Motion corrected fetal body magnetic resonance imaging provides reliable 3D lung volumes in normal and abnormal fetuses

Objectives: To calculate 3D-segmented total lung volume (TLV) in fetuses with thoracic anomalies using deformable slice-to-volume registration (DSVR) with comparison to 2D-manual segmentation. To establish a normogram of TLV calculated by DSVR in healthy control fetuses. Methods: A pilot study at a single regional fetal medicine referral centre included 16 magnetic resonance imaging (MRI) datasets of fetuses (22–32 weeks gestational age). Diagnosis was CDH (n = 6), CPAM (n = 2), and healthy controls (n = 8). Deformable slice-to-volume registration was used for reconstruction of 3D isotropic (0.85 mm) volumes of the fetal body followed by semi-automated lung segmentation. 3D TLV were compared to traditional 2D-based volumetry. Abnormal cases referenced to a normogram produced from 100 normal fetuses whose TLV was calculated by DSVR only. Results: Deformable slice-to-volume registration-derived TLV values have high correlation with the 2D-based measurements but with a consistently lower volume; bias −1.44 cm3 [95% limits: −2.6 to −0.3] with improved resolution to exclude hilar structures even in cases of motion corruption or very low lung volumes. Conclusions: Deformable slice-to-volume registration for fetal lung MRI aids analysis of motion corrupted scans and does not suffer from the interpolation error inherent to 2D-segmentation. It increases information content of acquired data in terms of visualising organs in 3D space and quantification of volumes, which may improve counselling and surgical planning

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

Automated template-based brain localization and extraction for fetal brain MRI reconstruction.

Most 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. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks

Brain development in fetal growth restriction: A volumetric approach using fetal MRI

Fetal growth restriction is the failure of a fetus to achieve its full growth potential, resulting in a neonate that is small for its gestational age. The aetiology of fetal growth restriction is varied and fetal growth restriction secondary to placental insufficiency is attributed to a failure of trophoblast invasion leading to under perfusion of the uteroplacental bed. In response to the adverse conditions in-utero, fetuses tend to compensate by increasing blood flow to the essential organs such as the brain, heart, and adrenals, at the expense of other organs (cerebral redistribution). As a consequence, growth tends to be asymmetric, with maintenance of the head growth velocity while the other growth parameters tail off; an effect which is also known as the ‘brain sparing effect’. Despite this apparent brain sparing effect, children who were growth restricted in utero are at increased risk of developmental delay and behavioural problems. 30 growth restricted and 48 normally grown fetuses were recruited into this study and were imaged using both conventional ultrasound with Doppler assessment, as well as fetal MRI with ssFSE sequences through the feto-placental unit and fetal brain. A dynamic approach was taken when imaging the fetal brain to compensate for the presence of fetal motion. MR imaging of the feto-placental unit detected significant differences in placental appearance, significantly smaller volumes of intra-abdominal and intra-thoracic organs, and significantly smaller regional brain growth among growth restricted fetuses. MR studies of the placenta in fetal growth restriction demonstrated a placental phenotype in growth restricted pregnancies that is characterised by smaller placental volumes, a significant increase in the placental volume affected by apparent pathology on MRI and a thickened, globular placenta. Although placental volume increased with gestation in both groups, the placental volume remained significantly smaller in the growth restricted fetuses (p = 0.003). There was also a significant correlation between the percentage of placental volume affected by abnormal heterogeneity and the severity of fetal growth restriction (r = 0.82, p < 0.001), and an increase in the maximal placental thickness to placental volume ratio above the 95th centile for gestational age was associated with fetal and early neonatal mortality (relative risk = 7, 95%CI = 2.96 – 16.55, p < 0.001) (figure 3.6) MR studies of fetal intra-thoracic and intra-abdominal volumes showed that although the volume of the intra-thoracic and intra-abdonimal organs (heart, lungs, thymus, liver and kidney) increased as gestation increased in both groups, the volumes of all three structures remained smaller in growth restricted fetuses (p < 0.01) (Figures 4.7 - 4.9) compared with normally grown fetuses. MR studies of the fetal brain demonstrated smaller intracranial volume, total brain volume and cerebellar volume in growth restricted fetuses. In addition, growth restricted fetuses with early onset fetal growth restriction demonstrated smaller vermis height and a corresponding increase in the tegmento-vermian angle. Growth restricted fetuses also demonstrated a disproportionate decrease in extra- and intra-cerebral fluid. This thesis showed evidence of changes in regional and global organ growth in growth restricted fetuses using high resolution fetal MRI. It is hoped that future imaging studies could offer useful insights into the origins and clinical significance of these findings and its consequences for later neurodevelopment

Fetal body MRI and its application to fetal and neonatal treatment: an illustrative review

This Review depicts the evolving role of MRI in the diagnosis and prognostication of anomalies of the fetal body, here including head and neck, thorax, abdomen and spine. 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. We discuss the benefits and limitations of fetal MRI, from anomaly detection, to classification and prognostication, and defines the role of imaging in the decision to proceed to fetal intervention, across the breadth of included conditions. We also consider the current capabilities of ultrasound and explore how MRI and ultrasound can complement each other in the future of fetal imaging

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

Brain growth and development in fetuses with congenital heart disease

Introduction and Objectives: In the current era of excellent surgical results for congenital heart disease (CHD), focus has become directed on quality of life for these children. Previous studies have shown that neurodevelopmental outcome in CHD is impaired. The mechanisms are incompletely understood but there is increasing evidence that the origins of this are in fetal life. This thesis aims to describe the in utero brain growth in a cohort of fetuses with CHD and relate this to the circulatory abnormalities and fetal Doppler parameters. Methods: Pregnant women with a fetus with CHD were prospectively recruited. The congenital heart defect was phenotyped using fetal echocardiography and patients subdivided into three physiological groups on the basis of the anticipated abnormality of cerebral blood flow and oxygen delivery: (1) isolated reduced flow to the brain; 2) reduced oxygen saturation of cerebral blood flow; (3) combination of reduced oxygen and flow. Fetal brain MRI was performed. In addition to standard biometric measurements, snapshot to volume reconstruction (SVR) was used to construct a 3D data set from the oversampled raw data. From these 3D volumes the total brain volume and ventricular volumes were measured by manual segmentation. Serial measurements of fetal growth were also made and umbilical artery and middle cerebral artery Doppler parameters were analysed. Results: 29 women were included; comparison was made with 83 normal MRI controls. Fetuses with CHD were found to have smaller brain volumes compared to controls when adjusting for advancing gestation (p<0.01). This difference becomes more pronounced with advancing gestation, suggesting a slower rate of in utero brain growth. Measurements of growth found that the fetuses with CHD were smaller throughout gestation with a highly significant difference at the later growth scan. (p<0.001). Cerebral and umbilical artery Doppler data showed evidence of reduced cerebrovascular resistance in fetuses with CHD but did not show a difference in the umbilical artery Doppler. Conclusion: Fetuses with CHD have evidence of impaired brain growth with advancing pregnancy and an increased rate of overall growth restriction. Doppler evidence of cerebral vasodilation supports the mechanism of reduced oxygen delivery as an underlying cause.Open Acces