title: A review of the automated detection of change in serial imaging studies of the brain.
abstract: Serial imaging is frequently performed on patients with diseases of the brain, to track and observe changes. Magnetic resonance imaging provides very detailed and rich information, and is therefore used frequently for this application. The data provided by MR can be so plentiful; however, that it obfuscates the information the radiologist seeks. A system which could reduce the large quantity of primitive data to a smaller and more informative subset of data, emphasizing change, would be useful. This article discusses motivating factors for the production of an automated process to this effect, and reviews the approaches of previous authors. The discussion is focused on brain tumors and multiple sclerosis, but many of the ideas are applicable to other disease processes, as well.
authors.affiliation: Department of Radiology, Mayo Clinic and Foundation, 200 First Street SW, Rochester, MN 55905, USA.
authors.forename: Julia
authors.initials: J
authors.lastname: Patriarche
authors.forename: Bradley
authors.initials: B
authors.lastname: Erickson
mesh_terms: D001932:Brain Neoplasms / Q000175:diagnosis*; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging; D009103:Multiple Sclerosis / Q000175:diagnosis*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed