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.
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.
|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|
|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|
|Rehder, Roberta; Yang, Edward; Cohen, Alan R (2016) Variation of the slope of the tentorium during childhood. Childs Nerv Syst 32:441-50|
|Akhondi-Asl, Alireza; Afacan, Onur; Balasubramanian, Mukund et al. (2016) Fast myelin water fraction estimation using 2D multislice CPMG. Magn Reson Med 76:1301-13|
|Scherrer, Benoit; Schwartzman, Armin; Taquet, Maxime et al. (2016) Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND). Magn Reson Med 76:963-77|
|Gholipour, Ali; Afacan, Onur; Aganj, Iman et al. (2015) Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI. Med Phys 42:6919-32|
|Gorthi, Subrahmanyam; Akhondi-Asl, Alireza; Warfield, Simon K (2015) Optimal MAP Parameters Estimation in STAPLE Using Local Intensity Similarity Information. IEEE J Biomed Health Inform 19:1589-97|
|Tomas-Fernandez, Xavier; Warfield, Simon K (2015) A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation. IEEE Trans Med Imaging 34:1349-61|
|Taquet, Maxime; Scherrer, Benoit; Boumal, Nicolas et al. (2015) Improved fidelity of brain microstructure mapping from single-shell diffusion MRI. Med Image Anal 26:268-86|
|Velasco-Annis, Clemente; Gholipour, Ali; Afacan, Onur et al. (2015) Normative biometrics for fetal ocular growth using volumetric MRI reconstruction. Prenat Diagn 35:400-8|
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