Literature DB >> 18044561

Longitudinal cortical registration for developing neonates.

Hui Xue1, Latha Srinivasan, Shuzhou Jiang, Mary Rutherford, A David Edwards, Daniel Rueckert, Joseph V Hajnal.   

Abstract

Understanding the rapid evolution of cerebral cortical surfaces in developing neonates is essential in order to understand normal human brain development and to study anatomical abnormalities in preterm infants. Several methods to model and align cortical surfaces for cross-sectional studies have been developed. However, the registration of cortical surfaces extracted from neonates across different gestational ages for longitudinal studies remains difficult because of significant cerebral growth. In this paper, we present an automatic cortex registration algorithm, based on surface relaxation followed by non-rigid surface registration. This technique aims to establish the longitudinal spatial correspondence of cerebral cortices for the developing brain in neonates. The algorithm has been tested on 5 neonates. Each infant has been scanned at three different time points. Quantitative results are obtained by propagating sulci across multiple gestational ages and computing the overlap ratios with manually established ground-truth.

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Year:  2007        PMID: 18044561     DOI: 10.1007/978-3-540-75759-7_16

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  8 in total

1.  Image registration driven by combined probabilistic and geometric descriptors.

Authors:  Linh Ha; Marcel Prastawa; Guido Gerig; John H Gilmore; Cláudio T Silva; Sarang Joshi
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

2.  Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes.

Authors:  Anqi Qiu; Marilyn Albert; Laurent Younes; Michael I Miller
Journal:  Neuroimage       Date:  2008-11-07       Impact factor: 6.556

3.  Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection.

Authors:  Yao Wu; Guorong Wu; Li Wang; Brent C Munsell; Qian Wang; Weili Lin; Qianjin Feng; Wufan Chen; Dinggang Shen
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

Review 4.  Computational neuroanatomy of baby brains: A review.

Authors:  Gang Li; Li Wang; Pew-Thian Yap; Fan Wang; Zhengwang Wu; Yu Meng; Pei Dong; Jaeil Kim; Feng Shi; Islem Rekik; Weili Lin; Dinggang Shen
Journal:  Neuroimage       Date:  2018-03-21       Impact factor: 6.556

5.  A new method to measure cortical growth in the developing brain.

Authors:  Andrew K Knutsen; Yulin V Chang; Cindy M Grimm; Ly Phan; Larry A Taber; Philip V Bayly
Journal:  J Biomech Eng       Date:  2010-10       Impact factor: 2.097

6.  Learning-based deformable registration for infant MRI by integrating random forest with auto-context model.

Authors:  Lifang Wei; Xiaohuan Cao; Zhensong Wang; Yaozong Gao; Shunbo Hu; Li Wang; Guorong Wu; Dinggang Shen
Journal:  Med Phys       Date:  2017-10-19       Impact factor: 4.071

7.  Longitudinal image registration with temporally-dependent image similarity measure.

Authors:  Istvan Csapo; Brad Davis; Yundi Shi; Mar Sanchez; Martin Styner; Marc Niethammer
Journal:  IEEE Trans Med Imaging       Date:  2013-07-03       Impact factor: 10.048

8.  Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs.

Authors:  Linh Ha; Marcel Prastawa; Guido Gerig; John H Gilmore; Cláudio T Silva; Sarang Joshi
Journal:  Int J Biomed Imaging       Date:  2011-08-17
  8 in total

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