A Digital Pediatric Brain Structure Atlas from T1-Weighted MR Images

Shan, Zack; Parra, Carlos; Ji, Qing; Ogg, Robert J.; Zhang, Yong; Laningham, Fred H.; Reddick, Wilburn E.

doi:10.1007/11866763_41

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…With these caveats in mind, we compare our template results with those in the literature: Shan et al (Shan, Parra et al 2006) created an atlas from the anatomy of a single 9-year-old subject. The atlases of Jelacic et al (Jelacic, de Regt et al 2006) allow the comparison of the anatomy of a given subject with those of other subjects, manually selected from a small group of standard normal scans.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Jelacic et al (Jelacic, de Regt et al 2006) built an interactive Web-based atlas for subjects under 4 years of age that facilitates the comparison of a given subject with standard datasets from a database. Shan et al (Shan, Parra et al 2006) built a digital pediatric brain structure atlas from T1w MRI scans from a single 9-year-old subject. However, the main problem with using single subject templates is that, despite being a typical healthy individual, the chosen subject may represent an extreme tail of the normal distribution for some brain regions.…”

Section: Introductionmentioning

confidence: 99%

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Unbiased average age-appropriate atlases for pediatric studies

et al. 2011

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Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5–18.5 years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. . Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5–6.9 years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unbiased average age-appropriate atlases for pediatric studies

et al. 2011

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“…In the resulting brain atlas, age-stratified features and regional asymmetries emerge that are not apparent in individual anatomies. Recently developed pediatric structural and white matter brain atlases form notable examples (Huang, Zhang et al 2006; Jelacic, de Regt et al 2006; Shan, Parra et al 2006). Figure 4 illustrates how processing workflows can be specifically designed to draw from large archives of individual subjects in order to produce customized age-stratified average brain spaces.…”

Section: Neuroimaging Databases and Their Role In Creating Brain Atlasesmentioning

confidence: 99%

Is it time to re-prioritize neuroimaging databases and digital repositories?

Horn

Toga

2009

NeuroImage

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The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented – collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

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“…Due to the principal differences in intensity and size between neonatal and adult or pediatric atlases, substantial bias may produce using adult or pediatric atlases. Hence several atlases have been proposed for the developing brain in contemporary researches [1,4,14]; Altaye et al (2008) applied MR images of 79 infants aged between 9 and 15 months to construct an intensity atlas and also tissue probability maps [16]. Habas et al (2010) developed intensity atlases and tissue probabilistic maps for fetuses aged between 20.75 and 24.71 weeks based on 20 MR images [17].…”

Section: Introductionmentioning

confidence: 99%

Precise spatial normalization through novel neonatal brain MRI and CT atlas templates

Dastjerdi

Moghaddam

Wallois

et al. 2014

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)

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The assessment of brain development is indubitably of critical importance specifically in the first two years of life. In comparison with the developments of other organs, the process of brain maturation from birth to two years of life is very meteoric. As a result of the complications of the study of the newborn brain, scarce researches have been done in this field. Spatial normalization, which modifies individual brain shape to match a reference space, is a necessary step in group level statistical analysis. In this paper, at first, we suggest a new approach for enhancing the normalization of CT scans to a reference space. We then explain our steps towards creating MR and CT atlases. Afterwards, we utilize 13 and 9 high resolution neonatal MR and CT scans to construct atlases based on our suggested methods. These scans are captured from a group of newborns with gestational ages between 39 and 40 weeks at the time of exam. We subsequently demonstrate the created multimodal atlas. Finally, we discuss the results of qualitative and quantitative assessment of our multimodal atlas, for example, through anatomical local deviations.

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A Digital Pediatric Brain Structure Atlas from T1-Weighted MR Images

Cited by 7 publications

References 11 publications

Unbiased average age-appropriate atlases for pediatric studies

Unbiased average age-appropriate atlases for pediatric studies

Is it time to re-prioritize neuroimaging databases and digital repositories?

Precise spatial normalization through novel neonatal brain MRI and CT atlas templates

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