6,172 research outputs found
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually
contaminated by severe and arbitrary fetal and maternal motion. Hence, stable
and robust motion correction is necessary to reconstruct high-resolution 3D
fetal brain volume for clinical diagnosis and quantitative analysis. However,
the conventional registration-based correction has a limited capture range and
is insufficient for detecting relatively large motions. Here, we present a
novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM)
correction of the multi-slice fetal brain MRI. It learns the sequential motion
from multiple stacks of slices and integrates the features between 2D slices
and reconstructed 3D volume using affinity fusion, which resembles the
iterations between slice-to-volume registration and volumetric reconstruction
in the regular pipeline. The method accurately estimates the motion regardless
of brain orientations and outperforms other state-of-the-art learning-based
methods on the simulated motion-corrupted data, with a 48.4% reduction of mean
absolute error for rotation and 61.3% for displacement. We then incorporated
AFFIRM into the multi-resolution slice-to-volume registration and tested it on
the real-world fetal MRI scans at different gestation stages. The results
indicated that adding AFFIRM to the conventional pipeline improved the success
rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%
Application of Advanced MRI to Fetal Medicine and Surgery
Robust imaging is essential for comprehensive preoperative evaluation, prognostication, and surgical planning in the field of fetal medicine and surgery. This is a challenging task given the small fetal size and increased fetal and maternal motion which affect MRI spatial resolution.
This thesis explores the clinical applicability of post-acquisition processing using MRI advances such as super-resolution reconstruction (SRR) to generate optimal 3D isotropic volumes of anatomical structures by mitigating unpredictable fetal and maternal motion artefact. It paves the way for automated robust and accurate rapid segmentation of the fetal brain. This enables a hierarchical analysis of volume, followed by a local surface-based shape analysis (joint spectral matching) using mathematical markers (curvedness, shape index) that infer gyrification. This allows for more precise, quantitative measurements, and calculation of longitudinal correspondences of cortical brain development.
I explore the potential of these MRI advances in three clinical settings: fetal brain development in the context of fetal surgery for spina bifida, airway assessment in fetal tracheolaryngeal obstruction, and the placental-myometrial-bladder interface in placenta accreta spectrum (PAS). For the fetal brain, MRI advances demonstrated an understanding of the impact of intervention on cortical development which may improve fetal candidate selection, neurocognitive prognostication, and parental counselling. This is of critical importance given that spina bifida fetal surgery is now a clinical reality and is routinely being performed globally. For the fetal trachea, SRR can provide improved anatomical information to better select those pregnancies where an EXIT procedure is required to enable the fetal airway to be secured in a timely manner. This would improve maternal and fetal morbidity outcomes associated with haemorrhage and hypoxic brain injury. Similarly, in PAS, SRR may assist surgical planning by providing enhanced anatomical assessment and prediction for adverse peri-operative maternal outcome such as bladder injury, catastrophic obstetric haemorrhage and maternal death
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Segmentation of the developing fetal brain is an important step in
quantitative analyses. However, manual segmentation is a very time-consuming
task which is prone to error and must be completed by highly specialized
indi-viduals. Super-resolution reconstruction of fetal MRI has become standard
for processing such data as it improves image quality and resolution. However,
dif-ferent pipelines result in slightly different outputs, further complicating
the gen-eralization of segmentation methods aiming to segment super-resolution
data. Therefore, we propose using transfer learning with noisy multi-class
labels to automatically segment high resolution fetal brain MRIs using a single
set of seg-mentations created with one reconstruction method and tested for
generalizability across other reconstruction methods. Our results show that the
network can auto-matically segment fetal brain reconstructions into 7 different
tissue types, regard-less of reconstruction method used. Transfer learning
offers some advantages when compared to training without pre-initialized
weights, but the network trained on clean labels had more accurate
segmentations overall. No additional manual segmentations were required.
Therefore, the proposed network has the potential to eliminate the need for
manual segmentations needed in quantitative analyses of the fetal brain
independent of reconstruction method used, offering an unbiased way to quantify
normal and pathological neurodevelopment.Comment: Accepted for publication at PIPPI MICCAI 202
Novel Image Processing Methods for Improved Fetal Brain MRI
Fetal magnetic resonance imaging (MRI) has been increasingly used as a powerful complement
imaging modality to ultrasound imaging (US) for the clinical evaluation of prenatal
abnormalities. Specifically, clinical application of fetal MRI has been significantly improved in
the nineties by hardware and software advances with the development of ultrafast multi-slice
T2-weighted (T2w) acquisition sequences able to freeze the unpredictable fetal motion and
provide excellent soft-tissue contrast. Fetal motion is indeed the major challenge in fetal
MRI and slice acquisition time should be kept as short as possible. As a result, typical fetal
MRI examination involves the acquisition of a set of orthogonally planned scans of thick
two-dimensional slices, largely free of intra-slice motion artifacts. The poor resolution in
the slice-select dimension as well as possible motion occurring between slices limits further
quantitative data analysis, which is the key for a better understanding of the developing
brain but also the key for the determination of operator-independent biomarkers that might
significantly facilitate fetal diagnosis and prognosis.
To this end, several research groups have developed in the past ten years advanced image
processing methods, often denoted by motion-robust super-resolution (SR) techniques, to
reconstruct from a set of clinical low-resolution (LR) scans, a high-resolution (HR) motion-free
volume. SR problem is usually modeled as a linear inverse problem describing the imaging
degradation due to acquisition and fetal motion. Typically, such approaches consist in iterating
between slice motion estimation that estimates the motion parameters and SR that recovers
the HR image given the estimated degradation model. This thesis focuses on the development
of novel advanced image processing methods, which have enabled the design of a completely
automated reconstruction pipeline for fetal MRI. The proposed techniques help in improving
state-of-the-art fetal MRI reconstruction in terms of efficiency, robustness and minimized
user-interactions, with the ultimate goal of being translated to the clinical environment.
The first part focuses on the development of a more efficient Total Variation (TV)-regularized
optimization algorithm for the SR problem. The algorithm uses recent advances in convex optimization
with a novel adaptive regularization strategy to offer simultaneously fast, accurate
and robust solutions to the fetal image recovery problem. Extensive validations on both
simulated fetal and real clinical data show the proposed algorithm is highly robust in front of
motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI
recovery as in comparison with state-of-the art methods.
The second part focuses on the development of a novel automatic brain localization and
extraction approach based on template-to-slice block matching and deformable slice-totemplate
registration. Asmost fetal brain MRI reconstruction algorithms rely only on brain
tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion
correction and image reconstruction, the fetal brain needs to be localized and extracted as
a first step. These tasks generally necessitate user interaction, manually or semi-automatically
done. Our methods have enabled the design of completely automated reconstruction pipeline
that involves intensity normalization, inter-slice motion estimation, and super-resolution.
Quantitative evaluation on clinical MRI scans shows that our approach produces brain masks
that are very close to manually drawn brain masks, and ratings performed by two expert
observers show that the proposed pipeline achieves similar reconstruction quality to reference
reconstruction based on manual slice-by-slice brain extraction without any further effort.
The third part investigates the possibility of automatic cortical folding quantification, one of
the best biomarkers of brain maturation, by combining our automatic reconstruction pipeline
with a state-of-the-art fetal brain tissue segmentation method and existing automated tools
provided for adult brain’s cortical folding quantification. Results indicate that our reconstruction
pipeline can provide HR MR images with sufficient quality that enable the use of surface
tessellation and active surface algorithms similar to those developed for adults to extract
meaningful information about fetal brain maturation.
Finally, the last part presents new methodological improvements of the reconstruction
pipeline aiming at improving the quality of the image for quantitative data analysis, whose
accuracy is highly dependent on the quality and resolution of the reconstructed image. In
particular, it presents a more consistent and global magnetic bias field correction method
which takes advantage of the super-resolution framework to provide a final reconstructed
image quasi free of the smooth bias field. Then, it presents a new TV SR algorithm that uses
the Huber norm in the data fidelity term to be more robust to non-Gaussian outliers. It
also presents the design of a novel joint reconstruction-segmentation framework and the
development of a novel TV SR algorithm driven by segmentation to produce images with
enhanced edge information that could ultimately improve their segmentation. Finally, it
preliminary investigates the capability of increasing the resolution in the in-plane dimensions
using SR to ultimately reduce the partial volume effect
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion
artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of
the whole uterus. Contrary to current slice-to-volume registration (SVR)
methods, requiring an inflexible anatomical enclosure of a single investigated
organ, the proposed patch-to-volume reconstruction (PVR) approach is able to
reconstruct a large field of view of non-rigidly deforming structures. It
relaxes rigid motion assumptions by introducing a specific amount of redundant
information that is exploited with parallelized patch-wise optimization,
super-resolution, and automatic outlier rejection. We further describe and
provide an efficient parallel implementation of PVR allowing its execution
within reasonable time on commercially available graphics processing units
(GPU), enabling its use in the clinical practice. We evaluate PVR's
computational overhead compared to standard methods and observe improved
reconstruction accuracy in presence of affine motion artifacts of approximately
30% compared to conventional SVR in synthetic experiments. Furthermore, we have
evaluated our method qualitatively and quantitatively on real fetal MRI data
subject to maternal breathing and sudden fetal movements. We evaluate
peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and
cross correlation (CC) with respect to the originally acquired data and provide
a method for visual inspection of reconstruction uncertainty. With these
experiments we demonstrate successful application of PVR motion compensation to
the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical
Imaging. v2: wadded funders acknowledgements to preprin
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Brain segmentation is a fundamental first step in neuroimage analysis. In the
case of fetal MRI, it is particularly challenging and important due to the
arbitrary orientation of the fetus, organs that surround the fetal head, and
intermittent fetal motion. Several promising methods have been proposed but are
limited in their performance in challenging cases and in real-time
segmentation. We aimed to develop a fully automatic segmentation method that
independently segments sections of the fetal brain in 2D fetal MRI slices in
real-time. To this end, we developed and evaluated a deep fully convolutional
neural network based on 2D U-net and autocontext, and compared it to two
alternative fast methods based on 1) a voxelwise fully convolutional network
and 2) a method based on SIFT features, random forest and conditional random
field. We trained the networks with manual brain masks on 250 stacks of
training images, and tested on 17 stacks of normal fetal brain images as well
as 18 stacks of extremely challenging cases based on extreme motion, noise, and
severely abnormal brain shape. Experimental results show that our U-net
approach outperformed the other methods and achieved average Dice metrics of
96.52% and 78.83% in the normal and challenging test sets, respectively. With
an unprecedented performance and a test run time of about 1 second, our network
can be used to segment the fetal brain in real-time while fetal MRI slices are
being acquired. This can enable real-time motion tracking, motion detection,
and 3D reconstruction of fetal brain MRI.Comment: This work has been submitted to ISBI 201
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
This paper aims to solve a fundamental problem in intensity-based 2D/3D
registration, which concerns the limited capture range and need for very good
initialization of state-of-the-art image registration methods. We propose a
regression approach that learns to predict rotation and translations of
arbitrary 2D image slices from 3D volumes, with respect to a learned canonical
atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks
(CNNs) to learn the highly complex regression function that maps 2D image
slices into their correct position and orientation in 3D space. Our approach is
attractive in challenging imaging scenarios, where significant subject motion
complicates reconstruction performance of 3D volumes from 2D slice data. We
extensively evaluate the effectiveness of our approach quantitatively on
simulated MRI brain data with extreme random motion. We further demonstrate
qualitative results on fetal MRI where our method is integrated into a full
reconstruction and motion compensation pipeline. With our CNN regression
approach we obtain an average prediction error of 7mm on simulated data, and
convincing reconstruction quality of images of very young fetuses where
previous methods fail. We further discuss applications to Computed Tomography
and X-ray projections. Our approach is a general solution to the 2D/3D
initialization problem. It is computationally efficient, with prediction times
per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under
review for MICCAI 201
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