Motion-robust super-resolution diffusion weighted MRI of early brain development The overall objective of this research is to dramatically improve technology and knowledge for in-vivo analysis of normal and abnormal white matter structure and neural connectivity before and early after birth when the brain undergoes its most rapid formative growth. Diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) is considered one of the most promising tools for in-vivo analysis of neural structure;however, our ability to image the fetal and neonatal brain with this technique is constrained by several limitations;including subject motion, limited spatial resolution, and geometric distortion. There s a critical need for motion-robust high- resolution DWI imaging of fetuses and neonates. Due to the lack of such imaging technology, our understanding of early brain growth and the most commonly seen neurodevelopmental abnormalities is largely limited to insights from postmortem (in-vitro) studies. This project aims to fill these gaps through the development of an innovative, motion-robust, super-resolution, C. This involves the development and evaluation of a novel approach, built upon the physics of MRI and advanced image processing techniques, which corrects motion and reconstructs high-resolution DWI data to delineate the neural microstructure of the small fetal and neonatal brain. The two specific aims of this project target 1) moving subjects (aiming at improving neonatal DWI), and 2) fetuses, respectively;
and aim at achieving high- resolution fractional anisotropy maps as well as single tensor and multi-tensor models of the neural fiber bundles in the developing brain despite subject movements. This contribution is important because it 1) enables in-vivo high-resolution mapping of the neural connectivity in fetal brain despite intermittent fetal and maternal motion, 2) significantly simpliies research MRI of neonates and preterm infants through a motion- robust imaging protocol that compensates for small head movements, 3) reduces the need for sedation and anesthesia in clinical MRI of neonates and non-cooperative patients, and 4) simultaneously corrects for motion and increases the spatial resolution of DWI, thus leads to dramatic improvements in the analysis of neural structure and connectivity in early brain development. Because the brain is incapable of self-repair and regeneration, interventions at early stages of brain growth are crucial. The development of neural rescue interventions, such as brain hypothermia, an intervention that has been shown to reduce brain damage due to birth asphyxia, is highly dependent upon accurate in-vivo analysis. Likewise, the evaluation of disruption or delay in neural development (due to premature birth or congenital anomalies) relies heavily on precise in-vivo analysis. The in-vivo analysis of early brain development proposed under this application is crucial to executing these research objectives.

Public Health Relevance

The proposed research is relevant to public health because it develops a medical imaging technology that enables in-vivo characterization of normal versus abnormal brain growth in three major patient populations: fetuses with congenital anomalies, prematurely born neonates, and newborns with developmental brain disorders. The proposed research is relevant to the NIH mission as it pertains to developing fundamental technology and knowledge that enables the development of early preventive interventions that lengthen life and reduce the burdens of human disability.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB018988-01
Application #
8764291
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Liu, Guoying
Project Start
2014-07-01
Project End
2018-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Children's Hospital Boston
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02115
Marami, Bahram; Mohseni Salehi, Seyed Sadegh; Afacan, Onur et al. (2017) Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. Neuroimage 156:475-488
Chamberland, Maxime; Scherrer, Benoit; Prabhu, Sanjay P et al. (2017) Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET). Hum Brain Mapp 38:509-527
Ferizi, Uran; Scherrer, Benoit; Schneider, Torben et al. (2017) Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison. NMR Biomed 30:
Gholipour, Ali; Rollins, Caitlin K; Velasco-Annis, Clemente et al. (2017) A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci Rep 7:476
Jia, Yuanyuan; Gholipour, Ali; He, Zhongshi et al. (2017) A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans. IEEE Trans Med Imaging 36:1182-1193
Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali (2017) Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging. IEEE Trans Med Imaging 36:2319-2330
Tourbier, S├ębastien; Velasco-Annis, Clemente; Taimouri, Vahid et al. (2017) Automated template-based brain localization and extraction for fetal brain MRI reconstruction. Neuroimage 155:460-472
Marami, Bahram; Scherrer, Benoit; Afacan, Onur et al. (2016) Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking. IEEE Trans Med Imaging 35:2258-2269
Afacan, Onur; Erem, Burak; Roby, Diona P et al. (2016) Evaluation of motion and its effect on brain magnetic resonance image quality in children. Pediatr Radiol 46:1728-1735
Afacan, Onur; Gholipour, Ali; Mulkern, Robert V et al. (2016) Fetal lung apparent diffusion coefficient measurement using diffusion-weighted MRI at 3 Tesla: Correlation with gestational age. J Magn Reson Imaging 44:1650-1655

Showing the most recent 10 out of 17 publications