Patent Description:
The coronary arteries, which include a tree of vessels, normally deliver arterial blood and thus Oxygen to the heart muscle or myocardium via the microvascular structure connecting the coronary arteries with the myocardium. With coronary artery disease (CAD), lipid- and calcium-composited coronary plaque deposits block one or more of the vessels (stenosis). Stenosis can cause heart and chest pain (angina) and also acute myocardial infarction and brain stroke when plaque ruptures and blocks a downstream artery. Coronary microvascular dysfunction (CMD) may also play a role in cardiovascular disease, e.g., myocardial ischemia in patients with angina.

Coronary Computed Tomography Angiography (CCTA) is a non-invasive test to detect CAD in patients with chest pain and a gatekeeper technique to invasive Coronary Angiography (CA) in the Catheterization Lab. During CA, assessment of coronary function with an invasive pressure- or flow-sensor tipped catheter may be performed as well to gauge the functional impact of a stenosis in a fractional flow reserve (FFR) or instant wave-free ratio (iFR) measurement. Non-invasive techniques include simulating FFR and IFR based on CT data using computational fluid dynamics (CFD) and related computational methods (FFR-CT, iFR-CT). These techniques rely not only on the anatomical image data but also on boundary conditions of blood flow and pressure at the ostium, the proximal inlet of the coronary tree, and the tips of the coronary arteries.

The boundary conditions, generally, are assumed, including at the point at the tips where they become too thin to be faithfully extracted from the image data. A fundamental limitation of all the approaches is the fact that patient-specific boundary conditions need to be assigned using a generic model. Models typically involve externally measured blood pressure and the diameters of the arterial tips. Unfortunately, since these are determined mainly by the quality of the CT scan rather than actual patient anatomy, errors are introduced to the FFR-CT results that may lead to a wrong recommendation or diagnosis. A problem is that the resistance to blood flow transitioning from the coronaries into the myocardial microvascular structure is not taken into account, and this is exacerbated by the prevalence of CMD, and microvascular resistance is not available to direct measurement.

<CIT> describes a system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

Aspects described herein address the above-referenced problems and others.

In one aspect, a computing system includes a computer readable storage medium with computer executable instructions, including a biophysical simulator and an electrocardiogram signal analyzer. The computing system further includes a processor configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate, from input cardiac image data and the determined myocardial infarction characteristics, a fractional flow reserve or an instant wave-free ratio index, wherein the biophysical simulator adjusts boundary conditions based on the determined myocardial infarction characteristics.

In another aspect, a computer readable storage medium is encoded with computer readable instructions, which, when executed by a processor of a computing system, causes the processor to receive cardiac image data, receive an electrocardiogram signal, determine myocardial infarction characteristics from the input electrocardiogram signal, and simulate, from the cardiac image data and myocardial infarction characteristics of the electrocardiogram signal, a fractional flow reserve or an instant wave-free ratio index, wherein the biophysical simulator adjusts boundary conditions based on the determined myocardial infarction characteristics.

In another aspect, a method includes receiving cardiac image data, receiving an electrocardiogram signal, determining myocardial infarction characteristics from the input electrocardiogram signal, and simulating, from the cardiac image data and myocardial infarction characteristics of the electrocardiogram signal, a fractional flow reserve or an instant wave-free ratio index, wherein the biophysical simulator adjusts boundary conditions based on the determined myocardial infarction characteristics.

Those skilled in the art will recognize still other aspects of the present disclosure upon reading and understanding the attached description.

The drawings are only for purposes of illustrating the preferred embodiments and examples.

<FIG> schematically illustrates a system <NUM> including an imaging system <NUM> such as a CT scanner. In a variation, the imaging system <NUM> includes an MR scanner. The illustrated imaging system <NUM> includes a generally stationary gantry <NUM> and a rotating gantry <NUM>, which is rotatably supported by the stationary gantry <NUM> and rotates around an examination region <NUM> about a z-axis. A subject support <NUM>, such as a couch, supports an object or subject in the examination region <NUM>.

A radiation source <NUM>, such as an x-ray tube, is rotatably supported by the rotating gantry <NUM>, rotates with the rotating gantry <NUM>, and emits radiation that traverses the examination region <NUM>. A radiation sensitive detector array <NUM> subtends an angular arc opposite the radiation source <NUM> across the examination region <NUM>. The array <NUM> detects radiation traversing the examination region <NUM> and generates an electrical signal(s) (projection data) indicative thereof. A reconstructor <NUM> reconstructs the projection data, generating volumetric image data indicative of the examination region <NUM>.

The system <NUM> further includes a computing system <NUM>, which, in this example, serves as an operator console. The console <NUM> includes a processor <NUM> (e.g., a microprocessor, a central processing unit, etc.) and a computer readable storage medium <NUM>, which excludes transitory medium, and includes non-transitory medium such as a physical memory device, etc. The console <NUM> further includes a human readable output device(s) such as a display monitor, and an input device(s) such as a keyboard, mouse, etc..

The computer readable storage medium <NUM> includes instructions <NUM> for a biophysical simulator <NUM> and an electrocardiogram (ECG, or EKG) analyzer <NUM>. The processor <NUM> is configured to execute the instructions <NUM> and/or software that allows the operator to interact with and/or operate the scanner <NUM> via a graphical user interface (GUI) or otherwise. The processor <NUM> may additionally, or alternatively, execute a computer readable instruction(s) carried by a carrier wave, a signal and/or other transitory medium.

In a variation, the biophysical simulator <NUM> and the ECG analyzer <NUM> are part of another computing system, which is separate from the console <NUM> and the system <NUM>. In this instance, the other computing system is similar to the console <NUM> in that it includes a processor, computer readable storage medium, an input device, and an output device, but it does not include the software that allows the operator to interact with and/or operate the scanner <NUM>.

The ECG analyzer <NUM> receives, an input, an ECG signal of a patient under evaluation. The ECG signal can be acquired concurrently with scanning a patient, before scanning the patient and/or after scanning the patient. In one instance, the ECG signal includes a <NUM>-lead ECG signal. Alternatively, or additionally, the ECG signal includes a <NUM>-lead, <NUM>-lead, a more than <NUM>-lead, etc. ECG signal. Alternatively, or additionally, the ECG signal is determined from a cardiac mapping using a vest of electrodes, such as the ECVUE vest, a product of CardioInsight, Ohio, USA. The ECG analyzer <NUM> analyzes the ECG signal and estimates an existence, a position and/or a size of a myocardial infarction (MI) therefrom, as described in detail below.

The biophysical simulator <NUM> is configured to process the volumetric image data and the ECG estimates and perform a biophysical simulation. With respect to FFR, the biophysical simulator determines the index based on CCTA image data. In one instance, this includes using CCTA image data to derive a geometrical model of the coronary tree and determine boundary conditions therefrom for the simulation. As described in detail below, the biophysical simulator <NUM> adjusts the boundary conditions (e.g., microvascular resistance) based on the ECG estimates and/or first integrates the ECG estimates into the CCTA image data. By taking into account the ECG signal, the biophysical simulator <NUM> can provide a more accurate index (e.g., less error introduced by assumptions, models, image quality, etc.), relative to a configuration which does not consider this information.

<FIG> schematically illustrates an example of the biophysical simulator <NUM>. In this example, the biophysical simulator <NUM> includes a segmentor <NUM>, a boundary condition determiner <NUM>, a boundary condition adapter <NUM>, and a flow simulator <NUM>. The biophysical simulator <NUM> receives, as input, CCTA image data from the imaging system <NUM>, a data repository (e.g., a radiology information system (RIS), a picture and archiving system (PACS), etc.), and/or other apparatus. The biophysical simulator <NUM> also receives, as input, the MI estimates (of the existence, the position, the size, etc. of an infarct) from the ECG analyzer <NUM>.

Briefly turning to <FIG>, an example of approximate electrode placement of the ten electrodes for a <NUM>-lead ECG is schematically illustrated. The electrodes include a right arm (RA) electrode <NUM>, a left arm (LA) electrode <NUM>, a right leg (RL) electrode <NUM>, a left leg (LG) electrode <NUM>, a V1 electrode <NUM> in the fourth intercostal space (between ribs <NUM> and <NUM>) just to the right of the sternum (breastbone), a V2 electrode <NUM> in the fourth intercostal space (between ribs <NUM> and <NUM>) just to the left of the sternum, a V3 electrode <NUM> over rib <NUM>, a V4 electrode <NUM> in the fifth intercostal space (between ribs <NUM> and <NUM>) in the mid-clavicular line, a V5 electrode <NUM> horizontally even with V4, in the left anterior axillary line, and a V6 electrode <NUM> horizontally even with V4 and V5 in the midaxillary line.

The <NUM> leads are: I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5 and V6. Lead I is a voltage between the electrode <NUM> and electrode <NUM> (I = LA - RA). Lead II is a voltage between the electrode <NUM> and the electrode <NUM> (II = LL - RA). Lead III is a voltage between the electrode <NUM> and the electrode <NUM> (III = LL - LA). Lead aVR is a voltage between the electrode <NUM> and a combination of the electrode <NUM> and the electrode <NUM> a (aVR = RA - <NUM>/<NUM> (LA + LL). Lead aVL is a voltage between the electrode <NUM> and a combination of the electrode <NUM> and the electrode <NUM> (aVL = LA - <NUM>/<NUM> (RA + LL). Lead aVF is a voltage between the electrode <NUM> and a combination of the electrode <NUM> and the electrode <NUM> (aVF = LL - <NUM>/<NUM> (RA + LA).

<FIG> schematically illustrates an example of an ECG signal <NUM> in "normal" sinus rhythm. The signal <NUM> includes a P wave <NUM>, a Q wave <NUM>, an R wave <NUM>, an S wave <NUM>, a T wave <NUM>, and a U wave <NUM>. The Q, R and S waves for a QRS complex <NUM>. The P wave <NUM> represents atrial depolarization, the QRS complex <NUM> represents ventricular depolarization, the T wave <NUM> represents ventricular repolarization, and the U wave <NUM> represents papillary muscle repolarization. A PR interval <NUM> is the interval from the beginning of the P wave <NUM> to the beginning of the QRS complex <NUM>. An ST segment <NUM> connects the QRS complex <NUM> and the T wave <NUM>, and represents the period when the ventricles are depolarized.

With reference to <FIG>, the ECG analyzer <NUM> analyzes the ECG signal and estimates an existence of a myocardial infarction (MI) therefrom. In one instance, the ECG analyzer <NUM> achieves this by analyzing one or more of the waves <NUM>-<NUM>. For example, the ECG analyzer <NUM> can analyze the ST segment <NUM>, where a depressed or elevated ST segment <NUM> may indicate an MI. In another example, the ECG analyzer <NUM> can analyze the T wave <NUM>, where an inverted T wave <NUM> may indicate an MI. This data can be analyzed for a patient through a comparison with a previously acquired and known normal ECG signal of the patient, through a comparison with a model normal and/or abnormal ECG signal, through a comparison of known normal and/or abnormal ECG signals from a population of patients, etc..

Additionally, or alternatively, the ECG analyzer <NUM> estimates a position of the MI. For this, the ECG analyzer <NUM> can use the leads V1 to V4, which measure electrical activity from the front of the heart, which is supplied by the left anterior descending coronary artery (LAD), to estimate an MI in an anterior region of the heart. The ECG analyzer <NUM> can use the leads I, aVL, V5 and V6, which measure electrical activity from the left of the heart, which is supplied by the left circumflex coronary artery (LC), to estimate an MI in a lateral region of the heart. The ECG analyzer <NUM> can use the II, III and aVF, which measure electrical activity from under the heart, which is supplied by the right coronary artery (RCA), to estimate an MI in an inferior region of the heart.

Additionally, or alternatively, the ECG analyzer <NUM> estimates a size of the MI. For example, MI size can be estimated by ECG signal characteristics such as a deepened Q wave, reduced R-wave amplitude, elevated ST segments and/or inverted T wave on various leads. The ECG analyzer <NUM> can estimates any or all of these characteristics. Furthermore, the ECG analyzer <NUM> can analyze characteristics as described in <CIT>. Additionally, or alternatively, a clinician may visually analyze the ECG signal and provide additional information to the ECG analyzer <NUM>, which can use this information to estimate an MI.

Generally, all of the waves of the ECG signal and the intervals between them may have a predictable time duration, a range of acceptable amplitudes (voltages), and a typical morphology. The ECG analyzer <NUM> can use any deviation from the normal tracing to estimate an MI. <FIG> show example ECG signals with deviations from the "normal" ECG signal shown in <FIG>. <FIG> shows an ECG signal indicating a Stage I extended front apical infarction. <FIG> shows an ECG signal indicating a Stage III anteroseptal infarction. <FIG> shows an ECG signal indicating an intermediate stage posterolateral infarction. <FIG> shows an ECG signal indicating a Stage I rear wall infarction.

With reference to <FIG> and <FIG>, the segmentor <NUM> employs a segmentation algorithm to segment the coronary tree from the CCTA imaging data. The segmentation can be performed automatically (e.g., machine learning, etc.) or semiautomatically (e.g., with user assistance). In one instance, the segmentation includes identifying and/or extracting coronary artery centerlines and/or lumen geometry (e.g., diameter, perimeter, cross-sectional area, etc.) therefrom. The segmentation can be based on voxel intensity, object shape, and/or other characteristics. <FIG> shows segmentation of a portion <NUM> of an individual vessel showing opposing walls <NUM> of the vessel lumen, and <FIG> shows a segmented coronary tree <NUM>.

Examples of suitable approaches for extracting a coronary tree from CCTA imaging data are discussed in <NPL>, <NPL>, and <NPL>. Other approaches are also contemplated herein.

The boundary condition determiner <NUM> determines boundary conditions for a computational fluid dynamic simulation of blood flow in vessels from the user adjusted coronary tree segmentation and/or the segmentor <NUM> adapted user adjusted coronary tree segmentation. With one approach, a parametric lumped model is employed. The model includes a centerline representation using nonlinear resistances, with elements indicating inflow and outflow boundary conditions, and elements representing tree segment transfer functions, which include a series of linear and nonlinear resistance elements reflecting vessel geometry (e.g., diameter, perimeter, cross-sectional area, etc.) and/or hydraulic effects.

An example of a lumped model is discussed in <NPL>. An example of deriving boundary conditions is described in <CIT>, and entitled "Enhanced Patient's Specific Modelling For FFR-CT".

Other approaches are also contemplated herein.

The boundary condition adapter <NUM> is configured to adapt the boundary conditions. For example, where the MI estimates indicate an infarction of the cardiac tissue supplied by the LAD (and/or the LC, the RCA, etc.), the boundary condition adapter <NUM> can increase the myocardial vascular resistance (MVR) boundary conditions (e.g., Ra = Ri + ΔR, where Ra = adapted resistance, Ro = initial resistance, and ΔR is the increase) for the LAD (and/or the LC, the RCA, etc.) to reflect an increase in MVR due to muscle cell damage and/or death that incurred during the infarct. The amount of change (ΔR) of the boundary conditions can be estimated from data known from cardiac physiology. For example, in Cardiac CT and MR, late enhancement describes the delayed myocardial influx of contrast media typically seen in post-ischemic myocardial infarction scar tissue and caused by the altered microcirculatory resistance in subendocardial tissue layer. Alternatively, or additionally, where a lumped parameter model is used and measured FFR (and/or iFR) data with known ECG modifications according to myocardial infarction is available, the model can be trained against data, and the boundary conditions can be trained such that the calculated and measured FFR (and/or iFR) data match.

The flow simulator <NUM> performs a flow simulation with the boundary conditions and generates and outputs FFR values. Flow simulations can be done, e.g., using a computational fluid dynamics (CFD) approach and/or other approach. Examples of computing FFR values are described in <CIT>, and entitled "Determination of a fractional flow reserve (FFR) value for a stenosis of a vessel," <CIT>, and entitled "Fractional flow reserve (FFR) index". The FFR index can be displayed via a display monitor, stored, conveyed to another device, etc. In General,.

<FIG> schematically illustrates a variation in which the biophysical simulator <NUM> includes an image data adapter <NUM> configured to integrate the MI estimates into spatial coordinates of the cardiac image data. In this example, the biophysical simulator <NUM> does not include boundary condition adapter <NUM>. In a variation, the biophysical simulator <NUM> includes both the boundary condition adapter <NUM> and the image data adapter <NUM>.

In one instance, the image data adapter <NUM> integrates the ECG estimates into the CCTA image data. This can be achieved via a personalized cardiac shape model, for example by inferring the coronary arteries associated with the involved cardiac feeding territory or territories and/or otherwise. The biophysical simulator <NUM> then processes the image data as described herein with the segmentor <NUM>, the boundary condition determiner <NUM>, and the flow simulator <NUM>. In this instance, the boundary conditions reflect the MI estimates, which were integrated with the image data prior to boundary condition determination.

In another variation, the approaches described in connection with <FIG> and <FIG> can be combined with invasive catheter measurements, which may deliver improved quantitative data of coronary flow.

<FIG>, <FIG> and -<NUM> described example that use FFR as a measure of a functional significance of coronary artery disease. In a variation, the approach described herein can also be applied to instantaneous wave-free ratio (iFR) and/or other measures. Generally, iFR is performed using pressure wires that are passed distal to the coronary stenosis and isolates a specific period in diastole, called the wave-free period, and computes a ratio of distal coronary pressure to a pressure observed in the aorta over this period.

<FIG> illustrates an example method in accordance with an embodiment described herein.

It is to be appreciated that the ordering of the acts is not limiting. As such, other orderings are contemplated herein. In addition, one or more acts may be omitted and/or one or more additional acts may be included.

At <NUM>, cardiac imaging data is acquired, as described herein and/or otherwise.

At <NUM>, an ECG signal is acquired, as described herein and/or otherwise.

At <NUM>, characteristics (e.g., an existence, a position, a size, etc.) of an MI is determined from the ECG signal, as described herein and/or otherwise.

At <NUM>, boundary conditions are determined from the image data, as described herein and/or otherwise.

At <NUM>, the boundary conditions are adapted based on the MI characteristics, as described herein and/or otherwise.

At <NUM>, the coronary function is assessed using the adapted boundary conditions, as described herein and/or otherwise.

The above may be implemented by way of computer readable instructions, encoded or embedded on computer readable storage medium, which, when executed by a computer processor(s), cause the processor(s) to carry out the described acts. Additionally, or alternatively, at least one of the computer readable instructions is carried by a signal, carrier wave or other transitory medium, which is not computer readable storage medium.

At <NUM>, the MI characteristics are integrated with the image data, as described herein and/or otherwise.

At <NUM>, boundary conditions are determined from the image data integrated with the MI characteristics, as described herein and/or otherwise.

Claim 1:
A computing system (<NUM>), comprising:
a computer readable storage medium (<NUM>) with computer executable instructions (<NUM>), including a biophysical simulator (<NUM>) and an electrocardiogram signal analyzer (<NUM>); and
a processor (<NUM>) configured
- to execute the electrocardiogram signal analyzer (<NUM>) to determine myocardial infarction characteristics from an input electrocardiogram;
- to execute the biophysical simulator to simulate, from input cardiac image data and the determined myocardial infarction characteristics, a fractional flow reserve or an instant wave-free ratio index,
wherein the biophysical simulator includes a boundary condition determiner (<NUM>) and a boundary condition adapter (<NUM>), the boundary condition determiner determines boundary conditions from a coronary tree segmented from the input cardiac image data, and the boundary condition adapter adapts the boundary conditions based on the determined myocardial infarction characteristics.