WO2023238110A1 - Systèmes et procédés de recommandation de lignes d'ablation - Google Patents

Systèmes et procédés de recommandation de lignes d'ablation Download PDF

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Publication number
WO2023238110A1
WO2023238110A1 PCT/IB2023/056006 IB2023056006W WO2023238110A1 WO 2023238110 A1 WO2023238110 A1 WO 2023238110A1 IB 2023056006 W IB2023056006 W IB 2023056006W WO 2023238110 A1 WO2023238110 A1 WO 2023238110A1
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WIPO (PCT)
Prior art keywords
ablation
line
anatomy
proposed
ablation line
Prior art date
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PCT/IB2023/056006
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English (en)
Inventor
Matityahu Amit
Liat Tsoref
Yariv Avraham Amos
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Biosense Webster (Israel) Ltd.
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Publication date
Priority claimed from US18/207,854 external-priority patent/US20230397950A1/en
Application filed by Biosense Webster (Israel) Ltd. filed Critical Biosense Webster (Israel) Ltd.
Publication of WO2023238110A1 publication Critical patent/WO2023238110A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B18/04—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating
    • A61B18/12—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by heating by passing a current through the tissue to be heated, e.g. high-frequency current
    • A61B18/14—Probes or electrodes therefor
    • A61B18/1492—Probes or electrodes therefor having a flexible, catheter-like structure, e.g. for heart ablation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00315—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body for treatment of particular body parts
    • A61B2018/00345—Vascular system
    • A61B2018/00351—Heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
    • A61B2018/00571—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body for achieving a particular surgical effect
    • A61B2018/00577—Ablation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/107—Visualisation of planned trajectories or target regions

Definitions

  • Atrial fibrillation is the most common diagnosed sustained arrhythmia and is characterized by rapid and irregular activation of the atria. Atrial fibrillation can be paroxysmal, lasting 7 days or less with or without intervention, or may be continuous beyond 7 days (persistent, PS-AF) or beyond 12 months (long-standing, LS-PS-AF). Permanent AF is the term used for longstanding persistent AF when any attempt to restore sinus rhythm has been abandoned or has proved impossible. (See e.g., Kaba, R. A., Momin, A. & Camm, J. Persistent atrial fibrillation: The role of left atrial posterior wall isolation and ablation strategies. J. Clin. Med. 10, (2021).
  • the system includes an ablation line editor which enables a physician to modify a selected one of the at least one proposed ablation line.
  • the system includes a treatment determiner which determines a recommended energy delivery per segment of a chosen ablation line.
  • the at least one proposed ablation line is a wide antral circumferential ablation (WACA) line.
  • WACA wide antral circumferential ablation
  • the method includes enabling a physician to modify a selected one of the at least one proposed ablation line.
  • the method includes determining a recommended energy delivery per segment of a chosen ablation line.
  • the same structure is a graph convolutional neural network (GCN) or a classifier.
  • GCN graph convolutional neural network
  • the at least one proposed ablation line is a wide antral circumferential ablation (WACA) line.
  • WACA wide antral circumferential ablation
  • the method includes comparing actual ablation points of a training case to intersection points or lines of neighboring parts on a segmented map of the training case and selecting those ablation points closest to the intersection points as key points.
  • FIGs. 1A and IB are schematic illustrations of an exemplary left atrium in the posterior anterior view, respectively without and with ablation lines marked thereon;
  • FIG. 4 is a schematic illustration of the exemplary left atrium of Fig. 1A with ablation lines thereon, useful in understanding the operation of the system of Fig. 2;
  • FIG. 5 is a schematic illustration of the exemplary left atrium of Fig. 1A with a point mesh face indicated thereon, useful in understanding the operation of the system of Fig. 2;
  • FIG. 6 is a schematic illustration of the exemplary left atrium of Fig. 1A with ablation points thereon, useful in understanding the operation of the system of Fig. 2;
  • Fig. 7 is a block diagram illustration of an alternative deep learning system for rendering recommended ablation line(s) on an anatomy, constructed and operative in accordance with an alternative preferred embodiment of the present invention.
  • Fig. 8 is a schematic illustration of the operation of a key point filterer, useful in the system of Fig. 7.
  • WACA wide antral circumferential ablation
  • Applicant has realized that deep learning systems may be utilized for planning ablation lines, such as wide antral circumferential ablation (WACA) lines and for checking how close the lines actually performed on the left atrium are to the planned ones.
  • WACA wide antral circumferential ablation
  • the problem of defining ablation lines is a segmentation problem.
  • the input to such a system may be an anatomical map of the patient’s heart and that the same type of deep learning structure may be utilized both for automatically segmenting the anatomical map as well as for defining the locations of the ablation lines.
  • FIG. 1 A illustrates an exemplary left atrium 2 in the posterior anterior (PA) view
  • Fig. IB illustrates exemplary ablation lines 4 marked on left atrium 2.
  • Left atrium 2 has multiple areas, such as a roof wall 3, and a posterior wall 5.
  • RSPV right superior pulmonary vein
  • RIPV right inferior PV
  • LIPV left inferior PV
  • LSPV left superior PV
  • Deep learning system 10 for rendering recommended ablation line(s) on an anatomy.
  • Deep learning system 10 comprises three sections, an ablation line trainer 12 which builds a trained deep learning unit for an ablation line proposer 13, an ablation line recommender 14 which generates ablation line recommendations using ablation line proposer 13, and an ablation line guider 16 which provides ablation line guidance during a procedure.
  • Ablation line trainer 12 may receive an anatomical map of the heart, which may be produced using an anatomical image produced by using a suitable medical imaging system 20, or using a fast anatomical mapping (FAM) technique available in the CARTOTM system, produced by Biosense Webster Inc.
  • FAM fast anatomical mapping
  • the map/model of the heart onto which the recommended ablation line(s) may be rendered may comprise any suitable type of three-dimensional (3D) anatomical map produced using any suitable technique.
  • the map may come from an intracardial ECG (electrocardiogram) and may comprise both location information, of a catheter moving around the atrium, and ECG measurements, such as voltage at each location.
  • Ablation line proposer 13 may allow the physician to specify the required ablation lines; which may be known ablation lines such as e.g.,: WACA, roofline, carina lines (right, left), posterior line, anterior line, inferior line, mitral line, posterior wall debulking, anterior wall debulking, inferior wall debulking, left lateral debulking, LAA isolation, etc.
  • ablation lines such as e.g.,: WACA, roofline, carina lines (right, left), posterior line, anterior line, inferior line, mitral line, posterior wall debulking, anterior wall debulking, inferior wall debulking, left lateral debulking, LAA isolation, etc.
  • Ablation line recommender 14 may also comprise an ablation line editor 27 to enable the physician to review and modify the selected ablation line according to his or her requirements, preferences, and findings during the case. For example, a physician may want to edit the initial ablation lines such that it will contain triggers observed in the posterior wall.
  • Ablation line editor 27 may also permit the physician to update the recommended treatment delivery per segment.
  • ablation line guidance unit 30 may present a dynamic display of a next ablation site based on the previous site and on the recommended ablation line (approved or edited by the physician). The next ablation site may then be displayed based on a pre-defined distance e.g., 4 mm from the previous site, with a minimum distance from the suggested line and in the direction of advancement of the line.
  • the goal of an ablation procedure is to form effective lesions that can interrupt the abnormal electrical pathways causing arrhythmias.
  • the ablation line guidance unit 30 may calculate an ablation index, indicating the quality of each radiofrequency (RF) ablation lesion during the procedure.
  • RF radiofrequency
  • Fig. 3 shows the deep learning structure, discussed in more detail hereinbelow, and indicates that the input is the anatomical map, discussed above, and an adjacency map, discussed hereinbelow.
  • Fig. 3 shows two types of output, segmentation codes for automatic map segmenter 22 and ablation line codes for ablation line neural network trainer 24.
  • an expert physician e.g., a KOL in the field
  • an expert physician initially may add manual annotations of ablation points on the anatomical maps of the cases, as generated by imaging system 20. Each point is associated to at least one of the ablation lines (1) - (13).
  • each ablation point may be rendered on the anatomical map using a particular radius (e.g., a 5mm radius in this example).
  • a particular radius e.g., a 5mm radius in this example.
  • Fig. 4 shows a posterior view of an exemplary left atrium 40 after projecting ablation points of 5 mm radius.
  • Fig. 4 shows left WACA lines 42 and right WACA lines 44..
  • each map be investigated, and maps with gaps/or blurred lines may be filtered out from training and testing sets.
  • further data preprocessing may be performed in which: a.
  • the atrium is converted into a fixed dimension (e.g., by producing a constant mesh file with a fixed number of points, such as, for example, 2000 points and a 2000 x 2000 adjacency matrix, where all values are configurable, such as values of 1000, 2000, 4000, 10000 etc.)
  • Feature extraction for 3D general segmentation To build a deep learning framework for 3D segmentation, graph data is characterized by improving the local features of each node.
  • each face contains at most three adjacent faces.
  • a face ⁇ F] on the mesh M has three corresponding nodes (vertices) on the graph ⁇ G] .
  • the adjacency matrix ⁇ Adjacency ⁇ is a 6000x6000 matrix in our case.
  • the adjacency matrix is based on ⁇ F] and represents the connections between vertices in M.
  • the spatial and structural geometric Features are used based on a single vertex of the simplified mesh (in this example composed of 6000 vertices).
  • the feature space is a 6000x9 matrix, for each vertex (a triangle composed of three vertices with 3 adjacent faces), the pre-processing utilizes the following features: pt - the normalized (.xt, yt, zt ) position of vertex Vi in the Euclidian space, where i represents the three indices of the vertices.; spt - spherical coordinate of vertex i cpt , 0i,rt) , where (pt is normalized by TT and 0 is normalized by 2TT; and ni - normal of the vertex i defined by (xni, yni, zni). [0060] c.
  • Automatic map segmenter 22 may utilize a machine-learning model composed of two layers, the first layer is composed of six graph convolutional neural network units, and the second layer is a decision layer comprised of another GCN with a Softmax unit. Each GCN unit gives a prediction of a given vertex to be associated to each one of the target categories (anatomical structures or ablation line).
  • the anatomical map and the adjacency matrix serve as inputs to all units in the network.
  • Two types of GCN units are used: 1) approximation personalized propagation of neural predictions (APPNP) and 2) auto regressive moving average layer (ARMA) that is highly efficient as a SoftMax layer for increasing the overall performance of the network during the training and cross validation stages and provides a more flexible frequency response, is more robust to noise, and better captures the global graph structure.
  • Personalized graph neural networks are described by Gasteiger, J., Bojchevski, A. & Unnemann, S. G.”. Predict then propogate: Graph neural networks meet personalized PageRank. Int. Conf. Learn. Represent. (2018).
  • Graph Neural networks with CNN ARMA filters have been described by Maria Bianchi, F., Grattarola, D., Livi, L. & Alippi, C. Graph Neural Networks with Convolutional ARMA Filters (2016).
  • the APPNP unit models the relationship between GCN networks and the PageRank algorithm (a link analysis algorithm, originally designed by Google, which assigns a numerical weight to each node based on the quantity and quality of links point to it) and extends it to a personalized PageRank. It uses the information from a large, adjustable neighborhood for classifying each node.
  • the model is computationally efficient and outperforms several state-of-the-art methods for semi-supervised classification on multiple graphs.
  • the APPNP has 13 channels with a multi-layer perceptron (MLP) having two hidden layers with 128, and 64 neurons in each layer, respectively.
  • MLP multi-layer perceptron
  • the network weights are trained using a weighted category cross entropy loss function.
  • Automatic map segmenter 22 may provide the segmentation codes to its segmentation loss function while ablation line neural network trainer 24 may provide the ablation line codes to its ablation line loss function.
  • the training set contains 5000 maps. Each map is represented using three matrixes, a 6000x9 feature matrix, a 6000x6000 adjacency matrix and a 6000x1 target matrices, one for the segmentation codes and one for the ablation codes. For the ablation codes, zero represents no line while non-zero represents the ablation line ID.
  • automatic map segmenter 22 may segment the anatomical map of each atrium and may provide the segmented map to ablation line display er 25.
  • From segmentation to line - Ablation line proposer 13 may create a centralized line with potential locations for point-by-point ablations on the anatomical map.
  • the map of Fig. 3 is depicted in Fig. 6 with the point-by-point ablations indicated by dots 50.
  • Point-by-point ablation “dots” may be updated on the fly based on actual ablation performed by the physician during each case.
  • the mechanism for updating the dots may involve minimizing the distance between the planned line and the next dot in the direction of advancement of the ablation line.
  • system 10 may use mapping data and automatic segmentation data to recommend which of the ablation lines is more suitable for a given case.
  • the recommendation may be set based on indications from a specific research hospital or specific physicians (e.g., a KOE in the field of cardiac ablation) or based on data from a multitude of physicians and/or research hospitals.
  • the structure used for both automatic map segmenter 22 and ablation line trainer 24 may comprise a classifier operating on data of the intracardiac ECG.
  • catheters are inserted into the heart to measure the electrical activity of the heart. The per anatomical location the electrical signals are represented as voltage amplitude.
  • the classifier may be any suitable classifier , such as a Random Forest Classifier, a Support- Vector Machine (SVM) classifier or a deep learning classifier, such as are described in the following articles:
  • SVM Support- Vector Machine
  • Each case in the training dataset may be preprocessed with an indication of whether one of given number of ablation lines (e.g., 14) were performed by a key opinion leader (KOL), so the target is a matrix of size Nxl4 where N represents the number of cases in the training dataset.
  • KOL key opinion leader
  • ECG data from the intracardiac ECG is saved as an additional feature matrix of 6000x22.
  • the following triggers and substrate maps from the intracardiac ECG data are used as feature spaces:
  • Segmentation hot one encoding is the output of automatic map segmenter 22: a matrix of size (6000xK) where K represents the number of anatomical structures (13 for the LA), (output from segmentation algorithm).
  • Ablation line encoding is the output of ablation line trainer 24; [0081] 10) Global voltage per segmentation region (6000x1) mV; and
  • Low voltage zone areas which are regions within the left atria where the recorded voltage amplitudes are relatively low compared to surrounding areas. For example, these may be areas whose voltages are between 0.2 - 0.5 mV. Typically, a scar may be defined as an area whose voltage is below 0.2mV. It will be appreciated that these thresholds may differ based on operator decision in the procedure.
  • the target vector has dimensions of Kxl, where K indicates the number of ablation approaches or IDs.
  • the data may be from successful cases of the procedure, specifically those with a 12-month freedom from any arrhythmia.
  • the Kxl vector will be set to 1 if a particular ablation approach was performed during the procedure.
  • the input 100 to key point filterer 23 may be an image or a mesh of the left atrium with the actual ablation lines, which may have been segmented (102) by automatic map segmenter 22.
  • Key point filterer 23 may detect the key points 104 by comparing the ablation points of each case to the intersection points on the segmented map 102 for that case and selecting those ablation points closest to the intersection points.
  • Key point filterer 23 may combine key points from multiple cases to generate resultant ablation lines 106 for training which may be thinner and better defined than otherwise.
  • Key point filterer 23 may provide the resultant ablation lines 106 to ablation line neural network trainer 24 for training.
  • Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk, including optical disks, magnetic-optical disks, read-only memories (ROMs), volatile and non-volatile memories, random access memories (RAMs), electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, Flash memory, disk-on-key or any other type of media suitable for storing electronic instructions and capable of being coupled to a computer system bus.
  • the computer readable storage medium may also be implemented in cloud storage.

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Abstract

Un système (10) pour améliorer une procédure d'ablation cardiaque comprend une unité de recommandation (14) configurée pour fournir une recommandation initiale pour au moins une ligne d'ablation proposée (4) pour une procédure d'ablation sur une anatomie d'un patient. Le système affiche la ou les lignes d'ablation proposées sur une carte anatomique de l'anatomie. L'unité de recommandation comprend un premier et un second modèle d'apprentissage automatique entraîné. Les premier et second modèles d'apprentissage automatique entraînés ont la même structure.
PCT/IB2023/056006 2022-06-10 2023-06-11 Systèmes et procédés de recommandation de lignes d'ablation WO2023238110A1 (fr)

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US202263350983P 2022-06-10 2022-06-10
US63/350,983 2022-06-10
US18/207,854 2023-06-09
US18/207,854 US20230397950A1 (en) 2022-06-10 2023-06-09 Systems and methods for recommending ablation lines

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Citations (5)

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US20200352652A1 (en) 2019-05-06 2020-11-12 Biosense Webster (Israel) Ltd. Systems and methods for improving cardiac ablation procedures
US20210137384A1 (en) * 2017-12-13 2021-05-13 Washington University System and method for determining segments for ablation
US11198004B2 (en) 2019-04-11 2021-12-14 Biosense Webster (Israel) Ltd. Goal-driven workflow for cardiac arrhythmia treatment
US20220036560A1 (en) * 2020-07-30 2022-02-03 Biosense Webster (Israel) Ltd. Automatic segmentation of anatomical structures of wide area circumferential ablation points

Patent Citations (5)

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Publication number Priority date Publication date Assignee Title
US20200022649A1 (en) * 2016-11-16 2020-01-23 Navix International Limited Estimation of effectiveness of ablation adjacency
US20210137384A1 (en) * 2017-12-13 2021-05-13 Washington University System and method for determining segments for ablation
US11198004B2 (en) 2019-04-11 2021-12-14 Biosense Webster (Israel) Ltd. Goal-driven workflow for cardiac arrhythmia treatment
US20200352652A1 (en) 2019-05-06 2020-11-12 Biosense Webster (Israel) Ltd. Systems and methods for improving cardiac ablation procedures
US20220036560A1 (en) * 2020-07-30 2022-02-03 Biosense Webster (Israel) Ltd. Automatic segmentation of anatomical structures of wide area circumferential ablation points

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CORTES, CORINNAVAPNIK, VLADIMIR: "Support-vector networks'' (PDF", MACHINE LEARNING, vol. 20, no. 3, 1995, pages 273 - 297
GASTEIGER, J.BOJCHEVSKI, A.UNNEMANN, S. G.: "Predict then propogate: Graph neural networks meet personalized PageRank", INT. CONF. LEARN. REPRESENT., 2018
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KABA, R. A.MOMIN, A.CAMM, J: "Persistent atrial fibrillation: The role of left atrial posterior wall isolation and ablation strategies", J. CLIN. MED., 2021, pages 10
MARIA BIANCHI, F.GRATTAROLA, D.LIVI, L.ALIPPI, C, GRAPH NEURAL NETWORKS WITH CONVOLUTIONAL ARMA FILTERS, 2018

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