Fetal face shape analysis from prenatal 3D ultrasound images.

3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.

Analysis of thrombogenicity under flow reveals new insights into the prothrombotic state of patients with post-COVID syndrome.

BACKGROUND: Post-COVID syndrome (PCS) affects millions of people worldwide, causing a multitude of symptoms and impairing quality of life months or even years after acute COVID-19. A prothrombotic state has been suggested; however, underlying mechanisms remain to be elucidated. OBJECTIVES: To investigate thrombogenicity in PCS using a microfluidic assay, linking microthrombi, thrombin generation, and the von Willebrand factor (VWF):a Disintegrin and Metalloproteinase with a Thrombospondin Type 1 motif, member 13 (ADAMTS13) axis. METHODS: Citrated blood was perfused through microfluidic channels coated with collagen or an antibody against the VWF A3 domain, and thrombogenicity was monitored in real time. Thrombin generation assays were performed and α(2)-antiplasmin, VWF, and ADAMTS13 activity levels were also measured. RESULTS: We investigated thrombogenicity in a cohort of 21 patients with PCS with a median time following symptoms onset of 23 months using a dynamic microfluidic assay. Our data show a significant increase in platelet binding on both collagen and anti-VWF A3 in patients with PCS compared with that in controls, which positively correlated with VWF antigen (Ag) levels, the VWF(Ag):ADAMTS13 ratio (on anti-VWF A3), and inversely correlated with ADAMTS13 activity (on collagen). Thrombi forming on collagen presented different geometries in patients with PCS vs controls, with significantly increased thrombi area mainly attributable to thrombi length in the patient group. Thrombi length positively correlated with VWF(Ag):ADAMTS13 ratio and thrombin generation assay results, which were increased in 55.5% of patients. α(2)-Antiplasmin levels were normal in 89.5% of patients. CONCLUSION: Together, these data present a dynamic assay to investigate the prothrombotic state in PCS, which may help unravel the mechanisms involved and/or establish new therapeutic strategies for this condition.

Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort.

The Multidomain Alzheimer Preventive Trial was designed to assess the effect of omega-3 supplementation and multidomain intervention on cognitive decline of subjects with subjective memory complaint. In terms of cognitive testing, no significant effect was found. In this paper, we evaluate the effect of the interventions on the brain morphological changes. Subjects with magnetic resonance imaging acquisitions at baseline and at 36 months were included (N = 376). Morphological changes were characterized by volume measurements and nonlinear deformation. The multidomain intervention was associated with a significant effect on the 3-year brain morphological changes in the deformation-based approach. Differences were mainly located in the left periventricular area next to the temporoparietal junction. These changes were associated with better cognitive performance and mood/behavior stabilization. No effect of the omega-3 supplementation was observed. This result suggests a possible effect on cognition, not yet observable after 3 years. We argue that neuroimaging could help define whether early intervention strategies are effective to delay cognitive decline and dementia.

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments.

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed regionally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The model is first estimated on a control population using longitudinal data, then, for each testing subject, the markers are computed cross-sectionally for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolutions. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quite high. The model is also generative since it can be used to simulate plausible morphological trajectories associated with the disease. Our method quantifies two interpretable scalar imaging biomarkers assessing respectively the effects of aging and disease on brain morphology, at the individual and population level. These markers confirm the presence of an accelerated apparent aging component in Alzheimer's patients but they also highlight specific morphological changes that can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.