Patent Description:
The human heart contains four heart valves, aortic, mitral (MV), pulmonary and tricuspid. Located between the left atrium and left ventricle, the MV has two key functions: maintaining a hemodynamic seal during ventricular ejection; and ensuring rapid ventricular re-filling following ventricular ejection. Optimal MV function depends upon several factors, such as the hemodynamic load, the biomechanical properties of the MV connected tissue and the functional anatomy of the left heart. Dysfunction of one or more of these factors may result in suboptimal filling or ejection.

Mitral valve regurgitation (MVR) is the most common MV dysfunction and accounts for approximately <NUM>% of native MV dysfunctions. Due to changes in MV function (such as MV prolapse or cardiomyopathy), the MV no longer maintains unidirectional flow in MVR. During ventricular ejection retrograde flow enters the left atrium. This increases the hemodynamic load in the left heart (also referred to as volume overload), which can lead to other cardiac related pathologies, such as atrial fibrillation.

MVR may be corrected by either: repairing the existing valve; or prosthetic replacement. However, repair has significant benefits, such as reduced mortality rates (<NUM>% repair, <NUM>% replacement) and minimization of other complications such as thromboses. One such repair technique for MVR is the edge-to-edge repair (ETER), which attempts to restore MV coaptation by physically joining prolapsing regions together.

The ETER was originally developed as an open chest surgery (i.e. midline sternotomy). However, minimally invasive approaches are now available whereby the repair is performed using a percutaneously delivered device. Such a device may be in the form of a clip, which brings the prolapsing regions together. However, during deployment of such devices, there remains some uncertainty in the number of devices required to restore proper function of the valve. Typically, during the procedure the clinician must determine if the current repair is sufficient, or if the subject requires further repair.

Although the ETER alleviates MVR, it may negatively affect ventricular filling. Accordingly, the ETER aims to find a balance between reducing retrograde flow into the atrium during ventricular contraction and avoiding a reduction in orifice area that would impair ventricular filling. For this reason a dilemma typically encountered is the decision between placing an additional repair device (thereby further reducing the orifice area and possibly impairing ventricular filling) or accepting the current configuration with the risk of having insufficiently reduced the MVR.

Imaging tools may be used to provide anatomical guidance to reduce the uncertainty of the decision to provide an additional repair device. However, these tools lack hemodynamic information relating to the flow of blood in the region of the repair.

Document <CIT> discloses a subject-specific simulation model of at least one component in the cardiovascular system for simulating blood flow and/or structural features.

In the scientific publication "<NPL>, it is shown how a 0D numerical model might be used to simulate a cardiac system.

According to an aspect of the invention, there is provided a system for determining a real-time valve function of a subject according to claim <NUM>.

The system provides for a means of determining the real-time hemodynamic function of a subject.

For example, when undergoing percutaneous valvular repair, a clinician may require data relating to the hemodynamic function of a subject in order to make a decision. Such data is typically determined based upon qualitative anatomic inspection and user dependent measurements, both of which can vary in accuracy.

However, these decisions and their outcomes often remain unclear. Accordingly, by providing an accurate model of the real-time hemodynamic function of the subject, based on subject-specific measurements, it is possible to generate a more accurate depiction of the hemodynamic function of the subject over time. This may then lead to a clearer insight into the status of the cardiac system and subsequently to a more beneficial decision by the clinician.

In an embodiment, the continuous stream of physiological data is obtained from a subject undergoing a change in valve function, and wherein the real-time valve function determined from the simulated real-time function of the cardiac system is representative of the change in valve function.

In this way, the change in valve function may be monitored, or even predicted, by the system taking the entire cardiac system into account by way of the numerical model.

In a further embodiment, the system is adapted for use while the subject is undergoing a valve repair.

In this way, the system may provide information for guidance and support to a user during the process of repairing a valve.

In an embodiment, the system further comprises a physiological sensor adapted to obtain physiological data from the subject, wherein the physiological sensor comprises one or more of:.

In a further embodiment, the volume waveform sensor comprises one or more of.

In an embodiment, the numerical model is based on a physical parameter, and wherein the processor is further adapted to adjust the physical parameter of the numerical model based on at least a portion of the continuous stream of physiological data.

In this way, the numerical model may be tailored to the subject according to variances in the physiological data over time.

In an embodiment, the numerical model is based on a physical parameter, and wherein the processor is further adapted to:.

In this way, the numerical model may be tailored to the subject before the simulation begins, thereby increasing the accuracy of the determined hemodynamic function.

In an embodiment, the numerical model is based on a physical parameter and the method further comprises predicting a future hemodynamic function of the subject, wherein the processor is further adapted to:.

In this way, a variety of different scenarios may be simulated that affect hemodynamic function in some way. For example, the effects of various stresses on the cardiac systems, or of various medications, may be investigated in the safety of the simulation.

In an embodiment, the physiological data comprises one or more of:.

In a further embodiment, the volume waveform data comprises one or more of.

In an embodiment, the volume waveform data comprises ultrasound data.

In an embodiment, the pressure waveform data comprises one or more of:.

In an embodiment, the physiological data comprises estimated physiological data.

In this way, intermittent data acquisition may be accounted for by providing estimated data.

According to another aspect of the invention, there is provided a computer-implemented method for determining a real-time valve function of a subject according to claim <NUM>.

According to a further aspect of the invention, there is provided a computer program according to claim <NUM>.

The invention provides a system for determining a real-time valve function of a subject. The system comprises a processing unit adapted to: obtain a numerical model of a cardiac system, the numerical model being a 0D numerical model or a 1D numerical model, wherein the numerical model is adapted to receive physiological data as an input and output a simulated function of the cardiac system in real-time, wherein the simulated function of the cardiac system comprises a simulated function of a valve within the cardiac system. The processor is further adapted to obtain a continuous stream of physiological data from the subject; provide the continuous stream of physiological data as an input to the numerical model of the cardiac system, thereby generating a simulated function of the cardiac system of the subject; and determine a real-time valve function of the subject based on the simulated real-time function of the cardiac system of the subject.

The general operation of an exemplary ultrasound system will first be described, with reference to <FIG>, and with emphasis on the signal processing function of the system since this invention relates to the processing of the signals measured by the transducer array.

The system comprises an array transducer probe <NUM> which has a transducer array <NUM> for transmitting ultrasound waves and receiving echo information. The transducer array <NUM> may comprise capacitive micromachined ultrasonic transducers (CMUTs); piezoelectric transducers, formed of materials such as PZT (lead zirconate titanate) or PVDF (polyvinylidene fluoride); or any other suitable transducer technology. In this example, the transducer array <NUM> is a two-dimensional array of transducers <NUM> capable of scanning either a 2D plane or a three dimensional volume of a region of interest. In another example, the transducer array may be a 1D array.

The transducer array <NUM> is coupled to a microbeamformer <NUM> which controls reception of signals by the transducer elements. Microbeamformers are capable of at least partial beamforming of the signals received by sub-arrays, generally referred to as "groups" or "patches", of transducers as described in <CIT>), <CIT>), and <CIT>).

It should be noted that the microbeamformer is entirely optional. Further, the system includes a transmit/receive (T/R) switch <NUM>, which the microbeamformer <NUM> can be coupled to and which switches the array between transmission and reception modes, and protects the main beamformer <NUM> from high energy transmit signals in the case where a microbeamformer is not used and the transducer array is operated directly by the main system beamformer. The transmission of ultrasound beams from the transducer array <NUM> is directed by a transducer controller <NUM> coupled to the microbeamformer by the T/R switch <NUM> and a main transmission beamformer (not shown), which can receive input from the user's operation of the user interface or control panel <NUM>. The controller <NUM> can include transmission circuitry arranged to drive the transducer elements of the array <NUM> (either directly or via a microbeamformer) during the transmission mode.

In a typical line-by-line imaging sequence, the beamforming system within the probe may operate as follows. During transmission, the beamformer (which may be the microbeamformer or the main system beamformer depending upon the implementation) activates the transducer array, or a sub-aperture of the transducer array. The sub-aperture may be a one dimensional line of transducers or a two dimensional patch of transducers within the larger array. In transmit mode, the focusing and steering of the ultrasound beam generated by the array, or a sub-aperture of the array, are controlled as described below.

For each line (or sub-aperture), the total received signal, used to form an associated line of the final ultrasound image, will be a sum of the voltage signals measured by the transducer elements of the given sub-aperture during the receive period. The resulting line signals, following the beamforming process below, are typically referred to as radio frequency (RF) data. Each line signal (RF data set) generated by the various sub-apertures then undergoes additional processing to generate the lines of the final ultrasound image. The change in amplitude of the line signal with time will contribute to the change in brightness of the ultrasound image with depth, wherein a high amplitude peak will correspond to a bright pixel (or collection of pixels) in the final image. A peak appearing near the beginning of the line signal will represent an echo from a shallow structure, whereas peaks appearing progressively later in the line signal will represent echoes from structures at increasing depths within the subject.

In addition, upon receiving the echo signals from within the subject, it is possible to perform the inverse of the above described process in order to perform receive focusing. In other words, the incoming signals may be received by the transducer elements and subject to an electronic time delay before being passed into the system for signal processing. The simplest example of this is referred to as delay-and-sum beamforming. It is possible to dynamically adjust the receive focusing of the transducer array as a function of time.

The structural and motion signals produced by the B mode and Doppler processors are coupled to a scan converter <NUM> and a multi-planar reformatter <NUM>. The scan converter <NUM> arranges the echo signals in the spatial relationship from which they were received in a desired image format. In other words, the scan converter acts to convert the RF data from a cylindrical coordinate system to a Cartesian coordinate system appropriate for displaying an ultrasound image on an image display <NUM>. In the case of B mode imaging, the brightness of pixel at a given coordinate is proportional to the amplitude of the RF signal received from that location. For instance, the scan converter may arrange the echo signal into a two dimensional (2D) sector-shaped format, or a pyramidal three dimensional (3D) image. The scan converter can overlay a B mode structural image with colors corresponding to motion at points in the image field, where the Doppler-estimated velocities to produce a given color. The combined B mode structural image and color Doppler image depicts the motion of tissue and blood flow within the structural image field. The multi-planar reformatter will convert echoes that are received from points in a common plane in a volumetric region of the body into an ultrasound image of that plane, as described in <CIT>). A volume renderer <NUM> converts the echo signals of a 3D data set into a projected 3D image as viewed from a given reference point as described in <CIT>).

The 2D or 3D images are coupled from the scan converter <NUM>, multi-planar reformatter <NUM>, and volume renderer <NUM> to an image processor <NUM> for further enhancement, buffering and temporary storage for display on an image display <NUM>. The imaging processor may be adapted to remove certain imaging artifacts from the final ultrasound image, such as: acoustic shadowing, for example caused by a strong attenuator or refraction; posterior enhancement, for example caused by a weak attenuator; reverberation artifacts, for example where highly reflective tissue interfaces are located in close proximity; and so on. In addition, the image processor may be adapted to handle certain speckle reduction functions, in order to improve the contrast of the final ultrasound image.

In addition to being used for imaging, the blood flow values produced by the Doppler processor <NUM> and tissue structure information produced by the B mode processor <NUM> are coupled to a quantification processor <NUM>. The quantification processor produces measures of different flow conditions such as the volume rate of blood flow in addition to structural measurements such as the sizes of organs and gestational age. The quantification processor may receive input from the user control panel <NUM>, such as the point in the anatomy of an image where a measurement is to be made.

Output data from the quantification processor is coupled to a graphics processor <NUM> for the reproduction of measurement graphics and values with the image on the display <NUM>, and for audio output from the display device <NUM>. The graphics processor <NUM> can also generate graphic overlays for display with the ultrasound images. These graphic overlays can contain standard identifying information such as patient name, date and time of the image, imaging parameters, and the like. For these purposes the graphics processor receives input from the user interface <NUM>, such as patient name. The user interface is also coupled to the transmit controller <NUM> to control the generation of ultrasound signals from the transducer array <NUM> and hence the images produced by the transducer array and the ultrasound system. The transmit control function of the controller <NUM> is only one of the functions performed. The controller <NUM> also takes account of the mode of operation (given by the user) and the corresponding required transmitter configuration and band-pass configuration in the receiver analog to digital converter. The controller <NUM> can be a state machine with fixed states.

The methods described herein may be performed on a processing unit. Such a processing unit may be located within an ultrasound system, such as the system described above with reference to <FIG>. For example, the image processor <NUM> described above may perform some, or all, of the method steps detailed below. Alternatively, the processing unit may be located in any suitable system, such as a monitoring system, that is adapted to receive an input relating to a subject.

<FIG> shows a method <NUM> for determining a real-time hemodynamic function of a subject. The real-time hemodynamic function may refer to any physical function where the movement, or flow, of blood is involved. For example, the real-time hemodynamic function may refer to the motion of blood passing through a valve, such as the mitral or aortic valve. Further, the real-time hemodynamic function may refer to the changes in volume of a given cardiac chamber, such as an atrium or ventricle.

The method begins in step <NUM> by obtaining a numerical model of a cardiac system, wherein the numerical model is adapted to take physiological data as an input and output a function of the cardiac system, the simulated function of the cardiac system comprising a simulated function of a valve within the cardiac system.

In other words, the numerical model simulates the function of a given cardiac system, such as a number of ventricles, atria and systemic arteries, a left side of the heart, an entire heart, and the like. For example, the cardiac system may include the left heart and the systemic arteries. An example of a numerical model for a cardiac system is described below with reference to <FIG>.

The numerical model may be constructed based on a variety of physical parameters. For example, the numerical model may be based on any one or more of: a pressure-volume relationship; a stiffness of the cardiac system; conservation of energy; conservation of mass; and conservation of momentum. The numerical model may also include a systemic arterial model, representing the arterial system of a subject.

The incorporation of physical parameters into the numerical model provides a framework within which data from various sources (such as: ultrasound data; peripheral, systemic or intra-cardiac blood pressure data; clinical guidelines; machine learning estimates; and the like) can be fused together following physical principals such as the conservation of mass, momentum and energy. Accordingly, a resultant estimate from the numerical model, such as a real-time hemodynamic function, may be made more consistent, particularly where inputs are noisy or are received from different imaging modalities and/or different moments in time are used.

Where the numerical model is based on one or more physical parameters, the method may further include adjusting the physical parameter(s) of the numerical model based on at least a portion of the continuous stream of physiological data. Alternatively, preliminary physiological data may be obtained from the subject and used to adjust the physical parameter(s) of the numerical model. Further, the physical parameter(s) of the numerical model may be adjusted based on physiological data collected from a number of subjects, i.e. using a population set of physiological data.

In other words, patient specific data may be used to tune the numerical model to an individual user. This may be performed during an examination, using the continuous stream of physiological data, or prior to the examination, using preliminary physiological data, which may be collected using the same sources as for the continuous stream of physiological data. By using patient-specific inputs (such as ultrasound volume segmentation taken from the ultrasound data or peripheral pressure measurements), the numerical model can be personalized to each patient, which in turn provides a patient-specific estimate of a cardiac function, for example real-time hemodynamic function.

The adjusting of the numerical model may include identifying a physical parameter based on the physiological data and providing the physical parameter to the numerical model.

For example, the physical parameter(s) may include one or more of: a systemic circulation parameter; a filling parameter; an ejection parameter; a heart rate parameter; a stiffness parameter; a valve related parameter, such as valve regurgitation or valve opening size; a blood flow parameter; and the like.

For example, an arterial blood pressure measurement may be used to tune the parameters of a systemic arterial model. The arterial blood pressure measurement may comprise one or more of a maximum, a minimum and a mean blood pressure value. The arterial blood pressure measurement may be used to adjust a resistance and a compliance of the systemic system in the numerical model, or vice versa.

In an example, a ventricular pressure measurement may be derived using an estimate of the pressure gradient over the aortic valve. The pressure gradient over the aortic valve may be estimated using Doppler ultrasound measurements. In the simplest implementation, the pressure gradient over the aortic valve may be assumed to be <NUM>.

The adjustment of the numerical model may be further improved using a full blood pressure waveform. In this way, the numerical model may be further personalized to the subject, for example by including pressure decay constants in the simulation of the cardiac system, which may be used to model the stiffness and relaxation of a cardiac chamber.

Blood pressure information used to adjust the numerical model may be obtained from a peripheral artery, such as a brachial or radial artery, or a systemic artery such as the aortic or femoral artery. Measurements of peripheral pressures in such locations are amplified with respect to central aortic pressure values due to variations in vessel diameter and stiffness.

In the numerical model, the physical parameters may be identified using a multi-step approach. For example, physical parameters that represent the systemic circulation may be identified first (i.e. the parameters associated with the afterload of the cardiac system), before the parameters representing the ejection and filling of the heart are identified. These patient-specific physical parameters may be estimated using a mixture of techniques such as physiology based rules, direct optimization methods, sequential filtering methods and the like.

In step <NUM>, a continuous stream of physiological data is obtained from the subject. The continuous stream of physiological data may be obtained from the subject in any suitable manner according to the application.

The physiological data may comprise one or more of: electrocardiogram data; pressure waveform data, which may include an atrial pressure waveform and/or an arterial pressure waveform; and volume waveform data, which may include a ventricular volume waveform and/or an atrial volume waveform. The volume waveform data may be based on ultrasound data collected from the subject. In addition, the physiological data comprises estimated physiological data, wherein the estimated physiological data is estimated based on available intermittent physiological data, thereby generating a continuous stream of physiological data based on the intermittent physiological data.

In step <NUM>, the continuous stream of physiological data is provided as an input to the numerical model of the cardiac system, thereby simulating a real-time function of a cardiac system of the subject. An example of a simulation of a real-time function of a cardiac system is discussed further below with reference to <FIG>.

In step <NUM>, the real-time hemodynamic function of the subject is determined based on the simulated real-time function of the cardiac system of the subject.

In patients undergoing MV repairs for MVR by a minimally invasive ETER, the goal is to reduce the level of MV regurgitation without significantly reducing impairing ventricular filling. In approximately <NUM>% of ETERs this requires the use of multiple devices, such as clips, and the benefit of the second device is typically determined intra-operatively after the successful deployment of a first device. The current decision-making process primarily relies upon echocardiography assessment (i.e. qualitative anatomic inspection) or user-dependent measurements (such as, the pressure gradient over the MV).

However, there remain situations in which the benefit of an additional device remains unclear. In such subjects, the hemodynamic function (for example, the level of regurgitation vs. filling volumes at specific phases in the cardiac cycle) of the ETER may be further tested by altering the hemodynamic state of the subject pharmacologically to mimic a hemodynamic status more representative of normal conditions (i.e. post-intervention). Observing the hemodynamic response to these altered states enables the cardiologist to determine the suitability of the current ETER configuration.

The method described above may provide a real-time evaluation of hemodynamic function during an ETER procedure using a numerical model. Using patient-specific inputs (such as ultrasound volume segmentation, pressure waveforms, and the like. ), such a model may be personalized on a beat-by-beat basis to each subject, providing a subject-specific estimate of the real-time hemodynamic function.

By way of example, the method may be deployed as follows. During ETER procedures, typically intermittent ultrasound is available, which would otherwise only provide a limited assessment of the hemodynamic function of the cardiac system of the subject. However, pressure waveforms, such as the left atrial and arterial pressures may be continuously recorded.

The numerical model may take the pressure waveforms and simultaneous volume waveforms (segmented from the ultrasound data when available) as an input and output a continuous estimate of the hemodynamic function of the subject. The volume waveforms may also be obtained by alternative techniques, such as using and inflatable finger cuff, thermodilution techniques, or by estimating complete volume waveforms based on available ultrasound data. The output of the numerical model may provide decision support on the effectiveness of deploying an additional repair device.

Furthermore, the numerical model may also produce the corresponding pressure-volume loops of the left heart (atrium and ventricle), thereby providing further physiological information for clinical assessment, such as the ability of the heart to adapt to altered conditions (such as exercise and/or stress).

In other words, the cardiac system may represent the heart, and specifically the left heart, of the subject and the method may further include determining a pressure-volume loop of a left ventricle and/or a left atrium of the cardiac system based on the simulated real-time function of the cardiac system of the subject.

In the methods described above, a real-time function of a valve of the subject is determined based on the real-time simulation generated from the continuous stream of physiological data. By real-time simulation, it is meant that there is little to no noticeable delay between the numerical model receiving the continuous stream of physiological data and the generation of the simulation of the cardiac system. Put another way, the real-time simulation of the cardiac system generates an output that is, or is almost, concurrent with the current state of the actual cardiac system of the subject. In this way, the system may provide an accurate real-time simulation of the functioning of the cardiac system that may be used during an interventional procedure, such as an edge-to-edge repair, in order to assess the progress of the procedure.

As it is not always possible, or desirable, to achieve an altered cardiac state pharmacologically, the numerical model may be employed to predict a future hemodynamic function of the subject. For example, a physical parameter of the numerical model, such as the ejection or regurgitation of a valve, may be adjusted according to a desired prediction scenario. The continuous stream of physiological data may then be provided as an input to the predictive numerical model, thereby simulating a predictive function of the cardiac system of the subject. Thus, the future hemodynamic function of the subject may be predicted based on the simulated predictive function of the cardiac system of the subject.

For example, during ETER procedures subjects are provided with anesthetic drugs, which temporarily reduce the vascular tone and therefore the afterload of the heart. Accordingly, the hemodynamic function of a subject as assessed during an ETER procedure may not be representative of normal situations. Accordingly, the numerical model may be used to predict the effect of an increased afterload virtually, thereby providing an estimate of hemodynamic function post-intervention. Thus, the numerical model may be used to provide additional decision support on the addition of a further repair device.

Put another way, the system for determining a real-time valve function of a subject may include one or more edge-to-edge valve repair devices and a processing unit adapted to implement the steps outlined above.

More specifically, the processing unit may be adapted to obtain a numerical model of a cardiac system, as described herein, and obtain a continuous stream of physiological data from the subject. The continuous stream of physiological data may be obtained from a subject undergoing a change in valve function, for example due to an edge-to-edge valve repair device being deployed at the valve.

The continuous stream of physiological data may be provided as an input to the numerical model of the cardiac system, thereby generating a simulated real-time function of the cardiac system of the subject and a real-time valve function of the subject may be determined based on the simulated real-time function of the cardiac system of the subject. The real-time valve function determined from the simulated real-time function of the cardiac system may be representative of the change in valve function.

Determining the real-time valve function of the subject may be performed by adjusting the simulated real-time function of the cardiac system based on a change in vascular tone of the cardiac system due to an anesthetic provided to the subject. Further, the numerical model may be further adapted to predict a future simulated valve function based on the provision of an additional edge-to-edge valve repair device to the valve based on the determined real-time valve function of the subject.

<FIG> shows an example of a schematic representation of a numerical model <NUM> of a part of the cardiac system, namely, the left heart <NUM> and the aorta <NUM>.

In this example, a simplified 0D approach to modelling blood flow, or hemodynamic function, during the heart cycle is represented. However, it is also possible to combine 1D/3D modelling approaches within the described framework and it is further possible to include a model of a complete circulatory system.

Zero dimensional (0D) models, also referred to as lumped-parameter models, give rise to a set of simultaneous ordinary differential equations (ODEs) describing the behavior of a cardiac system as discussed in detail in <NPL>. In representations of the vasculature, there are often two ODEs for each compartment, representing the conservation of mass and conservation of momentum, which may be complemented by an algebraic equilibrium equation relating compartment volume to pressure. Numerical models constructed from 0D components generally feature the major components of the cardiac system, such as the heart, the heart valves and compartments of the vasculature, and are suitable for the examination of global distributions of pressure, flow and blood volume over a range of physiological conditions.

One dimensional (1D), two dimensional (2D) and three dimensional (3D) models give rise to a series of partial differential equations describing conservation of mass and momentum (the Navier-Stokes equations), which may be complemented by algebraic equilibrium equations. In the context of cardiovascular numerical models, 1D models have the facility to represent wave transmission effects within the vasculature, which can be used to represent the aorta and larger systemic arteries. 3D models may be used to compute complex flow patterns, for example in the ventricles, around heart valves, near bifurcations, or in any region with vortical or separated flows. No analytical solutions are available for any 3D models but the simplest geometries, and recourse is always made to a numerical solution.

For example, in during an ETER procedure, the numerical model may be required to estimate a real-time hemodynamic function of a mitral valve of a subject. <FIG> illustrates an example of a 0D numerical model <NUM>, represented as an electronic circuit.

In carrying out 0D modelling, the concept of a hydraulic-electrical analog is often applied. Generally, there is a large amount of similarity between blood flow in the circulatory system and electric conduction in a circuit. For example, the blood pressure gradient in the circulatory loop drives the blood to flow against the hydraulic impedance and the voltage gradient in a circuit drives current to flow against the electric impedance. Hydraulic impedance represents the combined effect of the frictional loss, vessel wall elasticity and blood inertia in the blood flow, whilst electric impedance represents the combination of the resistance, capacitance and inductance in the circuit.

In the model shown in <FIG>, the voltage represents the blood pressure and the current represents the blood flow. In this approach the different compartments of the arterial system (such as the atria, ventricles, large arteries, and so on) are grouped together into electrical components which are related to hemodynamic analogues, such as resistance and capacitance.

Beginning with the left heart <NUM>, the source (Pla) will charge the variable capacitor <NUM>, mimicking the left atrium pumping blood to the left ventricle. The left atrium will fill the left ventricle (variable capacitor) to its passive limit. The physiological data obtained from the subject may include a continuous stream of left atrium pressure, which may be used to inform the model on the behavior of the left atrium.

The capacitance of the variable capacitor <NUM> represents the stiffness of the ventricle, i.e. the muscular contraction. The volume is a state variable that may be derived from the physiological data of the user.

Elv refers to the elastance of the left ventricle. This relates the ventricular volume to the pressure within the left ventricle. Although it is a measure of the stiffness (i.e. muscular contraction of the ventricle), it is not strictly a material stiffness. This relationship has been experimentally measured from simultaneous ventricular pressure and volume waveforms.

The charge travels along the circuit, with the diodes <NUM> acting as valves to define a direction of flow. The blood (charge) then moves into the aorta <NUM> and enters the systemic circulation system.

The resistance terms (mitral valve resistance, Rmv, aortic valve resistance, Rav, proximal systemic resistance, Rsys=p, distal systemic resistance, Rsys=d) represent the resistance of the blood vessels and valves to the blood flow and are directly related to the pressure in a given area. Csys represents systemic compliance. The resistance terms may represent various stenoses within the cardiac system.

In this example, the model parameters are represented using electrical analogues. However, such models do follow physical principle such as conservation of mass, for example the blood flow (current) into each node of the model is conserved. Furthermore, the electrical analogue can be derived from the linearization of the mass and momentum conservation equation for blood flow in deformable vessels.

By taking a continuous stream of physiological data and an input (for example a combination of ultrasound data and pressure waveforms obtained from the subject) it is possible for the numerical model to output a representation of the real-time hemodynamic function of the cardiac system of the subject.

It should be noted that the example shown in <FIG> is only one of many possible models of the left heart. The different components of the model may be interchanged dependent upon the specific application. In the example of an ETER procedure, the numerical model may be adapted to include a regurgitant mitral valve. Further, the model may be adapted to include a dynamic left atrium, dynamic left ventricle and the like. As described above, the numerical model may also include a systemic arterial model.

The model may be adapted to incorporate additional ultrasound data for specific applications, such as Doppler waveforms in heart diagnosis; however, this additional data must be routinely collected.

The numerical model described above may be integrated into an ultrasound analysis platform. This may enable the model to utilize the patient-specific segmentation of the left heart.

Put another way, in the example shown in <FIG>, a 0D numerical model is utilized to simulate the physics of blood flow in the heart. The numerical model is adapted to provide real-time patient-specific cardiac output or mitral regurgitation computations, such as a real-time hemodynamic function of the cardiac system of the subject. The input for this model may, for example, include quantitative echocardiography data of cardiac chambers (which may be based on ultrasound data obtained from the subject) and invasively, or non-invasively, measured pressure waves, relating to the blood pressure of the subject.

The numerical model may be a 0D numerical model or a 1D numerical model. As 0D and 1D numerical models are computationally simple, it is possible to process incoming physiological data from the subject with little to no delay, thereby providing a simulated function of the cardiac system in real-time.

In this model-based framework, pre-computed information, for example from complex 3D numerical models, may be provided. The pre-computed information may provide for an initial estimate for key physical parameters to be made, which may then be further personalized according to physiological data (for example, the non-linear valve resistance in ETER). The mapping of such non-linear pressure-flow relationships may be pre-computed over a range of different valve sizes and repair permutations using coupled fluid and solid numerical simulations. The pre-computed values may then be mapped to the patient based on the collected physiological data via a suitable technology (such as machine learning algorithms, proper orthogonal decomposition and the like).

In other words, computations having a high computational load may be performed before the application of the numerical model to the subject's physiological data, thereby reducing the computing resources and time required to simulate the cardiac system.

<FIG> shows a visual representation of the simulated hemodynamic function of a repaired mitral valve <NUM> following an ETER procedure. In the example shown in <FIG>, the ribbons <NUM> represent the path of blood flow through the mitral valve and may be used to assess the hemodynamic function of the valve.

<FIG> shows a schematic representation of a system for determining a real-time hemodynamic function of the subject <NUM>.

In the example of an ETER procedure, the physiological data that may be available is shown below in Table <NUM>.

Referring to <FIG>, the ventricular volume waveform and the atrial volume waveform may be obtained by way of an ultrasound system <NUM>. The arterial pressure waveform may be obtained by way of an anesthesiology set <NUM> and the atrial pressure waveform may be obtained by way of an interventional pressure measurement device <NUM>.

Using the input data described above, the patient-specific physical parameters (such as resistance, compliance and the like) of the numerical model <NUM> may be estimated using, for example, a combination of physiological rules, direct optimization and sequential estimation. The numerical model may then output a real-time hemodynamic function <NUM> of the cardiac system of the subject, such as the mitral valve.

As detailed in Table <NUM>, volume data, for example obtained by way of an ultrasound system, may be intermittent. Accordingly, the numerical model may need to operate for periods without volume waveform input. In this case, the physical parameters that govern the contractility of the heart (such as maximum contractility) may be used to automatically estimate the missing physiological data. The physiological data may be estimated, for example, by way of a combination of direct estimation techniques and non-linear state estimation (such as unscented Kalman filtering). When the volume waveform data is next available, the numerical model may then be updated using the new volume data, thereby recalibrating the numerical model.

In addition to an ultrasound system, volume waveform data may be obtained by way of an inflatable cuff, adapted to be worn by the subject and/or a thermistor-tipped catheter, wherein the volume waveform data is derived using a thermodilution technique.

Claim 1:
A system for determining a real-time valve function of a subject (<NUM>), the system comprising:
a processing unit adapted to:
obtain a numerical model (<NUM>, <NUM>) of a cardiac system, the numerical model being a 0D numerical model (<NUM>, <NUM>), wherein the numerical model is represented as electronic circuit and is adapted to receive a continuous stream of physiological data as an input and to output a simulated hemodynamic function of the cardiac system in real-time, wherein the simulated function of the cardiac system comprises a simulated function of a valve within the cardiac system;
wherein within the model, voltage represents blood pressure, current represents blood flow, and model parameters are represented by electrical analogues;
obtain a continuous stream of physiological data from the subject;
provide the continuous stream of physiological data as an input to the numerical model of the cardiac system, thereby generating a simulated real-time function of the cardiac system of the subject; and
determine a real-time valve function of the subject based on the simulated real-time function of the cardiac system of the subject.