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Principal Investigator: Malte Hoffmann
Organization: MASSACHUSETTS GENERAL HOSPITAL
Fiscal Year: 2024
Award: $245,712
Funding agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development
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.
Terms: <2-dimensional><3-D><3-D Imaging><3-Dimensional><3D><3D imaging><Address><Algorithmic Analyses><Algorithmic Analysis><Amniotic Fluid><Analyses of Algorithms><Analysis of Algorithms><Anatomic Sites><Anatomic structures><Anatomy><Aqua Amnii><Archives><Artifacts><Automation><Body Tissues><Brain><Brain Nervous System><Brain imaging><Calibration><Cell Communication and Signaling><Cell Signaling><Childhood><Clinical><Clinical Research><Clinical Study><Clinical assessments><ConvNet><Data Set><Developing fetus><Development><Diagnostic><Echo-Planar Imaging><Echo-Planar Magnetic Resonance Imaging><Echoplanar Imaging><Echoplanar Magnetic Resonance Imaging><Encephalon><Exclusion><Fetal Development><Fetus><Geometry><Goals><Head><History><Image><Individual><Intracellular Communication and Signaling><Label><Lead><Learning><Liquor Amnii><MR Imaging><MR Tomography><MRI><MRIs><Magnetic Resonance Imaging><Manuals><Masks><Measures><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><Morphologic artifacts><Motion><NMR Imaging><NMR Tomography><Neurosciences><Nuclear Magnetic Resonance Imaging><Patients><Pb element><Phase><Physics><Physiologic pulse><Planar Medical Imaging><Population><Position><Positioning Attribute><Pulse><Radial><Radius><Recording of previous events><Research><Residual><Residual state><Resolution><Sampling><Scanning><Scientist><Signal Transduction><Signal Transduction Systems><Signaling><Slice><Speed><Technology><Thick><Thickness><Three-Dimensional Imaging><Time><Tissues><Training><Training Programs><Translating><Update><Work><Zeugmatography><biological signal transduction><brain MR imaging><brain MRI><brain magnetic resonance imaging><brain visualization><cerebral MR imaging><cerebral MRI><cerebral magnetic resonance imaging><convolutional network><convolutional neural nets><convolutional neural network><cost><deep learning><deep learning method><deep learning strategy><developmental><echo detection><experience><fetal><heavy metal Pb><heavy metal lead><histories><imaging><improved><innovate><innovation><innovative><interest><neural imaging><neuro-imaging><neuroimaging><neurological imaging><novel><pediatric><prospective><radiologist><reconstruction><repair><repaired><research study><resolutions><response><skills><success><three dimensional><tool><two-dimensional><ultrasound>