title: Three-dimensional blind deconvolution of SPECT images.
abstract: Thanks to its ability to yield functionally rather than anatomically-based information, the three-dimensional (3-D) SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, the 3-D single photon emission computed tomography (SPECT) images are very blurred and, consequently, their interpretation by the clinician is often difficult and subjective. In order to improve the resolution of these 3-D images and then to facilitate their interpretation, we propose herein to extend a recent image blind deconvolution technique (called the nonnegativity support constraint-recursive inverse filtering deconvolution method) in order to improve both the spatial and the interslice resolution of SPECT volumes. This technique requires a preliminary step in order to find the support of the object to be restored. In this paper, we propose to solve this problem with an unsupervised 3-D Markovian segmentation technique. This method has been successfully tested on numerous real and simulated brain SPECT volumes, yielding very promising restoration results.
authors.affiliation: Institut National de Recherche en Informatique et Automatique, France. mignotte@iro.umontreal.ca
authors.forename: M
authors.initials: M
authors.lastname: Mignotte
authors.forename: J
authors.initials: J
authors.lastname: Meunier
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008390:Markov Chains; D019047:Phantoms, Imaging; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*