Innovative approach for first-trimester fetal organ volume measurements using a Virtual Reality system:The Generation R <i>Next</i> Study

INTRODUCTION: To investigate the reproducibility of first‐trimester fetal organ volume measurements using three‐dimensional (3D) ultrasound and a Virtual Reality system. METHODS: Within a population‐based prospective cohort study, 3D ultrasound datasets of 25 first‐trimester fetuses were collected by three sonographers. We used the V‐scope application to perform Virtual Reality volume assessments of the fetal heart, lungs, and kidneys. All measurements were performed by two independent researchers. RESULTS: Intraobserver analyses for volume measurements of the fetal heart, lungs, and kidneys showed intraclass correlation coefficients ≥0.86, mean differences ≤8.3%, and coefficients of variation ≤22.8%. Interobserver analyses showed sufficient agreement for right lung volume measurements, but consistent measurement differences between observers for left lung, heart, and kidney volume measurements (p‐values <0.05). CONCLUSION: We observed sufficient intraobserver reproducibility, but overall suboptimal interobserver reproducibility for first‐trimester fetal heart, lung, and kidney volume measurements using an innovative Virtual Reality approach. In the current stage, these measurements might be promising for the use in research settings. The reproducibility of the measurements might be further improved by novel post‐processing algorithms

How 3D immersive visualization is changing medical diagnostics

Originally the only way to look inside the human body without opening it up was by means of two dimensional (2D) images obtained using X-ray equipment. The fact that human anatomy is inherently three dimensional leads to ambiguities in interpretation and problems of occlusion. Three dimensional (3D) imaging modalities such as CT, MRI and 3D ultrasound remove these drawbacks and are now part of routine medical care. While most hospitals 'have gone digital', meaning that the images are no longer printed on film, they are still being viewed on 2D screens. However, this way valuable depth information is lost, and some interactions become unnecessarily complex or even unfeasible. Using a virtual reality (VR) system to present volumetric data means that depth information is presented to the viewer and 3D interaction is made possible. At the Erasmus MC we have developed V-Scope, an immersive volume visualization system for visualizing a variety of (bio-)medical volumetric datasets, ranging from 3D ultrasound, via CT and MRI, to confocal microscopy, OPT and 3D electron-microscopy data. In this talk we will address the advantages of such a system for both medical diagnostics as well as for (bio)medical research.</p

How 3D immersive visualization is changing medical diagnostics

Originally the only way to look inside the human body without opening it up was by means of two dimensional (2D) images obtained using X-ray equipment. The fact that human anatomy is inherently three dimensional leads to ambiguities in interpretation and problems of occlusion. Three dimensional (3D) imaging modalities such as CT, MRI and 3D ultrasound remove these drawbacks and are now part of routine medical care. While most hospitals 'have gone digital', meaning that the images are no longer printed on film, they are still being viewed on 2D screens. However, this way valuable depth information is lost, and some interactions become unnecessarily complex or even unfeasible. Using a virtual reality (VR) system to present volumetric data means that depth information is presented to the viewer and 3D interaction is made possible. At the Erasmus MC we have developed V-Scope, an immersive volume visualization system for visualizing a variety of (bio-)medical volumetric datasets, ranging from 3D ultrasound, via CT and MRI, to confocal microscopy, OPT and 3D electron-microscopy data. In this talk we will address the advantages of such a system for both medical diagnostics as well as for (bio)medical research.</p

Volume visualization using virtual reality medical applications of volume rendering in immersive virtual environments

In this chapter we take a look at the possible applications of volume visualization in immersive virtual environments, and how they can be implemented. Nowadays, the availability of many kinds of 3D imaging modalities, like CT, MRI and 3D ultrasound, gives clinicians an unprecedented ability to look inside a patient without the need to operate. However, while these datasets are three dimensional, they are still presented on 2D (flat) screens. While most systems and workstations offer a volume rendering option to present the data, when projected onto a normal screen (or printed on paper or film), the images are not truly 3D and are often termed 2.5D. By using a virtual reality system that immerses the viewer(s) in a truly three-dimensional world, we hope to improve the understanding of these images.</p

Volume visualization using virtual reality medical applications of volume rendering in immersive virtual environments

In this chapter we take a look at the possible applications of volume visualization in immersive virtual environments, and how they can be implemented. Nowadays, the availability of many kinds of 3D imaging modalities, like CT, MRI and 3D ultrasound, gives clinicians an unprecedented ability to look inside a patient without the need to operate. However, while these datasets are three dimensional, they are still presented on 2D (flat) screens. While most systems and workstations offer a volume rendering option to present the data, when projected onto a normal screen (or printed on paper or film), the images are not truly 3D and are often termed 2.5D. By using a virtual reality system that immerses the viewer(s) in a truly three-dimensional world, we hope to improve the understanding of these images.</p

Towards a 4D Spatio-Temporal Atlas of the Embryonic and Fetal Brain Using a Deep Learning Approach for Groupwise Image Registration

Brain development during the first trimester is of crucial importance for current and future health of the fetus, and therefore the availability of a spatio-temporal atlas would lead to more in-depth insight into the growth and development during this period. Here, we propose a deep learning approach for creation of a 4D spatio-temporal atlas of the embryonic and fetal brain using groupwise image registration. We build on top of the extension of Voxelmorph for the creation of learned conditional atlases, which consists of an atlas generation and registration network. As a preliminary experiment we trained only the registration network and iteratively updated the atlas. Three-dimensional ultrasound data acquired between the 8th and 12th week of pregnancy were used. We found that in the atlas several relevant brain structures were visible. In future work the atlas generation network will be incorporated and we will further explore, using the atlas, correlations between maternal periconceptional health and brain growth and development

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review

Background: Early screening of the brain is becoming routine clinical practice. Currently, this screening is performed by manual measurements and visual analysis, which is time-consuming and prone to errors. Computational methods may support this screening. Hence, the aim of this systematic review is to gain insight into future research directions needed to bring automated early-pregnancy ultrasound analysis of the human brain to clinical practice. Methods: We searched PubMed (Medline ALL Ovid), EMBASE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar, from inception until June 2022. This study is registered in PROSPERO at CRD42020189888. Studies about computational methods for the analysis of human brain ultrasonography acquired before the 20th week of pregnancy were included. The key reported attributes were: level of automation, learning-based or not, the usage of clinical routine data depicting normal and abnormal brain development, public sharing of program source code and data, and analysis of the confounding factors. Findings: Our search identified 2575 studies, of which 55 were included. 76% used an automatic method, 62% a learning-based method, 45% used clinical routine data and in addition, for 13% the data depicted abnormal development. None of the studies shared publicly the program source code and only two studies shared the data. Finally, 35% did not analyse the influence of confounding factors. Interpretation: Our review showed an interest in automatic, learning-based methods. To bring these methods to clinical practice we recommend that studies: use routine clinical data depicting both normal and abnormal development, make their dataset and program source code publicly available, and be attentive to the influence of confounding factors. Introduction of automated computational methods for early-pregnancy brain ultrasonography will save valuable time during screening, and ultimately lead to better detection, treatment and prevention of neuro-developmental disorders. Funding: The Erasmus MC Medical Research Advisor Committee (grant number: FB 379283)

First trimester fetal proportion volumetric measurements using a Virtual Reality approach

Objective: To establish feasibility and reproducibility of fetal proportion volumetric measurements, using three-dimensional (3D) ultrasound and a Virtual Reality (VR) system. Methods: Within a population-based prospective birth cohort, 3D ultrasound datasets of 50 fetuses in the late first trimester were collected by three ultrasonographers in a single research center. V-scope software was used for volumetric measurements of total fetus, extremities, head-trunk, head, trunk, thorax, and abdomen. All measurements were performed independently by two researchers. Intraobserver and interobserver reproducibility were analyzed using Bland and Altman methods. Results: Intraobserver and interobserver analyses of volumetric measurements of total fetus, head-trunk, head, trunk, thorax and abdomen showed intraclass correlation coefficients above 0.979, coefficients of variation below 7.51% and mean difference below 3.44%. The interobserver limits of agreement were within the ±10% range for volumetric measurements of total fetus, head–trunk, head and trunk. The interobserver limits of agreement for extremities, thorax and abdomen were −26.09% to 4.77%, −14.14% to 10.00% and −14.47% to 8.83%, respectively. Conclusion: First trimester fetal proportion volumetric measurements using 3D ultrasound and VR are feasible and reproducible, except volumetric measurements of the fetal extremities. These novel volumetric measurements may be used in future research to enable detailed studies on first trimester fetal development and growth

Assessment of First-Trimester Utero-Placental Vascular Morphology by 3D Power Doppler Ultrasound Image Analysis Using a Skeletonization Algorithm: The Rotterdam Periconception Cohort

Abnormal uteroplacental vascular development is one of the primary causes of the major disorders of pregnancy. Identification of this aberrant development in the preconception stage and in the first trimester, when measures to prevent complications are most effective, remains a challenge. Recent advances in offline imaging processing and Doppler techniques have allowed ultrasonographic analysis of placental volume (PV) and uteroplacental vascular volume (uPVV) in the first trimester. The ability of ultrasound to assess specific vascular morphology is not fully understood. The aim of this study was to investigate morphologic development of the first-trimester human uteroplacental vasculature in vivo using 3-dimensional power Doppler ultrasound and advanced image processing to generate the uteroplacental vascular skeleton (uPVS). Data were obtained from the VIRTUAL Placenta study, an ongoing observational study including adult women carrying a singleton pregnancy at less than 10-week gestational age (GA). At least 2 study visits were scheduled in the first trimester, at 7, 9, and 11 weeks' GA. The image quality of ultrasound scans was scored on a 4-point scale ranging between 0 (optimal) and 3 (unusable), and PV was measured using VOCAL software. The uPVV was measured using a virtual reality desktop system with the V-scope volume-rendering application. A skeletonization algorithm was then applied to the uPVV segmentation to generate the uPVS, and 7 morphological uPVS characteristics were used to determine the density of vascular branching within the placenta. A total of 214 women from the VIRTUAL cohort were eligible for inclusion, and 81% of all ultrasound data were of sufficient quality for generation of the uPVS. The distribution of endpoints, vessel points, bifurcation points, and crossing points in the uPVS and the distribution of uPVS characteristics per cm3 uPVV show consistent morphologic patterns throughout the first trimester. Moderate to strong positive correlations between uPVS characteristics and PV and strong positive correlations between uPVS characteristics and uPVV were identified. All uPVS characteristics increased significantly through the first trimester, except for uPVS average length, which remained constant between 7, 9, and 11 weeks' GA. When stratifying for placenta-related complications among the cohort, no significant differences in the uPVS characteristics were seen at 7 weeks' GA (n = 94). However, at 9 weeks (n = 170), there were significantly fewer number of vessel points (P = 0.040), bifurcation points (P = 0.050), crossing points (P = 0.020), and a shorter total network length (P = 0.023) among pregnancies with placenta-related complications. At 11 weeks' GA (n = 129), uPVS average vascular thickness was significantly lower in pregnancies with placenta-related complications (P = 0.007), but no other uPVS differences were observed. At 11 weeks' GA, pregnancies with placenta-related complications were found to have an increased density of vascular branching in the uPVV. The results of this study demonstrate a quantitative morphologic analysis of the human uteroplacental vasculature in the first trimester of pregnancy and key differences in the vascular morphology between pregnancies with and without placenta-related complications