PROJECT SUMMARY/ABSTRACT
Fetal-brain magnetic resonance imaging (MRI) has become an invaluable tool for studying the early development
of the brain and can resolve diagnostic ambiguities that may remain after routine ultrasound exams.
Unfortunately, high levels of fetal and maternal motion (1) limit fetal MRI to rapid two-dimensional (2D) sequences
and frequently introduce dramatic artifacts such as (2) image misorientation relative to the standard sagittal,
coronal, axial planes needed for clinical assessment and (3) partial to complete signal loss.
These factors lead to the inefficient practice of repeating ~30 s stack-of-slices acquisitions until motion-free
images have been obtained. Throughout the session, technologists manually adjust the orientation of scans in
response to motion, and about 38% of datasets are typically discarded. Thus, subject motion is the fundamental
impediment to reaping the full benefits of MRI for answering clinical and investigational questions in the fetus.
The overarching goal of this project is to overcome the challenges posed by motion by exploiting innovations in
deep learning, which have enabled image-analysis algorithms with unprecedented speed and reliability. We
propose to integrate these into the MRI acquisition pipeline to unlock the potential of fetal MRI. We will develop
practical pulse-sequence technology for automated and dynamically motion-corrected fetal neuroimaging
without the need for external hardware or calibration. We hypothesize that this will radically improve the quality
and success rates of clinical and research studies, while dramatically reducing patient discomfort and cost.
We propose as Aim 1 to eradicate (2) the vulnerability of acquisitions to image-brain misorientation with rapid,
automated prescription of standard anatomical planes. In Aim 2, we propose to address (3) motion during the
scan with real-time correction of fetal-head motion. An anatomical stack-of-slices acquisition will be interleaved
with volumetric navigators. These will be used to measure motion as it happens in the scanner and to adaptively
update the slice tilt/position. We propose as Aim 3 to develop a 3D radial sequence and estimate motion between
subsets of radial spokes for real-time self-navigation. Adaptively updating the orientation of spokes and
selectively re-acquiring corrupted subsets at the end of the scan will enable 3D imaging of the fetal brain (1).
Since the applicant has a physics background, the proposed training program at MIT and HMS will focus on
deep learning and fetal development/neuroscience during the K99 phase to develop the skills needed for
transitioning to independence in the R00 phase. The applicant’s goal is to become a fetal image acquisition and
analysis scientist acting as bridge between deep learning, MRI and clinical fetal-imaging applications to shift the
boundaries of what is currently possible with state-of-the-art technology. Fulfilling the research aims will promote
this, as it will result in a practical framework for automation and motion correction, applicable to a wide variety of
fetal neuroimaging sequences.