27 research outputs found
Placental Flattening via Volumetric Parameterization
We present a volumetric mesh-based algorithm for flattening the placenta to a
canonical template to enable effective visualization of local anatomy and
function. Monitoring placental function in vivo promises to support pregnancy
assessment and to improve care outcomes. We aim to alleviate visualization and
interpretation challenges presented by the shape of the placenta when it is
attached to the curved uterine wall. To do so, we flatten the volumetric mesh
that captures placental shape to resemble the well-studied ex vivo shape. We
formulate our method as a map from the in vivo shape to a flattened template
that minimizes the symmetric Dirichlet energy to control distortion throughout
the volume. Local injectivity is enforced via constrained line search during
gradient descent. We evaluate the proposed method on 28 placenta shapes
extracted from MRI images in a clinical study of placental function. We achieve
sub-voxel accuracy in mapping the boundary of the placenta to the template
while successfully controlling distortion throughout the volume. We illustrate
how the resulting mapping of the placenta enhances visualization of placental
anatomy and function. Our code is freely available at
https://github.com/mabulnaga/placenta-flattening .Comment: MICCAI 201
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Star-convex shapes arise across bio-microscopy and radiology in the form of
nuclei, nodules, metastases, and other units. Existing instance segmentation
networks for such structures train on densely labeled instances for each
dataset, which requires substantial and often impractical manual annotation
effort. Further, significant reengineering or finetuning is needed when
presented with new datasets and imaging modalities due to changes in contrast,
shape, orientation, resolution, and density. We present AnyStar, a
domain-randomized generative model that simulates synthetic training data of
blob-like objects with randomized appearance, environments, and imaging physics
to train general-purpose star-convex instance segmentation networks. As a
result, networks trained using our generative model do not require annotated
images from unseen datasets. A single network trained on our synthesized data
accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence
microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM,
and placental cotyledons in human fetal MRI, all without any retraining,
finetuning, transfer learning, or domain adaptation. Code is available at
https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We present a method for fast biomedical image atlas construction using neural
fields. Atlases are key to biomedical image analysis tasks, yet conventional
and deep network estimation methods remain time-intensive. In this preliminary
work, we frame subject-specific atlas building as learning a neural field of
deformable spatiotemporal observations. We apply our method to learning
subject-specific atlases and motion stabilization of dynamic BOLD MRI
time-series of fetuses in utero. Our method yields high-quality atlases of
fetal BOLD time-series with 5-7 faster convergence compared to
existing work. While our method slightly underperforms well-tuned baselines in
terms of anatomical overlap, it estimates templates significantly faster, thus
enabling rapid processing and stabilization of large databases of 4D dynamic
MRI acquisitions. Code is available at
https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202
Spatiotemporal alignment of in utero BOLD-MRI series
Purpose: 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.
Materials and Methods: 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.
Results: 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).
Conclusion: 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.National Institutes of Health (NIH) . Grant Numbers: U01 HD087211 , R01 EB017337 Consejeria de Educacion, Juventud y Deporte de la Comunidad de Madrid (Spain) through the Madrid-MIT M+Vision Consortium
In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study
Fetal health is critically dependent on placental function, especially placental transport of oxygen from mother to fetus. When fetal growth is compromised, placental insufficiency must be distinguished from modest genetic growth potential. If placental insufficiency is present, the physician must trade off the risk of prolonged fetal exposure to placental insufficiency against the risks of preterm delivery. Current ultrasound methods to evaluate the placenta are indirect and insensitive. We propose to use Blood-Oxygenation-Level-Dependent (BOLD) MRI with maternal hyperoxia to quantitatively assess mismatch in placental function in seven monozygotic twin pairs naturally matched for genetic growth potential. In-utero BOLD MRI time series were acquired at 29 to 34 weeks gestational age. Maps of oxygen Time-To-Plateau (TTP) were obtained in the placentas by voxel-wise fitting of the time series. Fetal brain and liver volumes were measured based on structural MR images. After delivery, birth weights were obtained and placental pathological evaluations were performed. Mean placental TTP negatively correlated with fetal liver and brain volumes at the time of MRI as well as with birth weights. Mean placental TTP positively correlated with placental pathology. This study demonstrates the potential of BOLD MRI with maternal hyperoxia to quantify regional placental function in vivo.National Institutes of Health (U.S.) (Grant U01 HD087211)National Institutes of Health (U.S.) (Grant R01 EB017337
Approximate Fourier Domain Expression for Bloch-Siegert Shift
PurposeIn this study, a new simple Fourier domain-based analytical expression for the Bloch-Siegert (BS) shift-based B-1 mapping method is proposed to obtain vertical bar b(1)(+)vertical bar more accurately while using short BS pulse durations and small off-resonance frequencies. Theory and MethodsA new simple analytical expression for the BS shift is derived by simplifying the Bloch equations. In this expression, the phase is calculated in terms of the Fourier transform of the radiofrequency pulse envelope, and thus making the off- and on-resonance effects more easily understandable. To verify the accuracy of the proposed expression, Bloch simulations and MR experiments are performed for the hard, Fermi, and Shinner-Le Roux pulse shapes. ResultsAnalyses of the BS phase shift-based B-1 mapping method in terms of radiofrequency pulse shape, pulse duration, and off-resonance frequency show that vertical bar b(1)(+)vertical bar can be obtained more accurately with the aid of this new expression. ConclusionsIn this study, a new simple frequency domain analytical expression is proposed for the BS shift. Using this expression, vertical bar b(1)(+)vertical bar values can be predicted from the phase data using the frequency spectrum of the radiofrequency pulse. This method works well even for short pulse durations and small offset frequencies. Magn Reson Med 73:117-125, 2015. (c) 2014 Wiley Periodicals, Inc
STRESS: Super-Resolution for Dynamic Fetal MRI Using Self-supervised Learning
Fetal motion is unpredictable and rapid on the scale of conventional MR scan
times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and
dynamics of fetal function, is limited to fast imaging techniques with
compromises in image quality and resolution. Super-resolution for dynamic fetal
MRI is still a challenge, especially when multi-oriented stacks of image slices
for oversampling are not available and high temporal resolution for recording
the dynamics of the fetus or placenta is desired. Further, fetal motion makes
it difficult to acquire high-resolution images for supervised learning methods.
To address this problem, in this work, we propose STRESS (Spatio-Temporal
Resolution Enhancement with Simulated Scans), a self-supervised
super-resolution framework for dynamic fetal MRI with interleaved slice
acquisitions. Our proposed method simulates an interleaved slice acquisition
along the high-resolution axis on the originally acquired data to generate
pairs of low- and high-resolution images. Then, it trains a super-resolution
network by exploiting both spatial and temporal correlations in the MR time
series, which is used to enhance the resolution of the original data.
Evaluations on both simulated and in utero data show that our proposed method
outperforms other self-supervised super-resolution methods and improves image
quality, which is beneficial to other downstream tasks and evaluations
Equivariant Filters for Efficient Tracking in 3D Imaging
We demonstrate an object tracking method for {3D} images with fixed
computational cost and state-of-the-art performance. Previous methods predicted
transformation parameters from convolutional layers. We instead propose an
architecture that does not include either flattening of convolutional features
or fully connected layers, but instead relies on equivariant filters to
preserve transformations between inputs and outputs (e.g. rot./trans. of inputs
rotate/translate outputs). The transformation is then derived in closed form
from the outputs of the filters. This method is useful for applications
requiring low latency, such as real-time tracking. We demonstrate our model on
synthetically augmented adult brain MRI, as well as fetal brain MRI, which is
the intended use-case
Placental Flattening via Volumetric Parameterization
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11767).We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our implementation is freely available at https://github.com/mabulnaga/placenta-flattening.NIH/NIBIB/NAC (Grant P41EB015902)NIH/NICHD (Grant U01HD087211)NSF (Grant IIS-1838071)Air Force Office of Scientific Research (Award FA9550-19-1-0319