Developmental brain ADC atlas creation from clinical ...you2/publications/BabyAt... ·...
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3/4/2014 OHBM https://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&subView=1&abs=4329 1/5 Developmental brain ADC atlas creation from clinical images Submission Number: 3973 Authors: Yangming Ou 1 , Nathaniel Reynolds 1 , Randy Gollub 2 , Rudolph Pienaar 3 , Yanbing Wang 4 , Taowei Wang 4 , Darren Sack 4 , Katherine Andriole 5 , Steven Pieper 6 , Christopher Herrick 4 , Shawn Murphy 4 , Patricia Grant 3 , Lilla Zollei 4 Institutions: 1 Massachusetts General Hospital, Boston, United States, 2 Massachusetts General Hospital, Charlestown, United States, 3 Children's Hospital Boston, Boston, United States, 4 MGH, Boston, United States, 5 BWH, Boston, United States, 6 Isomics, Boston, United States First Author: Yangming Ou - Lecture Information | Contact Me Massachusetts General Hospital Boston, United States Introduction: Development of age-specific brain atlases for diffusion-weighted images would significantly enhance clinicians' ability to consistently detect subtle abnormalities. However, the utility of such an atlas depends on the ability to accurately characterize the range of normal development and therefore necessitates large number of datasets. Here we test the ability to leverage large number of MRIs within the clinical PACS to create age-specific normative brain atlases. The Apparent Diffusion Coefficient (ADC) provides a measure of water content in the brain, and thus a surrogate marker for myelin development [1]. Myelination undergoes dramatic changes from birth to 6 yrs of age and thus ADC can track brain development over time within and potentially across subjects [2]. Prior developmental brain atlases have focused primarily on T1 or T2 showing volumetric changes with age [7,8,10], with only one including an FA atlases and ADC values [9]. Also, most cover narrow age ranges (29-44 gestational weeks [10], 0-1-2 year old [7], 37-53 post-conceptional weeks, or >4 years old [8]). Our proposed atlases densely cover 0-6 years. Methods: At Partners HealthCare institutions, the Research Patient Data Registry (RPDR) and the Medical Imaging Informatics Bench to Bedside (mi2b2) workbench enable the repurposing of electronic medical records (EMRs) and medical images for clinical research. The RPDR allows users to perform IRB-approved queries to query and obtain patient cohorts that match demographic, clinical, and medical history facts. The mi2b2 workbench allows the retrieval of clinical images from PACS in an institutionally approved manner. We submitted a detailed data request to RPDR for all pediatric patients who had a head MRI after 2000. By identifying an exclusionary set of diagnoses from the ICD9 records, extracting gestational age from inpatient billing records, determining the desired brain MRI from the Radiology records, and regular expression searching of the Radiology reports for indications of normal brain morphometry, we identified "potentially normative" brain MRI studies acquired after 2006 for patients who were under the age of 6 at the time of scan. An expert medical history review of these patients is currently under way. All DICOM images were converted into NIfTI format. Non-brain structures were removed by a multi-atlas skull-stripping framework [3], re- implemented specifically for neonatal and pediatric brain ADC images: 6 representative ADC images with ages spanning 0-6 years were manually skull stripped to serve as references to skull strip all other ADC images using DRAMMS deformable registration [4] and STAPLE label fusion [5]. The skull stripping succeeded in more than 95% of the subjects and the few failed cases were manually corrected. The skull-stripped ADC images were divided into 12 age bins (see Fig 3), and for each age bin, an unbiased, age-specific atlas was constructed by averaging the geometry and intensity of all subjects within this age bin [6]. Results: The detailed data request submitted to RPDR returned the EMRs of 4745 pediatric patients with a head MRI. From them 1600 patients were <6yr at the time of scan, with potentially normative brain MRI acquired after 2006. In this preliminary study we included 152 pre-computed ADC maps with visually good image quality and visually free of major pathologies. Fig 1 shows representative results of automated skull stripping on ADC images of 0- 6 yr subjects, which visually highly agree with expert knowledge. Fig 2 shows how multiple subjects in an age range were averaged into an unbiased atlas. Figure 3 shows the constructed ADC atlases from weeks old neonates to 6 yr old pediatrics. Conclusions: Mi2b2 workbench can query and access large number of PACS data. In this pilot study, we established a pipeline to construct atlases densely sampling 0-6 yr age range. The atlases display ADC values. Our future work will be to study ADC trends for whole brain, various tissue types and structures. Informatics: Atlases
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3/4/2014 OHBM
https://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&subView=1&abs=4329 1/5
Developmental brain ADC atlas creation from clinical images
Submission Number:
3973
Authors:
Yangming Ou1, Nathaniel Reynolds1, Randy Gollub2, Rudolph Pienaar3, Yanbing Wang4, Taowei Wang4, Darren Sack4, Katherine Andriole5, Steven
Pieper6, Christopher Herrick4, Shawn Murphy4, Patricia Grant3, Lilla Zollei4
Institutions:
1Massachusetts General Hospital, Boston, United States, 2Massachusetts General Hospital, Charlestown, United States, 3Children's Hospital Boston,
Boston, United States, 4MGH, Boston, United States, 5BWH, Boston, United States, 6Isomics, Boston, United States
First Author:
Yangming Ou - Lecture Information | Contact Me Massachusetts General Hospital Boston, United States
Introduction:
Development of age-specific brain atlases for diffusion-weighted images would significantly enhance clinicians' ability to consistently detect subtleabnormalities. However, the utility of such an atlas depends on the ability to accurately characterize the range of normal development and thereforenecessitates large number of datasets.
Here we test the ability to leverage large number of MRIs within the clinical PACS to create age-specific normative brain atlases. The ApparentDiffusion Coefficient (ADC) provides a measure of water content in the brain, and thus a surrogate marker for myelin development [1]. Myelinationundergoes dramatic changes from birth to 6 yrs of age and thus ADC can track brain development over time within and potentially across subjects[2]. Prior developmental brain atlases have focused primarily on T1 or T2 showing volumetric changes with age [7,8,10], with only one including anFA atlases and ADC values [9]. Also, most cover narrow age ranges (29-44 gestational weeks [10], 0-1-2 year old [7], 37-53 post-conceptionalweeks, or >4 years old [8]). Our proposed atlases densely cover 0-6 years.
Methods:
At Partners HealthCare institutions, the Research Patient Data Registry (RPDR) and the Medical Imaging Informatics Bench to Bedside (mi2b2)workbench enable the repurposing of electronic medical records (EMRs) and medical images for clinical research. The RPDR allows users to performIRB-approved queries to query and obtain patient cohorts that match demographic, clinical, and medical history facts. The mi2b2 workbench allowsthe retrieval of clinical images from PACS in an institutionally approved manner.
We submitted a detailed data request to RPDR for all pediatric patients who had a head MRI after 2000. By identifying an exclusionary set ofdiagnoses from the ICD9 records, extracting gestational age from inpatient billing records, determining the desired brain MRI from the Radiologyrecords, and regular expression searching of the Radiology reports for indications of normal brain morphometry, we identified "potentially normative"brain MRI studies acquired after 2006 for patients who were under the age of 6 at the time of scan. An expert medical history review of thesepatients is currently under way.
All DICOM images were converted into NIfTI format. Non-brain structures were removed by a multi-atlas skull-stripping framework [3], re-implemented specifically for neonatal and pediatric brain ADC images: 6 representative ADC images with ages spanning 0-6 years were manually skullstripped to serve as references to skull strip all other ADC images using DRAMMS deformable registration [4] and STAPLE label fusion [5]. The skullstripping succeeded in more than 95% of the subjects and the few failed cases were manually corrected. The skull-stripped ADC images were dividedinto 12 age bins (see Fig 3), and for each age bin, an unbiased, age-specific atlas was constructed by averaging the geometry and intensity of allsubjects within this age bin [6].
Results:
The detailed data request submitted to RPDR returned the EMRs of 4745 pediatric patients with a head MRI. From them 1600 patients were <6yr atthe time of scan, with potentially normative brain MRI acquired after 2006. In this preliminary study we included 152 pre-computed ADC maps withvisually good image quality and visually free of major pathologies. Fig 1 shows representative results of automated skull stripping on ADC images of 0-6 yr subjects, which visually highly agree with expert knowledge. Fig 2 shows how multiple subjects in an age range were averaged into an unbiasedatlas. Figure 3 shows the constructed ADC atlases from weeks old neonates to 6 yr old pediatrics.
Conclusions:
Mi2b2 workbench can query and access large number of PACS data. In this pilot study, we established a pipeline to construct atlases denselysampling 0-6 yr age range. The atlases display ADC values. Our future work will be to study ADC trends for whole brain, various tissue types andstructures.
Informatics:
Atlases
3/4/2014 OHBM
https://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&subView=1&abs=4329 2/5
3/4/2014 OHBM
https://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&subView=1&abs=4329 3/5
3/4/2014 OHBM
https://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&subView=1&abs=4329 4/5
Abstract Information
Would you accept an oral presentation if your abstract is selected for an oral session?
Yes
Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Internal Review Board (IRB) or Animal Use and Care Committee (AUCC) Approval. Please indicate approval below. Please note: Failure to have IRB orAUCC approval, if applicable will lead to automatic rejection of abstract.
Yes, I have IRB or AUCC approval
Please indicate which method was used in your research:
Diffusion MRI
For human MRI, what field strength scanner do you use?
3.0T
What post processing software packages do you use?
Other, Please list - DRAMMS
3/4/2014 OHBM
https://ww4.aievolution.com/hbm1401/index.cfm?do=abs.viewAbs&subView=1&abs=4329 5/5
References
Reference
1) Hagmann, P., Sporns, O., Madan, N., Cammoun, L., Pienaar, R., Wedeen, V. J., ... & Grant, P. E. (2010). White matter maturation reshapesstructural connectivity in the late developing human brain. Proceedings of the National Academy of Sciences, 107(44), 19067-19072.2) Hermoye L. (2006) 'Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood'Neuroimage 29: pp. 493-504.
3) Doshi, J., Erus, G., Ou, Y., Gaonkar, B., & Davatzikos, C. (2013). Multi-Atlas Skull-Stripping. Academic radiology, 20(12), 1566-1576.4) Ou, Y., Sotiras, A., Paragios, N., & Davatzikos, C. (2011). DRAMMS: Deformable registration via attribute matching and mutual-saliencyweighting.Medical image analysis, 15(4), 622-639.
5) Warfield, S. K., Zou, K. H., & Wells, W. M. (2004). Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validationof image segmentation. Medical Imaging, IEEE Transactions on, 23(7), 903-921.
6) Guimond, A., Meunier, J., & Thirion, J. P. (2000). Average brain models: A convergence study. Computer vision and image understanding, 77(2),192-210.
7) Shi, F., Yap, P. T., Wu, G., Jia, H., Gilmore, J. H., Lin, W., & Shen, D. (2011). Infant brain atlases from neonates to 1-and 2-year-olds. PLoS One,6(4), e18746.
8) Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatricstudies.NeuroImage, 54(1), 313-327.
9) Oishi, K., Mori, S., Donohue, P. K., Ernst, T., Anderson, L., Buchthal, S., ... & Chang, L. (2011). Multi-contrast human neonatal brain atlas:application to normal neonate development analysis. NeuroImage, 56(1), 8-20.
10) Kuklisova-Murgasova, M., Aljabar, P., Srinivasan, L., Counsell, S. J., Doria, V., Serag, A., ... & Rueckert, D. (2011). A dynamic 4D probabilisticatlas of the developing brain. NeuroImage, 54(4), 2750-2763.
