Cross contrast multi-channel image registration using image synthesis for MR brain images

Min Chen, Aaron Carass, Amod Jog, Junghoon Lee, Snehashis Roy, Jerry Ladd Prince

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information.

Original languageEnglish (US)
Pages (from-to)2-14
Number of pages13
JournalMedical Image Analysis
Volume36
DOIs
StatePublished - Feb 1 2017

Keywords

  • Brain imaging
  • Image processing
  • Image synthesis
  • Multi-channel image registration
  • Multi-contrast magnetic resonance imaging
  • Multi-modal image registration
  • Multi-modal imaging

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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