Automatic alignment of anatomical maps to previous anatomical maps

Abstract

To provide registration of anatomical cardiac maps.SOLUTION: A method includes calculating a first medial-axis tree graph of a volume of an organ of a patient in a first computerized anatomical map of the volume, acquired at a first time. A second medial-axis tree graph of a volume of the organ of the patient is calculated in a second computerized anatomical map of the volume, acquired at a second time that is different from the first time. A deviation between the first and second tree-graphs is detected and estimated. Using the estimated deviation, the first and second medial-axis tree graphs are registered with one another. Using the registered first and second tree graphs, the first and second computerized anatomical maps are combined.SELECTED DRAWING: Figure 1

Description

Technical Field

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[0001] The present invention generally relates to cardiac mapping, and more specifically to alignment of an anatomical heart map.

Background Art

[0002] Methods for aligning organ visualizations have been proposed in patent literature. For example, US Patent Application Publication No. 2005 / 0197568 describes a method for aligning cardiac image data in an intervention system. The method includes inserting a first plurality of reference points into an acquired 3D anatomical image and exporting the 3D anatomical image with the first plurality of reference points inserted to the intervention system. A second plurality of reference points are inserted into the 3D anatomical image exported using the intervention system, and the first plurality of reference points and the second plurality of reference points are aligned with each other so that the exported 3D anatomical image is registered in the intervention system.

[0003] As another example, US Patent Application Publication No. 2011 / 0026794 describes a method for performing deformable non-rigid alignment of 2D and 3D images of a vascular structure for assistance in a surgical intervention, including acquiring 3D image data. The abdominal aorta is segmented from the 3D image data using graph cut-based segmentation to generate a segmentation mask. A centerline is generated from the segmentation mask using a continuous topology thinning process. A three-dimensional graph is generated from the centerline. 2D image data is acquired. The 2D image data is segmented to generate a distance map. An energy function is defined based on the 3D graph and the distance map. The energy function is minimized to perform non-rigid alignment between the 3D image data and the 2D image data. The alignment may be optimized.

Summary of the Invention

Means for Solving the Problems

[0004] One embodiment of the present invention described herein provides a method comprising calculating a first central axis tree graph of the volumes of a patient's organs in a first digitized anatomical map of volumes acquired at a first time. A second central axis tree graph of the volumes of the patient's organs is calculated in a second digitized anatomical map of volumes acquired at a second time different from the first time. A deviation between the first tree graph and the second tree graph is detected and estimated. The estimated deviation is used to align the first and second central axis tree graphs with respect to each other. The aligned first and second tree graphs are used to combine the first digitized anatomical map and the second digitized anatomical map.

[0005] In some embodiments, detecting deviations involves identifying the movement of one or more landmarks between a first digitized anatomical map and a second digitized anatomical map.

[0006] In some embodiments, the landmark comprises either or both a coronary sinus catheter and / or a body surface patch.

[0007] In one embodiment, detecting a deviation includes detecting a discontinuity between corresponding edge points of a first central axis tree graph and a second central axis tree graph.

[0008] In another embodiment, detecting and estimating the deviation includes detecting and estimating the displacement between a first tree graph and a second tree graph.

[0009] In some embodiments, combining a first digitized anatomical map and a second digitized anatomical map includes generating a continuous anatomical map of volume.

[0010] In some embodiments, the method further includes estimating two or more deviations between three or more tree graphs calculated in each of the three or more anatomical maps. The estimated deviations are used to align the three or more tree graphs with each other. The aligned tree graphs are used to combine the three or more anatomical maps.

[0011] In one embodiment, the method further includes presenting a combined anatomical map to the user.

[0012] In some embodiments, combining a first digitized anatomical map and a second digitized anatomical map includes combining at least a first ablation site in the first anatomical map and a second ablation site in the second anatomical map.

[0013] Furthermore, according to another embodiment of the present invention, a system including a processor and a monitor is provided. The processor is configured to: calculate a first central axis tree graph of the volume of a patient's organs in a first electronic anatomical map of volumes acquired at a first time; calculate a second central axis tree graph of the volume of a patient's organs in a second electronic anatomical map of volumes acquired at a second time different from the first time; detect and estimate the deviation between the first tree graph and the second tree graph; align the first and second central axis tree graphs relative to each other using the estimated deviation; and combine the first and second electronic anatomical maps using the aligned first and second tree graphs. The monitor is configured to display the combined first and second electronic anatomical maps to the user. [Brief explanation of the drawing]

[0014] This invention will be more fully understood by considering the following "Modes for Carrying Out the Invention" in conjunction with the drawings. [Figure 1] This is a schematic diagram of a system for electroanatomical mapping and ablation according to an exemplary embodiment of the present invention. [Figure 2] An exemplary embodiment of the present invention is a schematic volume rendering of an automatically generated patient motion-compensated anatomical map of the left atrium using map skeletal alignment. [Figure 3] This flowchart schematically illustrates an automated method for generating a patient motion-compensated anatomical map, as shown in Figure 2, using map-skeletal alignment, according to an exemplary embodiment of the present invention. [Modes for carrying out the invention]

[0015] Overview Catheter-based anatomical mapping techniques can create electronic anatomical maps of organ cavities (e.g., volumetric surfaces). Such maps can then be used when performing ablation. For example, a cardiac mapping system can generate maps of cardiac cavities, such as the left atrium (LA), to be used during ablation of pulmonary vein (PV) holes to treat atrial fibrillation.

[0016] However, during invasive procedures involving mapping and ablation of pulmonary veins, patient movement may cause map shifts. Mapping and ablation may be continued, but the new map needs to be aligned with the previous map to understand if premature ablation has occurred.

[0017] To overcome this problem, physicians may first align those maps to CT or MRI images. In this case, if there is a map shift, the physician may re-align the new map to the CT / MR image, resulting in both maps being registered. However, such initial alignment is manual, time-consuming, and depends on how well the physician identifies the points used for alignment.

[0018] Some embodiments of the present invention described below provide a method for generating a ventricular skeleton while a procedure (mapping and ablation) is being performed. In these embodiments, the processor receives an anatomically mapped volume of the ventricle of the heart (e.g., the LA). In the case of the LA, the processor automatically identifies the PV and atrial appendage opening region on the anatomical map of the LA. For this purpose, the processor calculates a central axis graph (hereinafter also abbreviated as "skeleton") from the anatomical map. Points on the central axis of the volume can be defined as points in a planar cross-section of the volume having two or more nearest points on the resulting boundary in 2D within the cross-sectional plane. Originally called a "topological skeleton," it was introduced in 1967 as a tool for biomorphological recognition.

[0019] A method for generating the skeleton is described in U.S. Patent Application No. 17 / 009715, filed on September 1, 2020, entitled “Automatic Identification and Processing of Anatomical Structures in an Anatomical Map,” the disclosure of which is incorporated herein by reference. A processor may use this method or any other preferred method for generating the central axis graph (“Skeleton”).

[0020] Such a framework can assist physicians performing subsequent PV ablation by removing irrelevant electrophysiological (EP) information that could cause confusion, such as electrical activity appearing in the electrophysiological (EP) version of the map in the form of map coloring, if the PV is not removed from the map. This EP information, which was considered irrelevant immediately before the ablation, can cause physicians to mistakenly place the ablation catheter deep inside the PV (for example, instead of placing the catheter at the PV opening).

[0021] In one embodiment of the present invention, if there is patient movement, typically identified by a shift in a landmark such as a coronary sinus catheter or a body surface patch, the procedure continues and a new skeleton is generated. The processor identifies the endpoints of the two skeletons, e.g., the centers of the pulmonary veins, and these endpoints are used to align the map before movement and the map after movement. As described above, some embodiments of the disclosed technique provide a method of using skeleton alignment to combine an existing portion of the map (e.g., the portion before the patient moved) with a portion of the map made after movement in order to generate an updated patient movement-compensated (e.g., continuous) map.

[0022] The processor automatically generates a patient motion-compensated anatomical map using skeletal alignment. For this purpose, in one embodiment, the processor calculates a first central axis tree graph of the patient's organ volumes in a first digitized anatomical map of volumes acquired at a first time. The processor then calculates a second central axis tree graph of the patient's organ volumes in a second digitized anatomical map of volumes acquired at a second time, different from the first time.

[0023] This second time period may occur after the patient has moved during the same session or in a subsequent session. In any case, the processor detects and estimates the deviation between the first tree graph and the second tree graph using the means described below.

[0024] Using the estimated deviation, the processor aligns the first and second central axis tree graphs with each other and combines the first and second electronic anatomical maps using the aligned tree graphs.

[0025] In one embodiment, the processor detects the deviation by identifying the movement of one or more landmarks between the first and second electronic anatomical maps. The landmark can include one or more of a coronary sinus catheter and a body surface patch.

[0026] In another embodiment, the processor detects the deviation by detecting an unexpected discontinuity between two edge points of the first and second central axis tree graphs.

[0027] The above method may be applied to estimate two or more displacement deviations between three or more tree graphs. Using the aligned tree graphs, the processor can combine three or more anatomical maps.

[0028] In some embodiments, combining the first and second electronic anatomical maps includes combining ablation positions (e.g., actual ablation lesions or planned positions) from the first and second anatomical maps. By this technique, in one embodiment, it is possible to visualize ablation lesions formed before movement on an aligned map generated after movement. In this way, the physician can apply the desired ablation pattern regardless of the patient's movement.

[0029] In another embodiment, patient movement is identified by a processor constructing a tree graph by identifying one or more erroneous tree graph edges (e.g., tree graph edges that should not exist but arise due to discontinuities). The processor further estimates the deviations between parts of the tree graph (e.g., the size and direction of discontinuities due to displacement) by estimating the displacement between each of those edges. Generally, the deviations may be heterogeneous and include position-dependent displacement and / or rotation and / or stretch and / or contraction.

[0030] In contrast to physician-assisted positioning methods using medical images, using the skeleton does not require such images (or positioning to such images). Additionally, the method described below is fully automated.

[0031] Typically, a processor is programmed with software that includes specific algorithms that enable it to perform each of the processor-related processes and functions outlined above.

[0032] The disclosed alignment technique can facilitate the work required of physicians when analyzing anatomical maps during invasive procedures. Therefore, the disclosed technique can make clinical diagnosis and subsequent treatment, such as catheter ablation, safer and more efficient.

[0033] System Description Figure 1 is a schematic diagram of a system for electroanatomical mapping and ablation 20 according to one embodiment of the present invention.

[0034] System 20 includes a catheter 21 having a shaft 22, which is navigated by a physician 30 into the heart 26 of a patient 28 lying on a table 29. In the example depicted, the physician 30 inserts the shaft 22 through the sheath 23 while manipulating the distal end of the shaft 22 using a remote control 32 near the proximal end and / or deflection from the sheath 23. As shown in insert 25, a basket catheter 40 is attached to the distal end of the shaft 22. The basket catheter 40 is inserted through the sheath 23 in a folded state and then expands inside the heart 26.

[0035] In one embodiment, the basket catheter 40 is configured to (i) perform spatial mapping of the ventricles of the heart 26 to acquire electrophysiological signals from the ventricular surface 50, and (ii) apply electrical ablation energy to the ventricular surface 50. Insertion figure 45 shows the basket catheter 40 in an enlarged view inside the ventricle of the heart 26. As can be seen, the basket catheter 40 comprises an array of electrodes 48 connected on a spline that forms a basket shape. In one embodiment, ablation is performed in a bipolar ablation mode by applying ablation energy between pairs of electrodes 48.

[0036] The proximal end of the catheter 21 is connected to the console 24. The console 24 comprises a general-purpose computer with a processor 41, which typically transmits and receives electrical signals entering and leaving the catheter 21, and controls other components of the system 20, and has a suitable front-end and interface circuit 38. In one embodiment, the surface of the surrounding anatomical structures is shown to the physician 30 on the monitor 27, for example, in the form of a mesh diagram 35.

[0037] The processor 41 is typically programmed with software to perform the functions described herein. The software can be downloaded electronically to a computer, for example, over a network, or alternatively or additionally, it can be provided and / or stored on a non-temporary physical medium such as magnetic memory, optical memory, or electronic memory.

[0038] The illustrative embodiment shown in Figure 1 specifically relates to the use of a basket catheter for cardiac mapping, but other distal end assemblies may be used, in particular the processor 41 executes a dedicated algorithm disclosed herein, including Figure 3, which enables the processor 41 to perform the disclosed steps, as will be further described below.

[0039] Automatic alignment of anatomical map sections Figure 2 is a schematic volume rendering of an automatically generated patient motion-compensated anatomical map 40 of the left atrium using map skeletal alignment, according to one embodiment of the present invention.

[0040] As can be seen, for the convenience of the user, the surface mesh map 40 is rotated (by the processor 41) in the posterior-forward (rear to frontal) PA direction so that the right side of the display corresponds to the observer's right side and the left side of the display is on the patient's left side. Figure 2 further illustrates the automatically identified pulmonary veins (PVs) 411-412 and 414-415.

[0041] In the illustrated embodiment, before the patient moved, the processor 41 calculated the “central axis” skeletal portion S 42 from the LA surface anatomical map 35. The skeletal portion 42 (i.e., tree graph) includes three of the five main branches (N=5), namely branches 441-445, namely branches 413-415. That is, of branches 441-445, branches 443-445 were generated before the patient began to move, which occurred while point 210 was still being generated as the skeleton.

[0042] As a result of the patient's movement, the remaining generation of the skeleton (i.e., portion S'', 242) is shifted. In the illustrated embodiment, the processor 41 identifies edge points such as the endpoint 448 and start point 450 of skeletal portion 42, and the endpoints 461 and 472 of skeletal portion 242. Using the start and end points, the processor 41 aligns the map skeletal portion before and after movement (circumstantial view by arrow 202) (i.e., creates a motion-compensated skeletal portion S'', 142). The processor uses the skeletal alignment to automatically generate a patient motion-compensated anatomical map. For example, the processor applies a transformation defined by the alignment of the two skeletal portions to align the map after movement with the map portion before movement.

[0043] In another embodiment where the skeleton is completed before the patient moves, the algorithm can use more endpoints, such as older versions of branches 441 and 442.

[0044] As shown in Figure 2, lesion 111 was mapped (at point 201) before the patient moved. As a result of the movement, position 111 is displaced relative to the planned ablation position 333. Using the alignment of the two map portions, lesion 333 is shifted to position 222 to form a consistent path for the ablation lesion (111, 222). Depending on the reference map in use, the consistent path for the ablation lesion (shift of 111, 333) may be achieved by displacing position 111 instead. The exemplary schematic volume rendering shown in Figure 2 is selected simply to clarify the concept. For example, if patient movement results in multiple skeletal portions (i.e., three or four or more), the processor 41 aligns all of them to generate a patient movement-compensated map.

[0045] Figure 3 is a schematic flowchart illustrating an automated method for generating a patient motion-compensated anatomical map 40 of Figure 2 using map-skeletal alignment, according to an embodiment of the present invention. The algorithm according to the presented embodiment performs a process that begins with a pre-map portion receive step 78, in which a processor 41 receives a portion of the anatomical map 40 created before the patient moves. In the illustrated case, this is the map portion of the left atrium.

[0046] Next, in the skeleton generation step 80, the processor generates the skeleton 42 of the preliminary part of the map.

[0047] In motion identification step 82, the processor 41 identifies an open endpoint 448 on the skeleton 42 corresponding to an event in which the patient moved. The processor may identify the patient's movement using at least one landmark from the coronary sinus catheter and a body surface patch. In another embodiment, the patient's movement is identified by a processor that constructs a tree graph by identifying two edge points (448, 450) of the tree graph that should not be (for example, due to discontinuity).

[0048] In step 84, receiving the post-movement map portion, the processor 41 receives a portion of the anatomical map 40 created after the patient has moved. In step 86, generating the skeleton portion, the processor generates the skeleton 242 of the post-movement portion of the map.

[0049] In the skeletal alignment step 88, the processor aligns the moved sub-skeleton with the previous sub-skeleton (i.e., creates a motion-compensated skeletal part S', 142). For example, the processor performs the alignment using endpoint 448 and starting point 450, as well as endpoints 461 and 472.

[0050] Finally, using alignment, the processor combines the pre-movement and post-movement map portions to generate a patient motion-compensated (e.g., artifact-free) map 40.

[0051] The exemplary flowchart shown in Figure 3 is selected solely for the purpose of illustrating the concept. In optional embodiments, various additional steps may be performed, for example, to automatically align the openings in the PV to the LA, which are identified as being separated from the patient motion-compensated map, with the medical image of the motion-compensated map.

[0052] While the embodiments described herein primarily address cardiac applications, the methods and systems described herein can also be used for other applications. For example, the disclosed methods may be used to align otolaryngeal maps.

[0053] Accordingly, it will be understood that the embodiments described above are cited as examples and that the present invention is not limited to those specifically shown and described above. Rather, the scope of the present invention includes both combinations and partial combinations of the various features described in the above specification, as well as variations and modifications thereof not disclosed in the prior art, which would be conceivable to those skilled in the art by reading the foregoing description. Documents incorporated into this patent application by reference shall be considered integral parts of this application, except that, in such incorporated documents, only the definitions herein shall be considered to the extent that any term is defined in a manner that contradicts the definitions expressed or implied herein.

[0054] [Implementation Method] (1) A method for automatically aligning an anatomical map to a previous anatomical map, The first central axis tree graph of the volume of the patient's organs is calculated in a first electronic anatomical map of the said volume acquired in a first time period, The second central axis tree graph of the volume of the organs of the patient is calculated in a second electronic anatomical map of the volume acquired at a second time different from the first time, The deviation between the first tree graph and the second tree graph is detected and estimated, Using the estimated deviation, the first central axis tree graph and the second central axis tree graph are aligned relative to each other. A method comprising combining the first digitized anatomical map and the second digitized anatomical map using the aligned first tree graph and the second tree graph. (2) The method according to Embodiment 1, wherein detecting the deviation includes identifying the movement of one or more landmarks between the first digitized anatomical map and the second digitized anatomical map. (3) The method according to Embodiment 2, wherein the landmark comprises one or both of a coronary sinus catheter and a body surface patch. (4) The method according to Embodiment 1, wherein detecting the deviation includes detecting discontinuities between corresponding edge points of the first central axis tree graph and the second central axis tree graph. (5) The method according to Embodiment 1, wherein detecting and estimating the deviation includes detecting and estimating the displacement between the first tree graph and the second tree graph.

[0055] (6) The method according to Embodiment 1, wherein combining the first electronic anatomical map and the second electronic anatomical map generates a continuous anatomical map of the volume. (7) Estimating two or more deviations between three or more tree graphs calculated in each of three or more anatomical maps, Using the estimated deviations, the three or more tree graphs are aligned relative to each other. The method according to Embodiment 1, comprising combining the three or four or more anatomical maps using the aligned tree graph. (8) The method according to Embodiment 1, comprising presenting the combined anatomical maps to the user. (9) The method according to Embodiment 1, wherein combining the first digitized anatomical map and the second digitized anatomical map includes combining at least a first ablation site in the first anatomical map and a second ablation site in the second anatomical map. (10) A system for automatically aligning an anatomical map with a previous anatomical map, It is a processor, The first central axis tree graph of the volume of the patient's organs is calculated in a first electronic anatomical map of the said volume acquired in a first time period, The second central axis tree graph of the volume of the organs of the patient is calculated in a second electronic anatomical map of the volume acquired at a second time different from the first time, The deviation between the first tree graph and the second tree graph is detected and estimated, Using the estimated deviation, the first central axis tree graph and the second central axis tree graph are aligned relative to each other. A processor is configured to combine the first digitized anatomical map and the second digitized anatomical map using the aligned first tree graph and the second tree graph, A system comprising: a monitor configured to display the combined first electronic anatomical map and the second electronic anatomical map to the user.

[0056] (11) The system according to Embodiment 10, wherein the processor is configured to detect the deviation by identifying the movement of one or more landmarks between the first digitized anatomical map and the second digitized anatomical map. (12) The system according to embodiment 11, wherein the landmark comprises one or both of a coronary sinus catheter and a body surface patch. (13) The system according to Embodiment 10, wherein the processor is configured to detect the deviation by detecting discontinuities between corresponding edge points of the first central axis tree graph and the second central axis tree graph. (14) The system according to Embodiment 10, wherein the processor is configured to detect and estimate the deviation by detecting and estimating the displacement between the first tree graph and the second tree graph. (15) The system according to Embodiment 10, wherein the processor is configured to generate a continuous anatomical map of the volume when combining the first electronic anatomical map and the second electronic anatomical map.

[0057] (16) The processor To estimate two or three deviations between three or more tree graphs calculated in each of three or more anatomical maps, Using the estimated deviations, the three or more tree graphs are aligned relative to each other. The system according to embodiment 10, further configured to combine the three or more anatomical maps using the aligned tree graph. (17) The system according to Embodiment 10, wherein when combining the first digitized anatomical map and the second digitized anatomical map, the processor is configured to combine at least a first ablation location in the first anatomical map and a second ablation location in the second anatomical map.

Claims

1. A system for automatically aligning an anatomical map with a previous anatomical map, It is a processor, The first central axis tree graph of the volume of the patient's organs is calculated in a first electronic anatomical map of the said volume acquired in a first time period, To detect the patient's movements, The second central axis tree graph of the volume of the patient's organs is calculated in a second electronic anatomical map of the volume acquired at a second time interval, which is the time interval after the patient's movement is detected. Identifying the edge points of the first central axis tree graph and the edge points of the second central axis tree graph, To estimate the displacement between the edge point of the first central axis tree graph and the edge point of the second central axis tree graph, Using the estimated displacement, the first central axis tree graph and the second central axis tree graph are aligned with each other so that their corresponding edge points overlap. A processor is configured to connect the first digitized anatomical map and the second digitized anatomical map using the aligned first and second central axis tree graphs. A system comprising: a monitor configured to display the first and second digitized anatomical maps, which are joined together, to a user.

2. The system according to claim 1, wherein the processor is configured to detect the movement of the patient by detecting the movement of one or more landmarks between the first digitized anatomical map and the second digitized anatomical map.

3. The system according to claim 2, wherein the landmark comprises one or both of a coronary sinus catheter and a body surface patch.

4. The system according to claim 1, wherein the processor is configured to estimate the displacement by detecting discontinuities between corresponding edge points of the first central axis tree graph and the second central axis tree graph.

5. The system according to claim 1, wherein the processor is configured to detect and estimate the displacement by detecting and estimating the displacement between the first central axis tree graph and the second central axis tree graph.

6. The system according to claim 1, wherein the processor is configured to generate a continuous anatomical map of the volume when joining the first digitized anatomical map and the second digitized anatomical map.

7. The aforementioned processor, To estimate two or more displacements between the edge points of three or more central axis tree graphs calculated in three or more electronic anatomical maps, The system according to claim 1, further configured to: align the three or more central axis tree graphs with each other using the estimated displacements so that the corresponding edge points of each central axis tree graph overlap; and connect the three or more anatomical maps using the aligned three or more central axis tree graphs.

8. The system according to claim 1, wherein, when aligning the first electronic anatomical map and the second electronic anatomical map, the processor is configured to detect the displacement by detecting a first ablation position in the first electronic anatomical map and a second ablation position in the second electronic anatomical map.

9. A method for automatically aligning an anatomical map to a previous anatomical map, The first central axis tree graph of the volume of the patient's organs is calculated in a first electronic anatomical map of the said volume acquired in a first time period, To detect the movement of the aforementioned patient, The second central axis tree graph of the volume of the patient's organs is calculated in a second electronic anatomical map of the volume acquired at a second time interval, which is the time interval after the patient's movement is detected. Identifying the edge points of the first central axis tree graph and the edge points of the second central axis tree graph, To estimate the displacement between the edge point of the first central axis tree graph and the edge point of the second central axis tree graph, Using the estimated displacement, the first central axis tree graph and the second central axis tree graph are aligned with each other so that their corresponding edge points overlap. A method comprising connecting the first digitized anatomical map and the second digitized anatomical map using the aligned first axial tree graph and the second axial tree graph.

10. The method according to claim 9, wherein detecting the patient's movement includes detecting the movement of one or more landmarks between the first digitized anatomical map and the second digitized anatomical map.

11. The method according to claim 10, wherein the landmark comprises one or both of a coronary sinus catheter and a body surface patch.

12. The method according to claim 9, wherein estimating the displacement includes detecting discontinuities between corresponding edge points of the first central axis tree graph and the second central axis tree graph.

13. The method according to claim 9, wherein estimating the displacement includes detecting and estimating the displacement between the first central axis tree graph and the second central axis tree graph.

14. The method according to claim 9, wherein connecting the first digitized anatomical map and the second digitized anatomical map is to generate a continuous anatomical map of the volume.

15. To estimate two or more displacements between the edge points of three or more central axis tree graphs calculated in three or more electronic anatomical maps, Using the estimated displacement, the three or more central axis tree graphs are aligned with each other so that the corresponding edge points of each central axis tree graph are superimposed. The method according to claim 9, comprising connecting the three or four or more anatomical maps using the three or four or more aligned central axis tree graphs.

16. The method according to claim 9, comprising presenting the combined first digital anatomical map and the second digital anatomical map to the user.

17. The method according to claim 9, wherein aligning the first digitized anatomical map and the second digitized anatomical map includes detecting the displacement by detecting a first ablation location in the first digitized anatomical map and a second ablation location in the second digitized anatomical map.

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