Estimation and visualization of electro-anatomical (EA) analysis quality of EA maps

Abstract

The name of the invention is' Estimation and Visualization of Electroanatomical (EA) Analysis Quality of EA Mapping '. A method includes receiving an electroanatomical (EA) map of at least a portion of a cardiac cavity. The quality of the EA analysis of the EA map is scored. Based on the score, one or more regions in the mapped portion are identified as failing to meet the defined quality criteria. The one or more regions are indicated on an EA map to be displayed to a user.

Description

Technical Field

[0001] The present disclosure relates generally to analysis of electroanatomical (EA) maps, and in particular to systems and methods for visualizing the quality of analysis in EA maps. Background Art

[0002] EA mapping visualization methods have been previously considered in the patent literature. For example, U.S. Patent No. 11,478,182, filed on January 7, 2021, describes a method comprising receiving (i) a modeled surface of at least a portion of a heart and (ii) a plurality of EA values ​​measured at a plurality of corresponding locations in the heart. A plurality of regions are defined on the modeled surface, and for each region, a confidence level of an EA value whose position falls within the region is estimated. The modeled surface is presented to a user, including (i) EA values ​​superimposed on the modeled surface, and (ii) a graphically visualized confidence level in each region of the modeled surface.

[0003] The present disclosure will be more fully understood through the following detailed description of examples of the present disclosure in conjunction with the accompanying drawings, in which: BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 is a schematic illustration of a catheter-based electroanatomical (EA) mapping and ablation system according to an example of the present disclosure.

[0005] Figure 2A is an EA map of the left atrium according to an example of the present disclosure;

[0006] Figure 2B is used to indicate an example according to the present disclosure Figure 2A A mask for areas of the EA map where the EA analysis quality is insufficient;

[0007] Figure 2C is an example according to the present disclosure by Figure 2B The mask covers Figure 2A a combined EA map generated on the EA map of

[0008] Figure 3 is a flow chart schematically illustrating a method of estimating and visualizing EA analysis quality of an EA map according to an example of the present disclosure. DETAILED DESCRIPTION

[0009] Overview

[0010] Electroanatomical (EA) mapping of cardiac chambers of patients with arrhythmias requires the acquisition of a sufficient number of data points that adequately capture each chamber wall location and the corresponding electrical information. The physician performing cardiac EA mapping aims to achieve an optimal diagnostic quality EA map of at least a portion of the cardiac chamber. The quality of the EA map depends on, for example, the stability of the arrhythmia and the ability to cover a sufficient portion of the chamber. EA quality is reflected in the output of a dedicated algorithm that analyzes EA characteristics based on the acquired set of data points.

[0011] Examples of analyses performed by such EA algorithms include calculation of the local conduction vector (LCV) based on the strongest detected trend of the local activation time (LAT), coherence mapping analysis, and contact proximity index (TPI). These calculations will result in outputs after spatial interpolation. Other examples include The distance of the point / "visitor" from the surface, and the density of the data points which if inaccurate / insufficient may lead to an inaccurate Feng Shui dissection.

[0012] As discovered by the inventors, the EP quality of the above-mentioned EA analysis algorithms may be insufficient or lacking in some areas of the EA map without the user being fully aware of this fact. For example, in the LCV algorithm, if the algorithm cannot detect a trend in the LAT values, it will not present a vector on the map. The lack of a trend may be due to insufficient data points at the specified area or insufficient intramural signals that may present similar LAT values. Another example is coherent mapping, where excessive interpolation may be applied due to a lack of EA data points. In addition, there is no general method for estimating the overall EA quality of a map. Specifically, there is no indication of the EA quality of the output of the EA algorithm used in analyzing and visualizing the EA map therein for each map area that can easily reflect the quality of the EA.

[0013] Examples of the present disclosure described herein provide a technique that includes an automatic algorithm that provides an overlay map that scores the EA quality of the output of one or more EA algorithms used to construct the EA map at each location on the EA map. The disclosed technique can guide physicians to avoid misinterpretation of EA maps. Based on instructions from the disclosed method, physicians can attempt to improve inadequate EA analysis by re-mapping or by correcting the analysis of specific cardiac chamber regions, rather than accepting the EA map as is.

[0014] When using the LCV algorithm, regions of the map may receive low scores due to, for example, areas where conduction vectors are intentionally omitted by the algorithm due to the fact that there is no mutual trend in LAT. Since the nature of conduction through diseased tissue does not exhibit a strong LAT trend, but rather exhibits circular multidirectional propagation, a physician who is unaware of this intentional omission may miss clinically important subregions. By masking regions in the map with low EA algorithm output quality, the disclosed technique warns the physician against making such mistakes.

[0015] Another example of a map area with a low quality score is a region in a coherent EA map that has been over-interpolated. A physician who is unaware of the over-interpolation trusts the EA map without suspecting that the actual data in that region is sparse. Again, by masking regions in the map with low EA quality, the disclosed technique warns the physician not to make such a mistake.

[0016] In one example, a method is provided, the method comprising receiving an EA mapping portion of a cardiac chamber. The portion includes EA characteristics of the cardiac chamber analyzed using one or more EA analysis algorithms. A processor runs an algorithm that scores the quality of the EA analysis output on the EA mapping portion. In one example, the EA quality score can be normalized to a range between 0 and 1, and based on the score, the processor estimates whether the EA quality in one or more regions of the EA mapping portion is insufficient (e.g., the EA quality fails to meet a defined quality standard). The processor marks any EA found to be insufficient in one or more regions on the EA mapping map. The indicated mapping map is displayed to the user.

[0017] The processor may indicate such insufficient EA analysis map areas by constructing a grayscale map of locations that are only above a high threshold or below a low threshold. The processor may blend (e.g., combine) the grayscale information with the current EA map coloring. The blending may be reflected in several visualizations, such as applying a shading (e.g., a lower level of transparency) to areas with a quality score below a given threshold. In another blending example, the processor overlays the EA map with a layer of graphical symbols that vary in format according to the quality score.

[0018] The inputs for the scoring algorithm depend on the clinical question. Different clinical scenarios (e.g., atrial versus ventricular arrhythmias) use different EA algorithms. The disclosed technology estimates the algorithm output quality in order to score the relevant EA map. In some examples, the EA algorithm is pre-selected, and the map is received and estimated as is. In other examples, the user can select or deselect all or part of the above EA analysis algorithms (or others) via a graphical user interface (GUI). The selection can be available, for example, as a checkbox in the GUI.

[0019] In the examples detailed below, the quality score is a weighted sum of the quality scores from the outputs of two or more EA analysis algorithms (eg, bipolar amplitude and TPI value).

[0020] In another example, to indicate deficiencies in EA analysis in one or more regions on an EA map, the disclosed techniques may apply the following steps: filtering outlier data points (based on the scores); projecting the data points onto the anatomical map surface; and then applying Laplace interpolation to the quality scores.

[0021] System Description

[0022] Figure 1 is a schematic illustration of a catheter-based electroanatomical (EA) mapping and ablation system 10 according to an example of the present disclosure.

[0023] The system 10 includes a plurality of catheters that are inserted by a physician 24 through the patient's vascular system percutaneously into a chamber or vascular structure of the heart 12 (see illustration 45). Typically, a delivery sheath catheter is inserted into a cardiac chamber (such as the left atrium or right atrium) near a desired location in the heart 12. Multiple catheters may then be inserted into the delivery sheath catheter to reach the desired location. The multiple catheters may include a catheter dedicated to pacing, a catheter for sensing intracardiac electrogram signals, a catheter dedicated to ablation, and / or a catheter dedicated to both EA mapping and ablation. The example catheter 14 shown herein is configured to sense bipolar electrograms. The physician 24 brings the distal end 28 of the catheter 14 (hereinafter also referred to as the distal end assembly 28) into contact with the heart wall for sensing a target site in the heart 12. For ablation, the physician 24 similarly brings the distal end of the ablation catheter to the target site.

[0024] As shown in inset 65, the catheter 14 is an example catheter including a basket-shaped distal end 28, which includes one and preferably multiple electrodes 26, which are optionally distributed over multiple splines 22 at the distal end 28 and are configured to sense IEGM signals. The catheter 14 may additionally include a position sensor 29 embedded in or near the distal end 28 on the shaft 46 of the catheter 14 for tracking the position and orientation of the distal end 28. Optionally and preferably, the position sensor 29 is a magnetic-based position sensor that includes three magnetic coils for sensing three-dimensional (3D) position and orientation. As shown, the distal end 28 also includes an expansion / collapse rod 42 of the expandable component 28, which is mechanically connected to the basket component 28 at the distal edge 41 of the component 28.

[0025] The magnetic-based position sensor 29 can operate with the positioning pad 25, which includes a plurality of magnetic coils 32 configured to generate a magnetic field in a predefined working space. The real-time position of the distal tip 28 of the catheter 14 can be tracked based on the magnetic field generated by the positioning pad 25 and sensed by the magnetic-based position sensor 29. Details of magnetic-based position sensing technology are described in U.S. Patents 5,5391,199, 5,443,489, 5,558,091, 6,172,499, 6,239,724, 6,332,089, 6,484,118, 6,618,612, 6,690,963, 6,788,967 and 6,892,091.

[0026] The system 10 includes one or more electrode patches 38 positioned in contact with the skin of the patient 23 to establish a position reference for impedance-based tracking of the positioning pad 25 and the electrodes 26. For impedance-based tracking, current is directed toward the electrodes 26 and sensed at the electrode skin patches 38 so that the position of each electrode can be triangulated via the electrode patches 38. Details of impedance-based position tracking techniques are described in U.S. Patents 7,536,218, 7,756,576, 7,848,787, 7,869,865, and 8,456,182.

[0027] Recorder 11 displays cardiac signals 21 acquired using surface ECG electrodes 18 (e.g., electrograms acquired at respectively tracked cardiac tissue locations) and intracardiac electrograms acquired using electrodes 26 of catheter 14. Recorder 11 may include pacing capabilities for pacing cardiac rhythms and / or may be electrically connected to a separate pacemaker.

[0028] The system 10 may include an ablation energy generator 50 adapted to conduct ablation energy to one or more electrodes at the distal end of a catheter configured for ablation. The energy generated by the ablation energy generator 50 may include, but is not limited to, radio frequency (RF) energy or pulsed field ablation (PFA) energy (including monopolar or bipolar high voltage DC pulses to be used to achieve irreversible electroporation (IRE)), or a combination thereof.

[0029] The patient interface unit (PIU) 30 is an interface configured to establish electrical communication between the catheter, electrophysiology equipment, power supply, and workstation 55 to control the operation of the system 10 and receive EA signals from the catheter. The electrophysiology equipment of the system 10 may include, for example, multiple catheters, positioning pads 25, surface ECG electrodes 18, electrode patches 38, ablation energy generators 50, and recorders 11. Optionally and preferably, the PIU 30 further includes processing capabilities for achieving real-time calculation of the catheter position and for performing ECG calculations. The interface 30 may also be configured to receive files (e.g., files of EA mapping images) using, for example, a communication line or a storage stick.

[0030] The workstation 55 includes a memory 57, a processor 56 unit with a memory or storage device having appropriate operating software loaded therein, and user interface capabilities. The workstation 55 may provide multiple functions, optionally including: (i) three-dimensional (3D) modeling of the endocardial anatomy and rendering the model or anatomical map 20 for display on the display device 27; (ii) displaying on the display device 27 an activation sequence (or other data) compiled from the recorded cardiac signals 21 as representative visual markers or images superimposed on the rendered anatomical map 20; (iii) displaying the real-time position and orientation of multiple catheters within the heart chamber; and (iv) displaying on the display device 27 a site of interest (such as where ablation energy has been applied). A commercial product embodying elements of the system 10 may be CARTO TM 3System was purchased from Biosense Webster, Inc., 31 A Technology Drive, Irvine, CA 92618.

[0031] In the disclosed example, the processor 56 runs an algorithm that provides an overlay map (e.g., including a masking layer superimposed on the anatomical map 20) that represents a score of the EA quality of the EA map at each location on the EA map. The EA analysis algorithms may be pre-selected, or the physician 24 may select / deselect at least a portion of the EA analysis algorithms listed above for use in the EA map analysis, e.g., via the GUI 111.

[0032] In some examples, processor 56 typically includes a general purpose computer that is programmed in software to perform the functions described herein. The software may be downloaded to the computer in electronic form over a network, for example, or it may alternatively or additionally be provided and / or stored on a non-transitory tangible medium such as magnetic, optical, or electronic memory.

[0033] This configuration of system 10 is shown by way of example in order to illustrate certain problems solved by the examples of the present disclosure and to demonstrate the application of these examples in enhancing the performance of such systems. However, the examples of the present disclosure are by no means limited to this particular class of exemplary systems, and the principles described herein may be similarly applied to other types of medical systems. For example, other multipolar catheter types may be used, such as OCTARAY TM catheter or flat catheter.

[0034] Visualization of EA analysis quality of EA maps of cardiac chambers

[0035] FIG. 2A to FIG. 2C as follows: Figure 2A It is the EA map of the left atrium; Figure 2B is a mask indicating areas of the EA map where the quality of the EA analysis is insufficient; Figure 2C is an example according to the present disclosure by Figure 2B The mask covers Figure 2A A combined EA map is generated on the EA map of the image.

[0036] EA Map 200 (see Figure 2A 200 ) includes an anatomical surface and corresponding electrical values ​​(e.g., activation amplitude and / or time) at each mapping location on the surface. Interpolation is used for EA values ​​in the EA map to provide an EA map in a continuous format. However, in some areas of the EA map 200 , the interpolation may have low EA analysis quality, such as in area 204.

[0037] The disclosed algorithm scores the EA analysis quality and uses a mask 201 (see Figure 2B The scores are provided in the form of (in). The occlusion mask 201 includes quality scores for at least some regions of the EA map 200, which are normalized in the range [0,1].

[0038] like Figure 2B As shown in , the occlusion mask 201 includes dark areas 205 where the score is low. The dark areas indicate to the user that the quality of the EA analysis in these areas is insufficient and therefore unreliable.

[0039] Combined EA map 202 (see Figure 2C ) shows the low quality EA analysis as a grey area 206. Based on this indication, the user can, for example, re-map the area 206 by EA.

[0040] More generally, blending the information from the quality mask with the EA map 200 coloring can be reflected by several visualizations:

[0041] 1. As a transparent layer, where transparency and grayscale color are also affected by the score.

[0042] 2. Bump mapping, where bumps are influenced by a score (eg, a high score indicates a small bump, a low score indicates a larger bump).

[0043] In an example implementation, a processor receives a region of interest (ROI) on an anatomical map.

[0044] The quality is calculated by the EA analysis algorithm based on the following criteria:

[0045] LCV incoherence (a possible indication of a graded signal)

[0046] ●The distance of the data point label from the surface (geometric anatomy inaccuracy)

[0047] ●Contact level TPI

[0048] The density of data points

[0049] ● Over-interpolated coherence regions

[0050] All or part of the above algorithm (or others) can be controlled by the user via Figure 1 The GUI 111 can be used to pre-select or select / deselect.

[0051] Finally, see Figure 2B The scoring visualization in the example is the EA analysis quality of atrial fibrillation (AFib) maps, but, in general, the scoring is problem-dependent. Different clinical scenarios (e.g., atrial vs. ventricular arrhythmias) use different EA analysis algorithms because the disclosed techniques use their outputs to score relevant EA maps.

[0052] Scoring of the quality of EA analysis of EA maps of cardiac chambers

[0053] This article provides an example of the disclosed method for calculating EA quality score. Assuming the feature F of the selected corresponding EA analysis algorithm (e.g., bipolar amplitude and TPI) i :

[0054] ●Each F i With m i <F i <M i Range

[0055] ● At every position p on the map there exists every feature F i The value of (p)

[0056] Then the EA quality score can be given by weighted sum

[0057]

[0058] in is any set of generating basis functions.

[0059] Method for estimating and visualizing the quality of EA analysis of EA maps of cardiac chambers

[0060] Figure 3 1 is a flow chart schematically illustrating a method of estimating and visualizing the quality of an EA map 200 according to an example of the present disclosure. According to the example presented, the algorithm performs a process starting at an EA map receiving step 302, where the processor 56 receives an EA map for EA analysis using an EA analysis algorithm (such as the aforementioned LCV). Note that the physician 24 can adjust the EA analysis parameters or algorithms used via the GUI 111.

[0061] Next, at a scoring step 304, the processor runs a disclosed algorithm that gives an EA quality score to the EA analysis at the EA map.

[0062] Based on the scores, at region estimation step 306 , the processor estimates one or more regions of the EA map (eg, of the EA map 200 ) as having too low (eg, insufficient) EA analysis quality.

[0063] At a region indication step 308 , the processor indicates (eg, marks) one or more regions (eg, areas) of the EA map as having insufficient EA analysis quality (eg, failing to satisfy defined quality criteria for EA quality).

[0064] Finally, at an EA quality indication map presentation step 310 , the processor 56 presents an EA quality indication EA map (eg, the EA map 202 ) to the user.

[0065] Figure 3 The flowchart shown is chosen purely for conceptual clarity.Other examples of the technique may include different algorithms and / or visualization steps, such as using flashing to highlight areas scored by the disclosed technique as having insufficient EA analysis quality.

[0066] Example

[0067] Example 1

[0068] A method includes receiving an electroanatomical (EA) map (200) of at least a portion of a cardiac chamber. Scoring the quality of an EA analysis of the EA map. Based on the scoring, identifying one or more regions (204) in the mapped portion as failing to meet defined quality criteria. Indicating (206) the one or more regions on the EA map (202) to be displayed to a user.

[0069] Example 2

[0070] A method according to embodiment 1, wherein the EA analysis is performed by at least one algorithm selected from a list of algorithms consisting of (i) local conduction vector (LCV), (ii) coherent EA (CEA) propagation, (iii) contact indication index (TPI), (iv) late activation time (LAT) and (v) grading score.

[0071] Example 3

[0072] The method of any one of embodiments 1 and 2, wherein scoring the quality comprises calculating a weighted sum of quality scores of outputs of two or more algorithms from the list.

[0073] Example 4

[0074] The method according to any one of embodiments 1 to 3, wherein indicating (206) the one or more regions on the EA map comprises overlaying the EA map with a transparent layer based on the score.

[0075] Example 5

[0076] A method according to any one of embodiments 1 to 4, wherein indicating (206) the one or more regions on the EA map comprises overlaying the EA map with a layer of graphical symbols, the graphical symbols varying in format according to the score.

[0077] Example 6

[0078] A method according to any one of embodiments 1 to 5, wherein indicating (206) the one or more regions (204) on the EA map comprises: (i) filtering outliers based on the scores; (ii) projecting the scores onto the surface of the map; and (iii) applying Laplace interpolation to the scores.

[0079] Example 7

[0080] A method according to any one of embodiments 1 to 6, and comprising displaying an EA map (202) including the identified one or more regions (204) to a user.

[0081] Example 8

[0082] A system (10) includes an interface (30) and a processor (56). The interface is configured to receive an electroanatomical (EA) map (200) of at least a portion of a cardiac chamber. The processor (56) is configured to (i) score the quality of an EA analysis of the EA map, (ii) based on the score, identify one or more regions (204) in the mapped portion as failing to meet defined quality criteria, and (iii) indicate (206) the one or more regions on the EA map (202) to be displayed to a user.

[0083] Although the examples described herein are primarily directed to cardiac diagnostic applications, the methods and systems described herein may also be used in other medical applications.

[0084] It should be understood that the above examples are cited by way of example, and the present disclosure is not limited to what is specifically shown and described above. On the contrary, the scope of the present disclosure includes combinations and sub-combinations of the aforementioned various features and their variations and modifications, which should be expected by those skilled in the art when reading the above description and which are not disclosed in the prior art.

Claims

1. A method, comprising: receiving an electroanatomical (EA) map of at least a portion of a cardiac chamber; scoring the quality of the EA analysis of the EA map; identifying one or more regions in the mapped portion as failing to meet defined quality criteria based on the scoring; as well as The one or more regions are indicated on the EA map to be displayed to a user.

2. The method according to claim 1, wherein: The EA analysis is performed by at least one algorithm selected from a list of algorithms consisting of (i) local conduction vector (LCV), (ii) coherent EA (CEA) propagation, (iii) contact indication index (TPI), (iv) late activation time (LAT) and (v) grading score.

3. The method according to claim 2, wherein: Scoring the quality includes calculating a weighted sum of the quality scores of outputs of two or more algorithms from the list.

4. The method according to claim 1, wherein: Indicating the one or more regions on the EA map includes overlaying the EA map with a transparent layer based on the score.

5. The method according to claim 1, wherein: Indicating the one or more regions on the EA map includes overlaying the EA map with a layer of graphical symbols that vary in format based on the score.

6. The method according to claim 1, wherein: Indicating the one or more regions on the EA map includes: (i) filtering outliers based on the scores; (ii) projecting the scores onto a surface of the map; and (iii) applying Laplace interpolation to the scores.

7. A method according to claim 1 and comprising displaying the EA map including the identified one or more regions to a user.

8. A system, comprising: an interface configured to receive an electroanatomical (EA) map of at least a portion of a cardiac chamber; and A processor, the processor being configured to: scoring the quality of the EA analysis of the EA map; identifying one or more regions in the mapped portion as failing to meet defined quality criteria based on the scoring; as well as The one or more regions are indicated on the EA map to be displayed to a user.

9. The system according to claim 8, wherein: The EA analysis is performed by at least one algorithm selected from a list of algorithms consisting of (i) local conduction vector (LCV), (ii) coherent EA (CEA) propagation, (iii) contact indication index (TPI), (iv) late activation time (LAT) and (v) grading score.

10. The system according to claim 9, wherein: The processor is configured to score the quality by calculating a weighted sum of the quality scores of the outputs of two or more algorithms from the list.

11. The system according to claim 8, wherein: The processor is configured to indicate the one or more regions on the EA map by overlaying the EA map with a transparent layer according to the score.

12. The system according to claim 8, wherein: The processor is configured to indicate the one or more regions on the EA map by overlaying the EA map with a layer of graphical symbols that vary in format depending on the score.

13. The system according to claim 8, wherein: The processor is configured to indicate the one or more regions on the EA map by (i) filtering outliers based on the scores; (ii) projecting the scores onto a surface of the map; and (iii) applying Laplace interpolation to the scores.

14. The system according to claim 8, wherein: The processor is further configured to display the EA map including the identified one or more regions to a user.

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