Search

Background: Image-domain motion correction of black-blood contrast T2-weighted fetal cardiovascular magnetic resonance imaging (CMR) using slice-to-volume registration (SVR) provides high-resolution three-dimensional (3D) images of the fetal heart providing excellent 3D visualisation of vascular anomalies [1]. However, 3D segmentation of these datasets, important for both clinical reporting and the application of advanced analysis techniques is currently a time-consuming process requiring manual input with potential for inter-user variability. Methods: In this work, we present novel 3D fetal CMR population-averaged atlases of normal and abnormal fetal cardiovascular anatomy. The atlases are created using motion-corrected 3D reconstructed volumes of 86 third trimester fetuses (gestational age range 29-34 weeks) including: 28 healthy controls, 20 cases with postnatally confirmed neonatal coarctation of the aorta (CoA) and 38 vascular rings (21 right aortic arch (RAA), 17 double aortic arch (DAA)). We used only high image quality datasets with isolated anomalies and without any other deviations in the cardiovascular anatomy.In addition, we implemented and evaluated atlas-guided registration and deep learning (UNETR) methods for automated 3D multi-label segmentation of fetal cardiac vessels. We used images from CoA, RAA and DAA cohorts including: 42 cases for training (14 from each cohort), 3 for validation and 6 for testing. In addition, the potential limitations of the network were investigated on unseen datasets including 3 early gestational age (22 weeks) and 3 low SNR cases. Results: We created four atlases representing the average anatomy of the normal fetal heart, postnatally confirmed neonatal CoA, RAA and DAA. Visual inspection was undertaken to verify expected anatomy per subgroup. The results of the multi-label cardiac vessel UNETR segmentation showed 100 % per-vessel detection rate for both normal and abnormal aortic arch anatomy. Conclusions: This work introduces the first set of 3D black-blood T2-weighted CMR atlases of normal and abnormal fetal cardiovascular anatomy including detailed segmentation of the major cardiovascular structures. Additionally, we demonstrated the general feasibility of using deep learning for multi-label vessel segmentation of 3D fetal CMR images. [ABSTRACT FROM AUTHOR]

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 <32 weeks of gestation (ten with ruptured membranes and six with intact membranes) and 80 control 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. [ABSTRACT FROM AUTHOR]

Deep learning based medical image segmentation has shown great potential in becoming a key part of the clinical analysis pipeline. However, many of these models rely on the assumption that the train and test data come from the same distribution. This means that such methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions, without requiring the use of labeled data in the target domain. In this work, we aim to predict tissue segmentation maps on T 2-weighted magnetic resonance imaging data of an unseen preterm-born neonatal population, which has both different acquisition parameters and population bias when compared to our training data. We achieve this by investigating two unsupervised domain adaptation techniques with the objective of finding the best solution for our problem. We compare the two methods with a baseline fully-supervised segmentation network and report our results in terms of Dice scores obtained on our source test dataset. Moreover, we analyse tissue volumes and cortical thickness measures of the harmonized data on a subset of the population matched for gestational age at birth and postmenstrual age at scan. Finally, we demonstrate the applicability of the harmonized cortical gray matter maps with an analysis comparing term and preterm-born neonates and a proof-of-principle investigation of the association between cortical thickness and a language outcome measure. [ABSTRACT FROM AUTHOR]

Purpose: To develop an MRI acquisition and reconstruction framework for volumetric cine visualization of the fetal heart and great vessels in the presence of maternal and fetal motion. Methods: Four‐dimensional (4D) depiction was achieved using a highly‐accelerated multi‐planar real‐time balanced steady‐state free precession acquisition combined with retrospective image‐domain techniques for motion correction, cardiac synchronization and outlier rejection. The framework was validated using a numerical phantom and evaluated in a study of 20 mid‐ to late‐gestational age human fetal subjects (23‐33 weeks gestational age). Reconstructed MR data were compared with matched ultrasound. A preliminary assessment of flow‐sensitive reconstruction using the velocity information encoded in the phase of real‐time images is included. Results: Reconstructed 4D data could be visualized in any two‐dimensional plane without the need for highly specific scan plane prescription prior to acquisition or for maternal breath hold to minimize motion. Reconstruction was fully automated aside from user‐specified masks of the fetal heart and chest. The framework proved robust when applied to fetal data and simulations confirmed that spatial and temporal features could be reliably recovered. Evaluation suggested the reconstructed framework has the potential to be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques. Conclusions: The proposed methods show promise as a framework for motion‐compensated 4D assessment of the fetal heart and great vessels. [ABSTRACT FROM AUTHOR]

Objectives: The fetal thymus gland has been shown to involute in response to intrauterine infection, and therefore could be used as a non-invasive marker of fetal compartment infection. The objective of this study was to evaluate how accurately 2D ultrasound-derived measurements of the fetal thymus reflect the 3D volume of the gland derived from motion corrected MRI images.Study Design: A retrospective study was performed using paired ultrasound and MRI datasets from the iFIND project (http://www.ifindproject.com). To obtain 3D volumetry of the thymus gland, T2-weighted single shot turbo spin echo (ssTSE) sequences of the fetal thorax were acquired. Thymus volumes were manually segmented from deformable slice-to-volume reconstructed images. To obtain 2D ultrasound measurements, previously stored fetal cine loops were used and measurements obtained at the 3-vessel-view (3VV) and 3-vessel-trachea view (3VT): anterior-posterior diameter (APD), intrathoracic diameter (ITD), transverse diameter (TD), perimeter and 3-vessel-edge (3VE). Inter-observer and intra-observer reliability (ICC) was calculated for both MRI and ultrasound measurements. Pearson correlation coefficients (PCC) were used to compare 2D-parameters with acceptable ICC to TV.Results: 38 participants were identified. Adequate visualisation was possible on 37 MRI scans and 31 ultrasound scans. Of the 30 datasets where both MRI and ultrasound data were available, MRI had good interobserver reliability (ICC 0.964) and all ultrasound 3VV 2D-parameters and 3VT 3VE had acceptable ICC (>0.75). Four 2D parameters were reflective of the 3D thymus volume: 3VV TD r = 0.540 (P = 0.002); 3VV perimeter r = 0.446 (P = 0.013); 3VV APD r = 0.435 (P = 0.110) and 3VT TD r = 0.544 (P = 0.002).Conclusions: MRI appeared superior to ultrasound for visualization of the thymus gland and reproducibility of measurements. Three 2D US parameters, 3VV TD, perimeter and 3VT APD, correlated well with TV. Therefore, these represent a more accurate reflection of the true size of the gland than other 2D measurements, where MRI is not available. [ABSTRACT FROM AUTHOR]

Background: Two-dimensional (2D) ultrasound echocardiography is the primary technique used to diagnose congenital heart disease before birth. There is, however, a longstanding need for a reliable form of secondary imaging, particularly in cases when more detailed three-dimensional (3D) vascular imaging is required, or when ultrasound windows are of poor diagnostic quality. Fetal MRI, which is well established for other organ systems, is highly susceptible to fetal movement, particularly for 3D imaging. The objective of this study was to investigate the combination of prenatal MRI with novel, motion-corrected 3D image registration software, as an adjunct to fetal echocardiography in the diagnosis of congenital heart disease.Methods: Pregnant women carrying a fetus with known or suspected congenital heart disease were recruited via a tertiary fetal cardiology unit. After initial validation experiments to assess the general reliability of the approach, MRI data were acquired in 85 consecutive fetuses, as overlapping stacks of 2D images. These images were then processed with a bespoke open-source reconstruction algorithm to produce a super-resolution 3D volume of the fetal thorax. These datasets were assessed with measurement comparison with paired 2D ultrasound, structured anatomical assessment of the 2D and 3D data, and contemporaneous, archived clinical fetal MRI reports, which were compared with postnatal findings after delivery.Findings: Between Oct 8, 2015, and June 30, 2017, 101 patients were referred for MRI, of whom 85 were eligible and had fetal MRI. The mean gestational age at the time of MRI was 32 weeks (range 24-36). High-resolution (0·50-0·75 mm isotropic) 3D datasets of the fetal thorax were generated in all 85 cases. Vascular measurements showed good overall agreement with 2D echocardiography in 51 cases with paired data (intra-class correlation coefficient 0·78, 95% CI 0·68-0·84), with fetal vascular structures more effectively visualised with 3D MRI than with uncorrected 2D MRI (657 [97%] of 680 anatomical areas identified vs 358 [53%] of 680 areas; p<0·0001). When a structure of interest was visualised in both 2D and 3D data (n=358), observers gave a higher diagnostic quality score for 3D data in 321 (90%) of cases, with 37 (10%) scores tied with 2D data, and no lower scores than for 2D data (Wilcoxon signed rank test p<0·0001). Additional anatomical features were described in ten cases, of which all were confirmed postnatally.Interpretation: Standard fetal MRI with open-source image processing software is a reliable method of generating high-resolution 3D imaging of the fetal vasculature. The 3D volumes produced show good spatial agreement with ultrasound, and significantly improved visualisation and diagnostic quality compared with source 2D MRI data. This freely available combination requires minimal infrastructure, and provides safe, powerful, and highly complementary imaging of the fetal cardiovascular system.Funding: Wellcome Trust/EPSRC Centre for Medical Engineering, National Institute for Health Research. [ABSTRACT FROM AUTHOR]