Assessment of myocardial blood flow using non-invasive measurements and porous structure engineering

Abstract

SOLUTION: The present invention provides a method for describing fluid dynamics in tissues or organs using a numerical model of blood flow. In one aspect, the present invention relates to a method for assessing myocardial blood flow in a human patient using non-invasive measurements including computational fluid dynamics analysis and porous structure engineering. The method can be implemented as a computer-implemented invention and includes generating an anatomical model of myocardium connected to multiple coronary vessels using patient-specific anatomical data obtained from the non-invasive measurements. The anatomical model is composed of fluid and porous regions with boundaries. The present invention overcomes the drawbacks of known in vivo methods, including all the risks associated with invasive procedures. Furthermore, virtually unlimited spatial and temporal resolution is achieved, and the results of the present invention completely eliminate motion artifacts and limit radiation exposure to the patient.

Description

[Technical field]

[0001] The present invention relates to myocardial perfusion imaging (MPI) and poromechanics. In particular, the present invention uses non-invasive measurements (e.g., computed angiography or nuclear magnetic resonance) to build models of the human heart with porous regions for use in computational fluid dynamics. The results of the present invention can be used to diagnose the human heart, for example, coronary heart disease. [Background technology]

[0002] One of the major challenges in applying perfusion assessment methods to medical diagnosis is coronary heart disease (CHD). According to the National Center for Health Statistics, 5.6% of US adults had CHD in 2018. Prevalence increases with age, with nearly 24% of those aged 75 years and older having CHD. The disease is more prevalent in men than women, 7.4% vs. 4.1% of all US adults (Villarroel MA, Blackwell DL, Jen A (2019) Tables of Summary Health Statistics for USAdults: 2018 National Health Interview Survey, National Center for Health Statistics. Accessed December 15, 2021). In 2018, 365,744 people died in the United States from coronary heart disease (CHD), accounting for approximately 13% of all deaths in the United States (see Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System: public use data file documentation: mortality multiple cause-of-death micro-data files, accessed December 15, 2021). In 2017, 623,276 people died in the European Union from ischemic heart disease, accounting for approximately 12% of all deaths in the European Union in 2018 (see Eurostat (2021) Causes of death-deaths by country of residence and occurrence [HLTH_CD_ARO], accessed December 15, 2021).

[0003] The heart is the center of the circulatory system. The human heart beats about 100,000 times a day and about 3 billion times in a lifetime. The pumping action and heartbeat of the heart are caused by alternating contractions and relaxations of the myocardium. This pumping action keeps the blood flowing through the blood vessels. Thus, the heart is responsible for performing various functions in the human body, such as supplying oxygen and nutrients, removing metabolites, protecting against foreign and toxic substances, and preventing blood loss. In addition, the heart is responsible for maintaining constant physical and chemical conditions of the human body, especially temperature and pH (Barrett KE et al (2019) Ganong's Review of Medical Physiology, 26th Edition, McGraw-Hill Education). In fact, modern medical diagnostics offers a wide range of methods and parameters for the evaluation of cardiac function. Nevertheless, as more than 100 years ago, the key role in medical diagnostics is played by parameters that describe the amount of blood pumped during a certain time interval (usually within one cycle or one minute). As long as this parameter is maintained at an adequate level, the prerequisites are met for the other organs to maintain their function. The recognition of this principle and the attempt to evaluate it can be observed in the very early experimental studies of physiology. Albrecht von Haller had already in 1761 studied the pulmonary circulation by injecting colored fluids into the vena cava of animal corpses immediately after death (see Haller A(1761)Elementa physiologiae corporis humani,Francisci Grasser & Sociorum,Lausanne).The turning point and foundation of modern indicator dilution methods of blood flow diagnostics, a technique commonly used in modern times to assess perfusion, were the work of George Stewart (see Argueta EE, Paniagua D (2019) Thermodilution cardiac output, Cardiology in Review, 27 (3), p. 138-144) and, within a century, Kenneth Zierler (Meier P, Zierler KL (1954) On the theory of the indicator-dilution method for measurement of blood flow and volume, The Journal of Applied Physiology, 6 (12), p. 731-744, Zierler K (2000) Indicator dilution methods for measuring blood flow, volume, and other properties of biological systems: a brief history and memoir, Annals of Biomedical Engineering, 28 (8), p. 836-848).

[0004] Myocardial perfusion, i.e., blood flow through a particular tissue mass, is a fundamental physiological parameter that can be determined based on cardiac imaging data. Perfusion imaging, including myocardial perfusion imaging (MPI), involves injecting a contrast agent into the blood and recording the concentration distribution of the contrast agent in the cardiovascular system over time. The important point in perfusion analysis is the calculation of so-called perfusion parameters or perfusion metrics, such as myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT), and time to peak (TTP) (Taylor SH (1966) Measurement of the cardiac output in man, Proc R Soc Med, 59 Suppl (Suppl 1), p. 35-53, Axel L (1980) Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis, Radiology, 137 (3), p. 679-86, Lee TY (2002) Functional CT: Physiological models, Trends Biotechnol, 20, p. S3-S10, Tomandl BF et al. (2003) Comprehensive imaging of ischemic stroke with multisection CT,Radiographics,23(3),p.565-92,Konstas AA et al.(2009)Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke.Part 1:Theoretic basis,AJNR Am J Neuroradiol,30(4),p.662-8). Assume a specific control volume of interest (VOI) with a uniquely defined entrance and exit. At the initial time T0=0, a certain amount (m) of contrast agent is introduced and the time Δt i A sample is then taken at the outlet to measure the concentration of the contrast agent.

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[0012] Analysis of in vivo recordings of the arterial input function (AIF) and time attenuation curve (TAC) shows that the change in contrast volume occurring over a given time interval is directly proportional to the difference between the inflow and outflow volumes. This is due to the tissue control volume V t The balance of the contrast agent mass flux in the

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[0023] The field of non-invasive cardiac imaging is rapidly evolving, and in that evolution, quantitative blood flow assessment techniques that can be used to evaluate coronary blood flow and myocardial ischemia are of interest. The main methods for myocardial blood flow assessment are single photon emission computed tomography (SPECT), positron emission tomography (PET), and stress dynamic computed tomography perfusion (CTP) (see AlJaroudi WA, Hage FG (2020) Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2019: Positron emission tomography, computed tomography and magnetic resonance, J Nucl Cardiol, 27 (3), p. 921-930).

[0024] For decades, single photon emission computed tomography (SPECT) has been established as a clinical cardiovascular imaging modality. Evaluation of the degree of myocardial ischemia based on SPECT combined with measurements of left ventricular function and volumes is an accepted diagnostic method for myocardial ischemia. The technical evolution of SPECT was primarily aimed at maximizing sensitivity, shortening acquisition times, and reducing the amount of radiopharmaceuticals injected. SPECT has a temporal resolution (acquisition time for one frame) of 10 seconds and a voxel size of 10 × 10 × 10 mm. 3In addition, there was also the problem of an increasing BMI (body mass index) in the patient population. The solution to this problem was cadmium-zinc-telluride solid-state detector technology (CZT), which provided increased sensitivity, improved spatial and energy resolution, shorter acquisition times, and reduced radiopharmaceutical dosage. CZT SPECT allowed dynamic imaging and kinetic evaluation of blood flow tracers in the blood and in the myocardium. This information also allowed compartmental modeling and description of absolute myocardial blood flow (MBF) and coronary flow reserve (see Dewey M (2020) Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia, Nature Reviews Cardiology, 17 (7), p. 427-450).

[0025] Another technique to confirm or exclude ischemia is positron emission tomography (PET). PET can accurately measure the concentration of radiopharmaceuticals in vivo. PET has a time resolution of 1-5 seconds and a voxel size of 4 × 4 × 4 mm. 3 Various markers are used for testing, 15 O water is considered the clinical gold standard. 15 Modeling of O water kinetics is well established, and tracer concentrations are directly proportional to the measured image signal. Due to the short half-life of the tracer, rest-stress protocols can be completed in 30 minutes, resulting in a lower total radiation dose compared to single-photon emission computed tomography (SPECT). Images of perfusion disorders can sometimes be crude and difficult to interpret. Myocardial blood flow (MBF) is obtained by modeling the tracer kinetics and correcting for the limited extraction volume. Recent advances in PET have allowed the calculation of parametric images showing MBF at the voxel level, making it possible to perform measurements in a clinical setting. 15The use of O water PET has become easier (see Dewey M (2020) Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia, Nature Reviews Cardiology, 17 (7), p. 427-450).

[0026] The last of these three techniques is stress dynamic computed tomography perfusion imaging (CTP). There are currently two techniques for CTP: static and dynamic. Evaluation of the myocardial enhancement area acquired at one time point during the first pass of the contrast sample is called static CTP imaging. On the other hand, dynamic CTP evaluates myocardial blood flow by measuring the myocardial enhancement area at multiple time intervals during the first pass of the contrast sample that is resistant to the timing of the bolus. Dynamic CTP allows a complete quantitative examination of myocardial blood flow. Recent advances in computed tomography (CT) technology have made it possible to perform dynamic CTP of the entire myocardium with a significantly reduced radiation dose (Kitagawa K(2018)Dynamic CT perfusion imaging:state of the art,Cardiovascular Imaging Asia,2(2),p. 38-48).

[0027] Each of the above methods has advantages and disadvantages. In terms of spatial and temporal resolution, image quality, and diagnostic accuracy, positron emission tomography (PET) is superior to single-photon emission computed tomography (SPECT). PET can also be performed on patients with implantable cardioverter defibrillators and pacemakers. However, PET is not widely used due to its high cost and high radiation exposure. SPECT is more suitable for clinical routine because the radionuclides are cheap and easy to prepare, and have a long half-life. Furthermore, SPECT is better at detecting ischemia because of its high sensitivity and specificity. SPECT also allows the evaluation of left ventricular function. SPECT is limited in terms of its high cost and time-consuming nature, limited anatomical information due to low spatial resolution, false positives due to attenuation artifacts (especially in obese patients), and the possibility of underestimating the actual extent of ischemia in patients with multivessel disease. Unlike the aforementioned methods, CTP imaging is the only non-invasive method to identify and quantify the functional significance of coronary artery stenosis. A single CTP examination can perform an integrated anatomical and functional evaluation. In addition, CTP is very fast, highly sensitive and specific, and widely available. CTP has a high spatial resolution (approximately 0.3 mm), allowing the detection of smaller blood flow defects. However, this method is affected by radiation exposure, respiratory motion artifacts, beam hardening, and high heart rate artifacts (Xu C, Yi Y & Wang Y(2020)Clinical Applicatons of CT Myocardial Perfusion Imaging,Cardiovascular Imaging Asia,4(4),p.86-90,Seitun S(2018)CT myocardial perfusion imaging:a new frontier in cardiac imaging,BioMed research international,2018).

[0028] Recent advances in computed tomography technology have enabled the clinical application of myocardial perfusion imaging by computed tomography (CTP). Similar to PET and magnetic resonance imaging (MRI), CTP can provide qualitative or quantitative blood flow data. Assessment of myocardial blood flow by CTP can be performed by acquiring static or dynamic scans. Although both techniques have proven feasible for clinical applications, significant barriers must be overcome before they can be used routinely. For example, successful blood flow assessment using static CTP is highly dependent on the timing of the contrast bolus and on body motion. In contrast, assessment of myocardial blood flow by dynamic CTP allows a complete quantitative analysis of myocardial blood flow. Myocardium is enhanced at multiple time points during the first pass of contrast, which allows the assessment of myocardial blood flow. The assessment of myocardial blood flow may also be affected by performing stress CTP examinations with or without β-blockers (Seitun S(2016)Stress computed tomography myocardial perfusion imaging:a new topic in cardiology,Revista Espanola de Cardiologia(English Edition),69(2),p.188-200). It is difficult to set a clear cut-off value for myocardial blood flow (MBF) during perfusion testing. Among researchers, the cut-off value of myocardial blood flow (MBF) obtained from CT varies from 75 to 164 mL / 100 mL / min. This range in MBF cut-off values ​​may be due to differences in study design, patient coronary risk profile, sample size, FFR threshold, and CAD prevalence (see Kitagawa K (2018) Dynamic CT perfusion imaging: state of the art, Cardiovascular Imaging Asia, 2 (2), p. 38-48). If the MBF is uniformly high (i.e., >110 mL / 100 mL / min for dual-source CT and maximum gradient methods), stress myocardial blood flow can be considered normal. Low myocardial blood flow (MBF) (75mL / 100mL / min) is rare, but is likely related to a lack or insufficient hyperemic response to vasodilators, making ischemia difficult to identify. When sufficient hyperemia is confirmed by high MBF in remote myocardium, the relative MBF in the vascular territory can be used to determine the hemodynamic significance of coronary stenosis. Because normal MBF varies from patient to patient, there may be no generally applicable absolute MBF threshold for myocardial ischemia (see Kitagawa K(2018)Dynamic CT perfusion imaging: state of the art,Cardiovascular Imaging Asia,2(2),p.38-48).

[0029] CTP imaging allows the assessment of in vivo phenomena with unprecedented spatial resolution. This leads to a better understanding of myocardial blood flow at the microvascular level. In recent years, advances in the field have led to reductions in scan time, motion artifacts, contrast agent use, and radiation dose. Such advances have also significantly improved spatial and temporal resolution. However, all aspects of the above methods remain problematic and limited. As mentioned above, myocardial perfusion imaging (MPI) faces the problem of artifacts that prevent accurate assessment of myocardial blood flow, namely artifacts due to cardiac motion. These artifacts may result in areas of low blood flow that may be mistaken for myocardial ischemia. In this situation, it is difficult to identify whether the disorder is related to blood flow or a false positive finding. The above-mentioned motion artifacts can occur for several reasons, such as patient movement during the scanning process or cardiac motion due to an increased or irregular heart rate. One way to reduce the impact of motion artifacts in myocardial perfusion imaging (MPI) examinations is to examine different cardiac phases. Specifically, real (or true) blood flow disorders can be observed in all phases of the cardiac cycle, whereas motion artifacts can only be observed in some cardiac phases. Furthermore, the occurrence of motion artifacts can cause problems in temporal resolution, especially in patients with high heart rates and high heart rate variability. Dynamic CTP imaging of the heart rate during stress must have sufficiently high temporal and spatial resolution to image the entire "working" myocardium at high heart rates (Seitun S(2016)Stress computed tomography myocardial perfusion imaging:a new topic in cardiology,Revista Espanola de Cardiologia(English Edition),69(2),p.188-200,Seitun S(2018)CT myocardial perfusion imaging:a new frontier in cardiac imaging,BioMed research international,Volume 2018,Article ID 7295460, 21 pages). Cardiac patients may undergo multiple imaging procedures that increase their radiation exposure to more than their expected lifetime radiation exposure due to their condition. For example, dynamic CTP involves high radiation doses because it requires multiple image acquisitions to generate a time attenuation curve (TAC).In static CTP, the average radiation dose of the patient ranges from 1.9 to 15.7 mSv, with a mean value of 5.93 mSv. In dynamic CTP imaging, the effective dose ranges from 3.8 to 12.8 mSv, with a mean value of 9.23 mSv. Dynamic myocardial perfusion imaging using CT requires a relatively high radiation exposure dose. Various techniques are used to reduce the radiation exposure dose, for example, for patients with a normal body mass index, a low tube voltage is set during dynamic CTP. Such methods can reduce the radiation dose by 40% while maintaining image quality and a good assessment of myocardial blood flow (MBF). However, further improvements are needed in the process of CTP imaging. Improvements include eliminating unnecessary radiation exposure and risks associated with the process (see Danad I (2016) Static and dynamic assessment of myocardial perfusion by computed tomography, European Heart Journal-Cardiovascular Imaging, 17 (8), p. 836-844).

[0030] Methods based on indicator dilution flow analysis have been developed for many years and are not limited to general medical applications. These methods have been applied in a very wide range of fields, including hydrology, hydrogeology, chemical engineering, and process engineering. They have been used especially when the riverbed is rocky or debris moving, the river cross section is uncertain, or the flow is physically inaccessible or unsafe (Florkowski T, Davis TG (1969) The measurement of high discharges in turbulent rivers using tritium tracer. Journal of Hydrology, 8 (3), p. 249-264, Kilpatrick FA, Cobb ED (1982) Measurement of discharge using tracers, USDepartment of the Interior, US Geological Survey, Evans GV (1983) Tracer techniques in hydrology, The International Journal of Applied Radiation and Isotopes, 34 (1), p. 451-475). Note the change in terminology between engineering and medical terminology. In particular, the two equivalent engineering terms "dye-dilution" and "tracer-dilution" are replaced by the medical term "indicator-dilution". Thus, in physiology, cardiac output is measured, and in hydrology, outflow is measured. The latter was described in 1927 by Charles Allen, who named this method the "salt-velocity" method (see Allen CM (1927) Hydraulic-turbine tests by the Allen method, Power Plant Engineering, 31(10), p.549-551).Tetrach of Trachonites (37 AD) traced the underground waterway that was the source of the Jordan River using rice husks (see Assaad FA, LaMoreaux PE, Hughes T (2012) Field methods for geologists and hydrogeologists, Springer-Verlag Berlin and Heidelberg GmbH & Co.KG, page 247). Based on circulatory physiology, the mean transit time (MMT or equivalent tissue transit time: TTT) is introduced. This is due to the fact that residence time or residence time distribution is a very important parameter in chemical engineering and reactor design (see Wingard LB, et al. (1972) Concepts of residence time distribution applied to the indicator-dilution method, J Appl Physiol, 33(2), p.264-275). Currently, methods based on flow analysis by indicator dilution method (also classified as indicator dilution class method) have been partially or (almost) completely replaced by computational fluid dynamics based on numerical simulation (see Chen K(2018) CFD simulation of particle residence time distribution in industrial scale horizontal fluidized bed,Powder Technology,345(1),p.129-139,Walker P Sheikholeslami R(2003)Assessment of the effect of velocity and residence time in CaSO4 precipitating flow reaction,Chemical Engineering Science,58(16),p.3807-3816).

[0031] Having reviewed the developments in the prior art, it is apparent that there is a need for a reliable method of diagnosing patients without the risks and problems discussed above, and the present invention addresses such a need. Summary of the Invention [Means for solving the problem]

[0032] The following description is intended to provide a better understanding of the principles and advantages of the invention described in the appended claims. Therefore, it is not meant to be limiting in any sense. In particular, specific descriptions regarding the meaning or function of equations and the like used in the present invention should not be understood as limiting or exhaustive. Such equations are provided for ease of understanding and are not intended to replace the complete understanding provided by the general knowledge of a person skilled in the art.

[0033] The present invention redefines the paradigm of myocardial perfusion imaging (MPI). At its core, and one of the biggest improvements to the paradigm, is the replacement of the in vivo imaging stage (usually achieved with Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT) or Magnetic Resonance (MR) imaging devices) with an in silico "imaging" stage, while providing medically relevant and consistent results. The central element of the invention is an arterial and myocardial blood flow model solved using computational fluid dynamics analysis methods. This eliminates all the deficiencies of known in vivo methods, including the risks to the overall health of the treated patient, and provides virtually unlimited spatial and temporal resolution. Furthermore, the results of the present invention are completely free of motion artifacts and limit the radiation exposure of the patient. At the same time, the cost of performing this kind of in silico analysis is significantly reduced.

[0034] Within the scope of this disclosure, the term "tracker" may be used interchangeably with the term "contrast agent." The term "porous" should be understood to include or be equivalent to the term "saturated porous."

[0035] In one aspect, the present invention relates to a method for assessing myocardial blood flow in a human patient using non-invasive measurement techniques and porous structure engineering, particularly the porous structure engineering includes computational fluid dynamics analysis, which may be implemented as a computer-implemented method.

[0036] The method uses patient-specific anatomical data of a human patient to generate an anatomical geometric model of myocardium connected to a plurality of coronary vessels.

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[0038] an anatomical geometric model of the myocardium connected to the plurality of coronary vessels;

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[0047] In addition to the above mentioned advantages and a novel approach to myocardial perfusion imaging (MPI), the use of the fluid and porous regions allows for a simplified description of myocardium connected to multiple coronary vessels, which allows for faster computations and faster determination of blood flow indices. At the same time, it is emphasized that without the use of fluid and porous regions, it is not always possible to build an accurate and complex model of myocardium connected to multiple coronary vessels.

[0048] In an embodiment, the patient-specific anatomical data is obtained by at least one of computed tomography angiography (CTA), nuclear magnetic resonance (NMR), and / or computed angiography. The anatomical geometric model can be used in a computational fluid dynamics analysis to calculate blood flow indices.

[0049] In one embodiment, the patient-specific anatomical data comprises images stored in DICOM format, which allows for the reconstruction of time attenuation curves (TAC) and arterial input function (AIF) curves at multiple time points.

[0050] an anatomical geometric model of the myocardium connected to the plurality of coronary vessels;

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[0053] The method may further comprise using patient-specific demographic and general medical data to determine at least one patient-specific boundary condition and to select at least one physical parameter to describe blood as a flow medium. The term "patient-specific" means specific to a patient and does not refer to data or parameters of a patient population. The use of patient-specific data allows for results to be obtained that are medically relevant to a particular patient and does not refer to results of a patient population.

[0054] In one embodiment, the patient-specific demographic data and the general medical data include at least one of the patient's age, sex, height, weight, systemic blood pressure, blood test results, and / or information regarding the patient's current medication, which may be selected from, but is not limited to, β-adrenergic blockers, angiotensin-converting enzyme inhibitors, and antiarrhythmic drugs.

[0055] In one embodiment, the physical parameters for describing blood as a fluid medium are selected from the group consisting of density and dynamic viscosity.

[0056] The method further comprises:

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[0058] In a preferred embodiment of the present invention, the fluid domain (Ω f ) can satisfy the following equation:

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[0060] In a preferred embodiment of the present invention, the porous region (Ωp) can satisfy the following formula:

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[0062] In a preferred embodiment of the present invention, the fluid domain (Ω f ) and the porous region (Ω p The blood flow rate through the ventricle can satisfy the following equation:

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[0064] In a particular embodiment, in said porous regions, the Navier-Stokes equations are supplemented with additional terms associated with the resistance induced by the porous medium. These additional terms are based on the Forchheimer model, which describes the effects of both viscous and inertial resistance. The combination of the Navier-Stokes equations and the Forchheimer model makes it possible to avoid the highly complex geometry of the porous regions, while reproducing the nature of the phenomena occurring in said regions. This novel approach directly extracts diagnostically important information, namely information about the flow distribution. In other words, this unique feature allows to obtain relevant information without the time and effort required to build and solve a mathematical model of the porous region. This has advantages in terms of computational power requirements and computational speed.

[0065] The present invention allows for the calculation of blood flow indices, which in embodiments include:

[0066] 1) Myocardial blood flow (MBF), which can be defined as the amount of blood passing through myocardial tissue at a specific rate; 2) Myocardial blood volume (MBV), which can be defined as the total volume of blood within a given unit volume of the myocardium; 3) the mean transit time (MTT), which can be defined as the expected time interval that blood spends within a particular portion of the myocardium, and / or Time to peak (TTP) can be defined as the time interval of radiodensity or the time interval at which the concentration index is maximum in a particular part of the myocardium. The present invention allows the myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT) and time to peak (TTP) to be calculated directly or in the form of indices, ratios, area values ​​or patient specific reference values.

[0067] The blood flow index can be calculated using local tracer dynamics reconstruction or continuous Monte Carlo streamline trajectory Lagrangian tracking. In particular, the step of calculating the blood flow index can include maximum gradient method (MXS), deconvolution of impulse response function (DRF), multi-pathway, multi-species, and nonlinear blood tissue exchange (BTX). In another embodiment, the step of calculating the blood flow index includes:

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[0070] Further features and advantages of the present invention will become more apparent from the detailed description of the non-limiting embodiments and the accompanying drawings. It should be understood that the embodiments shown in the specification and claims can be combined in any order and number to form new embodiments that form part of this disclosure, unless otherwise stated. This specification contains numerous references to prior art documents, particularly scientific documents, the disclosures of which are incorporated herein in their entireties. [Brief description of the drawings]

[0071] The present invention will now be described in more detail with reference to the accompanying drawings. [Figure 1]FIG. 1 shows a process flow diagram incorporating method steps according to one embodiment of the present invention. [Diagram 2] Figure 2 shows the myocardial blood flow calculated using the tomography results. Figure 2(A) shows the tissue attenuation curve and the arterial input curve, and Figure 2(B) shows the long-axis and short-axis plots of myocardial blood flow (MBF) and time to peak (TTP). [Diagram 3] A 3D reconstruction of the myocardium and coronary arteries from a computed tomography angiography projection is shown in Figure 3. Each point marked on the reconstructed 3D mesh in the angiography projection represents an identified stenosis and its corresponding portion. [Figure 4] FIG. 4 is an unstructured tetrahedral 3D mesh of the myocardium and coronary arteries. [Diagram 5] Figure 5 is a visualization of normalized flow fields (A, B) and pressure fields (C, D) calculated in a numerical simulation of blood flow. [Figure 6] Figure 6 is a visualization of the AHA (American Heart Association) 17-segment model showing a comparison of the numerical simulation results of myocardial blood flow (B) with the reference myocardial blood flow results calculated from computed tomography perfusion images (CTP) for the same human patient (A). The location of the coronary arteries is marked in both images. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0072] Although engineering is often referred to herein as the basis for the medical techniques used in the present invention, there are significant differences between engineering and medical techniques. Such differences will be apparent to one of ordinary skill in the art and are not limited to the differences in the names used in both fields. For example, a particular engineering measurement may measure the flow of a pipe or a river in a given example and can be made directly since the shape of the pipe or river does not change significantly during the measurement. In contrast, the heart changes significantly in a cyclical manner, but there is no indication of at what stage in the cycle a given measurement was made. This limited example is intended only to provide a general overview of the differences between engineering and medicine. The description of the present invention is not intended to describe all aspects of the engineering and medical practices used herein, as would be understood by one of ordinary skill in the art.

[0073] The indicator dilution method used in cardiovascular diagnosis, as well as the well-known engineering dye dilution, tracer dilution, or Allen's salt velocity method, are based on the very basic concept of three traditional measures: time, volume, and mass. If we label these conventionally as T, V, and M, then T=V / Q (Q: volumetric flow rate), M / V=C (C: concentration of indicator), and considering the change in M ​​as a function of time, we obtain the relationship dM / dt=Q·ΔC (see Peters AM(1993)A unified approach to quantification by kinetic analysis in nuclear medicine.J Nucl Med.34(4):706-13). The entire analysis of these three traditional measures, as well as diagnostic parameters such as myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT), and time to peak (TTP), determined by the software accompanying the computed tomography device, is carried out according to equations (1)...(25). The results of in vivo imaging consist of a series of images, usually stored in DICOM format. The results of this imaging and the specific format allow the reconstruction of the time decay curves (TAC) and arterial input function (AIF) curves for many time points. Each time point is associated with several dozen images that need to be stored by the device to reconstruct the time decay curve of the tissue. It is technically difficult to perform such processing within one second, or within the time required to record one cardiac cycle. In fact, the time points on the time axis are quite far from each other, and only their common time intervals are measured by tracking the patient's ECG (electrocardiogram). A representative example of the above is Figure 2, which shows the results of a single patient examination. The upper part of the figure (A) shows the arterial input function (AIF) curve (black circles), the time decay curve (TAC) (white circles), and their approximations for the entire myocardium (in this figure, the volume of interest, VOI), and the lower part of the figure (B) shows the distribution of myocardial blood flow (MBF) and time to peak (TTP) in the cardiac axial plane. When using the maximum gradient method (see Equation 22 above), as implemented for the results shown in Figure 2, max(AIF) and max(dTAC / dt) need to be known to determine myocardial blood flow (MBF).This task is difficult, and the first values ​​are determined by approximating the empirical AIF (the formula below the horizontal axis) due to the numerous variations that occur. In comparison, finding max(dTAC / dt) is even more difficult. This is usually achieved by determining the angular slope of the tangent (the tangent itself is drawn in bold), as shown in the formula in Grubb, TAC(t) = -102.347 + 21.082 · t. It can be clearly seen that this tangent is constructed with only three points. It is very rare to use more than four points for this purpose. As can be seen more clearly in the arterial input function (AIF) curve and also in the time decay curve (TAC), the radiation concentration curve in vivo varies locally from the expected curve. And not only local variations, but in fact the entire curve may deviate from the expected curve. As can be seen, the radiation concentration value does not decrease unexpectedly, but after reaching a local minimum, it gradually increases. Such effects are difficult to observe at a global level, but are commonly seen locally in single voxels and are due to recirculation of the indicator (contrast) material.

[0074] The idea of ​​estimating the degree of circulatory insufficiency based on myocardial perfusion imaging (MPI) tests is laudable and will undoubtedly become the new gold standard of diagnostic tests in due course. However, those implemented based on developments in engineering (e.g. civil engineering or groundwater hydraulic engineering) have not been successful, and there is a reason for this: the heart is a constantly moving organ with periodic contractions, and it is not possible to stop the human heart for several minutes as required for diagnostic procedures. On the other hand, the time resolution cannot be significantly improved due to technical limitations of diagnostic procedures. This problem was already recognized very early in the application of perfusion diagnostic methods. For example, a team from the University of Münster concluded that "absolute CBF and TTP values ​​are significantly modified when the sampling interval of PCT imaging calculated using software relying on the maximum slope model exceeds 1 second..." (see Kloska SP et al(2010)Increasing sampling interval in cerebral perfusion CT:limitation for the maximum slope model,Acad Radiol,17(1),p.61-66). In fact, due to the interrelationship between the cardiac cycle and the imaging time scales, as well as the volume of distribution, the derivative of dTAC / dt has to be calculated from a section of the time-attenuation curve (TAC) constructed with two, three or four time point-layers. To make matters worse, the injection site of the contrast agent is usually located far from the target organ, and the duration of the injection must be sufficiently long compared to other time scales (as required by the patient's health).

[0075] In one embodiment of the present invention, the maximum gradient method (MXS) and in particular relations (22)-(25) can be used for the recovery of regional tracer kinetics (TKR).

[0076] In another preferred embodiment of the invention, the reconstruction of the local tracer kinetics (TKR) is based on an evaluation of the way in which the tracer is maintained in the vasculature instead of the tracer velocity: If within a time interval Δt, Δm(t) contrast agent introduced at the inlet from a total amount m of contrast agent flows into a region of interest (ROI), the empirical probability of this event is given by:

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[0088] In a third preferred embodiment of the present invention, the reconstruction of regional tracer kinetics (TKR) is performed via a blood tissue exchange model (BTX) (see Wirestam R (2012) Using contrast agents to obtain maps of regional perfusion and capillary wall permeability, Imaging in Medicine, 4(4), p. 423-438, Bindschadler M et al. (2014) Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT, Phys Med Biol, 59(7), p. 1533-56, Tofts PS et al. (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols, J Magn Reson Imaging, 10(3), p. 223-32). From the viewpoint of pharmacokinetic and pharmacodynamic (PK / PD) modeling, the local tracer kinetic (TKR) model presented above is a one-compartment model. In fact, it is easy to build a model corresponding to the blood-tissue exchange process on the n-compartment PK / PD model. For example, a two-compartment exchange model can be defined as follows:

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[0090] The present invention involves the in silico determination of blood flow parameters (blood flow indices) myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT) and time to peak (TTP) using computational fluid dynamics (CFD) methodology. In a particular embodiment of the invention, said determination is achieved using one of the following local tracer kinetic reconstruction (TKR) methods: maximum gradient method (MXS), deconvolution of impulse response function (DRF), multi-pathway, multi-species, nonlinear blood tissue exchange (BTX). The use of any of the above methods assumes familiarity with the time evolution of the marker concentration in the volumetric region of interest (ROI) of each point (or voxel), and provides a metric time decay curve TAC(t) that is the result of the arterial input. For in vivo testing, the temporal evolution of the marker concentration in the volumetric region of interest (ROI) for each point (or voxel) is directly provided by the imaging results, expressed in Hounsfield units (the same is not provided by computational fluid dynamics analysis (CFD), but pressure and velocity distributions). After image registration and removal of motion artifacts, the tissue attenuation curves in the volume of interest (VOI) are reconstructed for each voxel. This commonly adopted method is used by all manufacturers of blood flow diagnostic devices, since it is the easiest, but such an easiest method cannot cover all relevant cases and conditions. As mentioned above, due to factors that affect the measurements, especially in the case of this commonly adopted method, differences arise between the results obtained by different devices, and / or different protocols, and / or different cut-off values ​​in myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT) and time to peak (TTP). The variability of the results provided by different researchers can amount to tens of percent (sometimes even more than 100%). Furthermore, the results obtained by this method do not have a direct physical interpretation that can be expressed in terms of established measures such as flow rate, volume, or time, and the results have very little correlation to flow rate, volume, or time.As a result, an increasing number of researchers are interested in in vivo blood flow imaging using the advection-diffusion method (Liu P et al. (2021) Perfusion imaging: An advection diffusion approach, IEEE Transactions on Medical Imaging, 40 (12), p. 3424-3435). When a substance is transported with flow velocity, its local concentration change ∂c(t,r) / ∂t is described by the advection-diffusion transport equation (Bird RB et al (2006) Transport phenomenon, John Wiley & Sons), and can be expressed by the following equation.

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[0096] In the preferred embodiment of the invention, the evolution of the velocity (u) and pressure (p) fields is described by a system of Euler mass balance equations and Navier-Stokes momentum equations. The myocardium (and muscle tissue in general) supported by a network of branching arteries is divided into two non-overlapping simply connected domains:

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[0107] One embodiment of the method of the present invention has six steps: 1) collection of patient-specific demographic data, general medical data, and patient-specific anatomical structure data; 2) quantification of boundary conditions; 3) creation of discrete model geometry; 4) numerical simulation in fluid and porous domains; 5) recovery of local blood flow indices; and 6) visualization of local blood flow indices.

[0108] In a first step, patient-specific demographic and general medical data are collected. The patient-specific demographic and general medical data may include, among other things, the age, sex, height, weight, systemic blood pressure, and / or blood test results (particularly hematocrit value, if available) of a particular patient. The patient's demographic and general medical data may further include information regarding the patient's medication. The patient's medication may include beta-adrenergic blockers, angiotensin-converting enzyme inhibitors, and / or antiarrhythmic drugs. Alternative embodiments include other medications. It should be noted that medications may also include different dosing schedules and other medical effects of medications. In certain embodiments, it is envisioned that only selected patient-specific demographic and general medical data are collected, such as, for example, sex, age, medication. The patient-specific demographic and general medical data may be collected through interviews with the patient or obtained from a database. In addition to the patient-specific demographic and general medical data that are subsequently physically interpreted, patient-specific anatomical data is also collected. In one embodiment, the patient-specific anatomical data consists of the results of a computed tomography angiography (CTA) of the coronary arteries and myocardium. In another embodiment, computed tomography and / or magnetic resonance techniques can be used. The patient-specific anatomical data includes information about the anatomy of the myocardium of the heart and the blood vessels connected to the myocardium. The patient-specific anatomical data can be collected before, after, or in parallel with the patient-specific demographic and general medical data. The patient-specific anatomical data can be obtained from a database.

[0109] In a second step, the patient's demographic and general medical data are used to determine one or more patient-specific boundary conditions and to select one or more physical parameters describing blood as a flow medium, which are necessary for the numerical simulation. The selected physical parameters may include density and / or dynamic viscosity. In the diagram of FIG. 1, this step is indicated with 2.A. In a preferred embodiment, at the same time, the imaging results from a computed tomography angiography (CTA), preferably in the form of a series of several hundred DICOM images, are segmented to finally provide a surface model of the arteries and muscles of the patient's heart. This step is preferred since it allows the method results to be obtained faster. In another embodiment, which is less computationally intensive, the segmentation may be performed before or after the step of determining one or more patient-specific boundary conditions. In the diagram of FIG. 1, said step is indicated with 2.B. As an example, Figure 3 shows the orientation of the main coronary arteries along the centerline as visualized by curved planar reconstruction (CPR), which those skilled in the art will know is sometimes called curved planar reformation. In the case shown in Figure 3, there were two stenoses in the left branch (a: 92%, d: 87%, OM branch) and three in the right branch (b: 57%, c: 64%, e: 69%). The top part of the figure shows a diagram of the curved planar reconstruction (CPR), while the bottom part of the figure shows a zoomed-in result of the segmentation and reconstruction of the surface model (again with the location of the stenosis indicated). The resulting surface model is discretized via an unstructured volumetric mesh shown in Figure 4. The top left corner of Figure 4 shows a computational mesh diagram of the complete fluid and porous regions, while the rest of the figure shows the zoomed-in arterial and myocardial meshes.In addition, changes in radiodensity recorded by computed tomography angiography (CTA) are used to quantify myocardial blood flow properties (Vinnakota KC, Bassingthwaighte JB(2004) Myocardial density and composition: a basis for calculating intracellular metabolite concentrations, American Journal of Physiology-Heart and Circulatory Physiology,286(5),p.H1742-H1749,Gheorghe AG et al.(2019) Cardiac left ventricular myocardial tissue density,evaluated by computed tomography and autopsy,BMC Medical Imaging,19,p.29,Cuong NLQ et al.(2018)Porosity estimation from high resolution CT SAN images of rock samples by using Housfield unit,Open Journal of Geology,8,p.1019-1026,Sudhyadhom A(2020)On the molecular relationship between Hounsfield Unit (HU), mass density, and electron density in computed tomography (CT), see PLoS ONE 15(12), p.e024486).

[0110] In a third step, the computational grid with the conditions (boundary conditions) set at its edges, the fluid specifications and the physical properties of the porous region are transferred to the solver engine. As mentioned above, two sets of equations are solved, albeit in a penalized manner. The fluid motion equations of the Euler and Navier-Stokes systems are solved for the entire computational grid. Those skilled in the art will understand that the boundary conditions set to obtain a particular solution are excluded from the entire computational grid. In the porous regions, the Navier-Stokes equations are complemented with additional terms associated with the resistance induced by the porous medium. These additional terms are based on the Forchheimer model, which describes the effects of both viscous and inertial resistance. The combination of the Navier-Stokes equations and the Forchheimer model allows to avoid the extremely complex geometry of the porous region, while reproducing the nature of the phenomena occurring in said regions. This novel approach directly extracts information that is important in diagnosis, namely information about the flow distribution. In other words, this unique feature allows to obtain relevant information without the time and effort required to build and solve a mathematical model of the porous region. This has advantages in terms of computational power requirements and computational speed, but at the same time it is emphasized that it is not always possible to build an accurate and complex model of the myocardium connected to multiple coronary arteries without using fluid and porous regions.

[0111] In the fourth step, an algorithmic diagnostic interpretation is performed on the results obtained using the computational fluid dynamics analysis method. It should be noted that the computational fluid dynamics analysis model, regardless of its type and degree of complexity, only provides information on the distribution of the flow velocity and pressure field. These results are not directly used for medical diagnosis. When evaluating blood flow, it is generally expected that the diagnostic method will provide contractile indices such as myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT), time to peak (TTP), etc. The fourth step can be performed in three ways, differing in the calculation input and the results obtained. In the most direct approach (step 4.A in Fig. 1), the flow and pressure-flow distributions are determined in the view and cross section along the long axis of the heart, as shown in Fig. 5. However, this method does not provide information on common blood flow indices such as myocardial blood flow (MBF). Therefore, in a second variant embodiment of the post-processing of the simulation results, the continuous Monte Carlo streamline trajectory Lagrangian tracking method (step 4.B in Fig. 1) is used. This kind of post-processing, which is usually used to visualize trajectories, is used here to reconstruct probability distribution functions such as density, distribution and residuals. This information can be analyzed using known techniques used to analyze the results of in vivo myocardial perfusion imaging. Although particle tracking is highly effective, its accuracy is limited and it is not a complete replacement for advection-diffusion contrast dynamics simulation. Only this method provides complete information about the flow of contrast agents in tissues and is generally equivalent to in vivo methods. The results of this method (4C) are subjected to the same post-processing as the results of the particle tracking method (4B).

[0112] In the fifth step, a reconstruction of the tracer dynamics is performed to determine standard blood flow indices and is related to the results obtained in steps 4.B or 4.C (the results of 4.A are not subject to this type of processing). It is possible to use any of the techniques for tracer dynamics reconstruction (TKR) detailed above (i.e. maximum gradient method (MXS), deconvolution of impulse response functions (DRF), blood tissue exchange method (BTX)). The final result is exported for visualization in subsequent steps.

[0113] In the sixth and last step, only the visualization of the blood flow indices obtained in the previous steps is performed. As shown, the visualization of Fig. 5 above may be limited to the display of normalized values ​​of flow and pressure distribution. If particle tracking or full diffuse contrast dynamics simulation is performed, the myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT) and time to peak (TTP) are visualized. These results are shown in Fig. 6, where they are further placed in the system of the American Heart Association's 17-segment model (see Cerqueira MD (2002) Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association, Circulation, 105 (4), p. 539-42). Since blood flow indices vary greatly between patients, index values ​​normalized for individual differences may be applied (see Kono AK et al. (2014) Relative myocardial blood flow by dynamic computed tomographic perfusion imaging predicts hemodynamic significance of coronary stenosis better than absolute blood flow, Invest Radiol, 49 (12), p. 801-7, Coenen A. (2014). 801-7, Coenen A et al. (2017) Integrating CT myocardial perfusion and CT-FFR in the work-up of coronary artery disease, JACC Cardiovasc Imaging, 10 (7), p. 760-770). Any computational steps or sub-steps of the method according to the invention can be implemented using a computer or a computer program. In some particular embodiments, part or all of the computations are carried out using a computer program stored on a computer or on any type of storage device, or both. In another embodiment, part or all of the computations for the purposes of the method can be carried out remotely, for example using a cloud-based infrastructure including the use of the Internet or a local network.

[0114] All the measurement methods and algorithms described herein are intended to define the parameters of the present invention and provide specific results for comparison with clinical trials, and are in no way limiting, but are exemplary. Terms such as "comprise" or "have" are not limiting, for example, if element A includes another element B, element A may include another element or other elements in addition to element B. The use of the singular or plural forms does not limit the scope of the disclosure, for example, a portion of the description indicating that element A includes element B also discloses an embodiment in which multiple elements B are included in one element A, and multiple elements A are included in one element B, as well as an embodiment in which multiple elements A include multiple elements B. It is believed that many other embodiments will be obvious to those skilled in the art upon perusal of the contents of this disclosure. Thus, the scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which such claims are entitled.

Claims

1. 1. A computer-implemented method for assessing myocardial blood flow in a human patient using non-invasive measurements and porous structure mechanics, comprising: Anatomical model of myocardium connected to multiple coronary vessels [Number 62] (3) simulating blood flow within the vessel using computational fluid dynamics analysis; the computational fluid dynamics analysis includes at least one patient-specific boundary condition and at least one physical parameter for describing blood as a flow medium; an anatomical shape model of the myocardium connected to the plurality of coronary vessels; [Number 63] is the fluid domain (Ω ｆ ) and the porous region (Ω p ) and the fluid domain (Ω ｆ ) and porous area (Ω p ) and an interface (Γ) between the anatomical shape model of the myocardium. [Number 64] is expressed as follows: [Number 65] wherein: [Number 66] is the fluid domain (Ω ｆ ) and the fluid region (Ω ｆ ) boundary (Γ ｆ ) or the fluid region (Ω ｆ ) and the fluid region (Ω ｆ ) boundary (Γ ｆ ) and [Number 67] is the porous region (Ω p ) and the porous region (Ω p ) boundary (Γ ｐ ) or the porous region (Ω p ) and the porous region (Ω p ) boundary (Γ ｐ ) and The interface (Γ) is expressed by the following formula: [Number 68] and the at least one patient-specific boundary condition is determined using patient-specific demographic data and general medical data, and the at least one physical parameter for describing the blood as a flow medium is selected using the patient-specific demographic data and the general medical data; an anatomical shape model of the myocardium connected to the plurality of coronary vessels; [Number 69] is generated using patient-specific anatomical data for the human patient; the patient-specific anatomical data of the human patient is obtained using at least one of the non-invasive measurement techniques; the porous area (Ω p ) is configured to describe the blood flow in the myocardial tissue; The method further comprises: an anatomical shape model of the myocardium connected to the plurality of coronary vessels; [Number 70] Calculating a blood flow index using the results of the blood flow simulation in method.

2. 10. The method of claim 1, wherein the patient-specific anatomical data is acquired by at least one of computed tomography angiography, nuclear magnetic resonance, and / or computed angiography.

3. 3. The method of claim 2, wherein the patient-specific anatomical data comprises images stored in DICOM format.

4. 4. The method according to claim 1, wherein an anatomical geometric model of the myocardium connected to the plurality of coronary vessels is provided. [Number 71] generating a surface model of the arteries and muscles of the human patient's heart comprises segmenting images contained in the patient-specific anatomical data to create a surface model of the arteries and muscles of the human patient's heart.

5. 5. The method of claim 4, wherein an anatomical geometric model of the myocardium connected to the plurality of coronary vessels is provided. [Number 72] The method of claim 2, wherein generating (2B) further comprises discretizing the surface model via an unstructured volumetric mesh.

6. 6. The method of claim 1, wherein the patient-specific demographic data and the general medical data include information about the patient's age, sex, height, weight, systemic blood pressure, blood test results, and / or current medications; The method wherein the patient's current medication is selected from medications including beta-adrenergic blockers, angiotensin-converting enzyme inhibitors, and antiarrhythmic agents.

7. The method according to any one of claims 1 to 6, wherein the at least one physical parameter is selected from the group consisting of density and dynamic viscosity.

8. The method according to any one of claims 1 to 7, wherein the porous region (Ω p ) is a saturated method.

9. The method according to any one of claims 1 to 8, wherein the fluid region (Ω ｆ ) satisfies the following equation, [Number 73] and / or the porous region (Ω p ) satisfies the following equation, [Number 74] and / or the fluid domain (Ω ｆ ) and the porous region (Ω p ) and the blood flow rate through the artery satisfies the following equation: [Number 75] where ρ is the fluid density, t is time, u and p are the flow velocity and pressure, η is the dynamic viscosity coefficient, and Ω is the p Inside χ=1 or Ω p Outside of Ｆ is the inertial resistance tensor, K is the permeability tensor, method.

10. 10. The method according to claim 1, wherein the computational fluid dynamics analysis comprises: [Number 76] and having blood rheological properties described by Where η is the dynamic viscosity coefficient and HCT is the hematocrit value.

11. 11. The method of claim 10, [Number 77] is selected according to the Carreau, Cross, Yasuda, or Walburn-Schneck model.

12. 10. The method of claim 1, wherein the step of calculating the blood flow index uses at least one of local tracer dynamics reconstruction methods, including maximum gradient (MXS), deconvolution of impulse response functions (DRF), multipathway, multi-species, and nonlinear blood tissue exchange (BTX); and / or [Number 78] using a continuous Monte Carlo streamline trajectory Lagrangian tracking method of the form and In the formula, s ｉ is the training vector {Δs １ , Δs ２ ,... ,Δs ｎ }, [Number 79] That's the method.

13. 13. The method of claim 12, wherein the reconstruction of the local tracer dynamics is performed using the following formula: [Number 80] and / or [Number 81] and / or [Number 82] and where τ is the instant in the observation interval [0, t], AIF is the arterial input function, IRF is the impulse response function, c is the tracer concentration (p is plasma, e is the extracellular space, extravascular space, ap is arterial plasma), v is the fraction of plasma volume in the compartment, PS is the permeability-surface area product, t is time, TAC is the time decay curve, AIF is the arterial input function, IRF is the impulse response function, MBF is myocardial blood flow, and MTT is the mean transit time. method.

14. In the method according to any one of claims 1 to 13, the blood flow index is Myocardial blood flow (MBF), defined as the amount of blood passing through the myocardial tissue at a specific rate; Myocardial blood volume (MBV), which is the total volume of blood in a given unit volume of the myocardium; Mean transit time (MTT), which is the expected time interval spent by blood within a particular portion of the myocardium; and / or Time to Peak (TTP), which represents the time interval of radiodensity or the time interval during which the concentration index reaches a maximum in a particular part of the myocardium. Including, The myocardial blood flow (MBF), myocardial blood volume (MBV), mean transit time (MTT), and / or time to peak (TTP) are calculated as absolute or relative values. method.

15. A computer readable storage medium having instructions which, when executed by a computer, cause the computer to perform the steps of the method according to any one of claims 1 to 14.

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