Abstract: A method for designing a patient-customized EA or selecting an existing EA that fits the patient best includes segmenting shapes of SOIs of the cochlea in a pre-operative CT image using a shape model; defining a 3D curve of interest within the shape model of the SOIs as a sequence of points-; automatically transforming the defined 3D curve to the pre-operative CT image so as to obtain a structure curve in the cochlea; determining a length and curvatures of the structure curve at the sequence of points; and designing a patient-customized EA or selecting an existing EA based on the determined length and curvatures of the structure curve such that after the EA shape model, which estimates the resting state shape of the EA, is rigidly registered to the structure curve in the cochlea, the EA shape model has a registration error smaller than a preset value.

Abstract: A deep-learning-based method for metal artifact reduction in CT images includes providing a dataset and a cGAN. The dataset includes CT image pairs, randomly partitioned into a training set, a validation set, and a testing set. Each Pre-CT and Post-CT image pairs is respectively acquired in a region before and after an implant is implanted. The Pre-CT and Post-CT images of each pair are artifact-free CT and artifact-affected CT images, respectively. The cGAN is conditioned on the Post-CT images, includes a generator and a discriminator that operably compete with each other, and is characterized with a training objective that is a sum of an adversarial loss and a reconstruction loss. The method also includes training the cGAN with the dataset; inputting the post-operatively acquired CT image to the trained cGAN; and generating an artifact-corrected image by the trained cGAN, where metal artifacts are removed in the artifact-corrected image.

Abstract: A method for active electrode selection in a cochlear implant having an electrode array with a plurality of electrodes implanted in a cochlea of a living subject. The method includes estimating an activation region (AR) of each electrode based on its distance to nerve sites; presenting the AR in a visualization representation, wherein each electrode is represented by a bar having a width or length representing the AR; identifying electrodes having substantial AR overlap if the AR of one electrode overlaps substantially with the AR bar of another electrode; and selecting and deactivating at least one of the identified electrodes with substantial AR overlap.

Abstract: Systems and methods are provided for performing model-based cochlear implant programming (MOCIP) on a living subject with a cochlear implant (CI) to determine stimulation settings of a patient-customized electro-neural interface (ENI) model. The method includes: localizing an electrode array of the CI and intracochlear structures of the living subject to determine patient-specific electrode positions of the CI and a patient-specific anatomy shape; generating a CI electric field model based on the patient-specific electrodes positions of the CI and the patient-specific anatomy shape; and establishing an auditory nerve fiber (ANF) bundle model using the CI electric field model, and estimating neural health of the living subject using the ANF bundle model applications of the same.

Abstract: A method for using information of patient-specific cochlea size and/or shape to determine a patient-customized cochlear implant electrode insertion and placement plan includes segmenting shapes of structures of interest (SOIs) of the cochlea in a pre-operative CT image of the cochlea using a shape model; defining a 3D modiolar hugging curve within the shape model of the SOIs as a sequence of points; automatically transforming the defined 3D modiolar hugging curve to the pre-operative CT image so as to obtain a modiolar curve in the cochlea; rigidly registering an EA shape model of the EA to the modiolar curve in the cochlea, thereby placing a resting state shape of the EA within the patient's SOIs such that the EA matches the modiolar curve in the cochlea; and determining a patient-customized insertion plan for electrode placement using the registered EA shape model.

Abstract: A deep-learning-based method for metal artifact reduction in CT images includes providing a dataset and a cGAN. The dataset includes CT image pairs, randomly partitioned into a training set, a validation set, and a testing set. Each Pre-CT and Post-CT image pairs is respectively acquired in a region before and after an implant is implanted. The Pre-CT and Post-CT images of each pair are artifact-free CT and artifact-affected CT images, respectively. The cGAN is conditioned on the Post-CT images, includes a generator and a discriminator that operably compete with each other, and is characterized with a training objective that is a sum of an adversarial loss and a reconstruction loss. The method also includes training the cGAN with the dataset; inputting the post-operatively acquired CT image to the trained cGAN; and generating an artifact-corrected image by the trained cGAN, where metal artifacts are removed in the artifact-corrected image.

Abstract: A method for designing a patient-customized EA or selecting an existing EA that fits the patient best includes segmenting shapes of SOIs of the cochlea in a pre-operative CT image using a shape model; defining a 3D curve of interest within the shape model of the SOIs as a sequence of points-; automatically transforming the defined 3D curve to the pre-operative CT image so as to obtain a structure curve in the cochlea; determining a length and curvatures of the structure curve at the sequence of points; and designing a patient-customized EA or selecting an existing EA based on the determined length and curvatures of the structure curve such that after the EA shape model, which estimates the resting state shape of the EA, is rigidly registered to the structure curve in the cochlea, the EA shape model has a registration error smaller than a preset value.

Abstract: A method for designing a patient-customized EA or selecting an existing EA that fits the patient best includes segmenting shapes of SOIs of the cochlea in a pre-operative CT image using a shape model; defining a 3D curve of interest within the shape model of the SOIs as a sequence of points-; automatically transforming the defined 3D curve to the pre-operative CT image so as to obtain a structure curve in the cochlea; determining a length and curvatures of the structure curve at the sequence of points; and designing a patient-customized EA or selecting an existing EA based on the determined length and curvatures of the structure curve such that after the EA shape model, which estimates the resting state shape of the EA, is rigidly registered to the structure curve in the cochlea, the EA shape model has a registration error smaller than a preset value.

Abstract: A method for using information of patient-specific cochlea size and/or shape to determine a patient-customized cochlear implant electrode insertion and placement plan includes segmenting shapes of structures of interest (SOIs) of the cochlea in a pre-operative CT image of the cochlea using a shape model; defining a 3D modiolar hugging curve within the shape model of the SOIs as a sequence of points; automatically transforming the defined 3D modiolar hugging curve to the pre-operative CT image so as to obtain a modiolar curve in the cochlea; rigidly registering an EA shape model of the EA to the modiolar curve in the cochlea, thereby placing a resting state shape of the EA within the patient's SOIs such that the EA matches the modiolar curve in the cochlea; and determining a patient-customized insertion plan for electrode placement using the registered EA shape model.

Abstract: A method for designing a patient-customized EA or selecting an existing EA that fits the patient best includes segmenting shapes of SOIs of the cochlea in a pre-operative CT image using a shape model; defining a 3D curve of interest within the shape model of the SOIs as a sequence of points-; automatically transforming the defined 3D curve to the pre-operative CT image so as to obtain a structure curve in the cochlea; determining a length and curvatures of the structure curve at the sequence of points; and designing a patient-customized EA or selecting an existing EA based on the determined length and curvatures of the structure curve such that after the EA shape model, which estimates the resting state shape of the EA, is rigidly registered to the structure curve in the cochlea, the EA shape model has a registration error smaller than a preset value.

Abstract: One aspect of the invention provides a method for customizing cochlear implant stimulation of a living subject. The cochlear implant includes an electrode array having a plurality of electrodes implanted in a cochlea of the living subject. The method includes determining a position for each of the plurality of electrodes and spiral ganglion nerves that the electrode array stimulates, determining a geometric relationship between neural pathways within the cochlea and the electrode array implanted therein, and using one or more electrodes of the electrode array to stimulate a group of SG neural pathways of the cochlea based on the location of the one or more electrodes and their geometric relationship with the neural pathways.

Abstract: A method for automatic segmentation of intra-cochlear anatomy in post-implantation CT image of bilateral cochlear implant recipients includes coarsely segmenting a labyrinth with a labyrinth surface chosen from a library of inner ear anatomy shapes; creating a target specific ASM for each of the labyrinth and the SOIs using a set of inner ear anatomy surfaces selected from the library of inner ear anatomy shapes such that the set of inner ear anatomy surfaces has the smallest dissimilarity quantity with the coarsely localized labyrinth surface in the post-implantation CT image; refining the coarsely segmented labyrinth surface by performing an ASM-based segmentation of the labyrinth using the target-specific ASM of the labyrinth to obtain a segmented labyrinth; and fitting the points of the target-specific ASM of the SOIs to their corresponding points on the segmented labyrinth to segment the SOIs in the post-implantation CT image.

Abstract: A method for automatic segmentation of intra-cochlear anatomy in post-implantation CT image of bilateral cochlear implant recipients includes coarsely segmenting a labyrinth with a labyrinth surface chosen from a library of inner ear anatomy shapes; creating a target specific ASM for each of the labyrinth and the SOIs using a set of inner ear anatomy surfaces selected from the library of inner ear anatomy shapes such that the set of inner ear anatomy surfaces has the smallest dissimilarity quantity with the coarsely localized labyrinth surface in the post-implantation CT image; refining the coarsely segmented labyrinth surface by performing an ASM-based segmentation of the labyrinth using the target-specific ASM of the labyrinth to obtain a segmented labyrinth; and fitting the points of the target-specific ASM of the SOIs to their corresponding points on the segmented labyrinth to segment the SOIs in the post-implantation CT image.

Abstract: One aspect of the invention provides a method for customizing cochlear implant stimulation of a living subject. The cochlear implant includes an electrode array having a plurality of electrodes implanted in a cochlea of the living subject. The method includes determining a position for each of the plurality of electrodes and spiral ganglion nerves that the electrode array stimulates, determining a geometric relationship between neural pathways within the cochlea and the electrode array implanted therein, and using one or more electrodes of the electrode array to stimulate a group of SG neural pathways of the cochlea based on the location of the one or more electrodes and their geometric relationship with the neural pathways.

Abstract: A method for automatic segmentation of intra-cochlear anatomy of a patient. The patient has an implanted ear and a normal contralateral ear. At least one computed tomography (CT) image is obtained to generate a first image corresponding to the normal contralateral ear and a second image corresponding to the implanted ear. Intra-cochlear surfaces of at least one first structure of interest (SOI) of the normal contralateral ear in the first image are segmented using at least one active shape model (ASM). Next, the segmented intra-cochlear surfaces in the first image is projected to the second image using a transformation function, thereby obtaining projected segmented intra-cochlear surfaces for the implanted ear in the second image.

Abstract: One aspect of the invention provides a method for customizing cochlear implant stimulation of a living subject. The cochlear implant includes an electrode array having a plurality of electrodes implanted in a cochlea of the living subject. The method includes determining a position for each of the plurality of electrodes and spiral ganglion nerves that the electrode array stimulates, determining a geometric relationship between neural pathways within the cochlea and the electrode array implanted therein, and using one or more electrodes of the electrode array to stimulate a group of SG neural pathways of the cochlea based on the location of the one or more electrodes and their geometric relationship with the neural pathways.

Abstract: A method for automatic segmentation of intra-cochlear anatomy of a patient. The patient has an implanted ear and a normal contralateral ear. At least one computed tomography (CT) image is obtained to generate a first image corresponding to the normal contralateral ear and a second image corresponding to the implanted ear. Intra-cochlear surfaces of at least one first structure of interest (SOI) of the normal contralateral ear in the first image are segmented using at least one active shape model (ASM). Next, the segmented intra-cochlear surfaces in the first image is projected to the second image using a transformation function, thereby obtaining projected segmented intra-cochlear surfaces for the implanted ear in the second image.

Abstract: One aspect of the invention provides a method for customizing cochlear implant stimulation of a living subject. The cochlear implant includes an electrode array having a plurality of electrodes implanted in a cochlea of the living subject. The method includes determining a position for each of the plurality of electrodes and spiral ganglion nerves that the electrode array stimulates, determining a geometric relationship between neural pathways within the cochlea and the electrode array implanted therein, and using one or more electrodes of the electrode array to stimulate a group of SG neural pathways of the cochlea based on the location of the one or more electrodes and their geometric relationship with the neural pathways.

Abstract: A system and method for automatic segmentation of a structure of interest of an ear are disclosed, where the structure of interest includes the facial nerve and chorda tympani. In one embodiment, the method combines an atlas-based approach with a minimum cost path finding algorithm. A structure model is constructed from a plurality of preoperative image volumes to have a centerline of the structure of interest in an atlas with each point along the centerline being associated with expected values for characteristic features, and used to create a spatially varying cost function that includes geometric information. A three-dimension minimum cost accumulating path is computed using the cost function, which is used to extract the centerline of the structure of interest. The centerlines are then expanded into the full structures using a level-set algorithm with a spatially-varying speed function, so as to segment of the structure of interest.

Abstract: A system and method for automatic segmentation of a structure of interest of an ear are disclosed, where the structure of interest includes the facial nerve and chorda tympani. In one embodiment, the method combines an atlas-based approach with a minimum cost path finding algorithm. A structure model is constructed from a plurality of preoperative image volumes to have a centerline of the structure of interest in an atlas with each point along the centerline being associated with expected values for characteristic features, and used to create a spatially varying cost function that includes geometric information. A three-dimension minimum cost accumulating path is computed using the cost function, which is used to extract the centerline of the structure of interest. The centerlines are then expanded into the full structures using a level-set algorithm with a spatially-varying speed function, so as to segment of the structure of interest.