque, and calcified plaque. Identifying the type of plaque deposit can be used by a physician to determine the level of risk associated with the plaque, such as the plaque's vulnerability to rupture, or to determine how to treat the plaque.

[0041] Various measurements of the plaque deposit 120 are extracted from the cross-sectional view in act S130. For example, the at least one measurement of the plaque deposit 120 includes at least one of the thickness of the plaque deposit, the depth of the plaque deposit from the centerline 160 of the blood vessel 130, the angular extent φ of the plaque deposit 120 around the centerline 160 of the blood vessel 130, and the eccentricity of the plaque deposit. As an example, the thickness of the plaque deposit can be calculated as the longest radial path extending through the plaque from the centerline of the lumen. The thickness of the plaque deposit can also be calculated in other ways, such as the shortest radial path extending through the plaque from the centerline of the lumen. The depth of the plaque deposit from the centerline 160 of the blood vessel 130 can define the depth of the plaque deposit in the intima or media. The angular extent φ of the plaque deposit 120 around the centerline 160 of the blood vessel 130 is defined, for example, as the angular extent of the largest connected component of the plaque in the cross-section.

[0042] As an example, Figure 6 is a schematic diagram illustrating a cross-sectional view 150 of a blood vessel 130 including a first example of a measurement of a plaque deposit 120 in the form of an angular extent φ of the plaque deposit about a centerline 160 of the blood vessel 130, according to some embodiments of the present disclosure. In Figure 6, the angular extent of the plaque deposit is less than 180 degrees.

[0043] As another example, Figure 7 is a schematic diagram illustrating a cross-sectional view 150 of a blood vessel 130 including a second example of a measurement of a plaque deposit 120 in the form of the angular extent φ' of the plaque deposit about a centerline 160 of the blood vessel 130, according to some embodiments of the present disclosure. In Figure 7, the angular extent φ' of the plaque deposit is approximately 360 degrees. Another example of the angular extent φ' of the plaque deposit about a centerline 160 of the blood vessel 130 is shown in Figure 5. The eccentricity of the plaque deposit is determined by fitting an ellipse to the plaque deposit and calculating the eccentricity of the ellipse.

[0044] In one example, the plaque data 110 extracted in operation S130 includes an estimate of the length of the plaque deposit 120 along the blood vessel 130. The estimate of the length of the plaque deposit 120 along the blood vessel 130 is calculated based on the distance between a position along the blood vessel corresponding to a proximal end and a position along the blood vessel corresponding to a distal end of a series of consecutive cross-sectional views where at least one measurement of the plaque deposit 120 exceeds a predetermined value.

[0045] In this example, the measurement of the plaque deposit may be, for example, the thickness of the plaque deposit or the angular extent φ of the plaque deposit 120 around the centerline 160 of the blood vessel 130. The predetermined value of the measurement establishes a threshold used to identify cross-sectional views that are likely to represent the same plaque deposit. As a result, the proximal and distal ends of a series of consecutive cross-sectional views define the length of the plaque deposit. As an example, the predetermined value of the angular extent φ of the plaque deposit 120 around the centerline 160 of the blood vessel 130 is defined as 180 degrees, and the length of the plaque deposit 120 is defined as the length of the blood vessel between the proximal and distal ends of a series of consecutive cross-sectional views where the angular extent φ of the plaque deposit 120 exceeds 180 degrees.

[0046] Returning to the method shown in FIG. 2 , in act S140, a graphical representation of the plaque data 110 is output. The plaque data 110 can be output in various ways. In some examples, the plaque data 110 is output to a display. For example, the plaque data 110 may be output to the display 230 shown in FIG. 3 . The graphical representation of the plaque data 110 may be output to the display 230 in any human-readable format. For example, the plaque data may be output in a shape, image, icon, text, or numeric format. In some examples, the plaque data 110 is output in combination with a graphical representation and / or cross-sectional view of the CT data 140. For example, the plaque data 110 may be superimposed on the graphical representation and / or cross-sectional view of the CT data 140. For example, when measurements of plaque deposit 120 include an angular range φ of plaque deposit 120 about centerline 160 of blood vessel 130, a graphical representation of plaque data 110 is output superimposed on a corresponding cross-sectional view 150, as shown in Figure 5. Other measurements of plaque deposit 120 can be output in a similar manner as superimposition on the corresponding cross-sectional view 150. Alternatively, measurements of plaque deposit 120 may be output graphically in another manner, such as, for example, a table.

[0047] In one example, both the graphical representation and the cross-sectional views of the CT data 140 are output to the display 230. In this example, the graphical representation of the CT data 140 is provided in the form of a reconstructed volumetric image or planar image. If the CT data 110 includes spectral CT data and a material decomposition algorithm is applied to the spectral CT data to identify the type of plaque deposit 120, the type of plaque deposit can be identified in the cross-sectional views. For example, color coding or shading can be applied to the cross-sectional views to identify the type of plaque deposit. Markers can be provided in the graphical representation of the CT data 140 to identify the location, e.g., A-A′, from which the displayed cross-sectional view was generated. A user-operable control, such as a slider control, can also be provided to allow a user to select a location in the graphical representation of the CT data 140 to generate the displayed cross-sectional view.

[0048] In addition to the plaque data described above, other types of data can be extracted from the CT data 110 .

[0049] In one example, the method described with reference to FIG. 2 also includes extracting lumen data and / or vessel wall data of the blood vessel 130 from the cross-sectional view, the lumen data having at least one measurement of the lumen 170 of the blood vessel 130, and the vessel wall data having at least one measurement of the wall of the blood vessel 130 at multiple positions A-A', B-B' along the blood vessel.

[0050] The extracted lumen data in this example may include, for example, a measurement of the area of ​​the lumen, a minimum or maximum diameter of the lumen, or a measurement of the shape of the lumen. The shape of the lumen may be, for example, the eccentricity of the lumen. The eccentricity may be calculated in the same manner as described above for the plaque data. The extracted vessel wall data in this example may include, for example, a measurement of the thickness of the vessel wall, the area enclosed by the vessel wall, or the minimum or maximum diameter enclosed by the vessel wall. The lumen data and / or vessel wall data may be output to the display 230 and used by a physician to perform a diagnosis on the vessel 130.

[0051] In this example, the lumen data is extracted in a manner similar to the plaque data described above. In other words, if the CT data 140 is conventional CT data, the plaque data is extracted using image processing techniques such as segmentation. However, if the CT data 140 is extracted from multiple different energy intervals DE 1..m With spectral CT data defining the X-ray attenuation of the blood vessel 130 in a CT image, the operation of extracting lumen data for the blood vessel 130 is performed by applying a material decomposition algorithm to the spectral CT data. As discussed above, the use of spectral CT data and a material decomposition algorithm facilitates improved differentiation between the lumen and surrounding tissue, as compared to the use of conventional CT data. Various material decomposition algorithms discussed above can be used for this purpose.

[0052] In many situations, the lumen in a cross-sectional view has a reduced size due to the presence of plaque deposits. In such situations, physicians are often interested in knowing what the lumen size would be if the plaque deposits were not present. This helps physicians determine the impact of the plaque deposits on blood flow and evaluate the benefits of treating the plaque deposits. In one example, an estimated plaque-free measurement of the lumen 170 in a cross-sectional view is determined from an adjacent cross-sectional view. In this example, the method described with reference to FIG. 2 includes, for a cross-sectional view in which at least one measurement of the plaque deposits 120 exceeds a threshold, estimating a plaque-free measurement of the lumen 170 at a corresponding location along the blood vessel 130, and the estimated plaque-free measurement of the lumen 170 is determined based on the corresponding measurement of the lumen from an adjacent cross-sectional view in which at least one measurement of the plaque deposits 120 is below the threshold.

[0053] As an example, a physician may be interested in knowing what the lumen area of ​​a cross-section would be if no plaque deposits were present. In this case, a threshold for the angular extent φ of the plaque deposit 120 around the centerline 160 of the blood vessel 130 can be set to 90 degrees, and the estimated plaque-free lumen area of ​​the cross-section can be obtained from adjacent cross-sections where the angular extent φ of the plaque deposit 120 around the centerline 160 of the blood vessel 130 is less than 90 degrees. The adjacent cross-sections may be the nearest adjacent cross-sections for which the condition applies. Alternatively, the adjacent cross-sections may be a predetermined number of cross-sections or cross-sections a predetermined distance away from the nearest adjacent cross-section for which the condition applies.

[0054] In another example, a computer program product is provided having instructions that, when executed by one or more processors, cause the one or more processors to perform a method for providing plaque data 110 of a plaque deposit 120 in a blood vessel 130, the method comprising: an operation S110 of receiving computed tomography (CT) data 140 representing a blood vessel 130; an operation S120 of generating, from the CT data 140, a cross-sectional view 150 of the blood vessel 130 at each of a plurality of positions A-A', B-B' along the blood vessel; an operation S130 of extracting plaque data 110 from the cross-sectional view, the plaque data 110 having at least one measurement of a plaque deposit 120 at a plurality of locations along the blood vessel; an operation S140 of outputting a graphical representation of the plaque data 110; It has.

[0055] In another example, a system is provided for providing plaque data 110 of a plaque deposit 120 in a blood vessel 130. The system includes: an operation of receiving (S110) computed tomography (CT) data 140 representing a blood vessel 130; An operation of generating cross-sectional views 150 of the blood vessel 130 at each of a plurality of positions A-A', B-B' along the blood vessel from the CT data 140 (S120); an act of extracting (S130) from the cross-sectional view plaque data 110 having at least one measurement of a plaque deposit 120 at a plurality of locations along the blood vessel; an operation of outputting a graphical representation of the plaque data 110 (S140); The system includes one or more processors 210 configured to:

[0056] An example of a system 200 is shown in Figure 3. It should be noted that the system 200 may also include one or more of a CT imaging system 220 for providing CT data 140, a display 230 for displaying a graphical representation of the plaque data 110, a graphical representation of the CT data, a cross-sectional view 150 of the blood vessel 130, etc., a patient bed 240, and a user input device (not shown in Figure 3) configured to receive user input, such as, for example, a keyboard, a mouse, a touch screen, etc.

[0057] As mentioned above, various endovascular treatment devices are available for treating vascular plaque, including intravascular lithotripsy (IVL) balloons that deliver shock wave pulses into the blood vessel to break up the plaque, laser atherectomy catheters that use laser irradiation to break up the plaque, orbital atherectomy devices that perform an orbital sanding action around the axis of the vessel, rotablator atherectomy devices that perform a rotational sanding or cutting action around the axis of the vessel, and scoring or cutting balloons that include blades or wires on the outer surface of the balloon that rotate within the vessel to scrape plaque from the vessel wall.

[0058] 8 is a schematic diagram illustrating an example of an endovascular treatment device 180 in the form of an IVL balloon for use in an endovascular plaque treatment procedure according to some aspects of the present disclosure. The IVL balloon includes multiple shock wave emitters 190 disposed on a catheter. During use, the IVL balloon is inflated with saline, and shock waves generated by the emitters 190 travel through the IVL balloon and into the plaque deposit 120, causing disruption of calcium in the plaque deposit. The IVL balloon can be used to disrupt calcium deposits in the intimal and medial layers.

[0059] As discussed above, after selecting a treatment device for treating a plaque deposit, it is difficult to determine the values ​​of parameters to use with the treatment device. For example, it is typically necessary to determine values ​​for parameters such as the size of the treatment device, the location of the treatment device within the blood vessel, and the amount of treatment to deliver using the treatment device. With reference to the IVL balloon shown in FIG. 8, it is necessary to determine values ​​for parameters such as the size of the IVL balloon, the location of the IVL balloon relative to the plaque deposit 120, the number of shock wave pulses to deliver to the blood vessel 130 from one or more shock wave emitters 190 of the IVL balloon, and the IVL balloon pressure to use during delivery of shock waves from the IVL balloon to the blood vessel 130.

[0060] 9 is a flowchart illustrating an example of a computer-implemented method for planning an intravascular plaque treatment procedure according to some aspects of the present disclosure. an operation 210 that performs operations S110, S120, S130, and S140; an operation S220 of determining, based on the plaque data 110, a recommended value for at least one parameter of an endovascular treatment device 180 for use in an endovascular plaque treatment procedure; an operation S230 of outputting a recommended value of at least one parameter; Includes.

[0061] In this example, the recommended values ​​of the parameters are determined based on the plaque data 110 so that the parameters are tailored to the plaque deposits, which results in a more effective treatment of the plaque deposits.

[0062] The values ​​of the various parameters are determined in operation S220 depending on the type of endovascular treatment device being used. As an example, when the endovascular treatment device 180 has an intravascular lithotripsy (IVL) balloon, the at least one parameter includes one or more of the following: a size of the IVL balloon, a position of the IVL balloon relative to the plaque deposit 120, a number of shock wave pulses to deliver to the blood vessel 130 from one or more shock wave emitters 190 of the IVL balloon, and an IVL balloon pressure to use during delivery of shock waves from the IVL balloon to the blood vessel 130.

[0063] As another example, when the endovascular treatment device has a laser atherectomy catheter, the at least one parameter includes one or more of: a size of the laser atherectomy catheter; a position of the laser atherectomy catheter relative to the plaque deposit 120; a fluence of the light radiation emitted by the laser atherectomy catheter; a repetition rate of the light pulses emitted by the laser atherectomy catheter; a duty cycle of the light radiation emitted by the laser atherectomy catheter; and an advancement speed of the laser atherectomy catheter.

[0064] As another example, when the endovascular treatment device has an orbital atherectomy device, the at least one parameter includes one or more of the advancement speed of the orbital atherectomy device, the size of the orbital atherectomy device, the position of the orbital atherectomy device relative to the plaque deposit 120, and the rotational speed of the orbital atherectomy device.

[0065] As another example, when the endovascular treatment device has a rotablator atherectomy device, the at least one parameter includes one or more of the position of the rotablator atherectomy device relative to the plaque deposit 120, the advancement speed or advancement length of the rotablator atherectomy device, the retraction length or retraction speed of the rotablator atherectomy device, the burr size of the rotablator atherectomy device, and the rotational speed of the rotablator atherectomy device.

[0066] As another example, when the endovascular treatment device includes a scoring or cutting balloon, at least one recommended parameter includes one or more of the position of the balloon relative to the plaque deposit 120, the size of the balloon, the inflation pressure of the balloon, the blade length of the balloon, and the inflation time of the balloon.

[0067] The parameter values ​​of the endovascular treatment device 180 can be determined from the plaque data 110 in various ways. In one example, a functional relationship between the parameter values ​​and the plaque data 110 is used. This functional relationship may be provided, for example, by a graph or a lookup table. In another example, a biomechanical model is used to determine the parameter values ​​of the endovascular treatment device 180 from the plaque data 110. In this example, a biomechanical model is constructed from the plaque data 110, and the biomechanical model is used to predict the effect of the parameters of the endovascular treatment device 180 on the plaque deposits. For example, a finite element biomechanical model is generated from the plaque data based on measurements of the plaque deposits and / or the composition of the plaque deposits. The biomechanical model can be further generated based on lumen data and / or vessel wall data of the blood vessel 130. In another example, a neural network is used to determine the parameter values ​​of the endovascular treatment device 180 from the plaque data 110. In this example, the neural network is trained to predict the parameter values ​​from the plaque data 110. The neural network is trained to predict the values ​​of the parameters using training data from previous endovascular plaque treatment procedures, including plaque data 110 from previous endovascular plaque treatment procedures and corresponding values ​​of the parameters of endovascular treatment devices 180 used in procedures that had successful outcomes.

[0068] An example will now be described in which the intravascular treatment device 180 is an IVL balloon, and values ​​for various parameters of the IVL balloon are determined from the plaque data 110 using functional relationships, biomechanical models, and neural networks.

[0069] A typical IVL balloon parameter determined during treatment is the number of shockwave pulses delivered to the blood vessel 130 from the IVL balloon's shockwave emitter 190. The number of shockwave pulses delivered by the IVL balloon affects the amount of damage inflicted on the plaque deposit. Treating thicker plaque deposits, i.e., plaque deposits with a large angular extent, requires a relatively greater number of shockwave pulses than treating thinner plaque deposits, i.e., plaque deposits with a relatively small angular extent. The effectiveness of the shockwave pulses delivered to the blood vessel has also been reported to be affected by the eccentricity of the plaque deposit, with shockwave pulses being more effective at disrupting circular plaque deposits than oval plaque deposits.

[0070] The number of shockwave pulses to deliver to the vessel 130 is determined using a functional relationship, such as a look-up table, relating the number of shockwave pulses to deliver to the vessel 130 to plaque data in the form of plaque deposit thickness, and / or the angular extent φ of the plaque deposit 120 around the centerline 160 of the vessel 130, and / or the eccentricity of the plaque deposit. The look-up table may be generated from past intravascular plaque treatments that include corresponding values ​​for the number of pulses used in treatments that had successful outcomes.

[0071] Instead of using a functional relationship to determine the number of shockwave pulses to deliver to the blood vessel 130, a biomechanical model can be used. In this case, a biomechanical model of the plaque deposit 120 is constructed from plaque data, such as measurements of one or more of the following: the thickness of the plaque deposit, the depth of the plaque deposit from the centerline 160 of the blood vessel 130, the angular extent of the plaque deposit 120 around the centerline 160 of the blood vessel 130, and the eccentricity of the plaque deposit. Simulations are then performed using the biomechanical model to determine the effect of the number of pulses from the shockwave emitter. An optimal number of shockwave pulses to deliver to the blood vessel 130 is then determined based on the desired amount of damage to the plaque deposit 120.

[0072] Instead of using a biomechanical model to determine the number of shockwave pulses to deliver to the vessel 130, a neural network can be used. In this case, the neural network may be trained to predict the number of shockwave pulses to deliver to the vessel 130 from plaque data such as one or more measurements of the thickness of the plaque deposit, the depth of the plaque deposit from the centerline 160 of the vessel 130, the angular extent of the plaque deposit 120 around the centerline 160 of the vessel 130, and the eccentricity of the plaque deposit. The neural network is trained to predict the number of pulses using training data from previous endovascular plaque procedures. The training data may include plaque data 110 from previous endovascular plaque procedures and corresponding values ​​for the number of pulses used in procedures with successful outcomes.

[0073] Another parameter of the IVL balloon that is typically determined during treatment is the IVL balloon pressure used during delivery of shock waves from the IVL balloon to the vessel 130. The optimal balloon pressure has been reported to be determined in part by the thickness of the plaque deposit. Therefore, the IVL balloon pressure is determined using a functional relationship in the form of a look-up table that relates balloon pressure to plaque data in the form of plaque deposit thickness.

[0074] Instead of using a functional relationship to determine the IVL balloon pressure, a biomechanical model can be used. In this case, a biomechanical model of the plaque deposit 120 is constructed from plaque data, such as measurements of one or more of the following: the thickness of the plaque deposit, the depth of the plaque deposit from the centerline 160 of the blood vessel 130, the angular extent of the plaque deposit 120 around the centerline 160 of the blood vessel 130, and the eccentricity of the plaque deposit. A biomechanical model of the IVL balloon within the biomechanical model of the plaque deposit 120 can then be used to simulate the effect of balloon pressure on the shape of the balloon relative to the plaque deposit 120. Simulations can then be performed using the two biomechanical models to determine the effect of balloon pressure on acoustic coupling between the shock wave emitter within the IVL balloon and the plaque deposit to determine the value of the balloon pressure that provides optimal acoustic coupling.

[0075] Instead of using a biomechanical model to determine the IVL balloon pressure, a neural network can be used. In this case, the neural network is trained to predict the IVL balloon pressure from plaque data such as one or more measurements of plaque deposit thickness, plaque deposit depth from the centerline 160 of the blood vessel 130, angular extent of the plaque deposit 120 around the centerline 160 of the blood vessel 130, and eccentricity of the plaque deposit. The neural network is trained to predict the IVL balloon pressure using training data from previous endovascular plaque procedures. The training data can include plaque data 110 from previous endovascular plaque procedures and corresponding values ​​of IVL balloon pressure used in procedures with successful outcomes.

[0076] Another parameter of the IVL balloon that is typically determined during treatment is the size of the IVL balloon. The size of the IVL balloon should be such that the IVL balloon overlaps the plaque deposit along its length. Therefore, the size of the IVL balloon is determined using a functional relationship in the form of a lookup table that relates the size of the balloon to plaque data in the form of an estimate of the length of the plaque deposit 120 along the blood vessel 130. The balloon should have an inflated size that matches a plaque-free, i.e., "healthy," lumen. Therefore, the size of the IVL balloon is alternatively or additionally determined using a functional relationship in the form of a lookup table that relates the size of the balloon to lumen data in the form of a measurement of the area of ​​the plaque-free lumen, or a minimum or maximum lumen diameter, or a measurement of the lumen shape.

[0077] Instead of using a functional relationship to determine the size of the IVL balloon, a biomechanical model can be used. In this case, a biomechanical model of the plaque deposit 120 is constructed from plaque data, such as measurements of one or more of the following: the thickness of the plaque deposit, the depth of the plaque deposit from the centerline 160 of the blood vessel 130, the angular extent of the plaque deposit 120 around the centerline 160 of the blood vessel 130, and the eccentricity of the plaque deposit. A biomechanical model of the IVL balloon within the biomechanical model of the plaque deposit 120 can then be used to simulate the effect of balloon size on the balloon shape relative to the plaque deposit. The balloon shape can be simulated at various stages of balloon inflation. The two biomechanical models can then be used to determine the effect of balloon size on the overlap between the balloon and the plaque deposit at various stages of balloon inflation, thereby determining the balloon size that provides the optimal overlap.

[0078] Instead of using a biomechanical model to determine IVL balloon size, a neural network can be used. In this case, the neural network is trained to predict IVL balloon size from plaque data such as one or more measurements of plaque deposit thickness, plaque deposit depth from the centerline 160 of the vessel 130, angular extent of the plaque deposit 120 around the centerline 160 of the vessel 130, and eccentricity of the plaque deposit. The neural network is trained to predict IVL balloon size using training data from previous endovascular plaque procedures. The training data can include plaque data 110 from previous endovascular plaque procedures and corresponding values ​​of IVL balloon sizes used in procedures with successful outcomes.

[0079] Another parameter of the IVL balloon that is typically determined during treatment is the position of the IVL balloon relative to the plaque deposit 120. To optimize the amount of damage delivered to the plaque deposit, the shockwave emitter 190 of the IVL balloon should be positioned so that it is optimally shielded by the plaque deposit. Therefore, the position of the IVL balloon is determined using a functional relationship in the form of a look-up table that relates the balloon's position to plaque data in the form of an estimate of the length of the plaque deposit 120 along the vessel 130. The position of the IVL balloon relative to the plaque deposit 120 can be determined from plaque data, such as the estimated length of the plaque deposit 120 along the vessel 130, by, for example, positioning the IVL balloon so that the emitter is within the estimated length of the plaque deposit 120 along the vessel 130.

[0080] Instead of using a functional relationship to determine the position of the IVL balloon relative to the plaque deposit 120, a biomechanical model can be used. In this case, a biomechanical model may be constructed to determine the size of the IVL balloon, as described above, and a simulation may be performed using the two biomechanical models to determine the effect of the balloon's position on the overlap between the balloon and the plaque deposit to determine the balloon's position that provides the optimal overlap. When the CT data 140 received in operation S110 includes spectral CT data, the biomechanical model of the plaque deposit includes the spatial distribution of calcified plaque determined from the spectral CT data. In this case, a balloon position that provides the optimal overlap between the shock wave emitter on the balloon and the spatial distribution of calcified plaque is determined.

[0081] Instead of using a biomechanical model to determine the IVL balloon position, a neural network can be used. In this case, the neural network is trained to predict the IVL balloon position from plaque data such as one or more measurements of the thickness of the plaque deposit, the depth of the plaque deposit from the centerline 160 of the blood vessel 130, the angular extent of the plaque deposit 120 around the centerline 160 of the blood vessel 130, and the eccentricity of the plaque deposit. For example, the neural network can predict the optimal position relative to the maximum thickness of the plaque deposit. The neural network is trained to predict the IVL balloon position using training data from previous endovascular plaque procedures. The training data can include plaque data 110 from previous endovascular plaque procedures and corresponding values ​​of the IVL balloon positions used in procedures with successful outcomes.

[0082] The values ​​of the parameters of other types of intravascular treatment devices can be determined in a similar manner.

[0083] In one example, the recommended value of at least one parameter is determined based on the value of the corresponding at least one parameter from one or more similar previous intravascular plaque treatment procedures. In this example, the parameter value is determined from plaque data using a neural network, as described above. Alternatively, a database containing data from one or more previous intravascular plaque treatment procedures can be used. If a database is used, the database is queried to determine the recommended value of the parameter. The one or more similar previous intravascular plaque treatment procedures can be: procedures performed using similar types of endovascular devices; Treatments performed on similar types of blood vessels 130; procedures performed on vessels with similar geometries; procedures performed on vessels with similar plaque data 110; procedures performed on vessels with similar lumen data; Treatments administered to subjects with similar body mass index (BMI); treatments given to subjects of the same sex; Treatment given to subjects of similar age It includes at least one of the following:

[0084] In this example, the database records, for each past procedure, the values ​​of the parameters used in that procedure. The database may record the type of endovascular treatment device, the type of vessel 130, the vessel geometry, plaque data 110, lumen data, BMI, gender, and age, etc. The database may also record the associated values ​​of outcome metrics for the procedure.

[0085] In this example, similar previous endovascular plaque treatment procedures are selected using a similarity metric that determines the similarity between the current procedure and the previous procedures based on the factors described above. One example of a suitable similarity metric is Mahalanobis distance. The parameter values ​​for the current procedure are then selected from the previous procedures with the highest similarity metric values.

[0086] In another example, the method described with reference to FIG. 9 also includes determining a value of an outcome metric for the endovascular plaque treatment procedure based on the plaque data 110. In this example, the value of the outcome metric represents factors such as the probability of the procedure being successful or failing, or the probability of a medical complication resulting from the procedure. The value of the outcome metric is determined from the database described above by selecting the value of the outcome metric for the previous procedure with the highest similarity metric value. Alternatively, when a neural network is used to predict the value of a parameter, the neural network is trained to predict the value of the outcome metric for the procedure. The neural network is trained to predict the value of the outcome metric for the procedure by including the corresponding value of the outcome metric for the procedure in the training data and training the neural network to predict the value of the outcome metric. By inference, the neural network can predict the value of the parameter of the endovascular treatment device 180 as well as the value of the outcome metric.

[0087] In another example, a computer program product is provided having instructions that, when executed by one or more processors, cause the one or more processors to perform a method for planning an intravascular plaque treatment procedure for a blood vessel 130. The method comprises: an operation S210 that performs operations S110, S120, S130, and S140; an operation S220 of determining, based on the plaque data 110, a recommended value for at least one parameter of an endovascular treatment device 180 for use in an endovascular plaque treatment procedure; an operation S230 of outputting a recommended value for said at least one parameter; It has.

[0088] In another example, a system for planning an intravascular plaque treatment procedure for a blood vessel 130 is provided, the system comprising: an operation S210 that performs operations S110, S120, S130, and S140; an operation S220 of determining, based on the plaque data 110, a recommended value for at least one parameter of an endovascular treatment device 180 for use in an endovascular plaque treatment procedure; an operation S230 of outputting a recommended value for said at least one parameter; The system includes one or more processors 210 configured to:

[0089] In another example, a computer-implemented method for providing guidance during an intravascular plaque treatment procedure on a blood vessel 130 is provided. The method is described with reference to FIG. 10 , which is a flowchart illustrating a first example of a computer-implemented method for providing guidance during an intravascular plaque treatment procedure according to some aspects of the present disclosure. The method described with reference to FIG. 10 is performed by one or more processors 310 of a system 300 shown in FIG. 11 , which is a schematic diagram illustrating an example of a system 300 for providing guidance during an intravascular plaque treatment procedure according to some aspects of the present disclosure. With reference to FIG. 10 , the computer-implemented method for providing guidance during an intravascular plaque treatment procedure on a blood vessel 130 includes: an operation S310 that performs operations S210, S220, and S230; an operation S320 of receiving x-ray projection image data representative of the blood vessel 130, generated during an intravascular plaque treatment procedure; an operation S330 of registering the CT data 140 with the X-ray projection image data; an operation S340 of outputting a graphical representation of the X-ray projection image data and the CT data 140 as a superimposed image; Includes.

[0090] In operation S310, operations S210, S220 and S230 described with reference to FIG. 9 are performed.

[0091] In operation S320, X-ray projection image data representing the blood vessel 130 is received. The X-ray projection image data may be generated by an X-ray projection imaging system. The X-ray projection image data may be generated by, for example, the X-ray projection imaging system 320 shown in FIG. 11. The X-ray projection image data may represent a still image of the blood vessel or a temporal sequence of images of the blood vessel. In the latter case, the temporal sequence of images may represent the blood vessel in substantially real time. The receiving operation S320, the registering operation S330, and the outputting operation S340 are performed in substantially real time to provide live guidance during an endovascular treatment procedure on the blood vessel 130. The X-ray projection image data may be received by, for example, one or more processors 310 shown in FIG. 11.

[0092] An X-ray projection imaging system typically includes a support arm, a so-called "C-arm," that supports an X-ray source and an X-ray detector. Alternatively, the X-ray projection imaging system may include a support arm having a different shape than the present example, such as an O-arm. In contrast to CT images, an X-ray projection imaging system generates attenuation data for a subject using an X-ray source and an X-ray detector that are in a stationary position relative to the subject. One example of an X-ray projection imaging system that may be used to generate the projection attenuation data 110 received in act S110 is the Azurion 7 X-ray projection imaging system, commercially available from Philips Healthcare of Vest, The Netherlands.

[0093] In operation S330, the CT data 140 is registered to the X-ray projection image data. Operation S330 may be performed using known image registration techniques. Examples of suitable image registration techniques include intensity-based registration techniques, feature-based registration techniques, rigid and non-rigid registration techniques, etc.

[0094] In operation S340, a graphical representation of the X-ray projection image data and the CT data 140 is output as a superimposed image. Operation S340 is performed using known image superimposition techniques. For example, one image from the X-ray projection image data and the CT data 140 may be superimposed on the other image, with the superimposed image having a predetermined level of transparency. The graphical representation can be output in various ways, such as on a display. For example, the graphical representation may be output to the display 330 shown in FIG. 11 . The output superimposed image provides guidance to a physician during an intravascular plaque treatment procedure on the blood vessel 130.

[0095] 12 is a flowchart illustrating a second example of a computer-implemented method for providing guidance during an intravascular plaque treatment procedure, according to some aspects of the present disclosure. The flowchart illustrated in FIG. 12 illustrates that the method for providing guidance during an intravascular plaque treatment procedure, i.e., the method described with reference to FIG. 10, relies on a method for planning an intravascular plaque treatment procedure for a blood vessel 130, i.e., operation S310, and a method for providing plaque data 110 of a plaque deposit 120 within the blood vessel 130, i.e., operation S210.

[0096] In one example, an indication of the delivery of treatment to the blood vessel 130 is included in the superimposed image. In this example, the method of providing guidance during an intravascular plaque treatment procedure described with reference to FIG. receiving, by an intravascular treatment device, input data representative of delivery of a treatment to a blood vessel 130; updating the superimposed image based on the received input data; , wherein the updating includes providing an indication of the delivery of treatment to the vessel 130 in the overlaid image.

[0097] The indication of the delivery of the treatment to the vessel 130 may indicate the location where the treatment was delivered and / or the difference between the planned and actual treatment delivery.

[0098] This example is performed by tracking the position of an endovascular treatment device with respect to X-ray projection image data generated during an endovascular plaque treatment procedure and indicating the tracked position of the endovascular treatment device in a superimposed image at the time the treatment is delivered to the vessel. As an example, an indication of the delivery of treatment to the vessel 130 may indicate that a specified number of pulses have been delivered to the vessel 130 by one or more shockwave emitters 190 of the IVL balloon 180 shown in FIG. 8 . An indication of the delivery of treatment to the vessel 130 may be provided in the superimposed image, for example, as an icon at the associated location. The tracked position of the treatment device may be determined in the X-ray projection image data or using a separate tracking system. In the former case, the tracked position of the endovascular treatment device is determined in the X-ray projection image data using a feature detector trained to detect the shape of the endovascular treatment device. Alternatively, the feature detector may be trained to detect the shape of fiducial markers attached to the treatment device. In the latter case, the position of the endovascular treatment device may be tracked using various interventional device tracking techniques. The position in the X-ray projection image is determined by aligning the coordinate system of the tracking system with the coordinate system of the X-ray projection imaging system 320, which generates the X-ray projection image data. Various tracking systems can be used to track the position of an endovascular treatment device, including electromagnetic tracking systems and fiber optic-based tracking systems. An example of an electromagnetic tracking system is disclosed in U.S. Patent Application Publication No. 2020 / 397510. An example of a fiber optic-based tracking technique that uses strain sensors to determine the position of an interventional device is disclosed in U.S. Patent Application Publication No. 2012 / 323115.

[0099] In this example, the received input data represents the location of the delivery of therapies to the vessel 130. The act of updating the overlaid image may include providing the overlaid image with an indication of the location of the delivery of therapies to the vessel 130. Alternatively or additionally, the act of updating the overlaid image may include providing the overlaid image with an indication of the total number of therapies delivered to the vessel 130. Thus, the overlaid image provides a record of the therapies delivered to the vessel.

[0100] In another example, a computer program product is provided having instructions that, when executed by one or more processors, cause the one or more processors to perform a method for providing guidance during an intravascular plaque treatment procedure on a blood vessel 130, the method comprising: an operation S310 that performs operations S210, S220, and S230; an operation S320 of receiving x-ray projection image data representative of the blood vessel 130, generated during an intravascular plaque treatment procedure; an operation S330 of registering the CT data 140 with the X-ray projection image data; and outputting 340 a graphical representation of the X-ray projection image data and the CT data 140 as a superimposed image. It has.

[0101] In another example, a system is provided for providing guidance during an intravascular plaque treatment procedure on a blood vessel 130, the system comprising: an operation S310 that performs operations S210, S220, and S230; an operation S320 of receiving X-ray projection image data representing a blood vessel 130, the X-ray projection image data being generated during an intravascular plaque treatment procedure; an operation S330 of registering the CT data 140 with the X-ray projection image data; an operation S340 of outputting a graphical representation of the X-ray projection image data and the CT data 140 as a superimposed image; The device is configured to:

[0102] The above examples should be understood as illustrative of the present disclosure, not limiting. Further examples may be contemplated. For example, examples described in connection with a computer-implemented method may also be provided by a corresponding computer program product, a corresponding computer-readable storage medium, or a corresponding system. It should be understood that features described with respect to any one example may be used alone or in combination with other described features, in combination with one or more features of another example, or in combination with other examples. Furthermore, equivalents and modifications not described above may be used without departing from the scope of the invention, as defined by the appended claims. In the claims, the word "comprising" does not exclude other elements or operations, and the absence of a plurality does not exclude a plurality of those elements. 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 the scope of the claims.

Claims

1. 1. A computer-implemented method for providing plaque data for plaque deposits in a blood vessel, the method comprising: receiving computed tomography (CT) data representative of the blood vessel; generating a cross-sectional view of the blood vessel at each of a plurality of locations along the blood vessel from the CT data; extracting plaque data from the cross-sectional view, the plaque data comprising at least one measurement of the plaque deposit at a plurality of locations along the blood vessel; outputting a graphical representation of the plaque data; 10. A computer-implemented method comprising:

2. generating a cross-sectional view of the blood vessel at each of a plurality of locations along the blood vessel includes identifying a centerline of the blood vessel from the CT data; cross-sectional views of the blood vessel are generated at multiple locations along a centerline of the blood vessel; The computer-implemented method of claim 1 .

3. The computer-implemented method of claim 2 , wherein identifying a centerline of the blood vessel comprises determining a centerline of a lumen of the blood vessel and defining the centerline of the blood vessel as the centerline of the lumen.

4. The at least one measure of plaque deposits comprises: the thickness of the plaque deposit; the depth of the plaque deposit from the centerline of the blood vessel; the angular extent of the plaque deposit around the centerline of the blood vessel; and the eccentricity of the plaque deposit The computer-implemented method of claim 1 , comprising at least one of:

5. 2. The computer-implemented method of claim 1, wherein the plaque data further comprises an estimate of a length of the plaque deposit along the blood vessel, the estimate of the length of the plaque deposit along the blood vessel being calculated based on a distance between a position along the blood vessel corresponding to a proximal end and a position along the blood vessel corresponding to a distal end of a series of consecutive cross-sectional views at which at least one measurement of the plaque deposit exceeds a predetermined value.

6. 6. The computer-implemented method of claim 1, further comprising extracting lumen data and / or vessel wall data of the blood vessel from the cross-sectional view, the lumen data comprising at least one measurement of a lumen of the blood vessel and the vessel wall data comprising at least one measurement of a wall of the blood vessel at a plurality of positions along the blood vessel.

7. The method further comprises, for cross-sectional views in which at least one measure of plaque deposits exceeds a threshold, estimating a plaque-free measure of the lumen at a corresponding location along the blood vessel; 7. The computer-implemented method of claim 6, wherein the estimated plaque-free measurement of the lumen is determined based on corresponding measurements of the lumen from adjacent cross-sectional views in which the at least one measurement of the plaque deposit is below the threshold.

8. the CT data comprises spectral CT data defining x-ray attenuation of the blood vessel at a plurality of different energy intervals; extracting the plaque data and / or determining the centerline of the lumen of the blood vessel and / or extracting the lumen data of the blood vessel comprises applying a material decomposition algorithm to the spectral CT data. A computer-implemented method according to any one of claims 1 to 7.

9. 9. The computer-implemented method of claim 8, wherein extracting the plaque data comprises applying a material decomposition algorithm to the spectral CT data to identify the type of plaque deposit at multiple locations along the blood vessel.

10. 1. A computer-implemented method for planning an endovascular plaque treatment procedure for a blood vessel, comprising:

10. Carrying out a method for providing plaque data according to any one of claims 1 to 9; determining a recommended value for at least one parameter of an endovascular treatment device for use in the endovascular plaque treatment procedure based on the plaque data; and outputting the recommended value of the at least one parameter; 10. A computer-implemented method comprising:

11. the endovascular treatment device comprises an IVL balloon for intravascular lithotripsy, and the at least one parameter comprises one or more of a size of the IVL balloon, a position of the IVL balloon relative to the plaque deposit, a number of shock wave pulses to deliver to the blood vessel from one or more shock wave emitters of the IVL balloon, and an IVL balloon pressure to use during delivery of shock waves from the IVL balloon to the blood vessel; the endovascular treatment device includes a laser atherectomy catheter, and the at least one parameter includes one or more of: a size of the laser atherectomy catheter; a position of the laser atherectomy catheter relative to the plaque deposit; a fluence of light radiation emitted by the laser atherectomy catheter; a repetition rate of light pulses emitted by the laser atherectomy catheter; a duty cycle of light radiation emitted by the laser atherectomy catheter; and an advancement speed of the laser atherectomy catheter. the endovascular treatment device comprises an orbital atherectomy device, and the at least one parameter comprises one or more of an advancement speed of the orbital atherectomy device, a size of the orbital atherectomy device, a position of the orbital atherectomy device relative to the plaque deposit, and a rotational speed of the orbital atherectomy device. the endovascular treatment device comprises a rotablator atherectomy device, and the at least one parameter comprises one or more of a position of the rotablator atherectomy device relative to the plaque deposit, an advancement speed or length of the rotablator atherectomy device, a retraction speed or length of the rotablator atherectomy device, a burr size of the rotablator atherectomy device, and a rotational speed of the rotablator atherectomy device; or the endovascular treatment device has a scoring or cutting balloon, and the at least one parameter comprises one or more of a position of the balloon relative to the plaque deposit, a size of the balloon, an inflation pressure of the balloon, a blade length of the balloon, and a balloon inflation time; The computer-implemented method of claim 10.

12. 12. The computer-implemented method of claim 11, wherein the recommended value of the at least one parameter is determined further based on values ​​of the corresponding at least one parameter from one or more similar previous intravascular plaque treatment procedures.

13. the one or more similar prior intravascular plaque treatment procedures: procedures performed using similar types of endovascular devices; Procedures performed on similar types of blood vessels, procedures performed on vessels with similar geometries; procedures performed on vessels with similar plaque data; procedures performed on vessels with similar lumen data; Treatments administered to subjects with similar body mass index (BMI), treatments given to subjects of the same sex; Treatment given to subjects of similar age The computer-implemented method of claim 12 , comprising at least one of:

14. 1. A computer-implemented method for providing guidance during an intravascular plaque treatment procedure on a blood vessel, the method comprising:

13. A method for planning an intravascular plaque treatment procedure according to any one of claims 10 to 12, comprising: receiving x-ray projection image data representative of the blood vessel generated during the intravascular plaque treatment procedure; registering the CT data to the X-ray projection image data; outputting a graphical representation of the X-ray projection image data and the CT data as a superimposed image; The computer-implemented method further comprising:

15. The method comprises: receiving, by the intravascular treatment device, input data representative of delivery of a treatment to the blood vessel; updating the superimposed image based on the received input data; and The computer-implemented method of claim 14 , wherein the updating comprises including in the superimposed image an indication of delivery of a therapy to the vessel.

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