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 Achten^2,3, and Hacene Serrai^2,3

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.

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