Patent Description:
The local activation time (LAT) at any portion of cardiac tissue is the difference between (i) the time at which the tissue becomes electrically activated during any cardiac cycle, and (ii) a reference time during the same cycle. The reference time may be set, for example, to a point in the QRS complex of a body-surface electrocardiogram (ECG) or a coronary-sinus electrogram signal.

<CIT>, describes a method including receiving an input mesh representation of a cardiac chamber, a set of measured locations on a wall tissue of the cardiac chamber, and a respective set of local activation times (LATs) measured at the locations. The input mesh is re-meshed into a regular mesh including regularized polygons. The set of measured locations and respective LATs is data-fitted to the regularized polygons. Respective LAT values, and respective probabilities that the wall tissue includes scar tissue, are iteratively calculated for the regularized polygons, so as to obtain an electrophysiological (EP) activation wave over the regular mesh that indicates scar tissue. An electroanatomical map overlaid on the regular mesh, the map including the EP activation wave and the scar tissue, is presented.

<CIT> describes a method and system for patient-specific planning and guidance of electrophysiological interventions. A patient-specific anatomical heart model is generated from cardiac image data of a patient. A patient-specific cardiac electrophysiology model is generated based on the patient-specific anatomical heart model and patient-specific electrophysiology measurements. Virtual electrophysiological interventions are performed using the patient-specific cardiac electrophysiology model. A simulated electrocardiogram (ECG) signal is calculated in response to each virtual electrophysiological intervention.

<CIT> describes a method for guiding electrophysiology (EP) intervention using a patient-specific electrophysiology model, which includes acquiring a medical image of a patient subject. Sparse EP signals are acquired over an anatomy using the medical image for guidance. The sparse EP signals are interpolated using a patient specific computational electrophysiology model and a three-dimensional model of EP dynamics is generated therefrom. A rendering of the three-dimensional model is displayed. Candidate intervention sites are received, effects on the EP dynamics resulting from intervention at the candidate intervention sites is simulated using the model, and a rendering of the model showing the simulated effects is displayed.

Atienza, Felipe Alonso, et al. , "A probabilistic model of cardiac electrical activity based on a cellular automata system," Revista Española de Cardiologia (English Edition) <NUM> (<NUM>): <NUM>-<NUM> describes a computer model of cardiac electrical activity able to simulate complex electrophysiological phenomena.

<NPL> describes techniques for cellular automata simulations on triangulated grids.

Sahli Costabal, Francisco, et al. , "Physics-informed neural networks for cardiac activation mapping," Frontiers in Physics <NUM> (<NUM>): <NUM> proposes a physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics, and quantifies the epistemic uncertainty associated with these predictions. The reference further illustrates the potential of this approach using a synthetic benchmark problem and a personalized electrophysiology model of the left atrium. A further prior art method of simulating activation maps is known from <CIT>.

There is provided, in accordance with the invention a system as defined in claim <NUM>.

In some embodiments, the processor is further configured to compute respective forecasted conduction velocities at the locations based on the forecasted LATs, and the processor is configured to generate the forecasted electroanatomical map based on the forecasted conduction velocities.

The processor is configured to simulate the propagation of the activation potential by evolving a cellular automata model, which represents the cardiac tissue, over multiple iterations, and the processor is configured to compute the forecasted LATs in response to identifying respective ones of the iterations during which cells of the model are first active.

In some embodiments, the processor is further configured to define the cells by partitioning a triangular mesh representing the cardiac tissue.

In some embodiments, the cells have respective conduction velocities, and the processor is configured to evolve the model by, during each one of the iterations, for at least one first cell of those of the cells that are inactive:.

In some embodiments, the processor is further configured to:.

In some embodiments, the processor is configured to evolve the model by, during each one of the iterations, inactivating any one of the cells that was active for a predefined number of the iterations.

In some embodiments, the predefined number is a first predefined number, and the processor is configured to evolve the model by, during each one of the iterations, refraining from activating any one of the cells that was last active within a second predefined number of the iterations.

There is further provided, in accordance with the present invention, a method as defined in claim <NUM>.

There is further provided, in accordance with the present invention, a computer software product including a tangible non-transitory computer-readable medium in which program instructions are stored, as defined in claim <NUM>.

Embodiments of the present invention facilitate the planning of an ablation of cardiac tissue of a subject by forecasting the LATs that will result from the ablation.

First, the physician is shown an electroanatomical map of the tissue. The physician marks, on the map, the portion of the tissue that is planned for ablation. Typically, the physician also indicates, with reference to the map, the anatomical location from which an activation wave originates during each cardiac cycle.

Subsequently, based on the aforementioned input from the physician, a computer processor simulates the propagation of the activation wave along the cardiac tissue following the planned ablation. Based on the simulation, the processor computes the forecasted LATs. Finally, the processor displays a forecasted electroanatomical map that is colored and/or otherwise annotated so as to indicate the forecasted LATs. Based on the forecasted map, the physician may ascertain whether the planned ablation will be effective. For example, if the subject suffers from a stable tachycardia, the physician may ascertain whether the planned ablation will interrupt the circuit that generates the tachycardia.

The processor simulates the propagation of the activation wave by evolving a cellular automata model, which represents the cardiac tissue, over multiple iterations. For each cellular automaton (hereinafter "cell") of the model that is active at least once during the simulation, the processor identifies the iteration during which the cell was first active, and computes the forecasted LAT for the cell based on the number of the identified iteration. Thus, for example, a cell activated during an earlier iteration will have a lower forecasted LAT than another cell activated during a later iteration.

The processor evolves the model based on the respective conduction velocities of the cells, which are derived from the original electroanatomical map. Thus, for example, a cell may be activated by a neighboring cell that was active during the immediately-preceding iteration, provided that the neighboring cell has a sufficiently large conduction velocity. On the other hand, if the neighboring cell has a smaller conduction velocity, the cell may be activated by the neighboring cell only if the neighboring cell was active during an earlier iteration.

Typically, the processor also computes forecasted conduction velocities based on the forecasted LATs, and the forecasted electroanatomical map is annotated so as to indicate the forecasted conduction velocities.

Reference is initially made to <FIG>, which is a schematic illustration of a system <NUM> for planning an ablation of cardiac tissue, in accordance with some embodiments of the present invention.

System <NUM> comprises a probe <NUM>, configured for insertion into the body of a subject <NUM> by a physician <NUM>. System <NUM> further comprises a console <NUM>, comprising user-interface controls <NUM> for facilitating interaction of physician <NUM> with system <NUM>.

System <NUM> further comprises circuitry <NUM>, which is typically contained within console <NUM>. Circuitry <NUM> comprises a processor <NUM> and a memory <NUM>, which may comprise any suitable volatile memory and/or non-volatile memory. Typically, circuitry <NUM> further comprises a noise-removal filter and an analog-to-digital (A/D) converter.

The distal end of probe <NUM> comprises one or more electrodes <NUM>. Subsequently to inserting probe <NUM> into the body of subject <NUM>, physician <NUM> navigates the probe to the heart <NUM> of the subject. Subsequently, physician <NUM> uses electrodes <NUM> to measure electrogram signals from tissue of heart <NUM>. Processor <NUM> receives the electrogram signals, typically via the aforementioned noise-removal filter and A/D converter.

System <NUM> further comprises a tracking subsystem, which, for ease of illustration, is omitted from <FIG>. The tracking subsystem is configured to facilitate tracking the respective locations of electrodes <NUM> within heart <NUM>, such that processor <NUM> may associate each received electrogram signal with the location on the cardiac tissue at which the signal was acquired. In some embodiments, processor <NUM> tracks the electrode locations by executing a tracker module <NUM>, which interacts with the tracking subsystem.

In some embodiments, the tracking subsystem comprises one or more electromagnetic sensors at the distal end of probe <NUM>, along with one or more magnetic-field generators configured to generate a magnetic field. The magnetic field induces tracking signals in the electromagnetic sensors. Based on the tracking signals, processor <NUM> (e.g., tracker module <NUM>) ascertains the locations of the sensors, and hence, of the electrodes. Such location-tracking techniques are disclosed, for example, in <CIT>, <CIT>, and <CIT>, in <CIT>, in <CIT> et al. , and in <CIT>, whose respective disclosures are incorporated herein by reference.

Alternatively or additionally, the tracking subsystem may comprise one or more reference electrodes electrically coupled to the body of the subject. Electric currents may be passed between electrodes <NUM> and the reference electrodes. Based on the resulting current or voltage distribution, processor <NUM> (e.g., tracker module <NUM>) may ascertain the locations of electrodes <NUM>. Such techniques may utilize a location map calibrated, in advance, using electromagnetic sensors, as described, for example, in <CIT> and <CIT>.

Alternatively or additionally, electric currents may be passed between the reference electrodes. Based on the resulting voltages at electrodes <NUM>, processor <NUM> (e.g., tracker module <NUM>) may ascertain the locations of electrodes <NUM>, as described, for example, in <CIT>and <CIT>.

System <NUM> further comprises one or more ECG electrodes <NUM>, which are electrically coupled to the skin of subject <NUM>. Processor <NUM> receives the ECG signals acquired by electrodes <NUM>, typically via the aforementioned noise-removal filter and A/D converter.

Based on the electrogram and ECG signals, processor <NUM> generates an electroanatomical map <NUM>, which combines a representation of the anatomy of the tissue with electrical properties of the tissue. For example, a three-dimensional triangular mesh representing the tissue may be colored in accordance with a color scale so as to indicate, for each element of the mesh, an LAT that was computed for the portion of tissue represented by the element. Alternatively or additionally, vectors representing conduction velocities may be overlaid on the mesh.

In some embodiments, the processor generates map <NUM> by executing a map-generating module <NUM>. In generating map <NUM>, processor <NUM> accounts for the "reentries" observed in cyclic arrhythmias, as described in <CIT>.

Subsequently to generating map <NUM>, processor <NUM> displays the map on a display <NUM> and, typically, stores the map in memory <NUM>. Subsequently, physician <NUM> (or another user) provides input relating to a planned ablation of the tissue. The input may be provided, for example, using a touch screen belonging to display <NUM> or using any suitable user-interface control <NUM>, such as a mouse or trackball. The input may include, for example, a marking of the portion of the tissue that the physician plans to ablate.

Subsequently, based on the input, the processor computes multiple forecasted LATs at different respective locations on the tissue, by simulating a propagation of a physiological activation potential (or "activation wave") along the tissue following the planned ablation. In other words, the processor simulates the propagation with the particular portion of tissue that the physician plans to ablate, which is currently conductive, being non-conductive. In some embodiments, the processor performs the simulation by executing a simulation module <NUM>.

Subsequently, based on the forecasted LATs, the processor (e.g., map-generating module <NUM>) generates a forecasted electroanatomical map representing the forecasted state of the tissue following the ablation. The forecasted electroanatomical map is then displayed on display <NUM> and, typically, stored in memory <NUM>. Based on the forecasted map, the physician may decide whether to proceed with the planned ablation.

Typically, system <NUM> further comprises an ablation-signal generator (not shown). Subsequently to deciding to proceed with the planned ablation, physician <NUM> may control the ablation-signal generator, using user-interface controls <NUM>, so as to deliver ablating signals to electrodes <NUM>.

In general, processor <NUM> may be embodied as a single processor, or as a cooperatively networked or clustered set of processors. The functionality of processor <NUM> may be implemented solely in hardware, e.g., using one or more fixed-function or general-purpose integrated circuits, Application-Specific Integrated Circuits (ASICs), and/or Field-Programmable Gate Arrays (FPGAs). Alternatively, this functionality may be implemented at least partly in software. For example, processor <NUM> may be embodied as a programmed processor comprising, for example, a central processing unit (CPU) and/or a Graphics Processing Unit (GPU). Program code, including software programs, and/or data may be loaded for execution and processing by the CPU and/or GPU. The program code and/or data may be downloaded to the processor in electronic form, over a network, for example. Alternatively or additionally, the program code and/or data may be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory. Such program code and/or data, when provided to the processor, produce a machine or special-purpose computer, configured to perform the tasks described herein.

Reference is now made to <FIG>, which is a flow diagram for an algorithm <NUM> for generating and displaying a forecasted electroanatomical map, in accordance with some embodiments of the present invention. Algorithm <NUM> may be executed by processor <NUM> (<FIG>).

Typically, algorithm <NUM> begins with a first displaying step <NUM>, at which the processor displays electroanatomical map <NUM> (<FIG>), which represents the current (pre-ablation) state of the cardiac tissue.

Typically, while map <NUM> is displayed, the physician marks, on the map, the portion of tissue planned for ablation, this marking typically having the form of a line referred to herein as an "ablation line. " This marking is received by the processor at a marking-receiving step <NUM>. Alternatively or additionally, the physician may indicate the portion of the map representing the anatomical location from which the activation wave originates, such as a reentry location. For example, the physician may click a mouse on the portion of the map. This indication is received by the processor at an indication-receiving step <NUM>.

Next, at a simulating step <NUM>, the processor computes forecasted LATs at different respective locations on the tissue, by simulating a propagation of an activation wave along the tissue.

In some embodiments, as further described below with reference to <FIG>, the processor simulates the propagation by evolving a cellular automata model, which represents the cardiac tissue, over multiple iterations. Each cell in the model may either be active (i.e., "on"), which simulates the anatomical state of depolarization, or inactive (i.e., "off"), which simulates the polarized (or "resting") state. For each cell that is active at any point during the simulation, the processor identifies the iteration during which the cell was first active, and computes the forecasted LAT for the cell in response thereto.

In other embodiments, the processor simulates the propagation by solving a system of diffusion equations, e.g., using finite elements. Alternatively, the processor may use the Fast Marching method, e.g., as described in <NPL>.

As yet another option, the processor may use a neural network simulator as described, for example, in <NPL>.

Typically, the processor then performs a computing step <NUM>, at which the processor computes respective forecasted conduction velocities at the locations based on the forecasted LATs.

Subsequently, at a generating step <NUM>, the processor generates a forecasted electroanatomical map based on the forecasted LATs and, optionally, the forecasted conduction velocities. For example, the processor may color the forecasted electroanatomical map so as to indicate the forecasted LATs, and/or overlay vectors representing the conduction velocities, as described for map <NUM> with reference to <FIG>. Finally, at a second displaying step <NUM>, the processor displays the forecasted map.

Reference is now made to <FIG>, which is a schematic illustration of a portion of a partitioned triangular mesh <NUM>, in accordance with some embodiments of the present invention. Reference is further made to <FIG>, which is a flow diagram for an embodiment of simulating step <NUM> of algorithm <NUM> (<FIG>) in which a cellular automata model is evolved, in accordance with some embodiments of the present invention.

Mesh <NUM>, which belongs to map <NUM> (<FIG>), represents the cardiac tissue. A solid border <NUM> in <FIG> delineates two triangles <NUM> in mesh <NUM>.

To increase the accuracy of the forecasted LATs, it may be advantageous for cells <NUM> of the cellular automata model to be smaller than triangles <NUM>. Hence, in some embodiments, simulating step <NUM> begins with a partitioning step <NUM>, at which the processor defines cells <NUM> by partitioning mesh <NUM>. For example, as indicated by dashed lines <NUM> in <FIG>, the processor may partition each triangle <NUM> into four triangular cells <NUM>. It is noted that the sizes of triangles <NUM> may differ from each other, and hence, the sizes of cells <NUM> may also differ from each other.

At an initializing step <NUM>, the processor initializes the model by setting one or more cells <NUM> to the active state, and all the remaining cells to the inactive state. The cells set to active are those corresponding to the portion of map <NUM> representing the anatomical location from which the activation wave originates, which, as described above with reference to <FIG>, may be indicated by the physician.

Following the initialization, the processor iteratively evolves the model. During each iteration, each cell <NUM> that is conductive (i.e., that represents conductive tissue) is selected at a selecting step <NUM>. Subsequently to selecting the cell, the processor checks, at a checking step <NUM>, whether the cell is active. If not, the processor checks, at another checking step <NUM>, whether the cell is in a refractory period, i.e., whether the cell was previously active within a predefined number of iterations. If yes, the processor refrains from activating the cell, and instead proceeds to another checking step <NUM>, described below. Otherwise, the processor checks, at another checking step <NUM>, whether the cell has at least one activator, as further described below. If not, the processor proceeds to checking step <NUM>. Otherwise, the cell is flagged for activation at a flagging step <NUM>.

Subsequently to performing flagging step <NUM>, the processor checks, at another checking step <NUM>, whether the cell was activated before. If not, the processor, at a recording step <NUM>, records the current iteration number for the cell. Subsequently, or if the cell was activated before, the processor proceeds to checking step <NUM>.

On the other hand, if, at checking step <NUM>, the processor ascertains that the cell is active, the processor checks, at another checking step <NUM>, whether the cell was active for a predefined number of iterations. If yes, the cell is flagged for deactivation at another flagging step <NUM>. Subsequently, or if the cell was not active for the predefined number of iterations, the processor performs checking step <NUM>.

At checking step <NUM>, the processor checks whether any more cells remain to be selected during the current iteration. If yes, the processor returns to selecting step <NUM> and selects the next cell. Otherwise, the processor, at an evolving step <NUM>, evolves the model by changing the state of the flagged cells. In other words, the processor activates the cells flagged for activation and deactivates the cells flagged for deactivation. Subsequently, the processor checks, at another checking step <NUM>, whether all of the conductive cells were activated during the simulation. If not, the processor returns to selecting step <NUM>, and performs another iteration of the evolution.

Upon ascertaining, at checking step <NUM>, that all of the conductive cells were activated, the processor, at a computing step <NUM>, computes forecasted LATs from the recorded iteration numbers. For example, given a recorded iteration number n (where n = <NUM> indicates the first iteration), the processor may compute the forecasted LAT as n*T/N, where T is the cycle length (as calculated during the generation of the current electroanatomical map) and N is the total number of performed iterations.

The cells have respective conduction velocities, which are based on the conduction velocities computed during the generation of the current electroanatomical map. For example, each cell may have the conduction velocity of the triangle <NUM> from which the cell was partitioned. (Cells representing nonconductive tissue may be assigned a conduction velocity of zero. ) The processor uses the conduction velocities when performing checking step <NUM>. For example, the processor may identify another cell as an activator for the selected cell in response to (i) the conduction velocity of the other cell, (ii) the distance of the other cell from the selected cell, and (iii) the number of iterations that passed since the other cell was last active.

For example, supposing the ith cell is selected, the processor may first identify the set of cells that neighbor the ith cell, i.e., that share at least one edge with the ith cell. The processor may then iterate through the set, checking each cell in the set until an activator is found or the set is exhausted. In particular, for each jth cell that is checked, the processor may perform the following sequence of steps:.

As noted above with reference to computing step <NUM> of algorithm <NUM> (<FIG>), the processor typically computes forecasted conduction velocities based on the forecasted LATs. For example, the processor may calculate a forecasted conduction velocity <MAT> for the ith cell as follows:.

Reference is now made to <FIG>, which is a schematic illustration of an example electroanatomical map <NUM> and a corresponding forecasted electroanatomical map <NUM>', in accordance with some embodiments of the present invention.

Typically, map <NUM> is colored per a color scale so as to indicate the computed LATs, as described above with reference to <FIG>. In such embodiments, a particular color that does not belong to the color scale may indicate slow-conducting (including non-conducting) tissue. To illustrate this, <FIG> "colors" map <NUM> using multiple brightness levels, with shades of gray in a region <NUM> of the map indicating slow-conducting tissue. Outside of region <NUM>, vectors <NUM> indicate conduction velocities.

In response to viewing map <NUM>, the physician may mark an ablation line <NUM> passing through region <NUM>. The physician may further indicate a reentry location. Subsequently, the processor may compute forecasted LATs and conduction velocities, as described above with reference to the previous figures. Based on these forecasted values, the processor may generate and display forecasted map <NUM>'.

In some embodiments, the forecasted map includes the finer mesh computed at partitioning step <NUM> (<FIG>), as shown in <FIG>. In other embodiments, the forecasted map includes the original triangular mesh of map <NUM>, the forecasted LATs and conduction velocities being projected onto this mesh.

Claim 1:
A system (<NUM>)
for ablation planning, the system comprising:
a display (<NUM>); and
a processor (<NUM>) configured to:
compute multiple forecasted local activation times (LATs) at different respective locations on cardiac tissue of a subject, by simulating a propagation of a physiological activation potential along the cardiac tissue with a particular portion of the cardiac tissue, which is currently conductive, being non-conductive,
based on the forecasted LATs, generate a forecasted electroanatomical map representing a forecasted state of the cardiac tissue following an ablation of the particular portion of the cardiac tissue, and
display the forecasted electroanatomical map on the display,
wherein the processor is configured to simulate the propagation of the activation potential by evolving a cellular automata model, which represents the cardiac tissue, over multiple iterations,
wherein cells of the cellular automata model have conduction velocities based on conduction velocities computed during generation of a current electroanatomical map, and
wherein the processor is configured to compute the forecasted LATs in response to identifying respective ones of the iterations during which cells of the model are first active.