title: An efficient strategy based on an individualized selection of registration methods. Application to the coregistration of MR and SPECT images in neuro-oncology.
abstract: An efficient registration strategy is described that aims to help solve delicate medical imaging registration problems. It consists of running several registration methods for each dataset and selecting the best one for each specific dataset, according to an evaluation criterion. Finally, the quality of the registration results, obtained with the best method, is visually scored by an expert as excellent, correct or poor. The strategy was applied to coregister Technetium-99m Sestamibi SPECT and MRI data in the framework of a follow-up protocol in patients with high grade gliomas receiving antiangiogenic therapy. To adapt the strategy to this clinical context, a robust semi-automatic evaluation criterion based on the physiological uptake of the Sestamibi tracer was defined. A panel of eighteen multimodal registration algorithms issued from BrainVisa, SPM or AIR software environments was systematically applied to the clinical database composed of sixty-two datasets. According to the expert visual validation, this new strategy provides 85% excellent registrations, 12% correct ones and only 3% poor ones. These results compare favorably to the ones obtained by the globally most efficient registration method over the whole database, for which only 61% of excellent registration results have been reported. Thus the registration strategy in its current implementation proves to be suitable for clinical application.
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, F-75013, Paris, France. ESME Sudria, Laboratoire PRIAM, Ivry-sur-Seine, France.
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Tacchella
authors.forename: Elodie
authors.initials: E
authors.lastname: Roullot
authors.forename: Muriel
authors.initials: M
authors.lastname: Lefort
authors.forename: Mike-Ely
authors.initials: ME
authors.lastname: Cohen
authors.forename: Rémy
authors.initials: R
authors.lastname: Guillevin
authors.forename: Grégorio
authors.initials: G
authors.lastname: Petrirena
authors.forename: Jean-Yves
authors.initials: JY
authors.lastname: Delattre
authors.forename: Marie-Odile
authors.initials: MO
authors.lastname: Habert
authors.forename: Nathanaëlle
authors.initials: N
authors.lastname: Yeni
authors.forename: Aurélie
authors.initials: A
authors.lastname: Kas
authors.forename: Frédérique
authors.initials: F
authors.lastname: Frouin
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000473:pathology*; D016208:Databases, Factual; D005910:Glioma / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D060787:Neoplasm Grading; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D017256:Technetium Tc 99m Sestamibi / Q000493:pharmacokinetics; D014018:Tissue Distribution; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*
chemical_list: D019275:Radiopharmaceuticals; D017256:Technetium Tc 99m Sestamibi