130 research outputs found
Registration of 3D fetal neurosonography and MRI.
We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image
Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
We propose an unsupervised deep learning method for atlas based registration
to achieve segmentation and spatial alignment of the embryonic brain in a
single framework. Our approach consists of two sequential networks with a
specifically designed loss function to address the challenges in 3D first
trimester ultrasound. The first part learns the affine transformation and the
second part learns the voxelwise nonrigid deformation between the target image
and the atlas. We trained this network end-to-end and validated it against a
ground truth on synthetic datasets designed to resemble the challenges present
in 3D first trimester ultrasound. The method was tested on a dataset of human
embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed
alignment of the brain in some cases and gave insight in open challenges for
the proposed method. We conclude that our method is a promising approach
towards fully automated spatial alignment and segmentation of embryonic brains
in 3D ultrasound
High-Performance Motion Correction of Fetal MRI
Fetal Magnetic Resonance Imaging (MRI) shows promising results for pre-natal diagnostics. The detection of potentially lifethreatening abnormalities in the fetus can be difficult with ultrasound alone. MRI is one of the few safe alternative imaging modalities in pregnancy. However, to date it has been limited by unpredictable fetal and maternal motion during acquisition. Motion between the acquisitions of individual slices of a 3D volume results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms to solve this problem have evolved from very slow implementations targeting a single organ to general high-performance solutions to reconstruct the whole uterus. In this paper we give a brief overview over the current state-of-the art in fetal motion compensation methods and show currently emerging clinical applications of these technique
Registration of 3D Fetal Brain US and MRI
We propose a novel method for registration of 3D fetal brain ultrasound and a reconstructed magnetic resonance fetal brain volumes. The reconstructed MR volume is first segmented using a probabilistic atlas and an ultrasound-like image volume is simulated from the segmentation of the MR image. This ultrasound-like image volume is then affinely aligned with real ultrasound volumes of 27 fetal brains using a robust block-matching approach which can deal with intensity artefacts and missing features in ultrasound images. We show that this approach results in good overlap of four small structures. The average of the co-aligned US images shows good correlation with anatomy of the fetal brain as seen in the MR reconstruction
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Fast volume reconstruction from motion corrupted stacks of 2D slices
Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available
Diffusion tensor driven image registration: a deep learning approach
Tracking microsctructural changes in the developing brain relies on accurate
inter-subject image registration. However, most methods rely on either
structural or diffusion data to learn the spatial correspondences between two
or more images, without taking into account the complementary information
provided by using both. Here we propose a deep learning registration framework
which combines the structural information provided by T2-weighted (T2w) images
with the rich microstructural information offered by diffusion tensor imaging
(DTI) scans. We perform a leave-one-out cross-validation study where we compare
the performance of our multi-modality registration model with a baseline model
trained on structural data only, in terms of Dice scores and differences in
fractional anisotropy (FA) maps. Our results show that in terms of average Dice
scores our model performs better in subcortical regions when compared to using
structural data only. Moreover, average sum-of-squared differences between
warped and fixed FA maps show that our proposed model performs better at
aligning the diffusion data
Motion corrected 3D reconstruction of the fetal thorax from prenatal MRI
In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature
Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the
developing brain but is not suitable for anomaly screening. For this ultrasound
(US) is employed. While expert sonographers are adept at reading US images, MR
images are much easier for non-experts to interpret. Hence in this paper we
seek to produce images with MRI-like appearance directly from clinical US
images. Our own clinical motivation is to seek a way to communicate US findings
to patients or clinical professionals unfamiliar with US, but in medical image
analysis such a capability is potentially useful, for instance, for US-MRI
registration or fusion. Our model is self-supervised and end-to-end trainable.
Specifically, based on an assumption that the US and MRI data share a similar
anatomical latent space, we first utilise an extractor to determine shared
latent features, which are then used for data synthesis. Since paired data was
unavailable for our study (and rare in practice), we propose to enforce the
distributions to be similar instead of employing pixel-wise constraints, by
adversarial learning in both the image domain and latent space. Furthermore, we
propose an adversarial structural constraint to regularise the anatomical
structures between the two modalities during the synthesis. A cross-modal
attention scheme is proposed to leverage non-local spatial correlations. The
feasibility of the approach to produce realistic looking MR images is
demonstrated quantitatively and with a qualitative evaluation compared to real
fetal MR images.Comment: MICCAI-MLMI 201
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