arXiv subjects

David Lloyd

Publications and source records attributed to David Lloyd.

16 recordsLinked to original sources

arXiv2026

SPARC: Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI

Fetal cardiac MRI (fCMR) provides valuable diagnostic information complementary to echocardiography, particularly for complex congenital heart disease (CHD). Dynamic cine imaging captures cardiac motion essential for assessment of cardiac function; however, the reconstruction of 3D+time cine volumes from 2D+time acquired slices remains challenging due to unpredictable fetal motion and the absence of automated and robust processing tools suitable for clinical deployment. We present the SPARC pipeline (Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI) which combines physics-informed slice-to-volume reconstruction (SVR) of Doppler ultrasound (DUS) gated stacks of slices, assisted by deep learning (DL) models for thoracic segmentation and anatomical reorientation. The proposed SVR algorithm achieves a tenfold reduction in reconstruction time relative to existing frame-wise approaches ($4.8 \pm 1.0$ vs $49.0 \pm 14.1$ min, $p < 0.0001$) while improving the reconstruction quality. Thoracic segmentation performance using ensemble aggregation exceeded inter-rater agreement (Dice $84.7 \pm 3.9\%$ vs $81.4 \pm 7.7\%$, $p<0.05$), while anatomical reorientation achieved a success rate of $90.1\%$. End-to-end evaluation on a large held-out clinical cohort ($n = 121$) demonstrated fully automatic processing in $82.6\%$ of cases with a mean runtime of $7.1 \pm 1.3$ min, compatible with clinical deployment. The complete SPARC pipeline is publicly available as a Docker container https://hub.docker.com/r/aboutill/sparc and is currently deployed at our institution as a clinical research tool.

cs.CV↗

arXiv2025

Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta and umbilical cord for fetal MRI at 0.55T

Purpose: This work aims to enable real-time automated intra-uterine volumetric reporting and fetal weight estimation for fetal MRI, deployed directly on the scanner. Methods: A multi-region segmentation nnUNet was trained on 146 bSSFP images of 73 fetal subjects (coronal and axial orientations) for the parcellation of the fetal head, fetal body, placenta, amniotic fluid and umbilical cord from whole uterus bSSFP stacks. A reporting tool was then developed to integrate the segmentation outputs into an automated report, providing volumetric measurements, fetal weight estimations, and z-score visualisations. The complete pipeline was subsequently deployed on a 0.55T MRI scanner, enabling real-time inference and fully automated reporting in the duration of the acquisition. Results: The segmentation pipeline was quantitatively and retrospectively evaluated on 36 stacks of 18 fetal subjects and demonstrated sufficient performance for all labels, with high scores (>0.98) for the fetus, placenta and amniotic fluid, and 0.91 for the umbilical cord. The prospective evaluation of the scanner deployment step was successfully performed on 50 cases, with the regional volumetric reports available directly on the scanner. Conclusions: This work demonstrated the feasibility of multi-regional intra-uterine segmentation, fetal weight estimation and automated reporting in real-time. This study provides a robust baseline solution for the integration of fully automated scanner-based measurements into fetal MRI reports.

physics.med-ph↗

arXiv2025

Numerical Continuation and Bifurcation in Nonlinear PDEs: Stability, invasion and wavetrains in the Swift-Hohenberg equation

We discuss some aspects of numerical continuation and bifurcation for partial differential equations, specifically pattern formation and coherent structures. For the sake of clarity we focus on wavetrains, stability and associated invasion processes in the paradigmatic cubic Swift-Hohenberg equation (SHE). We do not aim at a review of numerical continuation for PDE or pattern formation in SHE in any generality, rather our goal is to provide an entry point for interested students and colleagues to the application of continuation methods. We provide access to numerical implementations and hope that our presentation provides an introductory guideline that can also be used for teaching.

nlin.PS↗

arXiv2024

AutOmatic floW planning for fetaL MRI (OWL)

Two subsequent deep learning networks, one localizing the fetal chest and one identifying a set of landmarks on a coronal whole-uterus balanced steady-state free precession scan, were trained on 167 and 71 fetal datasets across field strengths, acquisition parameters, and gestational ages and implemented in a real-time setup. Next, a phase-contrast sequence was modified to use the identified landmarks for planning. The OWL pipeline was evaluated retrospectively in 10 datasets and prospectively in 7 fetal subjects (gestational ages between 36+3 and 39+3 weeks). The prospective cases were additionally manually planned to enable direct comparison both qualitatively, by scoring the planning quality, and quantitatively, by comparing the indexed flow measurements. OWL enabled real-time fully automatic planning of the 2D phase-contrast scans in all but one of the prospective participants. The fetal body localization achieved an overall Dice score of 0.94+-0.05 and the cardiac landmark detection accuracy was 5.77+-2.91 mm for the descending aorta, 4.32+-2.44 mm for the spine, and 4.94+-3.82 mm for the umbilical vein. For the prospective cases, overall planning quality was 2.73/4 for the automated scans, compared to 3.0/4 for manual planning, and the flow quantitative evaluation showed a mean difference of -1.8% (range -14.2% to 14.9%) by comparing the indexed flow measurements obtained from gated automatic and manual acquisitions. Real-time automated planning of 2D phase-contrast MRI was effectively accomplished for 2 major vessels of the fetal vasculature. While demonstrated here on 0.55T, the achieved method has wider implications, and training across multiple field strengths enables generalization. OWL thereby presents an important step towards extending access to this modality beyond specialised centres.

physics.med-ph↗

arXiv2024

Nonlinear Propagation of Non-Gaussian Uncertainties

This paper presents a novel approach for propagating uncertainties in dynamical systems building on high-order Taylor expansions of the flow and moment-generating functions (MGFs). Unlike prior methods that focus on Gaussian distributions, our approach leverages the relationship between MGFs and distribution moments to extend high-order uncertainty propagation techniques to non-Gaussian scenarios. This significantly broadens the applicability of these methods to a wider range of problems and uncertainty types. High-order moment computations are performed one-off and symbolically, reducing the computational burden of the technique to the calculation of Taylor series coefficients around a nominal trajectory, achieved by efficiently integrating the system's variational equations. Furthermore, the use of the proposed approach in combination with event transition tensors, allows for accurate propagation of uncertainties at specific events, such as the landing surface of a celestial body, the crossing of a predefined Poincar\'e section, or the trigger of an arbitrary event during the propagation. Via numerical simulations we demonstrate the effectiveness of our method in various astrodynamics applications, including the unperturbed and perturbed two-body problem, and the circular restricted three-body problem, showing that it accurately propagates non-Gaussian uncertainties both at future times and at event manifolds.

physics.space-ph↗

arXiv2023

Adhesion of 2D MoS$_2$ to Graphite and Metal Substrates Measured by a Blister Test

Using a blister test, we measured the work of separation between MoS$_2$ membranes from metal, semiconductor, and graphite substrates. We found a work of separation ranging from 0.11 +- 0.05 J/m^2 for chromium to 0.39 +- 0.1 J/m^2 for graphite substrates. In addition, we measured the work of adhesion of MoS$_2$ membranes over these substrates and observed a dramatic difference between the work of separation and adhesion which we attribute to adhesion hysteresis. Due to the prominent role that adhesive forces play in the fabrication and functionality of devices made from 2D materials, an experimental determination of the work of separation and adhesion as provided here will help guide their development.

cond-mat.mes-hall↗

arXiv2021

Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome

Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a model architecture based on the $α$-GAN network and find evidence that the proposed model performs significantly better than the state-of-the-art in image-based anomaly detection, yielding average $0.81$ AUC \emph{and} a better robustness towards initialisation compared to previous works.

eess.IV↗

arXiv2020

Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.

eess.IV↗

arXiv2019

Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers

Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose a novel probabilistic Deep Learning approach for real-time automated estimation of fetal HC. This system feeds back statistics on measurement robustness to inform users how confident a deep neural network is in evaluating suitable views acquired during free-hand ultrasound examination. In real-time scenarios, this approach may be exploited to guide operators to scan planes that are as close as possible to the underlying distribution of training images, for the purpose of improving inter-operator consistency. We train on free-hand ultrasound data from over 2000 subjects (2848 training/540 test) and show that our method is able to predict HC measurements within 1.81$\pm$1.65mm deviation from the ground truth, with 50% of the test images fully contained within the predicted confidence margins, and an average of 1.82$\pm$1.78mm deviation from the margin for the remaining cases that are not fully contained.

eess.IV↗

arXiv2019

Voltage gated inter-cation selective ion channels from graphene nanopores

With the ability to selectively control ionic flux, biological protein ion channels perform a fundamental role in many physiological processes. For practical applications that require the functionality of a biological ion channel, graphene provides a promising solid-state alternative, due to its atomic thinness and mechanical strength. Here, we demonstrate that nanopores introduced into graphene membranes, as large as 50 nm in diameter, exhibit inter-cation selectivity with a ~20x preference for K+ over divalent cations and can be modulated by an applied gate voltage. Liquid atomic force microscopy of the graphene devices reveals surface nanobubbles near the pore to be responsible for the observed selective behavior. Molecular dynamics simulations indicate that translocation of ions across the pore likely occurs via a thin water layer at the edge of the pore and the nanobubble. Our results demonstrate a significant improvement in the inter-cation selectivity displayed by a solid-state nanopore device and by utilizing the pores in a de-wetted state, offers an approach to fabricating selective graphene membranes that does not rely on the fabrication of sub-nm pores.

cond-mat.mes-hall↗

arXiv2019

Robotic-assisted Ultrasound for Fetal Imaging: Evolution from Single-arm to Dual-arm System

The development of robotic-assisted extracorporeal ultrasound systems has a long history and a number of projects have been proposed since the 1990s focusing on different technical aspects. These aim to resolve the deficiencies of on-site manual manipulation of hand-held ultrasound probes. This paper presents the recent ongoing developments of a series of bespoke robotic systems, including both single-arm and dual-arm versions, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). After a brief review of the development history of the extracorporeal ultrasound robotic system used for fetal and abdominal examinations, the specific aim of the iFIND robots, the design evolution, the implementation details of each version, and the initial clinical feedback of the iFIND robot series are presented. Based on the preliminary testing of these newly-proposed robots on 42 volunteers, the successful and re-liable working of the mechatronic systems were validated. Analysis of a participant questionnaire indicates a comfortable scanning experience for the volunteers and a good acceptance rate to being scanned by the robots.

cs.RO↗

arXiv2018

Transient thermal characterization of suspended monolayer MoS$_2$

We measure the thermal time constants of suspended single layer molybdenum disulfide drums by their thermomechanical response to a high-frequency modulated laser. From this measurement the thermal diffusivity of single layer MoS$_2$ is found to be 1.14 $\times$ 10$^{-5}$ m$^2$/s on average. Using a model for the thermal time constants and a model assuming continuum heat transport, we extract thermal conductivities at room temperature between 10 to 40 W/(m$\cdot$K). Significant device-to-device variation in the thermal diffusivity is observed. Based on statistical analysis we conclude that these variations in thermal diffusivity are caused by microscopic defects that have a large impact on phonon scattering, but do not affect the resonance frequency and damping of the membrane's lowest eigenmode. By combining the experimental thermal diffusivity with literature values of the thermal conductivity, a method is presented to determine the specific heat of suspended 2D materials, which is estimated to be 255 $\pm$ 104 J/(kg$\cdot$K) for single layer MoS$_2$.

cond-mat.mes-hall↗

arXiv2018

Monolayer MoS$_2$ Strained to 1.3\% with a Microelectromechanical System

We report on a modified transfer technique for atomically thin materials integrated onto microelectromechanical systems (MEMS) for studying strain physics and creating strain-based devices. Our method tolerates the non-planar structures and fragility of MEMS, while still providing precise positioning and crack free transfer of flakes. Further, our method used the transfer polymer to anchor the 2D crystal to the MEMS, which reduces the fabrication time, increases the yield, and allowed us to exploit the strong mechanical coupling between 2D crystal and polymer to strain the atomically thin system. We successfully strained single atomic layers of molybdenum disulfide (MoS$_2$) with MEMS devices for the first time and achieved greater than 1.3\% strain, marking a major milestone for incorporating 2D materials with MEMS We used the established strain response of MoS$_2$ Raman and Photoluminescence spectra to deduce the strain in our crystals and provide a consistency check. We found good comparison between our experiment and literature.

cond-mat.mtrl-sci↗

arXiv2017

Adhesion, Stiffness and Instability in Atomically Thin MoS2 Bubbles

We measured the work of separation of single and few-layer MoS2 membranes from a SiOx substrate using a mechanical blister test, and found a value of 220 +- 35 mJ/m^2. Our measurements were also used to determine the 2D Young's modulus of a single MoS2 layer to be 160 +- 40 N/m. We then studied the delamination mechanics of pressurized MoS2 bubles, demonstrating both stable and unstable transitions between the bubbles' laminated and delaminated states as the bubbles were inflated. When they were deflated, we observed edge pinning and a snap-in transition which are not accounted for by the previously reported models. We attribute this result to adhesion hysteresis and use our results to estimate the work of adhesion of our membranes to be 42 +- 20 mJ/m^2.

cond-mat.mes-hall↗

arXiv2017

Visualizing the Cu and Cu2O Interface Transition in Nanoparticles with Environmental Scanning Transmission Electron Microscopy

Understanding the oxidation and reduction mechanisms of catalytically active transition metal nanoparticles is important to improve their application in a variety of chemical processes. In nanocatalysis the nanoparticles can undergo oxidation or reduction in situ, and thus the redox species are not what are observed before and after reactions. We have used the novel environmental scanning transmission electron microscope (ESTEM) with 0.1 nm resolution in systematic studies of complex dynamic oxidation and reduction mechanisms of copper nanoparticles. The oxidation of copper has previously been reported to be dependent on its crystallography and its interaction with the substrate. By following the dynamic oxidation process in situ, in real time, with high-angle annular dark-field imaging in the ESTEM, we use conditions ideal to track the oxidation front as it progresses across a copper nanoparticle by following the changes in the atomic number (z) contrast with time. The oxidation occurs via the nucleation of the oxide phase (Cu2O) from one area of the nanoparticle which then progresses unidirectionally across the particle, with the Cu-to-Cu2O interface having a relationship of Cu{111} parallel to Cu2O{111}. The oxidation kinetics are related to the temperature and oxygen pressure. When the process is reversed in hydrogen, the reduction process is observed to be similar to the oxidation, with the same crystallographic relationship between the two phases. The dynamic observations provide unique insights into redox mechanisms which are important to understanding and controlling the oxidation and reduction of copper-based nanoparticles.

physics.chem-ph↗

arXiv2016

Band Gap Engineering with Ultralarge Biaxial Strains in Suspended Monolayer MoS2

We demonstrate the continuous and reversible tuning of the optical band gap of suspended monolayer MoS2 membranes by as much as 500 meV by applying very large biaxial strains. By using chemical vapor deposition (CVD) to grow crystals that are highly impermeable to gas, we are able to apply a pressure difference across suspended membranes to induce biaxial strains. We observe the effect of strain on the energy and intensity of the peaks in the photoluminescence (PL) spectrum, and find a linear tuning rate of the optical band gap of 99 meV/%. This method is then used to study the PL spectra of bilayer and trilayer devices under strain, and to find the shift rates and Grüneisen parameters of two Raman modes in monolayer MoS2. Finally, we use this result to show that we can apply biaxial strains as large as 5.6% across micron sized areas, and report evidence for the strain tuning of higher level optical transitions.

cond-mat.mes-hall↗