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.
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.
|Ortinau, Cynthia M; Mangin-Heimos, Kathryn; Moen, Joseph et al. (2018) Prenatal to postnatal trajectory of brain growth in complex congenital heart disease. Neuroimage Clin 20:913-922|
|Marami, Bahram; Scherrer, Benoit; Khan, Shadab et al. (2018) Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition. Magn Reson Med :|
|Sourati, Jamshid; Gholipour, Ali; Dy, Jennifer G et al. (2018) Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support ( 11045:83-91|
|Tourbier, Sébastien; Velasco-Annis, Clemente; Taimouri, Vahid et al. (2017) Automated template-based brain localization and extraction for fetal brain MRI reconstruction. Neuroimage 155:460-472|
|Eaton-Rosen, Zach; Scherrer, Benoit; Melbourne, Andrew et al. (2017) Investigating the maturation of microstructure and radial orientation in the preterm human cortex with diffusion MRI. Neuroimage 162:65-72|
|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:|
|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|
|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|
|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|
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