Abstract

The overall objective of this project is to dramatically improve the capability of fetal MRI for diagnosis, analysis, and prognosis of high-risk pregnancies. Accurate analysis obtained by fetal MRI is crucial in evaluating the highly variable aetiology and poorly understood pathophysiology of fetal central nervous system developmental disorders which affect about one-half percent of pregnancies. Nevertheless, fetal MRI is limited to two- dimensional acquisitions by the small signal available from the small fetal brain, and by intermittent fetal motion that disrupts spatial encoding necessary for advanced three-dimensional volumetric MRI. In order to address this limitation and reveal the power of fetal MRI, we propose novel imaging and image processing technology pursuing four specific aims in this project;
Aim 1, which is pivotal to our overall objective, involves super- resolution reconstruction of three-dimensional high spatial resolution volumetric T2w images of the fetal brain.
Aim 2 involves the construction of a spatiotemporal fetal brain atlas.
Aim 3 involves the comparison of fetal brain biometry and evaluation using 2D MRI, 2D sonography and 3D MRI. Finally in Aim 4 improved assessment of ventriculomegaly is considered using 3D fetal MRI. Ventriculomegaly is the most frequently observed fetal brain abnormality affecting about 0.1-0.2 percent of fetuses.
Aims 1 and 2 involve the development of new technology for clinical use and Aims 3 and 4 involve both technical developments and hypothesis tests to see how much improvement is achieved in the evaluation, diagnosis, and analysis of fetal brain abnormalities using the developed technology as compared to the current practice.

Public Health Relevance

This research challenges current fetal brain MRI practice, which is limited to expert evaluation of 2D slices. This research will develop 3D fetal brain MRI reconstruction, and quantitative 3D fetal biometry, and demonstrate the superiority of 3D fetal brain MRI for the in vivo evaluation of fetal brain developmental disorders. This will lead to more accurate diagnosis and prognosis, and improved patient outcomes.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB013248-01A1
Application #
8239348
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2012-03-01
Project End
2016-02-29
Budget Start
2012-03-01
Budget End
2013-02-28
Support Year
1
Fiscal Year
2012
Total Cost
$391,500
Indirect Cost
$166,500

  Institution

Name
Children's Hospital Boston
Department
Type
DUNS #
076593722
City
Boston
State
MA
Country
United States
Zip Code
02115

  Publications

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
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
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
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; He, Zhongshi; Gholipour, Ali et al. (2016) Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices. IEEE J Biomed Health Inform 20:1552-1561
Marami, Bahram; Scherrer, Benoit; Afacan, Onur et al. (2016) Motion-Robust Reconstruction based on Simultaneous Multi-Slice Registration for Diffusion-Weighted MRI of Moving Subjects. Med Image Comput Comput Assist Interv 9902:544-552
Pier, Danielle B; Gholipour, Ali; Afacan, Onur et al. (2016) 3D Super-Resolution Motion-Corrected MRI: Validation of Fetal Posterior Fossa Measurements. J Neuroimaging 26:539-44
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

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