Showing papers by "Mario Ceresa published in 2014"

Patient-specific simulation of implant placement and function for cochlear implantation surgery planning.

Abstract: We present a framework for patient specific electrical stimulation of the cochlea, that allows to perform in-silico analysis of implant placement and function before surgery. A Statistical Shape Model (SSM) is created from high-resolution human μCT data to capture important anatomical details. A Finite Element Model (FEM) is built and adapted to the patient using the results of the SSM. Electrical simulations based on Maxwell’s equations for the electromagnetic field are performed on this personalized model. The model includes implanted electrodes and nerve fibers. We present the results for the bipolar stimulation protocol and predict the voltage spread and the locations of nerve excitation.

Patient Specific Simulation for Planning of Cochlear Implantation Surgery

Abstract: Cochlear implantation is a surgical procedure that can restore the hearing capabilities to patients with severe or complete functional loss However, the level of restoration varies highly between subjects and depends on patient-specific factors This paper presents a software application for planning cochlear implantation procedures that includes patient-specific anatomy estimation using high resolution models, implant optimization for patient-specific implant selection, simulation of mechanical and electrical properties of the implant as well as clinical reporting

Hierarchical Shape Modeling of the Cochlea and Surrounding Risk Structures for Minimally Invasive Cochlear Implant Surgery

Abstract: Knowing the anatomical shape and position of structures surrounding the cochlea is essential in planning minimally invasive cochlear implant surgery. In this work, a Multiobject Hierarchical Statistical Shape Model (MO-SSM) based of wavelet decomposition is created from clinical cone-beam CT datasets of the inner, middle and outer auditory system and surrounding structures. The methodology incorporates an algorithm that automatically segregates structures as the level of detail is increased, leading to a global description of the whole surgical site at the lowest resolution and detailed anatomic models at the highest resolution. This model is the basis for the automatic segmentation of patient data, allowing to quantify the relative position of risk structures in planning the intervention.