title: PCA-based groupwise image registration for quantitative MRI.
abstract: Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI.
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands. Electronic address: wykehuizinga@gmail.com.
authors.forename: W
authors.initials: W
authors.lastname: Huizinga
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
authors.forename: D H J
authors.initials: DH
authors.lastname: Poot
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: J-M
authors.initials: JM
authors.lastname: Guyader
authors.affiliation: Department of Medical Oncology, Academic Medical Center, Amsterdam, The Netherlands.
authors.forename: R
authors.initials: R
authors.lastname: Klaassen
authors.affiliation: Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands.
authors.forename: B F
authors.initials: BF
authors.lastname: Coolen
authors.affiliation: Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Cardiology, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: van Kranenburg
authors.affiliation: Department of Radiology, Erasmus MC, Rotterdam, The Netherlands; Department of Cardiology, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: R J M
authors.initials: RJ
authors.lastname: van Geuns
authors.affiliation: Department of Cardiology, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: A
authors.initials: A
authors.lastname: Uitterdijk
authors.affiliation: Vrije Universiteit Brussel, Department of Electronics and Informatics (ETRO), Brussels, Belgium; iMinds, Department of Medical IT, Ghent, Belgium.
authors.forename: M
authors.initials: M
authors.lastname: Polfliet
authors.affiliation: Vrije Universiteit Brussel, Department of Electronics and Informatics (ETRO), Brussels, Belgium; iMinds, Department of Medical IT, Ghent, Belgium.
authors.forename: J
authors.initials: J
authors.lastname: Vandemeulebroucke
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, The Netherlands.
authors.forename: A
authors.initials: A
authors.lastname: Leemans
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
authors.forename: W J
authors.initials: WJ
authors.lastname: Niessen
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: S
authors.initials: S
authors.lastname: Klein
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Groupwise image registration; Motion compensation; Principal component analysis; Quantitative MRI