Yao Wu1, Guorong Wu2, Li Wang2, Brent C Munsell3, Qian Wang4, Weili Lin2, Qianjin Feng5, Wufan Chen5, Dinggang Shen6. 1. 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. 2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599. 3. Department of Computer Science, College of Charleston, Charleston, South Carolina 29424. 4. Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China. 5. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. 6. 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.
Abstract
PURPOSE: 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.
PURPOSE: 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: Myong-Sun Choe; Silvia Ortiz-Mantilla; Nikos Makris; Matt Gregas; Janine Bacic; Daniel Haehn; David Kennedy; Rudolph Pienaar; Verne S Caviness; April A Benasich; P Ellen Grant Journal: Cereb Cortex Date: 2012-07-06 Impact factor: 5.357
Authors: Hui Xue; Latha Srinivasan; Shuzhou Jiang; Mary Rutherford; A David Edwards; Daniel Rueckert; Joseph V Hajnal Journal: Med Image Comput Comput Assist Interv Date: 2007