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Wu Y, Ridwan AR, Niaz MR, Bennett DA, Arfanakis K. High resolution 0.5mm isotropic T 1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas. Neuroimage 2023; 282:120387. [PMID: 37783362 PMCID: PMC10625170 DOI: 10.1016/j.neuroimage.2023.120387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.
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2
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Gu D, Shi F, Hua R, Wei Y, Li Y, Zhu J, Zhang W, Zhang H, Yang Q, Huang P, Jiang Y, Bo B, Li Y, Zhang Y, Zhang M, Wu J, Shi H, Liu S, He Q, Zhang Q, Zhang X, Wei H, Liu G, Xue Z, Shen D. An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images. Hum Brain Mapp 2022; 44:861-875. [PMID: 36269199 PMCID: PMC9875934 DOI: 10.1002/hbm.26126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/23/2022] [Accepted: 10/01/2022] [Indexed: 01/28/2023] Open
Abstract
It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.
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Affiliation(s)
- Dongdong Gu
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Rui Hua
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Ying Wei
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Yufei Li
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
- School of Mathematics and Computer ScienceChifeng UniversityChifengChina
| | - Jiayu Zhu
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
| | - Weijun Zhang
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
| | - Han Zhang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Qing Yang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Peiyu Huang
- Department of RadiologyThe Second Affiliated Hospital, Zhejiang University School of MedicineHangzhouChina
| | - Yi Jiang
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Bin Bo
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Yao Li
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Yaoyu Zhang
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Minming Zhang
- Department of RadiologyThe Second Affiliated Hospital, Zhejiang University School of MedicineHangzhouChina
| | - Jinsong Wu
- Glioma Surgery Division, Neurologic Surgery DepartmentHuashan HospitalShanghaiChina
- Medical CollegeFudan UniversityShanghaiChina
| | - Hongcheng Shi
- Department of Nuclear MedicineZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Siwei Liu
- Department of Nuclear MedicineZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Qiang He
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
- United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| | - Qiang Zhang
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
| | - Xu Zhang
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Hongjiang Wei
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | | | - Zhong Xue
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Dinggang Shen
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Shanghai Clinical Research and Trial CenterShanghaiChina
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3
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Chen L, Wu Z, Hu D, Wang Y, Zhao F, Zhong T, Lin W, Wang L, Li G. A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort. Neuroimage 2022; 253:119097. [PMID: 35301130 PMCID: PMC9155180 DOI: 10.1016/j.neuroimage.2022.119097] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022] Open
Abstract
Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Dan Hu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.
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Zhong T, Wei J, Wu K, Chen L, Zhao F, Pei Y, Wang Y, Zhang H, Wu Z, Huang Y, Li T, Wang L, Chen Y, Ji W, Zhang Y, Li G, Niu Y. Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age. Neuroimage 2021; 247:118799. [PMID: 34896583 DOI: 10.1016/j.neuroimage.2021.118799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 10/19/2022] Open
Abstract
Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.
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Affiliation(s)
- Tao Zhong
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA; Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jingkuan Wei
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Kunhua Wu
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Yuchen Pei
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Hongjiang Zhang
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Ying Huang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Tengfei Li
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Weizhi Ji
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Yu Zhang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA.
| | - Yuyu Niu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China.
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5
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Zhao F, Wu Z, Wang L, Lin W, Xia S, Li G. Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12902:262-272. [PMID: 36053245 PMCID: PMC9432861 DOI: 10.1007/978-3-030-87196-3_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three main issues: 1) constructing templates at discrete time points; 2) resulting in longitudinal inconsistency among different age's atlases; and 3) taking extremely long runtime. To address these issues, in this paper, we propose a fast unsupervised learning-based surface atlas construction framework incorporating longitudinal constraints to enforce the within-subject temporal correspondence in the atlas space. To well handle the difficulties of learning large deformations, we propose a multi-level multimodal spherical registration network to perform cortical surface registration in a coarse-to-fine manner. Thus, only small deformations need to be estimated at each resolution level using the registration network, which further improves registration accuracy and atlas quality. Our constructed 4D infant cortical surface atlas based on 625 longitudinal scans from 291 infants is temporally continuous, in contrast to the state-of-the-art UNC 4D Infant Surface Atlas, which only provides the atlases at a few discrete sparse time points. By evaluating the intra- and inter-subject spatial normalization accuracy after alignment onto the atlas, our atlas demonstrates more detailed and fine-grained cortical patterns, thus leading to higher accuracy in surface registration.
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Affiliation(s)
- Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China gang
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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6
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Kim M, Yan C, Yang D, Liang P, Kaufer DI, Wu G. Constructing Connectome Atlas by Graph Laplacian Learning. Neuroinformatics 2021; 19:233-249. [PMID: 32712763 PMCID: PMC7855351 DOI: 10.1007/s12021-020-09482-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.
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Affiliation(s)
- Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA
| | - Chenggang Yan
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, Hangzhou, China
| | - Defu Yang
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, Hangzhou, China
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Peipeng Liang
- Department of Psychology, Capital Normal University, Beijing, 100073, China
| | - Daniel I Kaufer
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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7
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Lynch KM, Shi Y, Toga AW, Clark KA. Hippocampal Shape Maturation in Childhood and Adolescence. Cereb Cortex 2020; 29:3651-3665. [PMID: 30272143 DOI: 10.1093/cercor/bhy244] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 06/29/2018] [Accepted: 09/07/2018] [Indexed: 11/14/2022] Open
Abstract
The hippocampus is a subcortical structure critical for learning and memory, and a thorough understanding of its neurodevelopment is important for studying these processes in health and disease. However, few studies have quantified the typical developmental trajectory of the structure in childhood and adolescence. This study examined the cross-sectional age-related changes and sex differences in hippocampal shape in a multisite, multistudy cohort of 1676 typically developing children (age 1-22 years) using a novel intrinsic brain mapping method based on Laplace-Beltrami embedding of surfaces. Significant age-related expansion was observed bilaterally and nonlinear growth was observed primarily in the right head and tail of the hippocampus. Sex differences were also observed bilaterally along the lateral and medial aspects of the surface, with females exhibiting relatively larger surface expansion than males. Additionally, the superior posterior lateral surface of the left hippocampus exhibited an age-sex interaction with females expanding faster than males. Shape analysis provides enhanced sensitivity to regional changes in hippocampal morphology over traditional volumetric approaches and allows for the localization of developmental effects. Our results further support evidence that hippocampal structures follow distinct maturational trajectories that may coincide with the development of learning and memory skills during critical periods of development.
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Affiliation(s)
- Kirsten M Lynch
- Keck School of Medicine of USC, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA.,Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089, USA
| | - Yonggang Shi
- Keck School of Medicine of USC, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Arthur W Toga
- Keck School of Medicine of USC, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Kristi A Clark
- Keck School of Medicine of USC, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
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Ahmad S, Wu Z, Li G, Wang L, Lin W, Yap PT, Shen D. Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brain. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2019; 11765:815-822. [PMID: 32128521 DOI: 10.1007/978-3-030-32245-8_90] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Infant brain atlases are essential for characterizing structural changes in the developing brain. Volumetric and cortical atlases are typically constructed independently, potentially causing discrepancies between tissue boundaries and cortical surfaces. In this paper, we present a method for surface-volume consistent construction of longitudinal brain atlases of infants from 2 weeks to 12 months of age. We first construct the 12-month atlas via groupwise surface-constrained volumetric registration. The longitudinal displacements of each subject with respect to different time points are then transported parallelly to the 12-month atlas space. The 12-month cortico-volumetric atlas is finally warped temporally to each month prior to the 12th month using the transported displacements. Experimental results indicate that the longitudinal atlases generated are consistent in terms of tissue boundaries and cortical surfaces, hence allowing joint surface-volume analysis to be performed in a common space.
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Affiliation(s)
- Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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9
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Kwak K, Yun HJ, Park G, Lee JM. Multi-Modality Sparse Representation for Alzheimer's Disease Classification. J Alzheimers Dis 2019; 65:807-817. [PMID: 29562503 DOI: 10.3233/jad-170338] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) and mild cognitive impairment (MCI) are age-related neurodegenerative diseases characterized by progressive loss of memory and irreversible cognitive functions. The hippocampus, a brain area critical for learning and memory processes, is especially susceptible to damage at early stages of AD. OBJECTIVE We aimed to develop prediction model using a multi-modality sparse representation approach. METHODS We proposed a sparse representation approach to the hippocampus using structural T1-weighted magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) to distinguish AD/MCI from healthy control subjects (HCs). We considered structural and function information for the hippocampus and applied a sparse patch-based approach to effectively reduce the dimensions of neuroimaging biomarkers. RESULTS In experiments using Alzheimer's Disease Neuroimaging Initiative data, our proposed method demonstrated more reliable than previous classification studies. The effects of different parameters on segmentation accuracy were also evaluated. The mean classification accuracy obtained with our proposed method was 0.94 for AD/HCs, 0.82 for MCI/HCs, and 0.86 for AD/MCI. CONCLUSION We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function.
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Affiliation(s)
- Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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10
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Wang L, Nie D, Li G, Puybareau É, Dolz J, Zhang Q, Wang F, Xia J, Wu Z, Chen J, Thung KH, Bui TD, Shin J, Zeng G, Zheng G, Fonov VS, Doyle A, Xu Y, Moeskops P, Pluim JP, Desrosiers C, Ayed IB, Sanroma G, Benkarim OM, Casamitjana A, Vilaplana V, Lin W, Li G, Shen D. Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:10.1109/TMI.2019.2901712. [PMID: 30835215 PMCID: PMC6754324 DOI: 10.1109/tmi.2019.2901712] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.
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Affiliation(s)
- Li Wang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Dong Nie
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Guannan Li
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Élodie Puybareau
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Jose Dolz
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), Ecole de Technologie Supérieure, Montreal, Canada
| | - Qian Zhang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Fan Wang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Jing Xia
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Jiawei Chen
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Toan Duc Bui
- Media System Lab., School of Electronic and Electrical Eng., Sungkyunkwan University (SKKU), Korea
| | - Jitae Shin
- Media System Lab., School of Electronic and Electrical Eng., Sungkyunkwan University (SKKU), Korea
| | - Guodong Zeng
- Information Processing in Medical Intervention Lab., University of Bern, Switzerland
| | - Guoyan Zheng
- Information Processing in Medical Intervention Lab., University of Bern, Switzerland
| | - Vladimir S. Fonov
- NeuroImaging and Surgical Technologies Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Andrew Doyle
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Yongchao Xu
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Pim Moeskops
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Josien P.W. Pluim
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Christian Desrosiers
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), Ecole de Technologie Supérieure, Montreal, Canada
| | - Ismail Ben Ayed
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), Ecole de Technologie Supérieure, Montreal, Canada
| | - Gerard Sanroma
- Simulation, Imaging and Modelling for Biomedical Systems (SIMBIOsys), Universitat Pompeu Fabra, Spain
| | - Oualid M. Benkarim
- Simulation, Imaging and Modelling for Biomedical Systems (SIMBIOsys), Universitat Pompeu Fabra, Spain
| | | | | | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, USA, and also Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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11
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Pietsch M, Christiaens D, Hutter J, Cordero-Grande L, Price AN, Hughes E, Edwards AD, Hajnal JV, Counsell SJ, Tournier JD. A framework for multi-component analysis of diffusion MRI data over the neonatal period. Neuroimage 2019; 186:321-337. [PMID: 30391562 PMCID: PMC6347572 DOI: 10.1016/j.neuroimage.2018.10.060] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 12/11/2022] Open
Abstract
We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
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Affiliation(s)
- Maximilian Pietsch
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
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12
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Zhang Y, Wei H, Cronin MJ, He N, Yan F, Liu C. Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping. Neuroimage 2018; 171:176-189. [PMID: 29325780 PMCID: PMC5857468 DOI: 10.1016/j.neuroimage.2018.01.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 12/02/2017] [Accepted: 01/05/2018] [Indexed: 11/28/2022] Open
Abstract
Longitudinal brain atlases play an important role in the study of human brain development and cognition. Existing atlases are mainly based on anatomical features derived from T1-and T2-weighted MRI. A 4D developmental quantitative susceptibility mapping (QSM) atlas may facilitate the estimation of age-related iron changes in deep gray matter nuclei and myelin changes in white matter. To this end, group-wise co-registered QSM templates were generated over various age intervals from age 1-83 years old. Registration was achieved by combining both T1-weighted and QSM images. Based on the proposed template, we created an accurate deep gray matter nuclei parcellation map (DGM map). Notably, we segmented thalamus into 5 sub-regions, i.e. the anterior nuclei, the median nuclei, the lateral nuclei, the pulvinar and the internal medullary lamina. Furthermore, we built a "whole brain QSM parcellation map" by combining existing cortical parcellation and white-matter atlases with the proposed DGM map. Based on the proposed QSM atlas, the segmentation accuracy of iron-rich nuclei using QSM is significantly improved, especially for children and adolescent subjects. The age-related progression of magnetic susceptibility in each of the deep gray matter nuclei, the hippocampus, and the amygdala was estimated. Our automated atlas-based analysis provided a systematic confirmation of previous findings on susceptibility progression with age resulting from manual ROI drawings in deep gray matter nuclei. The susceptibility development in the hippocampus and the amygdala follow an iron accumulation model; while in the thalamus sub-regions, the susceptibility development exhibits a variety of trends. It is envisioned that the newly developed 4D QSM atlas will serve as a template for studying brain iron deposition and myelination/demyelination in both normal aging and various brain diseases.
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Affiliation(s)
- Yuyao Zhang
- Electrical Engineering and Computer Science, University of California at Berkeley, CA, USA
| | - Hongjiang Wei
- Electrical Engineering and Computer Science, University of California at Berkeley, CA, USA
| | - Matthew J Cronin
- Electrical Engineering and Computer Science, University of California at Berkeley, CA, USA
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Chunlei Liu
- Electrical Engineering and Computer Science, University of California at Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California at Berkeley, CA, USA.
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13
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Makropoulos A, Counsell SJ, Rueckert D. A review on automatic fetal and neonatal brain MRI segmentation. Neuroimage 2018; 170:231-248. [DOI: 10.1016/j.neuroimage.2017.06.074] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 03/06/2017] [Accepted: 06/26/2017] [Indexed: 01/18/2023] Open
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14
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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15
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Natali M, Tagliafico G, Patanè G. Local up-sampling and morphological analysis of low-resolution magnetic resonance images. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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16
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Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2017; 10433:49-56. [PMID: 29568823 DOI: 10.1007/978-3-319-66182-7_6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Constructing longitudinal diffusion-weighted atlases of infant brains poses additional challenges due to the small brain size and the dynamic changes in the early developing brains. In this paper, we introduce a novel framework for constructing longitudinally-consistent diffusion-weighted infant atlases with improved preservation of structural details and diffusion characteristics. In particular, instead of smoothing diffusion signals by simple averaging, our approach fuses the diffusion-weighted images in a patch-wise manner using sparse representation with a graph constraint that encourages spatiotemporal consistency. Diffusion-weighted atlases across time points are jointly constructed for patches that are correlated in time and space. Compared with existing methods, including the one using sparse representation with l2,1 regularization, our approach generates longitudinal infant atlases with much richer and more consistent features of the developing infant brain, as shown by the experimental results.
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17
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Saghafi B, Kim J, Chen G, Shi F, Lin W, Yap P, Shen D. Spatio-angular consistent construction of neonatal diffusion MRI atlases. Hum Brain Mapp 2017; 38:3175-3189. [PMID: 28345171 PMCID: PMC5427004 DOI: 10.1002/hbm.23583] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 03/09/2017] [Accepted: 03/13/2017] [Indexed: 01/20/2023] Open
Abstract
Atlases constructed using diffusion-weighted imaging are important tools for studying human brain development. Atlas construction is in general a two-step process involving spatial registration and fusion of individual images. The focus of most studies so far has been on improving the accuracy of registration while image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this article, we propose a patch-based method for diffusion-weighted (DW) atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free and generated directly from the diffusion-weighted images. Instead of independently generating an atlas for each gradient direction and hence neglecting angular image correlation, we propose to construct the atlas by jointly considering DW images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results confirm that our atlas, constructed for neonatal data, reveals more structural details with higher fractional anisotropy than the atlas generated without angular consistency as well as the average atlas. Also the normalization of test subjects to the proposed atlas results in better alignment of brain structures. Hum Brain Mapp 38:3175-3189, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Behrouz Saghafi
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
| | - Jaeil Kim
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
| | - Geng Chen
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
| | - Feng Shi
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
- Cedars‐Sinai, Biomedical Imaging Research Institute8700 Beverly BlvdLos AngelesCalifornia
| | - Weili Lin
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
| | - Pew‐Thian Yap
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
| | - Dinggang Shen
- Department of Radiology and the Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth Carolina
- Department of Brain and Cognitive EngineeringKorea UniversitySeoul02841Republic of Korea
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18
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Ou Y, Zöllei L, Retzepi K, Castro V, Bates SV, Pieper S, Andriole KP, Murphy SN, Gollub RL, Grant PE. Using clinically acquired MRI to construct age-specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6-year old. Hum Brain Mapp 2017; 38:3052-3068. [PMID: 28371107 PMCID: PMC5426959 DOI: 10.1002/hbm.23573] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 03/03/2017] [Accepted: 03/07/2017] [Indexed: 12/19/2022] Open
Abstract
Diffusion imaging is critical for detecting acute brain injury. However, normal apparent diffusion coefficient (ADC) maps change rapidly in early childhood, making abnormality detection difficult. In this article, we explored clinical PACS and electronic healthcare records (EHR) to create age-specific ADC atlases for clinical radiology reference. Using the EHR and three rounds of multiexpert reviews, we found ADC maps from 201 children 0-6 years of age scanned between 2006 and 2013 who had brain MRIs with no reported abnormalities and normal clinical evaluations 2+ years later. These images were grouped in 10 age bins, densely sampling the first 1 year of life (5 bins, including neonates and 4 quarters) and representing the 1-6 year age range (an age bin per year). Unbiased group-wise registration was used to construct ADC atlases for 10 age bins. We used the atlases to quantify (a) cross-sectional normative ADC variations; (b) spatiotemporal heterogeneous ADC changes; and (c) spatiotemporal heterogeneous volumetric changes. The quantified age-specific whole-brain and region-wise ADC values were compared to those from age-matched individual subjects in our study and in multiple existing independent studies. The significance of this study is that we have shown that clinically acquired images can be used to construct normative age-specific atlases. These first of their kind age-specific normative ADC atlases quantitatively characterize changes of myelination-related water diffusion in the first 6 years of life. The quantified voxel-wise spatiotemporal ADC variations provide standard references to assist radiologists toward more objective interpretation of abnormalities in clinical images. Our atlases are available at https://www.nitrc.org/projects/mgh_adcatlases. Hum Brain Mapp 38:3052-3068, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Yangming Ou
- Psychiatric Neuroimaging, Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
- Laboratory for Computational NeuroimagingAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
- Quantitative Tumor Imaging at Martinos, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical SchoolBostonMassachusetts
| | - Lilla Zöllei
- Laboratory for Computational NeuroimagingAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
| | - Kallirroi Retzepi
- Psychiatric Neuroimaging, Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
- Laboratory for Computational NeuroimagingAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
| | - Victor Castro
- Research Computing, Partners Healthcare, 1 Constitution CenterCharlestownMassachusetts
- Laboratory of Computer ScienceMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusetts
| | - Sara V. Bates
- Division of Newborn Medicine, Department of PediatricsMassachusetts General Hospital for Children, Harvard Medical SchoolBostonMassachusetts
| | | | - Katherine P. Andriole
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusetts
| | - Shawn N. Murphy
- Research Computing, Partners Healthcare, 1 Constitution CenterCharlestownMassachusetts
- Laboratory of Computer ScienceMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusetts
| | - Randy L. Gollub
- Psychiatric Neuroimaging, Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
- Laboratory for Computational NeuroimagingAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusetts
| | - Patricia Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical SchoolBostonMassachusetts
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19
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Wilke M, Altaye M, Holland SK. CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation. Front Comput Neurosci 2017; 11:5. [PMID: 28275348 PMCID: PMC5321046 DOI: 10.3389/fncom.2017.00005] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 01/24/2017] [Indexed: 12/28/2022] Open
Abstract
Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.
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Affiliation(s)
- Marko Wilke
- Department of Pediatric Neurology and Developmental Medicine, Children's Hospital and Experimental Pediatric Neuroimaging Group, Children's Hospital and Department of Neuroradiology, University of TübingenTübingen, Germany
| | - Mekibib Altaye
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Pediatrics, Division of Biostatistics and Epidemiology, University of Cincinnati College of MedicineCincinnati, OH, USA
| | - Scott K. Holland
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Radiology, University of Cincinnati College of MedicineCincinnati, OH, USA
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20
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Benkarim OM, Sanroma G, Zimmer VA, Muñoz-Moreno E, Hahner N, Eixarch E, Camara O, González Ballester MA, Piella G. Toward the automatic quantification of in utero brain development in 3D structural MRI: A review. Hum Brain Mapp 2017; 38:2772-2787. [PMID: 28195417 DOI: 10.1002/hbm.23536] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 01/13/2017] [Accepted: 01/25/2017] [Indexed: 11/08/2022] Open
Abstract
Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
| | | | | | - Emma Muñoz-Moreno
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, Spain.,Experimental 7T MRI Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, Spain
| | - Oscar Camara
- DTIC, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Gemma Piella
- DTIC, Universitat Pompeu Fabra, Barcelona, Spain
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21
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Zhang Y, Shi F, Wu G, Wang L, Yap PT, Shen D. Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2568-2577. [PMID: 27392345 PMCID: PMC6537598 DOI: 10.1109/tmi.2016.2587628] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain atlases are an essential component in understanding the dynamic cerebral development, especially for the early postnatal period. However, longitudinal atlases are rare for infants, and the existing ones are generally limited by their fuzzy appearance. Moreover, since longitudinal atlas construction is typically performed independently over time, the constructed atlases often fail to preserve temporal consistency. This problem is further aggravated for infant images since they typically have low spatial resolution and insufficient tissue contrast. In this paper, we propose a novel framework for consistent spatial-temporal construction of longitudinal atlases for developing infant brain MR images. Specifically, for preserving structural details, the atlas construction is performed in spatial-temporal wavelet domain simultaneously. This is achieved by a patch-based combination of results from each frequency subband. Compared with the existing infant longitudinal atlases, our experimental results indicate that our approach is able to produce longitudinal atlases with richer structural details and also better longitudinal consistency, thus leading to higher performance when used for spatial normalization of a group of infant brain images.
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Affiliation(s)
- Yuyao Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
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22
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Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency. PATCH-BASED TECHNIQUES IN MEDICAL IMAGING : SECOND INTERNATIONAL WORKSHOP, PATCH-MI 2016, HELD IN CONJUNCTION WITH MICCAI 2016, ATHENS, GREECE, OCTOBER 17, 2016 : PROCEEDINGS. PATCH-MI (WORKSHOP) (2ND : 2016 : ATHENS, GREECE) 2016; 9993:9-16. [PMID: 28070568 DOI: 10.1007/978-3-319-47118-1_2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Atlases constructed using diffusion-weighted imaging (DWI) are important tools for studying human brain development. Atlas construction is in general a two-step process involving image registration and image fusion. The focus of most studies so far has been on improving registration thus image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this paper, we propose a patch-based method for DWI atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free. Instead of generating an atlas for each gradient direction independently and hence neglecting inter-image correlation, we propose to construct the atlas by jointly considering diffusion-weighted images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results verify that our atlas, constructed for neonatal data, reveals more structural details compared with the average atlas especially in the cortical regions. Our atlas also yields greater accuracy when used for image normalization.
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Zhang Y, Shi F, Yap PT, Shen D. Detail-preserving construction of neonatal brain atlases in space-frequency domain. Hum Brain Mapp 2016; 37:2133-50. [PMID: 26987787 DOI: 10.1002/hbm.23160] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 01/24/2016] [Accepted: 02/17/2016] [Indexed: 11/11/2022] Open
Abstract
Brain atlases are commonly utilized in neuroimaging studies. However, most brain atlases are fuzzy and lack structural details, especially in the cortical regions. This is mainly caused by the image averaging process involved in atlas construction, which often smoothes out high-frequency contents that capture fine anatomical details. Brain atlas construction for neonatal images is even more challenging due to insufficient spatial resolution and low tissue contrast. In this paper, we propose a novel framework for detail-preserving construction of population-representative atlases. Our approach combines spatial and frequency information to better preserve image details. This is achieved by performing atlas construction in the space-frequency domain given by wavelet transform. In particular, sparse patch-based atlas construction is performed in all frequency subbands, and the results are combined to give a final atlas. For enhancing anatomical details, tissue probability maps are also used to guide atlas construction. Experimental results show that our approach can produce atlases with greater structural details than existing atlases. Hum Brain Mapp 37:2133-2150, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yuyao Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
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Rekik I, Li G, Wu G, Lin W, Shen D. Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework. ACTA ACUST UNITED AC 2016; 9467:197-204. [PMID: 28393147 DOI: 10.1007/978-3-319-28194-0_24] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Magnetic resonance imaging (MRI) of pediatric brain provides invaluable information for early normal and abnormal brain development. Longitudinal neuroimaging has spanned various research works on examining infant brain development patterns. However, studies on predicting postnatal brain image evolution remain scarce, which is very challenging due to the dynamic tissue contrast change and even inversion in postnatal brains. In this paper, we unprecedentedly propose a dual image intensity and anatomical structure (label) prediction framework that nicely links the geodesic image metamorphosis model with sparse patch-based image representation, thereby defining spatiotemporal metamorphic patches encoding both image photometric and geometric deformation. In the training stage, we learn the 4D metamorphosis trajectories for each training subject. In the prediction stage, we define various strategies to sparsely represent each patch in the testing image using the training metamorphosis patches; as we progressively increment the richness of the patch (from appearance-based to multimodal kinetic patches). We used the proposed framework to predict 6, 9 and 12-month brain MR image intensity and structure (white and gray matter maps) from 3 months in 10 infants. Our seminal work showed promising preliminary prediction results for the spatiotemporally complex, drastically changing brain images.
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Affiliation(s)
- Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
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Wang L, Gao Y, Shi F, Li G, Chen KC, Tang Z, Xia JJ, Shen D. Automated segmentation of dental CBCT image with prior-guided sequential random forests. Med Phys 2016; 43:336. [PMID: 26745927 PMCID: PMC4698124 DOI: 10.1118/1.4938267] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Revised: 10/25/2015] [Accepted: 11/16/2015] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. METHODS In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. RESULTS Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors' method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). CONCLUSIONS The authors have developed and validated a novel fully automated method for CBCT segmentation.
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Affiliation(s)
- Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
| | - Yaozong Gao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513
| | - Ken-Chung Chen
- Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030
| | - Zhen Tang
- Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030
| | - James J Xia
- Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065; and Department of Oral and Craniomaxillofacial Surgery, Shanghai Jiao Tong University School of Medicine, Shanghai Ninth People's Hospital, Shanghai 200011, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
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Akazawa K, Chang L, Yamakawa R, Hayama S, Buchthal S, Alicata D, Andres T, Castillo D, Oishi K, Skranes J, Ernst T, Oishi K. Probabilistic maps of the white matter tracts with known associated functions on the neonatal brain atlas: Application to evaluate longitudinal developmental trajectories in term-born and preterm-born infants. Neuroimage 2015; 128:167-179. [PMID: 26712341 DOI: 10.1016/j.neuroimage.2015.12.026] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Revised: 12/08/2015] [Accepted: 12/16/2015] [Indexed: 01/23/2023] Open
Abstract
Diffusion tensor imaging (DTI) has been widely used to investigate the development of the neonatal and infant brain, and deviations related to various diseases or medical conditions like preterm birth. In this study, we created a probabilistic map of fiber pathways with known associated functions, on a published neonatal multimodal atlas. The pathways-of-interest include the superficial white matter (SWM) fibers just beneath the specific cytoarchitectonically defined cortical areas, which were difficult to evaluate with existing DTI analysis methods. The Jülich cytoarchitectonic atlas was applied to define cortical areas related to specific brain functions, and the Dynamic Programming (DP) method was applied to delineate the white matter pathways traversing through the SWM. Probabilistic maps were created for pathways related to motor, somatosensory, auditory, visual, and limbic functions, as well as major white matter tracts, such as the corpus callosum, the inferior fronto-occipital fasciculus, and the middle cerebellar peduncle, by delineating these structures in eleven healthy term-born neonates. In order to characterize maturation-related changes in diffusivity measures of these pathways, the probabilistic maps were then applied to DTIs of 49 healthy infants who were longitudinally scanned at three time-points, approximately five weeks apart. First, we investigated the normal developmental pattern based on 19 term-born infants. Next, we analyzed 30 preterm-born infants to identify developmental patterns related to preterm birth. Last, we investigated the difference in diffusion measures between these groups to evaluate the effects of preterm birth on the development of these functional pathways. Term-born and preterm-born infants both demonstrated a time-dependent decrease in diffusivity, indicating postnatal maturation in these pathways, with laterality seen in the corticospinal tract and the optic radiation. The comparison between term- and preterm-born infants indicated higher diffusivity in the preterm-born infants than in the term-born infants in three of these pathways: the body of the corpus callosum; the left inferior longitudinal fasciculus; and the pathway connecting the left primary/secondary visual cortices and the motion-sensitive area in the occipitotemporal visual cortex (V5/MT+). Probabilistic maps provided an opportunity to investigate developmental changes of each white matter pathway. Whether alterations in white matter pathways can predict functional outcomes will be further investigated in a follow-up study.
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Affiliation(s)
- Kentaro Akazawa
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Linda Chang
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Robyn Yamakawa
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Sara Hayama
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Steven Buchthal
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Daniel Alicata
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Tamara Andres
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Deborrah Castillo
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Kumiko Oishi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jon Skranes
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas Ernst
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Shi F, Cheng J, Wang L, Yap PT, Shen D. LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2459-66. [PMID: 26641727 PMCID: PMC5572670 DOI: 10.1109/tmi.2015.2437894] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.
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Zhang Y, Shi F, Yap PT, Shen D. Space-Frequency Detail-Preserving Construction of Neonatal Brain Atlases. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2015; 9350:255-262. [PMID: 27169138 PMCID: PMC4860280 DOI: 10.1007/978-3-319-24571-3_31] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Brain atlases are an integral component of neuroimaging studies. However, most brain atlases are fuzzy and lack structural details, especially in the cortical regions. In particular, neonatal brain atlases are especially challenging to construct due to the low spatial resolution and low tissue contrast. This is mainly caused by the image averaging process involved in atlas construction, often smoothing out high-frequency contents that indicate fine anatomical details. In this paper, we propose a novel framework for detail-preserving construction of atlases. Our approach combines space and frequency information to better preserve image details. This is achieved by performing reconstruction in the space-frequency domain given by wavelet transform. Sparse patch-based atlas reconstruction is performed in each frequency subband. Combining the results for all these subbands will then result in a refined atlas. Compared with existing atlases, experimental results indicate that our approach has the ability to build an atlas with more structural details, thus leading to better performance when used to normalize a group of testing neonatal images.
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Affiliation(s)
- Yuyao Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
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Li G, Wang L, Shi F, Gilmore JH, Lin W, Shen D. Construction of 4D high-definition cortical surface atlases of infants: Methods and applications. Med Image Anal 2015; 25:22-36. [PMID: 25980388 PMCID: PMC4540689 DOI: 10.1016/j.media.2015.04.005] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 04/07/2015] [Accepted: 04/09/2015] [Indexed: 11/24/2022]
Abstract
In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two postnatal years, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at seven time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development.
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Affiliation(s)
- Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
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A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning. Med Image Anal 2014; 19:176-86. [PMID: 25461336 DOI: 10.1016/j.media.2014.10.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2014] [Revised: 08/28/2014] [Accepted: 10/10/2014] [Indexed: 11/21/2022]
Abstract
Shape prior plays an important role in accurate and robust liver segmentation. However, liver shapes have complex variations and accurate modeling of liver shapes is challenging. Using large-scale training data can improve the accuracy but it limits the computational efficiency. In order to obtain accurate liver shape priors without sacrificing the efficiency when dealing with large-scale training data, we investigate effective and scalable shape prior modeling method that is more applicable in clinical liver surgical planning system. We employed the Sparse Shape Composition (SSC) to represent liver shapes by an optimized sparse combination of shapes in the repository, without any assumptions on parametric distributions of liver shapes. To leverage large-scale training data and improve the computational efficiency of SSC, we also introduced a homotopy-based method to quickly solve the L1-norm optimization problem in SSC. This method takes advantage of the sparsity of shape modeling, and solves the original optimization problem in SSC by continuously transforming it into a series of simplified problems whose solution is fast to compute. When new training shapes arrive gradually, the homotopy strategy updates the optimal solution on the fly and avoids re-computing it from scratch. Experiments showed that SSC had a high accuracy and efficiency in dealing with complex liver shape variations, excluding gross errors and preserving local details on the input liver shape. The homotopy-based SSC had a high computational efficiency, and its runtime increased very slowly when repository's capacity and vertex number rose to a large degree. When repository's capacity was 10,000, with 2000 vertices on each shape, homotopy method cost merely about 11.29 s to solve the optimization problem in SSC, nearly 2000 times faster than interior point method. The dice similarity coefficient (DSC), average symmetric surface distance (ASD), and maximum symmetric surface distance measurement was 94.31 ± 3.04%, 1.12 ± 0.69 mm and 3.65 ± 1.40 mm respectively.
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