PMID- 20879367
OWN - NLM
STAT- MEDLINE
DCOM- 20101115
LR  - 20211020
VI  - 13
IP  - Pt 2
DP  - 2010
TI  - Registration of longitudinal image sequences with implicit template and 
      spatial-temporal heuristics.
PG  - 618-25
AB  - Accurate measurement of longitudinal changes of anatomical structure is important 
      and challenging in many clinical studies. Also, for identification of 
      disease-affected regions due to the brain disease, it is extremely necessary to 
      register a population data to the common space simultaneously. In this paper, we 
      propose a new method for simultaneous longitudinal and groupwise registration of 
      a set of longitudinal data acquired from multiple subjects. Our goal is to 1) 
      consistently measure the longitudinal changes from a sequence of longitudinal 
      data acquired from the same subject; and 2) jointly align all image data 
      (acquired from all time points of all subjects) to a hidden common space. To 
      achieve these two goals, we first introduce a set of temporal fiber bundles to 
      explore the spatial-temporal behavior of anatomical changes in each longitudinal 
      data of the same subject. Then, a probabilistic model is built upon the hidden 
      state of spatial smoothness and temporal continuity on the fibers. Finally, the 
      transformation fields that connect each time-point image of each subject to the 
      common space are simultaneously estimated by the expectation maximization (EM) 
      approach, via the maximum a posterior (MAP) estimation of probabilistic models. 
      Promising results are obtained to quantitatively measure the longitudinal changes 
      of hippocampus volume, indicating better performance of our method than the 
      conventional pairwise methods.
FAU - Wu, Guorong
AU  - Wu G
AD  - Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 
      USA. grwu@med.unc.edu
FAU - Wang, Qian
AU  - Wang Q
FAU - Jia, Hongjun
AU  - Jia H
FAU - Shen, Dinggang
AU  - Shen D
LA  - eng
GR  - R01 EB008374/EB/NIBIB NIH HHS/United States
GR  - R01 EB006733/EB/NIBIB NIH HHS/United States
GR  - R01 EB008374-01A2/EB/NIBIB NIH HHS/United States
GR  - RC1 MH088520-01/MH/NIMH NIH HHS/United States
GR  - R01 EB009634/EB/NIBIB NIH HHS/United States
GR  - R01 EB009634-01A1/EB/NIBIB NIH HHS/United States
GR  - RC1 MH088520/MH/NIMH NIH HHS/United States
PT  - Journal Article
PL  - Germany
TA  - Med Image Comput Comput Assist Interv
JT  - Medical image computing and computer-assisted intervention : MICCAI ... 
      International Conference on Medical Image Computing and Computer-Assisted 
      Intervention
JID - 101249582
SB  - IM
MH  - *Algorithms
MH  - Brain/*pathology
MH  - Data Interpretation, Statistical
MH  - Humans
MH  - Image Enhancement/methods
MH  - Image Interpretation, Computer-Assisted/*methods
MH  - Longitudinal Studies
MH  - Magnetic Resonance Imaging/*methods
MH  - Pattern Recognition, Automated/*methods
MH  - Reproducibility of Results
MH  - Sensitivity and Specificity
MH  - *Subtraction Technique
PMC - PMC3021473
MID - NIHMS217224
EDAT- 2010/10/01 06:00
MHDA- 2010/11/16 06:00
PMCR- 2011/01/14
CRDT- 2010/10/01 06:00
PHST- 2010/10/01 06:00 [entrez]
PHST- 2010/10/01 06:00 [pubmed]
PHST- 2010/11/16 06:00 [medline]
PHST- 2011/01/14 00:00 [pmc-release]
AID - 10.1007/978-3-642-15745-5_76 [doi]
PST - ppublish
SO  - Med Image Comput Comput Assist Interv. 2010;13(Pt 2):618-25. doi: 
      10.1007/978-3-642-15745-5_76.