Cross contrast multi-channel image registration using image synthesis for MR brain images.
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.
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: minchen1@upenn.edu.
Min
M
Chen
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA. Electronic address: aaron_carass@jhu.edu.
Aaron
A
Carass
Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA. Electronic address: amod@cs.jhu.edu.
Amod
A
Jog
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA. Electronic address: junghoon@jhu.edu.
Junghoon
J
Lee
CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA. Electronic address: snehashis.roy@nih.gov.
Snehashis
S
Roy
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA; Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA. Electronic address: prince@jhu.edu.
Jerry L
JL
Prince
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D055815:Young Adult
Brain imaging; Image processing; Image synthesis; Multi-channel image registration; Multi-contrast magnetic resonance imaging; Multi-modal image registration; Multi-modal imaging