Fetal Ultrasound Volumes Research Articles

VP31.12: Trends in fetal ultrasound referrals and case severity throughout the first and second waves of the COVID‐19 pandemic

To determine the combined and differential impact of individual waves of the COVID-19 pandemic on fetal ultrasound referral patterns and antenatal case severity. This was a cross-sectional study evaluating temporal trends in fetal ultrasound referral patterns and case severity at a regional fetal assessment unit. Fetal assessment scans performed during the first and second ‘waves’ of the pandemic (March to May 2020 and November to March 2021) were compared head-to-head and to baseline pre-COVID-19 patterns. Clinic lists and stored ultrasound reports were reviewed to collate maternal demographics and medical history, referral details, daily scan volumes, ultrasound findings, and level of case severity, in order to compare outcomes across the pandemic. 9,451 fetal ultrasounds were performed during the first and second waves of the pandemic, and represent a significant reduction from baseline volumes in the unit (p = 0.008). In the first wave, there was a significant decrease in referrals for early pregnancy complications, genetic ultrasounds, and diabetes surveillance (p < 0.01): there were also fewer referrals from rural communities and general practitioners. The second-wave of the pandemic represented a further decline in referrals from remote locations, but less disruption in number of scans for maternal medical complications. The level of high-acuity cases increased significantly throughout the pandemic compared to baseline (4.9% vs 3.3%; p = 0.0031), but was higher in the first as compared to the second-waves. Also, there was an almost 10% increase in the relative proportion of stillbirths diagnosed during the pandemic, although the absolute numbers remain small. Fetal ultrasound volumes decreased and case severity increased throughout the pandemic, as well as an increase in stillbirths. Enhanced understanding of the impact of access to prenatal care and utilisation of services on the direct and indirect risks for pregnant women is critical for improving care and outcomes during future pandemic planning.

VP45.12: A cross‐sectional evaluation of fetal ultrasound utilisation and acuity during the COVID‐19 pandemic

To evaluate the impact of the COVID-19 pandemic on fetal assessment utilisation and acuity of cases seen. This is a cross-sectional study comparing temporal trends in fetal assessment unit volumes and acuity at a single, tertiary-care centre. All fetal assessment scans performed during the first ‘wave’ of the pandemic (March to May 2020) were compared to scans from the same time last year (March to May 2019). Clinic lists and stored ultrasound reports were reviewed to collate maternal demographics and medical history, referral patterns, number of scans, ultrasound findings, and level of acuity, in order to compare outcomes between time periods. There were 5811 scans performed during the study period, and an overall reduction in the number of fetal assessment scans performed during the first wave of the COVID pandemic compared to last year. The most dramatic decline was seen in the number of scans performed for first-trimester complications (0.9% vs 0.2%; p < 0.001) and genetic ultrasounds including amniocentesis (2.5% vs 1.7%; p < 0.05). Interestingly, there was in increase in the relative proportion of nuchal translucency scans performed between the 2 periods (10.8% vs 13.7%; p < 0.001). The causes of these changes in volumes and acuity were attributable to changes in referral practices (including modified prenatal visit schedules), changes to timing of follow-up scans, and patient preferences. There was also a significant increase in the acuity of cases seen during the pandemic, with several referrals requiring emergency delivery or urgent hospital admission due to fetal and/or maternal complications (p < 0.05). There was a decrease in fetal assessment utilisation during the first wave of the pandemic combined with an overall increase in the level of acuity of cases seen, which appears multi-factorial in nature. An enhanced awareness of changes in fetal ultrasound volumes and the relationship with adverse outcomes will be essential in planning obstetrical care for the next wave of the pandemic.

Probabilistic Learning Coherent Point Drift for 3D Ultrasound Fetal Head Registration

Quantification of brain growth is crucial for the assessment of fetal well being, for which ultrasound (US) images are the chosen clinical modality. However, they present artefacts, such as acoustic occlusion, especially after the 18th gestational week, when cranial calcification appears. Fetal US volume registration is useful in one or all of the following cases: to monitor the evolution of fetometry indicators, to segment different structures using a fetal brain atlas, and to align and combine multiple fetal brain acquisitions. This paper presents a new approach for automatic registration of real 3D US fetal brain volumes, volumes that contain a considerable degree of occlusion artefacts, noise, and missing data. To achieve this, a novel variant of the coherent point drift method is proposed. This work employs supervised learning to segment and conform a point cloud automatically and to estimate their subsequent weight factors. These factors are obtained by a random forest-based classification and are used to appropriately assign nonuniform membership probability values of a Gaussian mixture model. These characteristics allow for the automatic registration of 3D US fetal brain volumes with occlusions and multiplicative noise, without needing an initial point cloud. Compared to other intensity and geometry-based algorithms, the proposed method achieves an error reduction of 7.4% to 60.7%, with a target registration error of only 6.38 ± 3.24 mm. This makes the herein proposed approach highly suitable for 3D automatic registration of fetal head US volumes, an approach which can be useful to monitor fetal growth, segment several brain structures, or even compound multiple acquisitions taken from different projections.

Optimization of Fetal Biometry With 3D Ultrasound and Image Recognition (EPICEA): protocol for a prospective cross-sectional study

ContextVariability in 2D ultrasound (US) is related to the acquisition of planes of reference and the positioning of callipers and could be reduced in combining US volume acquisitions and anatomical...

Virtual reality objects improve learning efficiency and retention of diagnostic ability in fetal ultrasound.

Virtual reality (VR) objects of fetal ultrasound volumes have been proposed for teaching and learning diagnostic ultrasound. The aim of this study was to determine if VR objects improve learning efficiency and retention of diagnostic ability in fetal ultrasound. Medical students and junior doctors were taught normal and abnormal sonographic fetal brain anatomy using conventional means (video lectures and review articles; control group) or additionally with selected VR objects from a novel fetal brain atlas (Pocket Brain, http://pb.fetal.ch; study group). Knowledge, speed of recognition and retention of diagnostic ability were tested in multiple-choice questionnaires 1 and 4 months after teaching, and the results were compared between those taught using conventional means only and those taught using VR objects. Participants taught using VR objects answered significantly more questions correctly and solved the tests quicker than those taught using conventional methods only, both 1 and 4 months after teaching. The use of VR objects in teaching fetal ultrasound significantly improves learning efficiency and knowledge retention. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.

Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to address the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, we optimize the network by simultaneously learning features shared within the input data pertaining to the correlated tasks, and later branching out into task-specific output streams. Brain alignment is achieved by defining a parametric coordinate system based on skull boundaries, location of the eye sockets, and head pose, as predicted from intracranial structures. This information is used to estimate an affine transformation to align a volumetric image to the skull-based coordinate system. Co-alignment of 140 fetal ultrasound volumes (age range: 26.0 ± 4.4 weeks) was achieved with high brain overlap and low eye localization error, regardless of gestational age or head size. The automatically co-aligned volumes show good structural correspondence between fetal anatomies.

Towards improved ultrasound-based analysis and 3D visualization of the fetal brain using the 3D Slicer

Magnetic resonance imaging (MRI) provides useful three-dimensional (3D) information; however, there are some restrictions on its use during pregnancy due to safety concerns. In addition, fetal movements can create artifacts on MR images, as image quality depends on position of the fetus and placenta. In the past decade, 3D ultrasound imaging has been used in clinical practice to investigate the formation and volumetric size of critical anatomical structures of the fetus. However, current techniques rely mainly on analysis of sections of interest that do not integrate anatomical information concerning the shape of these structures. We provide a brief description of a workflow for semi-automated segmentation and 3D visualization of fetal ultrasound volumes in the second trimester using the 3D Slicer open source software1,2. Our workflow allowed quantitative image analysis of the choroid plexus and cerebrum from 3D ultrasound images. We acquired 3D ultrasound volumes from five healthy pregnant women at 12 (n = 2), 14 (n = 2) and 19 (n = 1) weeks of gestation. Informed consent was obtained in each case. We used a Voluson E6 (GE Medical Systems, Zipf, Austria) ultrasound machine with a RAB4-8-D/OB D/4D 8-MHz transabdominal transducer. Our workflow consisted of four steps (Figure 1). Firstly, we imported DICOM (digital imaging and communications in medicine) ultrasound volumes into the 3D Slicer. We then used the ‘Grow Cut Segmentation’ algorithm3 of the interactive Editor module to extract critical structures from the ultrasound volumes. We reconstructed 3D surface models from segmented regions using the ‘Marching Cubes’ algorithm4, and finally computed the volume of 3D anatomical models using the ‘Label Statistics’ module of the software. Figure 2 shows a 3D surface model of the choroid plexus and cerebrum reconstructed from the original 3D ultrasound volumes. The corresponding volumes of these structures at 12, 14 and 19 weeks' gestation were, respectively: 431.1 mm3, 698.9 mm3 and 1203.3 mm3 for the choroid plexus and 183.6 mm3, 282.8 mm3 and 469.8 mm3 for the cerebrum. Using the 3D Slicer, we were able to obtain patient-specific quantitative information and 3D visualization of anatomical structures within the fetal brain. We anticipate being able to perform segmentation that accurately matches the anatomy using different methods. We believe this method, combined with ultrasound or MRI data, will be helpful in monitoring fetal development and detecting anomalies of the brain as well as other anatomical structures.

OC078: Three-dimensional ultrasound fetal volume measurement: a study comparing the multiplanar technique with the rotational (VOCAL) technique

OC078: Three-dimensional ultrasound fetal volume measurement: a study comparing the multiplanar technique with the rotational (VOCAL) technique