title: New methods for MRI denoising based on sparseness and self-similarity.
abstract: This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings.
authors.affiliation: Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. jmanjon@fis.upv.es
authors.forename: José V
authors.initials: JV
authors.lastname: Manjón
authors.forename: Pierrick
authors.initials: P
authors.lastname: Coupé
authors.forename: Antonio
authors.initials: A
authors.lastname: Buades
authors.forename: D
authors.initials: D
authors.lastname: Louis Collins
authors.forename: Montserrat
authors.initials: M
authors.lastname: Robles
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity