Vanderbilt University

Benoit M Dawant

Jack H Noble

Robert F Labadie

Rene H Gifford

Michael I Miga

Peter E Konrad

Tuhin K Sinha

Fitsum A Reda

J Michael Fitzpatrick

Pierre-Francois Dominique D'Haese

Zhujiang Cao

Jianing Wang

Ahmet Cakir

A patient-customized electrode array (EA) includes a plurality of electrodes, {E}, assembled in a pre-curved form, wherein the curvature of the EA at the i-th electrode Eis characterized with a curvature that matches the curvature of the i-th point Pof a structure curve in the cochlea of a patient where the i-th electrode Eis to be placed, wherein i=1, 2, 3, . . . N, N is an integer greater than zero.

Patient-customized electrode arrays based on patient-specific cochlear information, and designing/selecting methods and applications of same

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.

Patient customized electro-neural interface models for model-based cochlear implant programming and applications of same

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.

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.

Deep-learning-based method for metal reduction in CT images and applications of same

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.

Methods and systems for optimizing selection and placement of cochlear implant electrode arrays using patient-specific cochlear information and applications of same

Systems and methods for performing current steering compatible image-guided cochlear implant (CI) electrode deactivation. The cochlear implant includes an electrode array having a plurality of electrodes implanted in a cochlea of a living subject. For each electrode, a corresponding distance-vs-frequency (DVF) curve is obtained. An analysis is performed on the DVF curves to identify the interfering electrodes, each having an interference with at least one other electrode. Then, rules may apply to the interfering electrodes in order to select one or more interfering electrodes to be deactivated. The rules may include: keeping the electrode having a corresponding DVF curve located at a left-most location on the plot to avoid a resulting sound frequency upshift; avoiding leaving any electrode stranded without a neighboring electrode; and deactivating a minimal number of the interfering electrodes to ensure that high interference is allowed only for each electrode with one neighboring electrode.

Current steering compatible image-guided cochlear implant electrode deactivation methods and applications of same

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.

Methods and systems for customizing cochlear implant stimulation and applications of same

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.

Methods for automatic segmentation of inner ear anatomy in post-implantation CT and applications of same

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.

Automatic segmentation of intra-cochlear anatomy in post-implantation CT of unilateral cochlear implant recipients

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.

System and methods for automatic segmentation of one or more critical structures of the ear

A method for programming a deep brain stimulator implanted in a target region of a brain of a living subject. In one embodiment, the method comprises the steps of creating an efficacy atlas; acquiring a position of each electrode contact of the at least one electrode; mapping the acquired position of each electrode contact of the at least one electrode onto a corresponding position in the efficacy atlas so as to determine the efficacy of stimulation at the acquired position; and selecting one or more electrode contacts having the highest efficacy for stimulation.

System and methods of deep brain stimulation for post-operation patients

A cortical surface registration procedure related to a diagnostic or surgical procedure. In one embodiment, the procedure includes the steps of pre-operatively obtaining a first textured point cloud of the cortical surface of a targeted region of a brain of a living subject, intra-operatively obtaining optically a second textured point cloud of the cortical surface of the brain of the living subject, and aligning the first textured point cloud of the cortical surface to the second textured point cloud of the cortical surface so as to register images of the brain of the living subject to the cortical surface of the living subject.

Apparatus and methods of cortical surface registration and deformation tracking for patient-to-image alignment in relation to image-guided surgery

A method for segmentation of an organ of a living subject with a variable shape and boundary and surrounded by structures and tissues in connection with an image containing the organ. In one embodiment, the method includes the steps of initializing a contour inside the organ in the image, introducing a speed function that is capable of accumulating spatial and temporal information of a propagating front of the contour, and evolving the contour toward the boundary of the organ using the introduced speed function so as to segment the organ.

Growing community of inventors

Nashville, TN, United States of America

Benoit M Dawant