Hierarchical and symmetric infant image registration by robust longitudinal‐example‐guided correspondence detection
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 br...
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| Vydané v: | Medical physics (Lancaster) Ročník 42; číslo 7; s. 4174 - 4189 |
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| Hlavní autori: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
American Association of Physicists in Medicine
01.07.2015
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| ISSN: | 0094-2405, 2473-4209, 2473-4209 |
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| 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. |
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| AbstractList | 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.
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.
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.
The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods. 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.PURPOSETo 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.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.METHODSTo 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.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.RESULTSTo 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.The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods.CONCLUSIONSThe 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. |
| Author | Wu, Guorong Wang, Li Wu, Yao Munsell, Brent C. Lin, Weili Wang, Qian Shen, Dinggang Feng, Qianjin Chen, Wufan |
| Author_xml | – sequence: 1 givenname: Yao surname: Wu fullname: Wu, Yao – sequence: 2 givenname: Guorong surname: Wu fullname: Wu, Guorong – sequence: 3 givenname: Li surname: Wang fullname: Wang, Li – sequence: 4 givenname: Brent C. surname: Munsell fullname: Munsell, Brent C. – sequence: 5 givenname: Qian surname: Wang fullname: Wang, Qian – sequence: 6 givenname: Weili surname: Lin fullname: Lin, Weili – sequence: 7 givenname: Qianjin surname: Feng fullname: Feng, Qianjin – sequence: 8 givenname: Wufan surname: Chen fullname: Chen, Wufan – sequence: 9 givenname: Dinggang surname: Shen fullname: Shen, Dinggang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26133617$$D View this record in MEDLINE/PubMed |
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| Notes | bmunsell@cofc.edu yaowu1588@gmail.com qianjinfeng08@gmail.com dgshen@med.unc.edu weili_lin@med.unc.edu , wang.qian@sjtu.edu.cn grwu@med.unc.edu and li_wang@med.unc.edu wufanchen@gmail.com Authors to whom correspondence should be addressed. Electronic addresses Electronic addresses ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 Electronic addresses: yaowu1588@gmail.com, grwu@med.unc.edu, li_wang@med.unc.edu, bmunsell@cofc.edu, wang.qian@sjtu.edu.cn, weili_lin@med.unc.edu, and wufanchen@gmail.com Authors to whom correspondence should be addressed. Electronic addresses: qianjinfeng08@gmail.com and dgshen@med.unc.edu |
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To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform... To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image... |
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| SubjectTerms | Biological material, e.g. blood, urine; Haemocytometers biomedical MRI brain Brain - anatomy & histology Brain - growth & development Clinical applications correspondence detection deformation Dictionaries Digital computing or data processing equipment or methods, specially adapted for specific applications hierarchical and symmetric registration Humans Image data processing or generation, in general image registration Imaging, Three-Dimensional - methods Infant infant brain registration Infant, Newborn Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging iterative methods Longitudinal Studies Magnetic Resonance Imaging - methods Magnetic Resonance Physics Medical image contrast medical image processing Medical image segmentation Medical magnetic resonance imaging MRI: anatomic, functional, spectral, diffusion Numerical approximation and analysis paediatrics Registration Testing procedures Three dimensional image processing Trajectory models |
| Title | Hierarchical and symmetric infant image registration by robust longitudinal‐example‐guided correspondence detection |
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