Vascular contrast based speculation of selective endoradiotherapy dosimetry

Abstract

A method comprises recording (S2) 3D data of an organ of interest after administration of beads into a patient to flow to the organ; segmenting (S3) visible accumulated arterial vascular structures with beads around the organ; determining (S4) a diameter or lumen of the arterial vascular structure; determining or estimating (S5) a degree of fill factors in the arterial vascular structure; generating (S6) a bead density map along the arterial vascular structure; inferring (S7) bead accumulation in the parenchyma in the organ and / or in the tumor tissue; and generating (S8) a composite profile of the microbeads comprising the inferred accumulation of the microbeads in the parenchyma and / or tumor tissue in the arterial vascular structure and organ with visible accumulation of the microbeads.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 342,487, filed May 16, 2022, which is hereby incorporated by reference into this application for all purposes as if fully set forth herein. Technical Field

[0003] The present disclosure relates to selective internal radiation therapy (SIRT) dosimetry. More specifically, the present disclosure relates to a method for providing dosimetry estimates for a partial volume of the liver or other organs based on single cone beam computed tomography (CBCT) angiography (a method of obtaining computed tomography (CT) using a C-arm system) without the need for spectral resolution or dual energy. Background Art

[0004] For SIRT dosimetry with X-ray visible microbeads, it is necessary to determine the number of microbeads present in the tissue volume. This is particularly important in parenchymal tissue (liver tissue outside of blood vessels), through which many tiny arterioles pass. This is challenging because after SIRT injection, once the imaging average exceeds the voxel size / pixel size, the blood vessels and parenchyma often show low concentrations of microbeads. Summary of the invention

[0005] The methods and devices described herein relate to embodiments in which X-ray images of tissue including microbeads are used to estimate the microbead concentration and distribution in the tissue of interest, and the estimate is used as input to dosimetry calculations for treating the tissue. The microbeads can be made of MAA (macro-aggregated albumin), which is a biodegradable macro-aggregate of human serum albumin. The microbeads can also be made of glass or resin matrices. This process can be used prior to or to treat highly vascular tumors, such as hepatocellular carcinoma (HCC), the most common type of primary liver cancer.

[0006] Based on the visible concentration of contrast beads in medium-sized and large vessels, the concentration of beads in arterioles and in the immediately adjacent parenchymal or tumor tissue is inferred. This inference can be made based on a model or via a learning network that is calibrated or trained based on comparisons with other imaging methods or histological examinations.

[0007] It has been found that the disclosed embodiments provide adequate estimates of the concentration of microbeads in arterioles and in the immediately adjacent parenchymal or tumor tissue without the need for conventional dual energy CBCT or subtraction procedures (which require complex patient motion compensation and further documentation).

[0008] According to one embodiment, a method includes recording 3D data of an organ of interest after administering microbeads into a patient's body to flow to the organ of interest; segmenting arterial vascular structures having visible accumulation of microbeads around the organ; determining the diameter or (multiple) lumens of the arterial vascular structures; determining or estimating the degree of filling factor in the arterial vascular structures; generating a microbead density map along the arterial vascular structures; inferring microbead accumulation in parenchyma and / or tumor tissue in the organ; and generating a composite distribution map of microbeads, the composite distribution map including arterial vascular structures having visible accumulation of microbeads and the inferred microbead accumulation in parenchyma and / or tumor tissue in the organ.

[0009] According to another embodiment, a system includes an X-ray CT system, the system comprising: an X-ray source; a detector; a control and processing system for controlling the X-ray CT system; and a data storage device; wherein the X-ray CT system records 3D data of an organ of a patient to which beads are administered to flow to the organ, and the processing system: segments arterial vascular structures having visible accumulation of beads around the organ; determines the diameter or (multiple) lumens of the arterial vascular structures; determines or estimates the degree of filling factor in the arterial vascular structures; generates a microbead density map along the arterial vascular structures; infers microbead accumulation in the parenchyma and / or tumor tissue in the organ; and generates a composite distribution map of the beads, the composite map including the arterial vascular structures having visible accumulation of beads and the inferred microbead accumulation in the parenchyma and / or tumor tissue in the organ.

[0010] According to another embodiment, a non-transitory computer-readable medium includes executable instructions that, when executed by a processor, cause the processor to perform the following steps: after administering microbeads into a patient's body to flow to an organ of interest, record 3D data of microbeads in the organ; segment arterial vascular structures around the organ with visible accumulation of microbeads; determine the diameter of the arterial vascular structures; determine or estimate the extent of a filling factor agent in the arterial vascular structures; generate a microbead density map along the arterial vascular structures; infer microbead accumulation in the parenchyma and / or tumor tissue in the organ; and generate a composite distribution map of microbeads, the composite map including arterial vascular structures with visible accumulation of microbeads and the inferred microbead accumulation in the parenchyma and / or tumor tissue in the organ.

[0011] In one aspect, the method, system, or non-transitory computer readable medium further comprises performing a contrast scan of an arterial structure of the organ of interest prior to the step of recording the 3D data of the organ.

[0012] In one aspect, the method, system, or non-transitory computer-readable medium further comprises simulating arteriolar structure or arteriolar density in a region of a tumor and a healthy portion of an organ.

[0013] In one aspect, the method, system, or non-transitory computer readable medium further comprises calculating the bead patency or resorption capacity of tumor tissue adjacent to an arteriole or healthy portion of an organ.

[0014] In one aspect, determining or estimating a degree of fill fraction in the arterial vasculature is performed based on a Hounsfield Unit value in the arterial vasculature and a diameter of the arterial vasculature.

[0015] In one aspect, inferring said microbead accumulation in parenchymal and / or tumor tissue in an organ is performed by a learning-based algorithm.

[0016] In one aspect, inference of microbead accumulation in parenchymal and / or tumor tissue in an organ is based on the size of visible contrasting vessels, the size of the blood supply area, and the number of microbeads delivered to the arterial vasculature via the supplying vessels.

[0017] In one aspect, inferring microbead accumulation in parenchyma and / or tumor tissue in an organ is based on dynamic images of microbead influx into the parenchyma and tumor tissue.

[0018] In one aspect, the composite profile contains the estimated density of beads for each voxel or volume element in the 3D data.

[0019] In one aspect, the method, system, or non-transitory computer-readable medium further comprises predicting a number of beads delivered to parenchymal and / or tumor tissue in an organ via the arterial vasculature based on a size of one of the arterial vasculatures.

[0020] In one aspect, the method, system, or non-transitory computer readable medium further comprises detecting clumping of the beads and thereby predicting whether reduced blood flow is due to an embolic effect of the beads. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The features and advantages of the present disclosure will be more fully disclosed or become apparent in the following detailed description of example embodiments. Figure 1 Consider together the drawings wherein like numerals refer to like parts, and further in the drawings:

[0022] Figure 1 Indicates blood vessels of an organ embedded in or surrounded by parenchyma or tumor tissue into which contrast medium has been injected.

[0023] Figure 2 Representation and Figure 1 Medium to large blood vessels of similar organs have been infused with microbeads.

[0024] Figure 3is an example of an X-ray CT system according to an embodiment of the present disclosure that can be used to image microbeads and perform dosimetry estimation.

[0025] Figure 4 is a flow chart including steps of a method of imaging microbeads and performing dosimetry estimation according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The description of the exemplary embodiments is intended to be read in conjunction with the accompanying drawings, which should be considered as part of the entire written description of these disclosures. Although the present disclosure is susceptible to various modifications and alternative forms, specific embodiments are shown by way of example in the drawings and will be described in detail herein. The objects and advantages of the claimed subject matter will become more apparent through the following detailed description of these exemplary embodiments in conjunction with the accompanying drawings.

[0027] However, it should be understood that the present disclosure is not intended to be limited to the particular forms disclosed. On the contrary, the present disclosure covers all modifications, equivalents, and alternatives falling within the spirit and scope of these exemplary embodiments.

[0028] Figure 1 and Figure 2 Used to describe a problem. Figure 1 The dark lines in the figure represent blood vessels of an organ surrounded by parenchymal or tumor tissue 20, which is represented as a gray oval. The medium to large blood vessels 10 and microvessels 15 are filled with liquid contrast agent, such as an iodine solution injected upstream from the tissue. The parenchymal or tumor tissue 20 has a measurable contrast agent uptake via the body perfusion blood volume (DynaPBV body) protocol. Small vessel and tissue uptake of liquid contrast agent can be imaged. This is achieved by directly resolving individual small vessels or by calculating the volume / spatial average uptake of contrast agent in tissues and small vessels.

[0029] Figure 2 The line in the middle represents Figure 1 Medium to large vessels of similar organs that have been infused with microbeads. When imaged, medium to large vessels are somewhat visible, but microvasculature and parenchymal / tumor tissue concentrations are not visible or measurable.

[0030] During SIRT, tiny glass or resin beads filled with the radioactive isotope yttrium Y-90 (or possibly non-radioactive) are placed in blood vessels that supply the tumor. By including highly X-ray visible material in the beads, the injected beads are directly visible in 3D X-ray images. Typically, individual beads cannot be distinguished in imaging, but all larger accumulations of beads (such as in arterial vessels of different sizes) are directly visible in the X-ray image. Without spectroscopic methods such as dual energy or photon counting, smaller accumulations or freely distributed beads cannot be distinguished and can only be diffusely observed by a slight increase in the Hounsfield Unit (HU) value. The diameter of the microbeads can be, for example, 15, 20, 30 or 50 microns, or another suitable size. Due to the larger diameter, the microbeads cannot diffuse / penetrate into the parenchyma as easily as liquid contrast agents. That is, most of the beads typically remain "stuck" in blood vessels of different sizes, or only penetrate a short distance from these vessels into the surrounding parenchyma or tumor tissue.

[0031] The present disclosure is directed to a flow phantom or human or animal organ infused with SIRT microbeads, and at least a portion of the microbeads are X-ray visible. A C-arm / CBCT scanner or CT or other 3D X-ray imaging system can be used to image the microbeads. A data processing system with associated data storage devices is used to interpret the data and perform simulations of arteriole structure or arteriole density in tumor regions and healthy portions of the organ and to calculate therapeutic dose determination.

[0032] Figure 3 3 is an example of an X-ray CT system 300 that can be used to image microbeads and perform dosimetry estimation according to an embodiment of the present disclosure. The X-ray CT system 300 may include X-ray sources 320 and 330, detectors 340 and 350, a gantry housing 360, a patient 370 on a patient bed 380, a system axis 310, a control and processing system 390, and a data storage device 395. The control and processing system 390 may be a single computer or a network of computers for controlling the X-ray CT system 300 and interacting with a technician to perform dosimetry estimation as described below. Figure 4 Image capture, image processing, data manipulation, calculations and map generation are performed in the steps of the described method.

[0033] Figure 4is a flow chart including the steps of a method for imaging microbeads and performing dosimetry estimation according to an embodiment of the present disclosure. The disclosed method is intended to be performed prior to providing radiation therapy to a patient and as an input to providing radiation therapy to a patient. Any procedure that injects microbeads or materials with flow / wetting dynamics different from liquid contrast agents can benefit from this method. For example, such procedures can include transarterial chemoembolization (FACE) with drug eluting beads, and injection of ultrasound contrast agent bubbles. Using the disclosed method, a technician captures a single CBCT or CT image of the patient after microbead injection without spectral information, and outputs a complete dosimetry or estimated total distribution of the microbeads. Dual-energy CBCT or subtraction recording is not required, but the method can also be used based on dual-energy or subtraction imaging to obtain even higher sensitivity.

[0034] Step S0 may optionally be performed to image the arterial structure in the organ of interest with a liquid contrast agent or to provide a contrast scan, such as 3D X-ray angiography, arterial phase of CT, MRI with contrast arteries. The optional contrast scan may be used to identify and segment arterial vessels.

[0035] Step S1 can optionally be performed with a computer to calculate / simulate possible arteriolar structures or arteriolar densities in tumor regions and healthy parts of an organ. In addition, the method can calculate / assume the bead patency or absorption capacity of tumor tissue adjacent to arterioles or healthy parenchyma. For this purpose, tumor type, blood values, metabolic tests (such as indocyanine green angiography (ICG) tests), measured contrast agent dynamics or other data / examination results can also be used. The arteriolar structure includes small arterial vessels and very small arterial vessels that supply parenchyma and tumor tissue. These vessels are large enough to be at least partially infiltrated by microbeads.

[0036] In step S2, after selective arterial injection of SIRT microbeads is performed on the patient and the organ of interest, a 3D dataset of the organ or tissue or the object of study is recorded or provided by a computer. For example, the 3D dataset can be recorded by an imaging device and characterize the vascular system and the visible microbeads included in the vascular system.

[0037] In step S3, arterial vessel structures with visible accumulation of contrast beads are segmented. This can be automatic segmentation or performed by a technician. Alternatively, this step can also be semi-automatic segmentation, i.e. any known combination of computer segmentation and human interaction / correction. For example, this can be threshold-based, or can be via a known vessel segmentation algorithm or network trained for this purpose.

[0038] In step S4, the cross-section diameter of the arterial vessel structure with the visible bead is determined, typically by computer. This determination can also be optionally performed via registration of the 3D dataset and / or segmentation of the arterial vessel produced in step S0.

[0039] In step S5, the degree of fill fraction of the arterial vessels with visible beads from step S3 is determined / estimated by computer. This is performed, for example, around the tissue with HU values ​​and vessel diameters for the vessel portion, thereby taking into account partial volume effects, i.e. including the effects of finite voxel size in the 3D image. Alternatively, averaged / smoothed / interpolated bead flow modeling along the arterial vessels may be used.

[0040] In step S6, the computer generates a bead density map along the arterial vessels visible in the image generated in step S2. The bead density map is the result of steps S5 and S6, which means that it will contain the visible vessels, their sizes and their filling fraction / filling density with beads.

[0041] From the visible arteries with microbeads, in step S7, the microbead accumulation is now extrapolated to the surrounding parenchyma / tumor tissue surrounded by these arteries and the arteriolar vessels contained therein.

[0042] In another aspect, model-based inference of contrast agent density in arterioles and infiltrates can be used. For example, the model or trained function can be calibrated / annotated with histological determinations of actual concentrations of beads.

[0043] In another aspect, a learning-based algorithm can be used that is trained based on paired images of the type captured in step S3 and "ground truth" images such as PET / CT or dual-energy CT. This means that based on CBCT images, where only medium-sized and larger arteries can be segmented with beads, such a learning-based algorithm can infer / estimate the density of beads in arterioles or parenchymal or tumor tissue provided by contrast arteries. An example network architecture for this task can be a Generative Adversarial Network (GAN).

[0044] In another aspect, in the modeling, the number of beads delivered to the arteriolar structure via the supply vessels can be predicted, in particular based on the size of the visible contrast vessels and based on the size of the supply area. In addition, based on the mass of beads, it can be predicted whether there has been a "backlog" or "blockage" of beads during SIRT infusion and / or a significant reduction in blood flow due to the embolic effect of the beads.

[0045] In another aspect, LiverPBV (NeuroPBV transferred to the liver) images can be used to estimate the blood volume supplied by arteries within the liver tissue. Based on the transport model and the tissue model, the density of arteriolar vessels can be generated from the LiverPBV values. In the case of tumors, in areas with very high PBV values, it can be assumed that the permeability of the arterial vessel wall is increased, which is associated with a stronger infiltration of the microbeads into the tumor tissue. This effect is roughly equivalent to the disturbance of the blood-brain barrier caused by brain tumors.

[0046] On the other hand, in the context of pulmonary shunt detection, additional Tc99-loaded biodegradable MAA particles or particles of magnetic resonance detectable contrast agents may also be introduced / introduced in combination with these particles, via which the distribution of microbeads in tissue (parenchymal or tumor tissue) in relation to the concentration of microbeads in the supplying artery may be used.

[0047] On the other hand, by measuring the dynamics of inflow, for example using 4D digital subtraction angiography (DSA) but recording during SIRT infusion rather than during test infusion, or by 2D-3D registration of projection images from previous imaging to the vascular tree, it is possible to estimate how much microbeads are caused by arterial vessels visible in the dynamic images to flow into the tissue they supply.

[0048] Note that model-based approaches will work particularly accurately in healthy / intact liver parenchyma, which can be used here for validated tissue models or models of arteriolar supply based on histological examination and previous imaging of other patients. Especially in healthy liver tissue, it is very important to calculate fairly small SIRT bead concentrations, as very small dose values ​​are to be detected in this step.

[0049] In step S8, a composite distribution map of beads is generated consisting of the visible arterial vessel structures segmented in step S3 and the inferred distribution in the tissue environment from step S7. For example, the composite distribution map may contain the estimated density of beads for each voxel or volume element in the 3D data set from step S3 that is outside of the vessels with visible bead accumulation, for which the density has been mapped in step S7. It can be assumed that the density of contrast agent in the arterioles will be similar to the corresponding density of beads. Thus, an infiltration model can be used for the directly adjacent tumor tissue / healthy parenchyma, such as in the closest / supplying visible arterial vessel segments.

[0050] In step S9, a dose determination calculation is performed based on the map generated in step S8. For example, see E Courtney Henry et al., Precision Dosimetry in Yttrium-90 Radio embolization through CTI imaging of Radiopaque Microspheres in a Rabbit Liver Model, EJNMMIPhysics 9, 21 (2022), available at DOI: https: / / doi.Org / 10.21203 / rs.3.rs-806070 / vl, which is fully incorporated herein by reference.

[0051] The above steps and features can be implemented in any of a variety of ways. For example, embodiments can be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or set of processors, whether arranged in a single computer or distributed among multiple computers. Such processors can be implemented as integrated circuits, with one or more processors in an integrated circuit assembly. However, any suitable form of circuitry can be used to implement the processor.

[0052] Additionally or alternatively, the above-described embodiments may be implemented as a non-transitory computer-readable storage medium having a program embodied thereon, which program may be executed by a processor for performing the methods of the various embodiments.

[0053] In addition, the various methods or processes outlined herein may be encoded as software that can be executed on one or more processors employing any of a variety of operating systems or platforms. In addition, such software may be written using any of a variety of suitable programming languages ​​and / or programming or scripting tools, and may also be compiled into executable machine language code or intermediate code that is executed on a framework or virtual machine. Typically, in various embodiments, the functionality of the program modules may be combined or distributed as desired.

[0054] In addition, embodiments of the present disclosure may be embodied as methods, examples of which have been provided. The steps or actions performed as part of the method may be ordered in any suitable manner. Thus, embodiments may be constructed that perform steps in an order different from the order shown, which may include performing some steps simultaneously, even though shown as sequential steps in the illustrative embodiments.

[0055] The above contents are provided to illustrate, explain and describe the embodiments of these disclosures. Modifications and adaptations to these embodiments are obvious to those skilled in the art and can be made without departing from the scope or spirit of these disclosures.

Claims

1. A method comprising: After the microbeads are administered into a patient's body to flow to an organ of interest, 3D data of the organ is recorded; segmenting arterial vascular structures having visible accumulation of the microbeads around the organ; determining a diameter of the arterial vascular structure; determining or estimating a degree of fill fraction in said arterial vascular structure; generating a microbead density map along the arterial vascular structure; inferring microbead accumulation in parenchymal and / or tumor tissue in said organ; and A composite distribution map of the beads is generated, the composite distribution map comprising the arterial vascular structure with visible accumulation of the beads and the inferred accumulation of the beads in the parenchyma of the organ and / or the tumor tissue.

2. The method according to claim 1, further comprising: Prior to the step of recording 3D data of the organ, a contrast scan is performed on the arterial structure of the organ of interest.

3. The method according to claim 1, further comprising: Arteriolar structure or arteriolar density in tumorous areas and healthy parts of the organ are simulated.

4. The method according to claim 1, further comprising: Bead patency or resorption capacity is calculated for tumor tissue adjacent to arterioles or healthy portions of the organ. 5 . The method according to claim 1 , wherein the determining or estimating the degree of filling fraction in the arterial vessel structure is performed based on a Hounsfield Unit value in the arterial vessel structure and a diameter of the arterial vessel structure.

6. The method of claim 1, wherein said inferring said microbead accumulation in parenchyma and / or tumor tissue in said organ is performed by a learning-based algorithm.

7. The method of claim 1, wherein the inference of the microbead accumulation in the parenchyma and / or tumor tissue in the organ is based on: the size of visible contrasting blood vessels, the size of the blood supply area, and the number of microbeads delivered to the arterial vascular structure through the supplying blood vessels.

8. The method of claim 1, wherein the inference of the microbead accumulation in the parenchyma and / or tumor tissue in the organ is based on dynamic images of microbead influx into the parenchyma and the tumor tissue.

9. The method according to any one of claims 1 to 9, further comprising: Dosimetry calculations are performed based on the composite profile.

10. A system comprising: X-ray CT system, including: X-ray source; Detector; A control and processing system for controlling the X-ray CT system; and Data storage device; wherein The X-ray CT system records 3D data of an organ of a patient to which the microbeads have been administered to flow to the organ, and The control and processing system: segmenting arterial vascular structures having visible accumulation of the microbeads around the organ; determining a diameter of the arterial vascular structure; determining or estimating a degree of filling factor in said arterial vascular structure; generating a microbead density map along the arterial vascular structure; inferring microbead accumulation in parenchymal and / or tumor tissue in said organ; and A composite distribution map of the beads is generated, the composite map including the arterial vascular structure with visible accumulation of the beads and the inferred accumulation of the beads in the parenchyma of the organ and / or the tumor tissue. 11 . The system of claim 10 , wherein the determining or estimating the degree of filling fraction in the arterial vascular structure is performed based on a Hounsfield Unit value in the arterial vascular structure and a diameter of the arterial vascular structure.

12. The system of claim 10, wherein said inferring said microbead accumulation in parenchyma and / or tumor tissue in said organ is performed by a learning-based algorithm.

13. The system of claim 10, wherein the inference of the microbead accumulation in the parenchyma and / or tumor tissue in the organ is based on: the size of visible contrasting blood vessels, the size of the blood supply area, and the number of microbeads delivered to the arterial vascular structure through the supplying blood vessels.

14. The system of claim 10, wherein the inference of the microbead accumulation in the parenchyma and / or tumor tissue in the organ is based on dynamic images of microbead influx into the parenchyma and the tumor tissue.

15. The system according to any one of claims 10 to 14, further comprising: Dosimetry calculations are performed based on the composite profile.

16. A non-transitory computer readable medium comprising executable instructions which, when executed by a processor, cause the processor to perform the following steps: After administering the microbeads into a patient's body to flow to an organ of interest, recording 3D data of the microbeads in the organ; segmenting arterial vascular structures having visible accumulation of the microbeads around the organ; determining a diameter of the arterial vascular structure; determining or estimating the extent of a filling factor agent in said arterial vascular structure; generating a microbead density map along the arterial vascular structure; inferring microbead accumulation in parenchymal and / or tumor tissue in said organ; and A composite distribution map of the beads is generated, the composite map including the arterial vascular structure with visible accumulation of the beads and the inferred accumulation of the beads in the parenchyma of the organ and / or the tumor tissue.

17. The method of claim 1, the system of claim 10, or the non-transitory computer-readable medium of claim 16, wherein the composite distribution map comprises an estimated density of beads for each voxel or volume element in the 3D data.

18. The method of claim 1, the system of claim 10, or the non-transitory computer readable medium of claim 16 or 17, comprising: Based on the size of one of the arterial vascular structures, the number of microbeads delivered to the parenchyma and / or the tumor tissue in the organ through the arterial vascular structure is predicted.

19. The method of claim 1, the system of claim 10, or the non-transitory computer readable medium of claim 16, 17, or 18, comprising: The clumping of the microbeads is detected, and thereby it is predicted whether the blood flow is reduced due to the embolic effect of the microbeads.

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