Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI
TL;DR: This work proposes a novel approach for non-rigid motion correction in 3D volumes based on an extension of the classical SVR method with hierarchical deformable registration scheme and structure-based outlier rejection and allows high resolution reconstruction of the fetal trunk.
Abstract: In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.
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TL;DR: In this article, the authors identify fetuses at risk of severe neonatal coarctation of the aorta (CoA) in clinical practice with a high rate of false positives.
Abstract: Background: Identifying fetuses at risk of severe neonatal coarctation of the aorta (CoA) can be lifesaving but is notoriously challenging in clinical practice with a high rate of false positives. ...
24 citations
TL;DR: In this paper, a review of the current literature on the latest developments in antenatal imaging for diagnosis and prognostication of congenital anomalies is coupled with illustrative cases in true radiological planes with viewable three-dimensional video models that show the potential of post-acquisition reconstruction protocols.
21 citations
TL;DR: To generate magnetic resonance imaging ‐derived fetal thymus volumes standardized for fetal weight, it is hoped that the presence of chorioamnionitis and funisitis at delivery with thymic volumes in utero in fetuses that subsequently deliver preterm birth will be correlated.
Abstract: INTRODUCTION Infection and inflammation have been implicated in the etiology and subsequent morbidity associated with preterm birth. At present, there are no tests to assess for fetal compartment infection. The thymus, a gland integral in the fetal immune system, has been shown to involute in animal models of antenatal infection, but its response in human fetuses has not been studied. This study aims: (a) to generate magnetic resonance imaging (MRI) -derived fetal thymus volumes standardized for fetal weight; (b) to compare standardized thymus volumes from fetuses that delivered before 32 weeks of gestation with fetuses that subsequently deliver at term; (c) to assess thymus size as a predictor of preterm birth; and (d) to correlate the presence of chorioamnionitis and funisitis at delivery with thymic volumes in utero in fetuses that subsequently deliver preterm. MATERIAL AND METHODS Women at high-risk of preterm birth at 20-32 weeks of gestation were recruited. A control group was obtained from existing data sets acquired as part of three research studies. A fetal MRI was performed on a 1.5T or 3T MRI scanner: T2 weighted images were obtained of the entire uterine content and specifically the fetal thorax. A slice-to-volume registration method was used for reconstruction of three-dimensional images of the thorax. Thymus segmentations were performed manually. Body volumes were calculated by manual segmentation and thymus:body volume ratios were generated. Comparison of groups was performed using multiple regression analysis. Normal ranges were created for thymus volume and thymus:body volume ratios using the control data. Receiver operating curves (ROC) curves were generated for thymus:body volume ratio and gestation-adjusted thymus volume centiles as predictors of preterm birth. Placental histology was analyzed where available from pregnancies that delivered very preterm and the presence of chorioamnionitis/funisitis was noted. RESULTS Normative ranges were created for thymus volume, and thymus volume was standardized for fetal size from fetuses that subsequently delivered at term, but were imaged at 20-32 weeks of gestation. Image data sets from 16 women that delivered 37 weeks were included. Mean gestation at MRI of the study group was 28+4 weeks (SD 3.2) and for the control group was 25+5 weeks (SD 2.4). Both absolute fetal thymus volumes and thymus:body volume ratios were smaller in fetuses that delivered preterm (P < .001). Of the 16 fetuses that delivered preterm, 13 had placental histology, 11 had chorioamnionitis, and 9 had funisitis. The strongest predictors of prematurity were the thymus volume Z-score and thymus:body volume ratio Z-score (ROC areas 0.915 and 0.870, respectively). CONCLUSIONS We have produced MRI-derived normal ranges for fetal thymus and thymus:body volume ratios between 20 and 32 weeks of gestation. Fetuses that deliver very preterm had reduced thymus volumes when standardized for fetal size. A reduced thymus volume was also a predictor of spontaneous preterm delivery. Thymus volume may be a suitable marker of the fetal inflammatory response, although further work is needed to assess this, increasing the sample size to correlate the extent of chorioamnionitis with thymus size.
14 citations
TL;DR: In this paper, the authors proposed a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2* scan in two steps: automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health.
13 citations
TL;DR: A fully automatic pipeline to predict the biological age and health of the placenta based on a rapid T2* scan in two steps has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency.
Abstract: Purpose: Artificial-intelligence population-based automated quantification of placental maturation and health from a rapid functional Magnetic Resonance scan. The placenta plays a crucial role for any successful human pregnancy. Deviations from the normal dynamic maturation throughout gestation are closely linked to major pregnancy complications. Antenatal assessment in-vivo using T2* relaxometry has shown great promise to inform management and possible interventions but clinical translation is hampered by time consuming manual segmentation and analysis techniques based on comparison against normative curves over gestation. Methods: This study proposes a fully automatic pipeline to predict the biological age and health of the placenta based on a rapid (sub-30 second) T2* scan in two steps: Automatic segmentation using a U-Net and a Gaussian Process regression model to characterize placental maturation and health. These are trained and evaluated on 110 3T MRI placental data sets including 20 high-risk pregnancies diagnosed with pre-eclampsia and/or fetal growth restriction. Results: Automatic segmentation achieves comparable performance to human experts (mean DICE coefficients automatic-manual 0.76, Pearson Correlation Coefficient 0.986 for mean T2* within the masks). The placental health prediction achieves an excellent ability to differentiate early cases of placental insufficiency before 32 weeks. High abnormality scores correlate with low birth weight, premature birth and histopathological findings. Retrospective application on a different cohort imaged at 1.5T illustrates the ability for direct clinical translation. Conclusion: The presented automatic pipeline facilitates a fast, robust and reliable prediction of placental maturation. It yields human-interpretable and verifiable intermediate results and quantifies uncertainties on the cohort-level and for individual predictions. The proposed machine-learning pipeline runs in close to real-time and, deployed in clinical settings, has the potential to become a cornerstone of diagnosis and intervention of placental insufficiency.
12 citations
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TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
40,609 citations
TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
Abstract: In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
5,490 citations
"Deformable Slice-to-Volume Registra..." refers methods in this paper
...ng motion involving large degree rotations or excessive bending, which should be taken into account with respect to selection of input stacks. B. Deformable registration The B-spline FFD registration [25] with NMI similarity measure was chosen for both deformable SVR and global registration steps due to the lower computational requirements compared to the diffeomorphic registration such as FFD paramet...
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...[22] or multimodal registration (e.g., histology to MRI) [23], [24]. The majority of monomodal methods are based on rigid SVR for global alignment followed by Free Form Deformation (FFD) registration [25] for correction of non-rigid shape changes. Recently, [26] formalised deformable graph-based SVR approach validated on a 3D heart MRI dataset. However, the existing implementation is limited to in-pla...
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TL;DR: This work proposes a new method for reconstruction of 3D fetal brain MRI from 2D slices that is interleaved with motion correction and shows excellent results for clinical and optimized data.
326 citations
"Deformable Slice-to-Volume Registra..." refers background or methods in this paper
...The proposed DSVR method is an extension of the rigid SVR SR reconstruction framework [5] described in Sec....
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...It is formalised as follows [5]:...
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...body position are either rejected as outliers [5] or contribute as an error to the reconstructed volume, resulting in blurring of local features (e....
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...02 similarly to the SVR settings in [5]....
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...In this paper we present a novel approach for non-rigid motion correction in 3D volumes based on an extension of the rigid SVR reconstruction method [5] with hierarchical deformable registration scheme and structure-based outlier rejection....
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TL;DR: A novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions and a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices are developed.
Abstract: Fast magnetic resonance imaging slice acquisition techniques such as single shot fast spin echo are routinely used in the presence of uncontrollable motion These techniques are widely used for fetal magnetic resonance imaging (MRI) and MRI of moving subjects and organs Although high-quality slices are frequently acquired by these techniques, inter-slice motion leads to severe motion artifacts that are apparent in out-of-plane views Slice sequential acquisitions do not enable 3-D volume representation In this study, we have developed a novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions The super-resolution volume reconstruction is formulated as an inverse problem of finding the underlying structure generating the acquired slices We have developed a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices The accuracy and robustness of this novel technique has been quantitatively assessed through simulations with digital brain phantom images as well as high-resolution newborn images We also report here successful application of our new technique for the reconstruction of volumetric fetal brain MRI from clinically acquired data
325 citations
"Deformable Slice-to-Volume Registra..." refers background or methods in this paper
... registration with scattered data interpolation based on weighed sum of Gaussian kernels [8] or multilevel Bsplines [9]. The SVR reconstruction framework was gradually extended with SR reconstruction [3], [4], edge-preserving regularisation [4], outlier rejection [3], [5], intensity matching [5], total variation regularisation [10], sinc PSF model and GPU-parallelisation [11]. More recent works propo...
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...en, 1iFIND Project: http://www.ifindproject.com AUTHOR et al.: DEFORMABLE SLICE-TO-VOLUME REGISTRATION FOR MOTION CORRECTION OF FETAL BODY AND PLACENTA MRI 3 we perform super-resolution reconstruction [3] of the volume X by iterative gradient descent optimisation based on minimisation of the sum of squared errors P jk e 2 jk + R(X), where R(X) is an edge preserving regularisation term [5] and e jk a...
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...rent time points. Slice-to-volume registration in combination with superresolution (SR) reconstruction is considered to be an efficient motion correction approach since it resolves out-of-plane motion [3]–[5]. The fact that the volumetric region of interest (ROI) is oversampled at different stack orientations ensures consistency of reconstructed volumes. Recent validation of SVR for fetal brain recons...
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TL;DR: Results indicate that this method promises a unique route to acquiring high-resolution MRI of the fetal brain in vivo allowing comparable quality to that of neonatal MRI.
229 citations
"Deformable Slice-to-Volume Registra..." refers methods in this paper
...Next, as initialisation of the SR reconstruction loop, the weighted Gaussian interpolation [8] is performed for estimation of X....
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...The original concept of SVR for reconstruction of fetal brain from motion-corrupted MRI stacks was to interleave slice-to-volume registration with scattered data interpolation based on weighed sum of Gaussian kernels [8] or multilevel Bsplines [9]....
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