Meeting Banner
Abstract #4091

Magnetic Resonance Imaging de-noising using the squared eigenfunctions of the Schrödinger operator: Application to brain MRI data.

Jiayu Zhang1, Taous Meriem Laleg1, Stephanie Bogaert2, Rik Achten2,3, and Hacene Serrai2,3

1Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, 2Department of Radiology, University Hospital of Gent, gent, Belgium, 3University of Gent, Gent, Belgium

A magnetic resonance imaging denoising method based upon the spectral analysis of the shrodinger operator is proposed.The method called semi-classical signal analysis SCSA, employs an adaptive filter to represent the MRI image as a set of useful vectors and others representing noise. The separation between signal and noise vectors is achieved using a soft and efficient threshold. Method validation is achieved on anatomical brain images acquired with low signal to noise ratio. The obtained results demonstrate that the SCSA is efficient in reducing noise while preserving image details necessary for accurate image diagnosis.

This abstract and the presentation materials are available to members only; a login is required.

Join Here