WO2022074054A1 - Procédé non effractif de détermination d'un gradient de pression pour un rétrécissement cardiovasculaire chez un sujet et produit de programme informatique - Google Patents

Procédé non effractif de détermination d'un gradient de pression pour un rétrécissement cardiovasculaire chez un sujet et produit de programme informatique Download PDF

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WO2022074054A1
WO2022074054A1 PCT/EP2021/077556 EP2021077556W WO2022074054A1 WO 2022074054 A1 WO2022074054 A1 WO 2022074054A1 EP 2021077556 W EP2021077556 W EP 2021077556W WO 2022074054 A1 WO2022074054 A1 WO 2022074054A1
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narrowing
cardiovascular
subject
flow
flow rate
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PCT/EP2021/077556
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Leonid Goubergrits
Titus Kühne
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Charité - Universitätsmedizin Berlin
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Publication of WO2022074054A1 publication Critical patent/WO2022074054A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02028—Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0263—Measuring blood flow using NMR
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1079—Measuring physical dimensions, e.g. size of the entire body or parts thereof using optical or photographic means
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/503—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data

Definitions

  • a non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject and computer program product
  • the present disclosure refers to a non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject, and a computer program product.
  • Simulations were divided in a training data set (135 cases) and a test data set (36 cases).
  • the training data was used to fit an adjusted Bernoulli model as a function of AVA and flow rate.
  • the disclosure in document US 2013/066229 A1 relates to an apparatus and methods for imaging-assisted determination of pressure gradient of blood flow across a valve orifice in a cardiovascular circuit without the use of velocity data measured at the valve orifice.
  • Document WO 2015/138555 A2 provides a method for non-invasively determining the functional severity of arterial stenosis in a selected portion of an arterial network.
  • a non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject of claim 1 is provided. Further, a computer program product of claim 13 is provided. Additional aspects are subject of dependent claims.
  • a computer program product is provided.
  • the present invention seeks to illustrate pressure loss because of physical reasons based on dimensionality as a square of speed.
  • the approach of the present invention is purely data driven where for local issues (domains of applicability) the coefficients are fitted with the purpose of minimizing errors and satisfying non-physical laws.
  • the present invention makes it possible to predict pressure loss under stress even from rest-based data.
  • the pressure gradient is to be determined by (only) taking into account orifice area of a cardiovascular narrowing, and the flow rate of the blood flow through the cardiovascular narrowing.
  • the cardiovascular narrowing may also be referred to as stenosis.
  • the method may further comprise: providing image data representing a plurality of images of a blood vessel section of the subject comprising the cardiovascular narrowing, the images acquired by imaging; and determining the geometric data from the image data, comprising reconstructing geometry of the cardiovascular narrowing from the image data.
  • Image data analysis known as such may be applied for determining one or more geometric parameter from 2D or 3D images acquired by at least one 2D imaging and 3D imaging.
  • the method may further comprise determining the flow data from at least one of the following: measurement of the blood flow rate for the blood vessel section comprising the cardiovascular narrowing, and flow rate modelling for the blood flow rate for the blood vessel section comprising the cardiovascular narrowing.
  • the method may further comprise, in the data processing device, providing first flow data indicative for a rest flow rate of the blood flow through the cardiovascular narrowing of the subject under rest condition, and determining a rest pressure gradient for the cardiovascular narrowing of the subject under rest condition, wherein the first flow data comprise first measured flow data detected in a flow rate measurement for the subject under rest condition.
  • rest condition it is referred to the subject (patient) not conducting physical activity.
  • stress condition it is referred to the subject (patient) not conducting physical activity.
  • the patient may receive some medication (stress stimulation) generating stress condition for the patients’ cardiovascular system.
  • stress conditions may cause an increase of cardiac output by a change of the heart rate and stroke volume depending on the kind and level of stress as well as patient disease and patient age.
  • the ability of the heart to respond to external stress causes an increased cardiac output as it applied by cardiac stress test.
  • the measurement of the cardio-vascular function under stress condition allows unmasking pathological condition, which are undetectable under the rest condition.
  • the stress response indicative of actual stress conditions is induced by exercise or by drug stimulations.
  • Such stress condition may also be referred to real or actual stress condition.
  • Commonly used drugs are vasodilators such as adenosine and dipyridamole, re- gadenoson and dobutamine. Physical activity or exercise could be done on a treadmill, by pedaling a bicycle ergometer or by a handgrip.
  • the method may further comprise, in the data processing device, providing second flow data indicative for a stress flow rate of the blood flow through the cardiovascular narrowing of the subject under stress condition.
  • the second flow data provide indication for the stress flow rate of the blood flow through the cardiovascular narrowing for the subject being under stress condition.
  • the providing of the second flow data may further comprise determining the second flow data indicative for the stress flow rate from the first flow data indicative for the rest flow rate by applying the flow rate modelling, the stress flow rate being different from the rest flow rate for the subject.
  • the subject is not under actual stress condition.
  • the second flow data indicative for the stress flow rate is not determined from measurement for the subject under stress condition.
  • the second flow data is determined from flow rate modelling for the blood flow rate for the blood vessel section comprising the cardiovascular narrowing.
  • stress condition may also be referred to modelled or simulated stress condition.
  • the second flow data indicative for the stress flow rate are determined by the flow rate modelling (without the subject actually being under stress condition).
  • the method may comprise the following. From the measurement data gathered by the imaging acquisition system, a shape profile of the stenosis (cardiovascular narrowing) is determined and characterized by few primary parameters such as stenosis cross-section area, stenosis length and / or cross-section area behind the stenosis.
  • the measurement data gathered by the imaging acquisition system, a flow profile of the target stenosis and a flow profile of the respective heart are assessed during rest condition.
  • the flow profile may be characterized by at least one from the following: peak systolic flow rate of the target stenosis, heart rate (HR), stroke volume (SV) and systolic time (ST) of the respective heart.
  • the flow profile or rate changes are determined modelling the stress flow rate, including the change in heart rate, stroke volume and systolic time as a function of a stress load (stress condition) such as dobutamine doses.
  • Stress conditions cause an increase of cardiac output (CO) by a change of the heart rate and stroke volume depending on the kind and level of stress as well as patient disease and patient age.
  • CO cardiac output
  • the changes in SV, HR and ST allows to predict peak flow rate during stress and thus to calculate the normalized peak flow rate as a target relationship between peak systolic flow rate under rest and during stress condition.
  • the normalized pressure gradient is determined during stress condition using the trained (parameters of polynomial or non-linear functions a fitted to minimize an error) computational unit (algorithm) represented by equation (1) as a function of the target flow (Q) and shape profiles (AVA).
  • the second flow data may be determined from measurement of the blood flow rate for the blood vessel section comprising the cardiovascular narrowing.
  • the second flow data may be provided as or may comprise measurement or experimental data detected for the subject under actual stress conditions.
  • the method may further comprise: determining the first flow data indicative for the rest flow rate of the blood flow through the cardiovascular narrowing of the subject under rest condition, wherein the determining of the first flow data comprises applying at least one physiological parameter of the subject; and determining the second flow data indicative for the stress flow rate of the blood flow through the cardiovascular narrowing of the subject under stress condition, wherein the determining of the second flow data comprises applying at least one amended physiological parameter of the subject determined by amending the physiological parameter in response to the stress condition.
  • the at least one physiological parameter may be selected from: heart rate of the subject, and volume of heart stroke of the subject.
  • Systolic time (STstress) amy be calculated: STstress A - B * H Rstress, wherein constants A and B depend on disease (heart state).
  • Peak systolic flow rate (Qstress) amy be calculated as follows: Qstress 1.5 * S stress / STstress .
  • the method may further comprise determining at least one of: a rest pressure gradient for the cardiovascular narrowing of the subject under rest condition, comprising applying as the rest flow rate of the blood flow through the cardiovascular narrowing the rest flow rate in equation (1); and a rest pressure gradient for the cardiovascular narrowing of the subject under rest condition, comprising applying as the stress flow rate of the blood flow through the cardiovascular narrowing the stress flow rate in equation (1).
  • the method may comprise following steps.
  • a shape profile of the stenosis is assessed and characterized by few primary parameters such as stenosis cross-section area, stenosis length and cross-section area behind the stenosis.
  • a flow profile of the target stenosis and a flow profile of the respective heart are assessed. The flow profile is characterized by peak systolic flow rate of the target stenosis as well as by heart rate, stroke volume and systolic time of the respective heart.
  • the rest pressure gradient under rest condition is determined by using trained (parameters of polynomial or non-linear functions a fitted to minimize an error) computational unit (algorithm) represented by equation (1).
  • algorithm computational unit
  • Rest condition constants c1 , a1 , and pi may be applied for determining the rest pressure gradient for the cardiovascular narrowing.
  • the method may further comprise applying stress condition constants c2, a2, and p2 for determining the stress pressure gradient for the cardiovascular narrowing, wherein at least one constant from the stress condition constants c2, a2, and p2 is different from the rest condition constants c1 , a1 , and pi .
  • the stress condition constants c2, a2, and p2 applied for determining the pressure gradient for the cardiovascular narrowing under stress condition may be identical to the rest condition constants c1 , a1 , and pi applied for determining the pressure gradient for the cardiovascular narrowing under rest condition.
  • the method further comprises determining the constants c, a, and p by training a non-linear regression model based on a set of training data, particularly for local issues (domains of applicability). Training of the non-linear regression model may be applied by curve fitting. A first set of training data indicative for training data referring a first degree of stenosis (cardiovascular narrowing) may be applied. Following, the model trained based on the first training data represented by constants C
  • a second set of training data indicative for training data referring a second degree of stenosis (higher than the first degree of stenosis) may be applied.
  • the model trained based on the second training data represented by constants Chigh, cihigh, and Phigh may be applied for determining a patient (subject) with high of stenosis.
  • the method may further comprise at least one of the following: determining the rest condition constants c1 , a1 , and pi by training the non-linear regression model based on a set of rest training data indicative for training data for rest condition; and determining the stress condition constants c2, a2, and p2 by training the non-linear regression model based on a set of stress training data indicative for training data for stress condition.
  • the pressure gradient may be determined with the same constants ex, ax, and Px for both the subject being under rest condition and the subject being under stress condition.
  • the pressure gradient determined for the rest condition and the stress condition, respectively, will dependent on AVA (orifice area of a cardiovascular narrowing), and / or Q (flow rate of the blood flow through the cardiovascular narrowing) being different for rest condition and stress condition.
  • first set of constants cn, an, pn and a second set of constants cm, am, pm determined by training the non-linear regression model based on a first and a second set of training data
  • first set of training data comprises training data assigned to a first section of the blood circulation system
  • second set of training data comprises training data assigned to a second section of the blood circulation system.
  • At least one constant of the second set of constants is different from the first set of constants.
  • the first and second set of constants can be applied for determining the pressure gradient for a cardiovascular narrowing located in the first or the second section of the blood circulation system, respectively.
  • different sets of constants may be applied for determining the pressure gradient for a cardiovascular narrowing provided in an aortic valve or a cardiovascular narrowing provided in different location of the aorta (coarctation of aorta).
  • the first and second set of constants can be applied for determining the pressure gradient for a cardiovascular narrowing (potentially) assigned to the first or the second kind of disease, respectively.
  • the constants c, a, and p can be determined by training the non-linear regression model based on the set of training data.
  • the model may be trained for subjects for which the specific condition (at least) is assumed to be present.
  • Fig.1 a schematic block diagram for a method for non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject under rest condition
  • Fig. 2 a schematic block diagram for a method for non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject under stress condition
  • Fig. 3 a schematic illustration of the method to train non-linear regression model predicting pressure gradient due to a narrowing: segmentation and 3D geometry reconstruction based on medical imaging data, flow simulation using computational fluid dynamics solver, and analysis pressure fields aiming to quantify pressure gradient;
  • FIG. 4 examples of evaluated geometric orifice areas of diseased (stenosed) aortic valves
  • Fig. 5 3D plot of over 100 simulated cases showing a dependence of pressure gradient (PG) simulated by CFD (Computational Fluid Simulation) from aortic valve orifice area (AVA) and simulated flow rate, whereas the curved surface represent trained (minimization of the root mean square error) non-linear regression power law model;
  • PG pressure gradient
  • AVA aortic valve orifice area
  • Fig. 7 Bland Altman plot comparing predicted pressure gradient in patients with coarctation of the aorta under stress conditions caused by dobutamine vs. catheter measured pressure gradients.
  • Fig.1 shows a schematic block diagram for a method for non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject under rest condition.
  • the pressure gradient may also be referred to as pressure difference or pressure drop.
  • the method comprises the following steps (under rest condition):
  • 3D imaging acquisition is conducted, for example, by CT.
  • MRI Echocardiography CT - Computer Tomography, MRI - Imaging
  • stenosis a local narrowing of the cardiovascular system under resting conditions.
  • Step 110 Automatic, semi-automatic or manual segmentation of the 3D anatomy of the compartment comprising the narrowing and the respective heart chamber is applied.
  • Step 120 The following parameters are determined: Stroke volume (SV res t), heart rate (HRrest), cross-section area of the narrowing A s , and cross-section area Ao downstream of the narrowing (under rest condition).
  • Fig. 2 shows a schematic block diagram for a method for non-invasive method for determining a pressure gradient for a cardiovascular narrowing of a subject under stress condition.
  • the pressure gradient under stress condition is determined by applying, compared to the rest condition, amended parameters for heart rate of the subject, and volume of heart stroke.
  • the subject (patient) is not under actual stress condition.
  • Ruther starting from or based on parameters for heart rate, and volume of heart stroke measured for the subject under rest condition, the amended parameters for heart rate, and volume of heart stroke applied for determining the pressure gradient for the cardiovascular narrowing of a subject under stress condition are derived by modelling (calculating).
  • the method comprises the following steps (under stress condition):
  • Determination of the pressure gradient may be conducted by a data processing device having one or more processors.
  • Fig. 3 shows a schematic illustration of the method to train non-linear regression model for determining pressure gradient caused by a cardiovascular narrowing.
  • 3D images 300 gathered by 3D imaging segmentation and 3D geometry reconstruction 310 is applied.
  • Flow simulation 320, 330 is conducted using computational fluid dynamics solver, and analysis pressure fields aiming to quantify pressure gradient.
  • Flow simulation 320 shows an exemplary numerically simulated flow field through the stenosed aortic valve by using CFD flow solver STARCCM+.
  • 3D velocity field is visualized by path lines distinguished by velocity magnitude.
  • Flow simulation 330 refers to a pressure curve along the centerline of the simulated geometry starting in the left ventricle tract located downstream of the aortic valve, going through the aortic valve and further through the ascending aorta. Locations corresponding to the planes Phigh, Plow are marked in the flow simulation 320.
  • Fig. 4 shows examples of evaluated geometric orifice areas of the diseased (stenosed) aortic valves, as were assessed from computer tomography 3D data used for CFD simulations in frames of the training. Variability (areas and shapes) of cases used for training was shown. However, all these shapes could be considered as a three-star orifice shape. This is because normal aortic valve has three leaflets forming this typical shape. There is however another type of aortic valves, so called bicuspid aortic valves. I am expecting that these valves form- ing another aortic valve orifice shape with two corners will affect coefficients of the power law. Respectively bicuspid and tricuspid aortic valve shapes could be trained separately.
  • Fig. 5 shows a 3D plot of over 100 simulated cases showing a dependence of pressure gradient (PG) simulated by CFD (Computational Fluid Simulation) from aortic valve orifice area (AVA) and simulated flow rate, whereas the curved surface represent trained (minimization of the root mean square error) non-linear regression power law model.
  • PG pressure gradient
  • AVA aortic valve orifice area
  • Fig. 6 shows a Bland Altman plot comparing CFD simulated and by the trained non-linear regression model predicted pressure gradient in over 30 cases, which were not used to train the model. This can be considered as in silico validation of the model.
  • a non-linear regression model predicting the pressure gradient due to aortic valve stenosis was developed based on computational fluid simulations of the blood flow through aortic valves.
  • Computed Tomography (CT) data of patients with stenosed aortic valves were segmented and 3D geometries of the LVOT, aortic root with aortic valve and aorta were reconstructed.
  • Reconstructed geometry of the aortic valve was used to define the geometric aortic valve orifice area (AVA, mm 2 ).
  • Flow with respective flow rate (Q, ml I s) was simulated using commercial software and the pressure drop (PG, mmHg) was calculated.
  • a non-linear regression model using power law was trained by using curve fitting toolbox of the MATLAB software. Coefficients of the equation (1):
  • PG c * AVA a * QP were fitted by minimizing root mean square error, whereas Q is the flow rate, 1 1 s.
  • the pressure gradient was determined for patients with coarctation of the aorta.
  • the piece-wise non-linear regression model predicting the pressure gradient through the stenosis of the aorta was developed using CFD simulations of altogether 2722 cases.
  • the model predicts the pressure gradient (PG, mmHg) based on the cross-section area of the stenosis AVA (mm 2 ) and flow rate Q (ml I s) through stenosis.
  • Pressure gradient under stress conditions was predicted by using the method described in the invention and compared with catheter-based measurements in a clinical cohort of 22 patients with coarctation of the aorta.
  • Fig. 7 shows Bland Altman plot comparing predicted and measured pressure gradients under stress conditions.
  • the model used as input pressure gradient under rest conditions measured by catheter, an information about weight normalized dobutamine doses, stenosis degree as measured used angiography images, heart rate measured during rest, and stroke volume measured during rest by echocardiography.
  • the non-invasive method for determining the pressure gradient for a cardiovascular narrowing of a subject may be conducted by applying a computational unit which, in an example, may include two interfaces.
  • One interface may be provided for the stenosis shape profile and one interface for the flow profile of the stenosis of a vessel or of the heart valve.
  • the computational unit may be arranged and configured to receive target shape and flow profiles and to predict the pressure gradient, for example, the pressure gradient under rest and during stress based on received information.
  • the target vessel may be any vessel of human or animal body or heart valve.
  • the vessel shape profile of the target vessel may include at least three (geometric) parameters: length of the stenosis, cross-section area of the stenosis and cross-section area of the vessel behind the stenosis. Preferably these parameters are extracted from an image data set. Both cross-section areas allow for calculating a fourth shape parameter the degree of stenosis.
  • the flow profile of the target vessel may include at least three parameters measured under rest conditions: peak systolic flow rate through stenosis, heart rate and stroke volume.
  • the fourth parameter, the systolic time can be measured using imaging data set or calculated using received first three flow parameters.
  • the computational unit which may also be referred to as data processing device and which can be considered as an Al system applying, for example, non-linear regression, may be trained for predicting first the pressure gradient under rest condition and second to predict normalized pressure gradient during stress condition for the subject (patient).
  • the computational unit determining the pressure gradient under rest condition may implement a non-linear regression model connecting at least two input parameters - for example, cross-section area of the stenosis (AV A) and flow rate through the stenosis (Q) - and the output parameter, the pressure gradient.
  • Other regressions models may be applied, e.g. piecewise regression.
  • RMSE root mean square error
  • the aim of the training is to achieve a situation with the model error which is below an error of the current clinical gold standard for the measurement of the pressure gradient that is a catheter-based measurement.
  • the accuracy of catheter measurements are specified with 4mmHg or 4% of the measured value for pressures >100 mmHg.
  • the training data may be provided by image-based computational fluid dynamics simulations.
  • a computational unit predicting the pressure gradient based on the shape, orifice areas, and flow profiles a relatively large data set covering a physiological range of these profiles with different combinations of shape (e.g. geometries with stenosis degrees covering a range between 0 and 100% stenosis with a step of 2% resulting in approximately 50 different stenosis degrees) and approximately 50 different flow parameters covering a physiological range thus resulting in set of at least 500 combinations with respective three-dimensional velocity and pressure fields should be provided.
  • shape e.g. geometries with stenosis degrees covering a range between 0 and 100% stenosis with a step of 2% resulting in approximately 50 different stenosis degrees
  • approximately 50 different flow parameters covering a physiological range thus resulting in set of at least 500 combinations with respective three-dimensional velocity and pressure fields should be provided.
  • image-based computational fluid dynamics using imaging data for 3D anatomy for simulations as described for example in (Goubergrits et al., J. Magn. Reson. Imaging 2015: 41 , 909-916).
  • flow solvers such as Lattice Boltzman Modelling or Smoothed Particle Hydrodynamics.
  • the second way to provide required data is a use of MRI for direct measurements of the 3D velocity field combined with a numerical approach to calculate relative pressure field as exemplary described in (Goubergrits et al., J. Magn. Res. Imag. 49(1):2019, 81-89).
  • the model may be preliminary clinically validated against current clinical “gold standard” for the pressure gradient measurement, the catheter-based pressure measurement.
  • imagebased data for shape and flow rate can be directly combined with catheter-based pressure gradient measurements.
  • the pressure gradient may be determined for a subject during stress.
  • the non-linear regression model is calculating the pressure gradient as a function of input parameters is used assuming that the stress condition does not affect the shape profile (AVA), whereas the change in flow profile (Q) may be calculated according the piece-wise linear model predicting changes in HR, SV and ST as a function of shape and flow profiles under rest and the stress level like dobutamine dose.
  • the pressure gradient during stress can be calculated if pressure gradient under rest is known (e.g. measured by catheter or calculated) and non- dimensional relationship (computational unit) describing a relationship connecting normalized pressure gradient and normalized flow rate is trained.
  • the function describing this connectivity is a polynomial of at least second order describing an increasing pressure gradient with increasing flow rate, which is also affected by shape profile like degree of stenosis or stenosis length.
  • the coefficients (constants) of the function may by fitted by minimizing RMSE. To fit this function a data sets of several target stenosis with several (at least four) output values for different input flow rates per each target stenosis is required.

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Abstract

La présente divulgation fait référence à un procédé non effractif qui permet de déterminer un gradient de pression pour un rétrécissement cardiovasculaire chez un sujet et qui comprend dans un dispositif de traitement de données ayant un ou plusieurs processeurs : la fourniture de données géométriques indiquant une zone d'orifice d'un rétrécissement cardiovasculaire chez un sujet ; la fourniture de données de flux indiquant le débit d'un débit sanguin à travers le rétrécissement cardiovasculaire du sujet ; la détermination d'un gradient de pression pour le rétrécissement cardiovasculaire selon l'équation PG = c * AVAα * Qβ, PG étant le gradient de pression pour le rétrécissement cardiovasculaire, AVA étant la zone d'orifice du rétrécissement cardiovasculaire, Q étant le débit du débit sanguin à travers le rétrécissement cardiovasculaire, et c, α et β étant des constantes, les constantes c, α et β étant déterminées par l'entraînement d'un modèle de régression non linéaire sur la base d'un ensemble de données d'apprentissage. En outre, l'invention concerne un produit programme informatique.
PCT/EP2021/077556 2020-10-07 2021-10-06 Procédé non effractif de détermination d'un gradient de pression pour un rétrécissement cardiovasculaire chez un sujet et produit de programme informatique WO2022074054A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130066229A1 (en) 2011-09-11 2013-03-14 Neosoft, Llc Noninvasive methods for determining the presure gradient across a heart valve without using velocity data at the valve orifice
WO2015138555A2 (fr) 2014-03-11 2015-09-17 The Johns Hopkins University Procédé pour estimer des débits et des gradients de pression dans des réseaux artériels à partir de données de distribution de contraste obtenues sur la base d'une angiographie de tomographie assistée par ordinateur

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130066229A1 (en) 2011-09-11 2013-03-14 Neosoft, Llc Noninvasive methods for determining the presure gradient across a heart valve without using velocity data at the valve orifice
WO2015138555A2 (fr) 2014-03-11 2015-09-17 The Johns Hopkins University Procédé pour estimer des débits et des gradients de pression dans des réseaux artériels à partir de données de distribution de contraste obtenues sur la base d'une angiographie de tomographie assistée par ordinateur

Non-Patent Citations (12)

* Cited by examiner, † Cited by third party
Title
BENEDIKT ET AL.: "Towards improving the accuracy of aortic transvalvular pressure gradients: rethinking Bernoulli", MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING, vol. 58, 26 May 2020 (2020-05-26), pages 1667 - 1679, XP055778816, DOI: 10.1007/s11517-020-02186-w
FARR ET AL., AM J CARDIOL, vol. 102, no. 2, 2008, pages 203 - 6
FRANKE BENEDIKT ET AL: "Towards improving the accuracy of aortic transvalvular pressure gradients: rethinking Bernoulli", vol. 58, no. 8, 26 May 2020 (2020-05-26), DE, pages 1667 - 1679, XP055778816, ISSN: 0140-0118, Retrieved from the Internet <URL:http://link.springer.com/article/10.1007/s11517-020-02186-w/fulltext.html> DOI: 10.1007/s11517-020-02186-w *
GOUBERGRITS ET AL., J. MAGN. RES. IMAG., vol. 49, no. 1, 2019, pages 81 - 89
GOUBERGRITS ET AL., J. MAGN. RESON. IMAGING, vol. 41, 2015, pages 909 - 916
JULIO A SOTELO ET AL: "Pressure gradient prediction in aortic coarctation using a computational-fluid-dynamics model: validation against invasive pressure catheterization at rest and pharmacological stress", JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, BIOMED CENTRAL LTD, LONDON UK, vol. 17, no. Suppl 1, 3 February 2015 (2015-02-03), pages Q78, XP021212522, ISSN: 1532-429X, DOI: 10.1186/1532-429X-17-S1-Q78 *
PENICKA ET AL., J AM COLL CARDIOL, vol. 55, no. 16, 2010, pages 1701 - 10
RUNTE ET AL., FRONTIERS IN CARDIOVASCULAR MEDICINE, vol. 6, 2019, pages 43
SOTELO ET AL.: "Journal of Cardiovascular Magnetic Resonance", vol. 17, 3 February 2015, BIOMED CENTRAL LTD., article "Pressure gradient prediction in aortic coarctation using a computational-fluid-dynamics model: validation against invasive pressure catheterization at rest and pharmacological stress"
SUGIMOTO ET AL., JACC CARDIOVASC IMAGING, vol. 10, pages 1253 - 1264
TYSZKA ET AL., JOURNAL OF MAGNETIC RESONANCE IMAGING, vol. 12, no. 2, 2000, pages 321 - 329
WANG ET AL., EUR J HEART FAIL, vol. 16, no. 8, 2014, pages 888 - 97

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