Spatiotemporal alignment of in utero BOLD-MRI series.
To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs.



The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison.



The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P < 0.01) and volume overlap and distance between region boundaries measures were significantly improved (P < 0.01).



The proposed approach to align MRI time series enables more accurate quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs.



1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:403-412.
Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.|Madrid-MIT M+Vision Consortium in RLE, Massachusetts Institute of Technology, Cambridge, MA, United States.
Esra Abaci
EA
Turk
Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.|Madrid-MIT M+Vision Consortium in RLE, Massachusetts Institute of Technology, Cambridge, MA, United States.
Jie
J
Luo
Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.|Department of Radiology, Harvard Medical School, Boston Children's Hospital, Boston, MA, United States.
Borjan
B
Gagoski
Madrid-MIT M+Vision Consortium in RLE, Massachusetts Institute of Technology, Cambridge, MA, United States.|Instituto de Investigación Sanitaria Gregorio Marañón, CIBERSAM, Madrid, Spain.|Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
Javier
J
Pascau
Maternal and Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Carolina
C
Bibbo
Maternal and Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Julian N
JN
Robinson
Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.
P Ellen
PE
Grant
Madrid-MIT M+Vision Consortium in RLE, Massachusetts Institute of Technology, Cambridge, MA, United States.|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States.|Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
Elfar
E
Adalsteinsson
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States.|Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, United States.
Polina
P
Golland
Madrid-MIT M+Vision Consortium in RLE, Massachusetts Institute of Technology, Cambridge, MA, United States.|Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain.
Norberto
N
Malpica
D003944:Diagnostic Techniques, Obstetrical and Gynecological; D005260:Female; D005333:Fetus / Q000000981:diagnostic imaging*; D006801:Humans; D018496:Hyperoxia; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D009038:Motion; D010100:Oxygen / Q000032:analysis*; D010920:Placenta / Q000000981:diagnostic imaging*; D011247:Pregnancy; D059285:Pregnancy, Twin; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D062211:Spatio-Temporal Analysis
D010100:Oxygen
BOLD-MRI; fetal MRI; hyperoxygenation; motion correction; placental MRI; signal nonuniformity