A review of the automated detection of change in serial imaging studies of the brain

Julia Patriarche, Bradley Erickson

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)158-174
Number of pages17
JournalJournal of Digital Imaging
Volume17
Issue number3
DOIs
StatePublished - Sep 2004

Keywords

  • Brain tumor
  • Change detection
  • Magnetic resonance
  • Multiple sclerosis

ASJC Scopus subject areas

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

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