Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.
The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.
Patriarche
Julia Willamena
JW
Department of Radiology, Mayo Clinic and Foundation, Rochester, MN 55905, USA.
Erickson
Bradley James
BJ
D000465:Algorithms; D001331:Automation; D001931:Brain Mapping; D001932:Brain Neoplasms / Q000473:pathology*; D003287:Contrast Media; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D019047:Phantoms, Imaging
D003287:Contrast Media