Patent Publication Number: US-2023157761-A1

Title: Smart image navigation for intracardiac echocardiography

Description:
TECHNICAL FIELD 
     The present invention relates generally to smart image navigation for ICE (intracardiac echocardiography), and in particular to automatic navigation of catheters for ICE. 
     BACKGROUND 
     ICE (intracardiac echocardiography) is an established imaging modality for cardiac electrophysiology procedures. ICE enables the operator to visualize cardiac anatomy, blood flow, and devices without general anesthesia and is therefore well suited for therapy guidance and adverse event detection and monitoring. Despite these advantages, ICE is used only sporadically during electrophysiology procedures. This is due to the complexity to performing ICE, the limited field-of-view of the ICE catheter, the requirement of two operators during the ICE procedure, and the demands of extensive training for effective manipulations and imaging. 
     BRIEF SUMMARY OF THE INVENTION 
     In accordance with one or more embodiments, systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions. 
     In one embodiment, a preoperative medical image of a patient is received. A first registration between the preoperative medical image and the image of the current view of the catheter is performed. A second registration between the preoperative medical image and an image of a view of the catheter navigated to a predefined position in the patient is received. The set of actions of the robotic navigation system for navigating the catheter from the current view towards a view corresponding to the preoperative medical image is determined based on the first registration and the second registration. The preoperative medical image is a medical image acquired for planning the same medical procedure for which the catheter is automatically navigated. 
     In one embodiment, a selection of a saved medical image of one or more saved medical images is received. A path in a graph from a first vertex representing a configuration of the robotic navigation system corresponding to the image of the current view of the catheter to a second vertex representing a configuration of the robotic navigation system corresponding to the selected saved medical image is determined. The set of actions of the robotic navigation system for navigating the catheter from the current view towards a view depicted in the selected saved medical image is determined based on the determined path. The one or more saved medical images were saved during the same medical procedure for which the catheter is automatically navigated. The graph is generated as a user navigates the catheter by adding a vertex to the graph in response to receiving user input. 
     In one embodiment, one or more images depicting standard anatomical views of the patient are received. The set of actions of the robotic navigation system for navigating the catheter from the current view towards a view depicted in the one or more images is determined. The one or more images depicting standard anatomical views comprise clinically significant views. 
     In one embodiment, the image of the current view is compared to an image of the target view to determine a similarity measure. The receiving, the determining, and the automatically navigating are repeated until a similarity threshold is satisfied. 
     In one embodiment, at least one of classification of current view of the catheter or identification of one or more anatomical objects of interest in the current view of the catheter are performed. The at least one of the classification of the current view of the catheter or the identification of the one or more anatomical objects of interest are identified in the current view of the catheter. 
     These and other advantages of the invention will be apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    shows a framework for automatically navigating a catheter using a robotic catheter navigation system for performing a medical procedure, in accordance with one or more embodiments; 
         FIG.  2    shows a method for automatically navigating a catheter using a robotic catheter navigation system for performing a medical procedure, in accordance with one or more embodiments; 
         FIG.  3    shows a method for navigating a catheter towards a target view depicted in preoperative medical images, in accordance with one or more medical images; 
         FIG.  4    shows a method for navigating a catheter towards a target view depicted in saved medical image, in accordance with one or more medical images; 
         FIG.  5    shows a schematic diagram for generating a graph, in accordance with one or more embodiments; 
         FIG.  6    shows a method for navigating a catheter towards a standard anatomical view of a patient, in accordance with one or more medical images; 
         FIG.  7    shows a workflow for determining a set of actions of a robotic navigation system for navigating a catheter from a current view towards a target view, in accordance with one or more embodiments; 
         FIG.  8    shows network architecture of a deep learning model for estimating the relative position of a current view of a catheter with respect to one or more target views, in accordance with one or more embodiments; 
         FIG.  9    shows exemplary views of a catheter with anatomical objects of interest labelled thereon, in accordance with one or more embodiment; 
         FIG.  10    shows exemplary views of a catheter with live quantification of patient anatomy and live tracking of therapy devices identified thereon, in accordance with one or more embodiment; 
         FIG.  11    shows an exemplary artificial neural network that may be used to implement one or more embodiments; 
         FIG.  12    shows a convolutional neural network that may be used to implement one or more embodiments; and 
         FIG.  13    shows a high-level block diagram of a computer that may be used to implement one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention generally relates to methods and systems for smart image navigation for ICE (intracardiac echocardiography). Embodiments of the present invention are described herein to give a visual understanding of such methods and systems. A digital image is often composed of digital representations of one or more objects (or shapes). The digital representation of an object is often described herein in terms of identifying and manipulating the objects. Such manipulations are virtual manipulations accomplished in the memory or other circuitry/hardware of a computer system. Accordingly, it is to be understood that embodiments of the present invention may be performed within a computer system using data stored within the computer system. 
     The present invention generally relates to automatic control of robotic catheter navigation systems to navigate a catheter towards a target view depicted in a medical image of a patient during a medical procedure. As used herein, a view depicted in a medical image refers to the depiction of the medical image. Accordingly, by navigating a catheter towards a target view depicted in a medical image, the catheter is navigated so that the view of the catheter is substantially the same as the target view depicted in the medical image. Various embodiments described herein provide for the target view being a view depicted in a preoperative planning medical image, a view depicted in a bookmarked medical image previously acquired during the medical procedure, or a standard anatomically significant view. Advantageously, embodiments described herein provide for efficient, precise, and reproducible view recovery and finding. 
       FIG.  1    shows a framework  100  for automatically navigating a catheter using a robotic catheter navigation system for performing a medical procedure, in accordance with one or more embodiments.  FIG.  1    will be further described below with reference to  FIGS.  1 - 10   .  FIG.  2    shows a method  200  for automatically navigating a catheter using a robotic catheter navigation system for performing a medical procedure, in accordance with one or more embodiments. The steps of method  200  may be performed by one or more suitable computing devices, such as, e.g., computer  1302  of  FIG.  13   . Framework  100  of  FIG.  1    and method  200  of  FIG.  2    may be performed for automatically navigating a catheter using a robotic catheter navigation system for various medical procedures, such as, e.g., ICE, TEE (transesophageal echo), etc. 
     At step  202  of  FIG.  2   , an image of a current view of a catheter in a patient is received. The current view of the catheter refers to the view of the catheter at its current location. In one example, as shown in  FIG.  1   , the image of the current view may be included in current probe pose and US (ultrasound) image appearance  102  in framework  100 . 
     The image of the current view may be of any suitable modality, such as, e.g., CT (computed tomography), MRI (magnetic resonance imaging), US, x-ray, or any other medical imaging modality or combinations of medical imaging modalities. The image of the current view may comprise 2D (two dimensional) images and/or 3D (three dimensional) volumes, and may comprise a single input medical image or a plurality of input medical images. The image of the current view may be received directly from an image acquisition device, such as, e.g., a CT scanner, as the image is acquired, or can be received by loading previously acquired medical images from a storage or memory of a computer system (e.g., a PACS (picture archiving and communication system)) or receiving medical images that have been transmitted from a remote computer system. 
     At step  204  of  FIG.  2   , a set of actions of a robotic navigation system is determined for navigating the catheter from the current view towards a target view using a machine learning based network. The robotic catheter navigation system may include any robotic catheter navigation system able to manipulate all 12 degrees of freedom of the catheter. The 12 degrees of freedom of the catheter represents all possible actions for moving the catheter: positive and negative translation in the X, Y, Z plane and clockwise and counterclockwise rotation in the yaw, pitch, and roll axes. The set of actions is selected from the possible actions for moving the catheter. 
     In a first embodiment, the target view is a view depicted in preoperative medical images. For example, as shown in  FIG.  1   , the set of actions is determined to navigate to pre-operative 3D data (e.g., CT, MR, US images), shown as option 1 in framework  100 . Navigating towards a target view depicted in preoperative medical images is further described below with respect to  FIG.  3   . 
     In a second embodiment, the target view is a view depicted in a saved medical image previously acquired during the same medical procedure for which the catheter is being navigated. For example, as shown in  FIG.  1   , the set of actions is determined to navigate to saved ICE views, shown as option 2 in framework  100 . Navigating towards a target view depicted in a saved medical image is further described below with respect to  FIG.  4   . 
     In a third embodiment, the target view is a view of a standard anatomical view. The standard anatomical view may be a clinically significant view, such as, e.g., the A4C (apical 4 chamber) of the heart. For example, as shown in  FIG.  1   , the set of actions is determined to navigate to standard ICE views  108 , shown as option 3 in framework  100 . Navigating towards a target view depicted in a medical image of a standard view is further described below with respect to  FIG.  6   . 
     In one embodiment, the set of actions is determined using a machine learning based network to predict a reward for each possible action for moving the catheter. For example, an Al (artificial intelligence) agent may be trained with deep reinforcement learning to predict a reward for each possible action of the robotic navigation system and the action or actions with the maximum reward is select as the set of actions. In one example, as shown in  FIG.  1   , Al view navigation system  110  predicts a reward  112  for each possible action and the action with the maximum reward is chosen  114  for navigating the catheter using the robotic catheter navigation system  116 . Determining the set of actions using the machine learning based network is further described below with respect to  FIG.  7   . 
     At step  206  of  FIG.  2   , the catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions. In one example, as shown in  FIG.  1   , the robotic navigation system  116  performs  118  the action with the highest reward. The current probe pose and US image appearance  102  is then updated and Al view navigation  110  may iteratively determine a next action for navigating the catheter towards the target view. Accordingly, method  200  may be iteratively repeated for any number of iterations (e.g., a predetermined number of iterations) to navigate the catheter towards the target view. 
       FIG.  3    shows a method  300  for navigating a catheter towards a target view depicted in preoperative medical images, in accordance with one or more medical images. The steps of method  300  may be performed by one or more suitable computing devices, such as, e.g., computer  1302  of  FIG.  13   . In one embodiment, method  300  may be performed at step  204  of  FIG.  2   . Method  300  of  FIG.  3    corresponds to option 1 in  FIG.  1   . 
     At step  302  of  FIG.  3   , a preoperative medical image of a patient is received. The preoperative medical image is a medical image acquired for planning the same medical procedure for which the catheter is being navigated (e.g., the medical procedure performed in framework  100  of  FIG.  1    or method  200  of  FIG.  2   ). In one example, as shown in  FIG.  1   , the preoperative medical image is pre-operative 3D data  104  in framework  100 . 
     The preoperative medical image may be of any suitable modality, such as, e.g., CT, MRI, US, x-ray, or any other medical imaging modality or combinations of medical imaging modalities. The preoperative medical image may comprise 2D images and/or 3D volumes, and may comprise a single input medical image or a plurality of input medical images. The preoperative medical image may be received directly from an image acquisition device, such as, e.g., a CT scanner, as the image is acquired, or can be received by loading previously acquired medical images from a storage or memory of a computer system (e.g., a PACS) or receiving medical images that have been transmitted from a remote computer system. 
     At step  304  of  FIG.  3   , a first registration between the preoperative medical image and a current image of a current view of a catheter is performed. In one example, as shown in  FIG.  1   , the first registration is performed by Al image fusion and planning system  120  of framework  100 . The current image may be the current image received at step  202  of  FIG.  2   . The current view of the catheter refers to the view of the catheter at its current location. The first registration may be performed as, e.g., CT-to-x-ray registration (e.g., 2D/3D registration), CT-to-DynaCT registration (3D/3D registration), or CT-to-US registration using any suitable approach. For example, the first registration may be performed based on known image-based registration techniques or known deep learning registration techniques. The first registration provides the location of the catheter with respect to the preoperative image. 
     At step  306  of  FIG.  3   , a second registration between the preoperative medical image and an image of a view of the catheter navigated to a predetermined position in the patient is performed. In one example, as shown in  FIG.  1   , the second registration is performed by Al image fusion and planning system  120  of framework  100 . After acquiring the current image (for step  304 ), the catheter is navigated to the predetermined position in the patient to acquire the image. The predetermined position in the patient may be a standard or otherwise known position and may have anatomical landmarks. The second registration may be performed as described with respect to the first registration at step  304 . The second registration provides the location of the catheter with respect to the patient’s anatomy and is only performed when the preoperative medical image is available for defining the target view. 
     In some embodiments, instead of performing the second registration, the location of the catheter with respect to the patient’s anatomy can be determined by acquiring two x-ray images to determine the precise pose estimation of the tip of the catheter, which can then be automatically mapped to the preoperative image (as determined at step  304 ). In another alternative embodiment, robotic sensors or other sensors at a tip of the catheter (e.g., inertial measurement units or fiber Bragg grating sensors) to geolocalize the tip of the catheter. 
     At step  308  of  FIG.  3   , a set of actions of a robotic navigation system is determined for navigating the catheter from the current view of the catheter towards a view depicted in the preoperative medical image based on the first registration and the second registration. In one example, as shown in  FIG.  1   , the set of actions is determined by Al view navigation system  110  of framework  100 . The set of actions may be determined using a machine learning based network as further described below with respect to  FIG.  7   . 
     In one embodiment, an Al (artificial intelligence) agent is trained for multi-task position estimation to estimate the relative position of current view to each target view depicted in the preoperative medical image based on the first registration and the second registration. The Al agent is trained using DRL (Deep Reinforcement Learning) to continuously move closer to the target view in each step. To further boost the accuracy of the target view localization obtained from multi-task position estimation, local search is then conducted via a deep action learning model. 
       FIG.  4    shows a method  400  for navigating a catheter towards a target view depicted in saved medical image, in accordance with one or more medical images. The steps of method  400  may be performed by one or more suitable computing devices, such as, e.g., computer  1302  of  FIG.  13   . In one embodiment, method  400  may be performed at step  204  of  FIG.  2   . Method  400  of  FIG.  4    corresponds to option 2 of  FIG.  1   . 
     At step  402  of  FIG.  4   , a selection of a saved medical image of one or more saved medical images is received. The one or more saved medical images were previously acquired and saved during the same medical procedure for which the catheter is being navigated (e.g., the medical procedure performed in framework  100  of  FIG.  1    or method  200  of  FIG.  2   ). In particular, during a prior initial stage of the medical procedure, a user navigates the catheter in the patient to one or more anatomical views of interest and user input is received from the user to acquire and save the one or more saved medical images in memory or storage as bookmarks, thereby creating a library of views that are easily reproducible. During a next stage of the medical procedure, at step  402 , the user selects one of the one or more saved medical images to navigate the catheter to view the one or more anatomical views of interest. In one example, as shown in  FIG.  1   , the one or more saved medical images are saved ICE views  106  in framework  100 . 
     The saved medical images may be of any suitable modality, such as, e.g., CT, MRI, US, x-ray, or any other medical imaging modality or combinations of medical imaging modalities. The saved medical images may comprise 2D images and/or 3D volumes, and may comprise a single input medical image or a plurality of input medical images. The saved medical images may be received directly from an image acquisition device, such as, e.g., a CT scanner, as the image is acquired, or can be received by loading previously acquired medical images from a storage or memory of a computer system (e.g., a PACS) or receiving medical images that have been transmitted from a remote computer system. 
     At step  404  of  FIG.  4   , a path in a graph is determined from a first vertex representing a configuration of a robotic navigation system corresponding to an image of a current view of a catheter to a second vertex representing a configuration of the robotic navigation system corresponding to the selected saved medical image. 
     The graph is generated during the prior initial stage of the medical procedure in substantially real time as the user navigates the catheter to the one or more anatomical views of interest.  FIG.  5    shows a schematic diagram  500  for generating a graph, in accordance with one or more embodiments. During the prior initial stage of the medical procedure, the user navigates the catheter along paths  502  in a patient. User input is received to acquire and save images  504 -A,  504 -B,  504 -C, and  504 -D (collectively referred to as saved images  504 ) depicting anatomical views of interest. Graph  506  is generated comprising vertices or nodes (shown as dots in  FIG.  5   ) and edges connecting the vertices. 
     Formally, let G(V,E) represent a topological graph  506  in which V is a set of vertices and E is edges connecting the vertices in the configuration space of the robotic navigation system. Vertices V represent configurations q of the robotic navigation system. As shown in  FIG.  5   , vertices  508 -A,  508 -B,  508 -C, and  508 -D (collectively referred to as vertices  508 ) correspond to configurations q (i.e., motor states) of the robotic navigation system at the time when user input is received for acquiring and saving the saved images  504 . Graph G  506  is generated during a generation phase in substantially real time by inserting vertices V according to a density parameter a so that vertices V are not spaced too closely. Density parameter a is a user-defined parameter for the resolution of the search space. A larger density parameter a results in a faster search, however the safety of the path would not be guaranteed as larger steps along the path could result in collisions with the anatomy. During a query phase (at step  404  of  FIG.  4   ), a pair of configurations is generated upon user request (i.e., selection of a saved medical image at step  402  of  FIG.  4   ) comprising a start configuration q s  and a target configuration q t . The start configuration q s  corresponds to the current configuration of the robotic navigation system and the target configuration q t  corresponds to the configuration of the robotic navigation system for the selected saved medical image. Since each configuration is represented as vertices V in graph G  506 , a discrete search algorithm may be applied to determine a sequence of edges E that forms a path between the start configuration q s  and the target configuration q t  in graph G  506 . The discrete search algorithm may be any suitable algorithm for determining a path between the start configuration q s  and the target configuration q t , such as, e.g., A* search algorithm. If the current position of the catheter does not correspond to a vertex of the graph G  506 , a new node is added to the graph G  506  for the current position (assuming that the density parameter a is satisfied). 
     At step  406  of  FIG.  4   , a set of actions of the robotic navigation system is determined for navigating the catheter from the current view of the catheter towards a view depicted in the selected saved medical image based on the determined path. The set of motions is given based on the targeted view depicted in the selected saved medical image. Since the current state (one node) is known, the path (set of motions) to the target view is identified. Method  400  in  FIG.  4    has all possible trajectories that were previously produced to easily reproduce a path. In one example, as shown in  FIG.  1   , the set of actions is determined by Al view navigation system  110  of framework  100 . The set of actions may be determined using a machine learning based network as further described below with respect to  FIG.  7   . 
     In one embodiment, local view refinement may be applied by comparing an image of the current view of the catheter to the selected saved medical image to generate a real time image similarity measure. The image similarity measure may be any suitable measure, such as, e.g., a normalized cross correlation, a structural similarity measure, a Dice similarity coefficient, or any other suitable metric. The real time image similarity measure is treated as an objective function, where the catheter is manipulated to maximize or satisfy an image similarity threshold between the image of the current view of the catheter and the selected saved medical image. This approach may be implemented in series, whereby the catheter is manipulated towards a target view based on the determined path and the catheter positioning is incrementally refined (e.g., by repeating iteratively repeating method  200 ) until a minimum image similarity criterion is satisfied. 
       FIG.  6    shows a method  600  for navigating a catheter to a standard anatomical view of a patient, in accordance with one or more medical images. The steps of method  600  may be performed by one or more suitable computing devices, such as, e.g., computer  1302  of  FIG.  13   . In one embodiment, method  600  may be performed at step  204  of  FIG.  2   . Method  600  of  FIG.  6    corresponds to option 3 of  FIG.  1   . 
     At step  602  of  FIG.  6   , one or more images depicting standard anatomical views of a patient are received. The standard anatomical views are clinically significant views of the patient. Exemplary standard anatomical views include PLAX (parasternal long axis), PSAX (parasternal short axis), A4C (apical 4 chamber), A3C (apical 3 chamber), A2C (apical 2 chamber), subcostal, and SSN (suprasternal). The standard anatomical views were not previously viewed or navigated to during the same medical procedure for which the catheter is being navigated (e.g., the medical procedure performed in framework  100  of  FIG.  1    or method  200  of  FIG.  2   ). In one example, as shown in  FIG.  1   , the one or more saved medical images are standard ICE views  108  in framework  100 . 
     The one or more images depicting standard anatomical views may be of any suitable modality, such as, e.g., CT, MRI, US, x-ray, or any other medical imaging modality or combinations of medical imaging modalities. The one or more images may comprise 2D images and/or 3D volumes, and may comprise a single input medical image or a plurality of input medical images. The one or more images may be received directly from an image acquisition device, such as, e.g., a CT scanner, as the image is acquired, or can be received by loading previously acquired medical images from a storage or memory of a computer system (e.g., a PACS) or receiving medical images that have been transmitted from a remote computer system. 
     At step  604  of  FIG.  6   , a set of actions of a robotic navigation system is determined for navigating a catheter from a current view to the one or more images depicting the standard anatomical views using a machine learning based network. In one example, as shown in  FIG.  1   , the set of actions is determined by Al view navigation system  110  of framework  100 . The set of actions may be determined using a machine learning based network as further described below with respect to  FIG.  7   . 
       FIG.  7    shows a workflow  700  for determining a set of actions of a robotic navigation system for navigating a catheter from a current view towards a target view, in accordance with one or more embodiments. In one example, workflow  700  may be performed by Al view navigation system  110  of  FIG.  1   . Workflow  700  may be performed to determine a set of actions of a robotic navigation system at step  204  of  FIG.  2   , step  308  of  FIG.  3   , step  406  of  FIG.  4   , or step  604  of  FIG.  6   . As shown in workflow  700 , a set of actions for navigating a catheter from a current view  702  towards one or more target views  708  according to a position estimation component  704  and a local search component  706 . 
     Position estimation component  704  estimates the relative position of current view  702  with respect to the one or more target views  708 .  FIG.  8    shows network architecture  800  of a deep learning model for estimating the relative position of a current view of a catheter with respect to one or more target views, in accordance with one or more embodiments. Considering the close-to-deterministic human cardiac structure, it is possible to learn the relative position of the current view to all target views simultaneously via multi-task learning. Given image I  802  of the current view of the catheter and N target views with relative positions denoted as P E1   808 -A, P E2   808 -B, and P EN  808-N (collectively referred to as relative positions  808 ), network architecture  800  comprises a feature extraction component  804  and a position estimation component  806 . Feature extraction component  804  comprises convolution (conv) layers and pooling layers and is parameterized by W to extract latent features from image I  802 . Position estimation component  806  comprises N branches for respectively estimating relative positions  808  of image I  802 . Each branch of position estimation component  806  comprises FC (fully connected) layers and is parameterized by {W į } n į=1   . The multi-task model training object may be defined according to the following objective: 
     
       
         
           
             
               
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     Comparing the training of the deep learning model of network architecture  800  for each target view individually, the multi-task model has the following advantages. As the relative position of all target views are highly correlated, using all information in the supervised learning can boost the accuracy of predicting each one of P E1   808 -A, P E2   808 -B, and P EN  808-N. Because the feature extraction component is shared by all position prediction components, the embodiments described in  FIG.  8    are more memory efficient than training N models for estimating the relative position to the N target views separately. 
     Local search component  706  of  FIG.  7    may be learned via deep reinforce learning to train Al (artificial intelligence) agents to predict proper rewards to move the catheter closer to the target view in each step. Such rewards may be predicted, for example, using known techniques. 
     In one embodiment, the spatial relationship the different target views may be further leveraged during training. For example, only probe rotation is needed in order to move the catheter from the target view of A4C to A3C. Suppose the A4C view has been acquired according to the methods described above. To predict the rotation parameter from the A4C view to the A3C view, a convolutional neural network may be learned according to the following objective: 
     
       
         
           
             
               
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      where A i  is the ground=truth rotation parameter of training image Ii and µ AAC-A3c  is the average rotation angle from A4C to A3C, which may be learned statistically from training samples. Empirically, using µ A4C-A3C  as prior knowledge can significantly improve the model prediction accuracy. The training samples may be acquired via various approaches. For example, the training samples may be acquired as simulations of US images from other 3D modalities (e.g., CT, MR, etc.) that have a global view of human anatomy over a large population of patients. In another example, the training samples may be acquired via synchronized acquisition of US images with recorded catheter positions over a large population of patients and a large variation of catheter positions. Such training samples may be used for offline training. In a further example, the training samples may be acquired by online update of the agent over a smaller number of patients starting from the model trained offline. 
     In one embodiment, during navigation of the catheter (e.g., during step  206  of  FIG.  2   ), the current view of the catheter may be automatically classified as being a certain standard anatomical view (e.g., PLAX, PSAX, A4C, A3C, A2C, subcostal, or SSN) and anatomical objects of interest (e.g., the atriums, ventricles, pulmonary veins, left atrial appendages, etc.) shown in the current view of the catheter may be identified and the classification of the current view and the identified anatomical objects of interest may be identified in the current view. In one example, as shown in  FIG.  1   , the classification of the current view of the catheter and the identification and labelling of the anatomical objects of interest may be performed by Al anatomy recognition  122  of framework  100 . In one embodiment, the classification of the current view and the labelling of the anatomical objects of interest may be performed using a machine learning based network. The classification of the current view and the labelling of the anatomical objects of interest facilitate the navigation of the catheter by recognizing anatomical objects of interest encountered during the navigation of the catheter and providing the recognized anatomical objects of interest to the Al agent for optimal planning of the next action.  FIG.  9    shows exemplary views of a catheter with anatomical objects of interest labelled thereon, in accordance with one or more embodiment. In view  902 , the LA (left atrium) and LAA (left atrium appendage) are labelled thereon. In view  904 , the LA, LAA, and LSPV (left superior pulmonary vein) are labelled thereon. 
     In one embodiment, clinician knowledge can be incorporated in the training and execution of the Al agent employed for navigating the catheter using, for example, deep Q-learning from demonstrations. In one example, as shown in  FIG.  1   , clinical knowledge is represented as clinical know-hows  124  of framework  100 . The clinician knowledge may comprise knowledge about the anatomy and common practice in path planning during specific procedures. An example of clinician knowledge may include that a typically ICE study starts at a home view obtained with a catheter placed in the mid-right atrium and the transducer in a neutral position facing the tricuspid valve. The home view provides imaging of the right atrium, tricuspid valve, right ventricle, and typically an oblique or short-axis view of the aortic valve. From the home view, clockwise rotation of the catheter brings into view the aortic valve in the long axis and the right ventricle outflow tract. Additional clockwise rotation allows visualization of the mitral valve and interatrial septum, with the left atrial appendage anteriorly and the coronary sinus posteriorly. 
     In one embodiment, a safety check may be performed using, for example, image-based approaches or sensor-based approaches. In one example, as shown in  FIG.  1   , the safety check may be performed by safety check  126  in framework  100 . To perform the safety check, tissue boundaries in the neighborhood of the catheter (e.g., the ICE catheter and therapy catheters) are clearly delineated (e.g., by the user). Real-time tracking of the catheters is performed so that an adequate safety zone could be allocated during the navigation of the catheters. In the case of navigating the ICE catheter, an augmented anatomical view provided by the co-registered preoperative image is utilized to provide the relative position of the ICE catheter with respect to the patient’s anatomy. 
     In one embodiment, live tracking and quantification is performed. In one example, as shown in  FIG.  1   , the live tracking and quantification may be performed by Al live quantification  128  of framework  100 . Live quantification of patient anatomy and live tracking of therapy devices provides optimal guidance and monitoring of therapy delivery.  FIG.  10    shows exemplary views of a catheter with live quantification of patient anatomy and live tracking of therapy devices identified thereon, in accordance with one or more embodiment. As shown in views  1002 , for a left atrial appendage closure procedure, an LAA ostium model  1006  is shown to provide shape detection and tracking of its measurement over a cardiac cycle, which is important for device selection and therapy outcome prediction. As shown in views  1004 , an atrium transseptal puncture needle is tracked by tracker  1008 . Tracking of therapy devices in ICE images and its display in the context of the 3D roadmap created from either 4D ICE or co-registered preoperative volumetric images (e.g., CT, MR, US) could help monitor the device position during therapy. In addition, the identified and tracked therapy devices and/or anatomies of interest may be used to feed the control loop of the Al agents for navigating the ICE catheter so that the therapy devices or anatomies of interest are maintained in the center of the view or otherwise optimally positioned in the view during the procedure. 
     Embodiments described herein are described with respect to the claimed systems as well as with respect to the claimed methods. Features, advantages or alternative embodiments herein can be assigned to the other claimed objects and vice versa. In other words, claims for the systems can be improved with features described or claimed in the context of the methods. In this case, the functional features of the method are embodied by objective units of the providing system. 
     Furthermore, certain embodiments described herein are described with respect to methods and systems utilizing trained machine learning based networks (or models), as well as with respect to methods and systems for training machine learning based networks. Features, advantages or alternative embodiments herein can be assigned to the other claimed objects and vice versa. In other words, claims for methods and systems for training a machine learning based network can be improved with features described or claimed in context of the methods and systems for utilizing a trained machine learning based network, and vice versa. 
     In particular, the trained machine learning based networks applied in embodiments described herein can be adapted by the methods and systems for training the machine learning based networks. Furthermore, the input data of the trained machine learning based network can comprise advantageous features and embodiments of the training input data, and vice versa. Furthermore, the output data of the trained machine learning based network can comprise advantageous features and embodiments of the output training data, and vice versa. 
     In general, a trained machine learning based network mimics cognitive functions that humans associate with other human minds. In particular, by training based on training data, the trained machine learning based network is able to adapt to new circumstances and to detect and extrapolate patterns. 
     In general, parameters of a machine learning based network can be adapted by means of training. In particular, supervised training, semi-supervised training, unsupervised training, reinforcement learning and/or active learning can be used. Furthermore, representation learning (an alternative term is “feature learning”) can be used. In particular, the parameters of the trained machine learning based network can be adapted iteratively by several steps of training. 
     In particular, a trained machine learning based network can comprise a neural network, a support vector machine, a decision tree, and/or a Bayesian network, and/or the trained machine learning based network can be based on k-means clustering, Q-learning, genetic algorithms, and/or association rules. In particular, a neural network can be a deep neural network, a convolutional neural network, or a convolutional deep neural network. Furthermore, a neural network can be an adversarial network, a deep adversarial network and/or a generative adversarial network. 
       FIG.  11    shows an embodiment of an artificial neural network  1100 , in accordance with one or more embodiments. Alternative terms for “artificial neural network” are “neural network”, “artificial neural net” or “neural net”. Machine learning networks described herein, such as, e.g., AI view navigation system  110  or AI anatomy recognition  122  of  FIG.  1   , the machine learning based network applied at step  204  of  FIG.  2   , the position estimation  704  of  FIG.  7   , and the network architecture  800  of  FIG.  8   , may be implemented using artificial neural network  1100 . 
     The artificial neural network  1100  comprises nodes  1102 - 1122  and edges  1132 ,  1134 , ...,  1136 , wherein each edge  1132 ,  1134 , ...,  1136  is a directed connection from a first node  1102 - 1122  to a second node  1102 - 1122 . In general, the first node  1102 - 1122  and the second node  1102 - 1122  are different nodes  1102 - 1122 , it is also possible that the first node  1102 - 1122  and the second node  1102 - 1122  are identical. For example, in  FIG.  11   , the edge  1132  is a directed connection from the node  1102  to the node  1106 , and the edge  1134  is a directed connection from the node  1104  to the node  1106 . An edge  1132 ,  1134 , ...,  1136  from a first node  1102 - 1122  to a second node  1102 - 1122  is also denoted as “ingoing edge” for the second node  1102 - 1122  and as “outgoing edge” for the first node  1102 - 1122 . 
     In this embodiment, the nodes  1102 - 1122  of the artificial neural network  1100  can be arranged in layers  1124 - 1130 , wherein the layers can comprise an intrinsic order introduced by the edges  1132 ,  1134 , ...,  1136  between the nodes  1102 - 1122 . In particular, edges  1132 ,  1134 , ...,  1136  can exist only between neighboring layers of nodes. In the embodiment shown in  FIG.  11   , there is an input layer  1124  comprising only nodes  1102  and  1104  without an incoming edge, an output layer  1130  comprising only node  1122  without outgoing edges, and hidden layers  1126 ,  1128  in-between the input layer  1124  and the output layer  1130 . In general, the number of hidden layers  1126 ,  1128  can be chosen arbitrarily. The number of nodes  1102  and  1104  within the input layer  1124  usually relates to the number of input values of the neural network  1100 , and the number of nodes  1122  within the output layer  1130  usually relates to the number of output values of the neural network  1100 . 
     In particular, a (real) number can be assigned as a value to every node  1102 - 1122  of the neural network  1100 . Here, x (n)   i  denotes the value of the i-th node  1102 - 1122  of the n-th layer  1124 - 1130 . The values of the nodes  1102 - 1122  of the input layer  1124  are equivalent to the input values of the neural network  1100 , the value of the node  1122  of the output layer  1130  is equivalent to the output value of the neural network  1100 . Furthermore, each edge  1132 ,  1134 , ...,  1136  can comprise a weight being a real number, in particular, the weight is a real number within the interval [-1, 1] or within the interval [0, 1]. Here, w (m,n)   i,j  denotes the weight of the edge between the i-th node  1102 - 1122  of the m-th layer  1124 - 1130  and the j-th node  1102 - 1122  of the n-th layer  1124 - 1130 . Furthermore, the abbreviation w (n)   i,j  is defined for the weight w (n,n+1)   i,j . 
     In particular, to calculate the output values of the neural network  1100 , the input values are propagated through the neural network. In particular, the values of the nodes  1102 - 1122  of the (n+1)-th layer  1124 - 1130  can be calculated based on the values of the nodes  1102 - 1122  of the n-th layer  1124 - 1130  by 
     
       
         
           
             
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     Herein, the function f is a transfer function (another term is “activation function”). Known transfer functions are step functions, sigmoid function (e.g. the logistic function, the generalized logistic function, the hyperbolic tangent, the Arctangent function, the error function, the smoothstep function) or rectifier functions. The transfer function is mainly used for normalization purposes. 
     In particular, the values are propagated layer-wise through the neural network, wherein values of the input layer  1124  are given by the input of the neural network  1100 , wherein values of the first hidden layer  1126  can be calculated based on the values of the input layer  1124  of the neural network, wherein values of the second hidden layer  1128  can be calculated based in the values of the first hidden layer  1126 , etc. 
     In order to set the values w (m,n)   i,j  for the edges, the neural network  1100  has to be trained using training data. In particular, training data comprises training input data and training output data (denoted as t i ). For a training step, the neural network  1100  is applied to the training input data to generate calculated output data. In particular, the training data and the calculated output data comprise a number of values, said number being equal with the number of nodes of the output layer. 
     In particular, a comparison between the calculated output data and the training data is used to recursively adapt the weights within the neural network  1100  (backpropagation algorithm). In particular, the weights are changed according to 
     
       
         
           
             
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      if the (n+1)-th layer is the output layer  1130 , wherein f′ is the first derivative of the activation function, and y (n+1)   j  is the comparison training value for the j-th node of the output layer  1130 . 
       FIG.  12    shows a convolutional neural network  1200 , in accordance with one or more embodiments. Machine learning networks described herein, such as, e.g., AI view navigation system  110  or AI anatomy recognition  122  of  FIG.  1   , the machine learning based network applied at step  204  of  FIG.  2   , the position estimation  704  of  FIG.  7   , and the network architecture  800  of  FIG.  8   , may be implemented using convolutional neural network  1200 . 
     In the embodiment shown in  FIG.  12   , the convolutional neural network comprises  1200  an input layer  1202 , a convolutional layer  1204 , a pooling layer  1206 , a fully connected layer  1208 , and an output layer  1210 . Alternatively, the convolutional neural network  1200  can comprise several convolutional layers  1204 , several pooling layers  1206 , and several fully connected layers  1208 , as well as other types of layers. The order of the layers can be chosen arbitrarily, usually fully connected layers  1208  are used as the last layers before the output layer  1210 . 
     In particular, within a convolutional neural network  1200 , the nodes  1212 - 1220  of one layer  1202 - 1210  can be considered to be arranged as a d-dimensional matrix or as a d-dimensional image. In particular, in the two-dimensional case the value of the node  1212 - 1220  indexed with i and j in the n-th layer  1202 - 1210  can be denoted as x (n )[i,j]. However, the arrangement of the nodes  1212 - 1220  of one layer  1202 - 1210  does not have an effect on the calculations executed within the convolutional neural network  1200  as such, since these are given solely by the structure and the weights of the edges. 
     In particular, a convolutional layer  1204  is characterized by the structure and the weights of the incoming edges forming a convolution operation based on a certain number of kernels. In particular, the structure and the weights of the incoming edges are chosen such that the values x (n)   k  of the nodes  1214  of the convolutional layer  1204  are calculated as a convolution x (n)   k  = K k  * x (n-1)  based on the values x (n-1)  of the nodes  1212  of the preceding layer  1202 , where the convolution * is defined in the two-dimensional case as 
     
       
         
           
             
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     Here the k-th kernel K k  is a d-dimensional matrix (in this embodiment a two-dimensional matrix), which is usually small compared to the number of nodes  1212 - 1218  (e.g. a 3x3 matrix, or a 5x5 matrix). In particular, this implies that the weights of the incoming edges are not independent, but chosen such that they produce said convolution equation. In particular, for a kernel being a 3x3 matrix, there are only 9 independent weights (each entry of the kernel matrix corresponding to one independent weight), irrespectively of the number of nodes  1212 - 1220  in the respective layer  1202 - 1210 . In particular, for a convolutional layer  1204 , the number of nodes  1214  in the convolutional layer is equivalent to the number of nodes  1212  in the preceding layer  1202  multiplied with the number of kernels. 
     If the nodes  1212  of the preceding layer  1202  are arranged as a d-dimensional matrix, using a plurality of kernels can be interpreted as adding a further dimension (denoted as “depth” dimension), so that the nodes  1214  of the convolutional layer  1204  are arranged as a (d+1)-dimensional matrix. If the nodes  1212  of the preceding layer  1202  are already arranged as a (d+1)-dimensional matrix comprising a depth dimension, using a plurality of kernels can be interpreted as expanding along the depth dimension, so that the nodes  1214  of the convolutional layer  1204  are arranged also as a (d+1)-dimensional matrix, wherein the size of the (d+1)-dimensional matrix with respect to the depth dimension is by a factor of the number of kernels larger than in the preceding layer  1202 . 
     The advantage of using convolutional layers  1204  is that spatially local correlation of the input data can exploited by enforcing a local connectivity pattern between nodes of adjacent layers, in particular by each node being connected to only a small region of the nodes of the preceding layer. 
     In embodiment shown in  FIG.  12   , the input layer  1202  comprises 36 nodes  1212 , arranged as a two-dimensional 6x6 matrix. The convolutional layer  1204  comprises 72 nodes  1214 , arranged as two two-dimensional 6x6 matrices, each of the two matrices being the result of a convolution of the values of the input layer with a kernel. Equivalently, the nodes  1214  of the convolutional layer  1204  can be interpreted as arranges as a three-dimensional 6x6x2 matrix, wherein the last dimension is the depth dimension. 
     A pooling layer  1206  can be characterized by the structure and the weights of the incoming edges and the activation function of its nodes  1216  forming a pooling operation based on a non-linear pooling function f. For example, in the two dimensional case the values x (n)  of the nodes  1216  of the pooling layer  1206  can be calculated based on the values x (n-1)  of the nodes  1214  of the preceding layer  1204  as 
     
       
         
           
             
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     In other words, by using a pooling layer  1206 , the number of nodes  1214 ,  1216  can be reduced, by replacing a number d1▪d2 of neighboring nodes  1214  in the preceding layer  1204  with a single node  1216  being calculated as a function of the values of said number of neighboring nodes in the pooling layer. In particular, the pooling function f can be the max-function, the average or the L2-Norm. In particular, for a pooling layer  1206  the weights of the incoming edges are fixed and are not modified by training. 
     The advantage of using a pooling layer  1206  is that the number of nodes  1214 ,  1216  and the number of parameters is reduced. This leads to the amount of computation in the network being reduced and to a control of overfitting. 
     In the embodiment shown in  FIG.  12   , the pooling layer  1206  is a max-pooling, replacing four neighboring nodes with only one node, the value being the maximum of the values of the four neighboring nodes. The max-pooling is applied to each d-dimensional matrix of the previous layer; in this embodiment, the max-pooling is applied to each of the two two-dimensional matrices, reducing the number of nodes from 72 to 18. 
     A fully-connected layer  1208  can be characterized by the fact that a majority, in particular, all edges between nodes  1216  of the previous layer  1206  and the nodes  1218  of the fully-connected layer  1208  are present, and wherein the weight of each of the edges can be adjusted individually. 
     In this embodiment, the nodes  1216  of the preceding layer  1206  of the fully-connected layer  1208  are displayed both as two-dimensional matrices, and additionally as non-related nodes (indicated as a line of nodes, wherein the number of nodes was reduced for a better presentability). In this embodiment, the number of nodes  1218  in the fully connected layer  1208  is equal to the number of nodes  1216  in the preceding layer  1206 . Alternatively, the number of nodes  1216 ,  1218  can differ. 
     Furthermore, in this embodiment, the values of the nodes  1220  of the output layer  1210  are determined by applying the Softmax function onto the values of the nodes  1218  of the preceding layer  1208 . By applying the Softmax function, the sum the values of all nodes  1220  of the output layer  1210  is 1, and all values of all nodes  1220  of the output layer are real numbers between 0 and 1. 
     A convolutional neural network  1200  can also comprise a ReLU (rectified linear units) layer or activation layers with non-linear transfer functions. In particular, the number of nodes and the structure of the nodes contained in a ReLU layer is equivalent to the number of nodes and the structure of the nodes contained in the preceding layer. In particular, the value of each node in the ReLU layer is calculated by applying a rectifying function to the value of the corresponding node of the preceding layer. 
     The input and output of different convolutional neural network blocks can be wired using summation (residual / dense neural networks), element-wise multiplication (attention) or other differentiable operators. Therefore, the convolutional neural network architecture can be nested rather than being sequential if the whole pipeline is differentiable. 
     In particular, convolutional neural networks  1200  can be trained based on the backpropagation algorithm. For preventing overfitting, methods of regularization can be used, e.g. dropout of nodes  1212 - 1220 , stochastic pooling, use of artificial data, weight decay based on the L1 or the L2 norm, or max norm constraints. Different loss functions can be combined for training the same neural network to reflect the joint training objectives. A subset of the neural network parameters can be excluded from optimization to retain the weights pretrained on another datasets. 
     Systems, apparatuses, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc. 
     Systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computer and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers. 
     Systems, apparatus, and methods described herein may be implemented within a network-based cloud computing system. In such a network-based cloud computing system, a server or another processor that is connected to a network communicates with one or more client computers via a network. A client computer may communicate with the server via a network browser application residing and operating on the client computer, for example. A client computer may store data on the server and access the data via the network. A client computer may transmit requests for data, or requests for online services, to the server via the network. The server may perform requested services and provide data to the client computer(s). The server may also transmit data adapted to cause a client computer to perform a specified function, e.g., to perform a calculation, to display specified data on a screen, etc. For example, the server may transmit a request adapted to cause a client computer to perform one or more of the steps or functions of the methods and workflows described herein, including one or more of the steps or functions of  FIGS.  1 - 4  and  6   . Certain steps or functions of the methods and workflows described herein, including one or more of the steps or functions of  FIGS.  1 - 4  and  6   , may be performed by a server or by another processor in a network-based cloud-computing system. Certain steps or functions of the methods and workflows described herein, including one or more of the steps of  FIGS.  1 - 4  and  6   , may be performed by a client computer in a network-based cloud computing system. The steps or functions of the methods and workflows described herein, including one or more of the steps of  FIGS.  1 - 4  and  6   , may be performed by a server and/or by a client computer in a network-based cloud computing system, in any combination. 
     Systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method and workflow steps described herein, including one or more of the steps or functions of  FIGS.  1 - 4  and  6   , may be implemented using one or more computer programs that are executable by such a processor. A computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. 
     A high-level block diagram of an example computer  1302  that may be used to implement systems, apparatus, and methods described herein is depicted in  FIG.  13   . Computer  1302  includes a processor  1304  operatively coupled to a data storage device  1312  and a memory  1310 . Processor  1304  controls the overall operation of computer  1302  by executing computer program instructions that define such operations. The computer program instructions may be stored in data storage device  1312 , or other computer readable medium, and loaded into memory  1310  when execution of the computer program instructions is desired. Thus, the method and workflow steps or functions of  FIGS.  1 - 4  and  6    can be defined by the computer program instructions stored in memory  1310  and/or data storage device  1312  and controlled by processor  1304  executing the computer program instructions. For example, the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform the method and workflow steps or functions of  FIGS.  1 - 4  and  6   . Accordingly, by executing the computer program instructions, the processor  1304  executes the method and workflow steps or functions of  FIGS.  1 - 4  and  6   . Computer  1302  may also include one or more network interfaces  1306  for communicating with other devices via a network. Computer  1302  may also include one or more input/output devices  1308  that enable user interaction with computer  1302  (e.g., display, keyboard, mouse, speakers, buttons, etc.). 
     Processor  1304  may include both general and special purpose microprocessors, and may be the sole processor or one of multiple processors of computer  1302 . Processor  1304  may include one or more central processing units (CPUs), for example. Processor  1304 , data storage device  1312 , and/or memory  1310  may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs). 
     Data storage device  1312  and memory  1310  each include a tangible non-transitory computer readable storage medium. Data storage device  1312 , and memory  1310 , may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices. 
     Input/output devices  1308  may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices  1308  may include a display device such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor for displaying information to the user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to computer  1302 . 
     An image acquisition device  1314  can be connected to the computer  1302  to input image data (e.g., medical images) to the computer  1302 . It is possible to implement the image acquisition device  1314  and the computer  1302  as one device. It is also possible that the image acquisition device  1314  and the computer  1302  communicate wirelessly through a network. In a possible embodiment, the computer  1302  can be located remotely with respect to the image acquisition device  1314 . 
     Any or all of the systems and apparatus discussed herein may be implemented using one or more computers such as computer  1302 . 
     One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that  FIG.  13    is a high level representation of some of the components of such a computer for illustrative purposes. 
     The foregoing Detailed Description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the Detailed Description, but rather from the claims as interpreted according to the full breadth permitted by the patent laws. It is to be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Those skilled in the art could implement various other feature combinations without departing from the scope and spirit of the invention.