title: Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection.
abstract: OBJECTIVE
  To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old.
  
  
  METHODS
  To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration.
  
  
  RESULTS
  To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance.
  
  
  CONCLUSIONS
  The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China and Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599.
authors.forename: Yao
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Computer Science, College of Charleston, Charleston, South Carolina 29424.
authors.forename: Brent C
authors.initials: BC
authors.lastname: Munsell
authors.affiliation: Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599.
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-701, Republic of Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007223:Infant; D007231:Infant, Newborn; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*