Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients

Julia Willamena Patriarche, Bradley James Erickson

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)203-222
Number of pages20
JournalJournal of Digital Imaging
Volume20
Issue number3
DOIs
StatePublished - Sep 1 2007

Keywords

  • Brain tumor
  • Change detection
  • Serial imaging

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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