Unsupervised image registration towards enhancing performance and explainability in cardiac and brain image analysis

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an unsupervised deep learning registration methodology that can accurately model affine and non-rigid transformations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent representations, and involves two factorised transformation networks (one per each encoder-decoder channel) and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named “FIRE”) shows improved performances against the reference standard baseline method (i.e., Symmetric Normalization implemented using the ANTs toolbox) on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments. We focus on explaining model-data components to enhance model explainability in medical image registration. On computational time experiments, we show that the FIRE model performs on a memory-saving mode, as it can inherently learn topology-preserving image registration directly in the training phase. We therefore demonstrate an efficient and versatile registration technique that can have merit in multi-modal image registrations in the clinical setting

Impact of thixotropy on flow patterns induced in a stirred tank : numerical and experimental studies

Agitation of a thixotropic shear-thinning fluid exhibiting a yield stress is investigated both experimentally and via simulations. Steady-state experiments are conducted at three impeller rotation rates (1, 2 and 8 s−1) for a tank stirred with an axial-impeller and flow-field measurements are made using particle image velocimetry (PIV) measurements. Threedimensional numerical simulations are also performed using the commercial CFD code ANSYS CFX10.0. The viscosity of the suspension is determined experimentally and is modelled using two shear-dependant laws, one of which takes into account the flow instabilities of such fluids at low shear rates. At the highest impeller speed, the flow exhibits the familiar outward pumping action associated with axial-flow impellers. However, as the impeller speed decreases, a cavern is formed around the impeller, the flow generated in the vicinity of the agitator reorganizes and its pumping capacity vanishes. An unusual flow pattern, where the radial velocity dominates, is observed experimentally at the lowest stirring speed. It is found to result from wall slip effects. Using blades with rough surfaces prevents this peculiar behaviour and mainly resolves the discrepancies between the experimental and computational results

Enabling Accurate Cross-Layer PHY/MAC/NET Simulation Studies of Vehicular Communication Networks

Vehicle-to-vehicle and vehicle-to-roadside communications is required for numerous applications that aim at improving traffic safety and efficiency. In this setting, however, gauging system performance through field trials can be very expensive especially when the number of studied vehicles is high. Therefore, many existing studies have been conducted using either network or physical layer simulators; both approaches are problematic. Network simulators typically abstract physical layer details (coding, modulation, radio channels, receiver algorithms, etc.) while physical layer ones do not consider overall network characteristics (topology, network traffic types, and so on). In particular, network simulators view a transmitted frame as an indivisible unit, which leads to several limitations. First, the impact of the vehicular radio channel is typically not reflected in its appropriate context. Further, interference due to frame collisions is not modeled accurately ( if at all) and, finally, the benefits of advanced signal processing techniques, such as interference cancellation, are difficult to assess. To overcome these shortcomings we have integrated a detailed physical layer simulator into the popular NS-3 network simulator. This approach aims to bridge the gap between the physical and network layer perspectives, allow for more accurate channel and physical layer models, and enable studies on cross-layer optimization. In this paper, we exemplify our approach by integrating an IEEE 802.11a and p physical layer simulator with NS-3. Further, we validate the augmented NS-3 simulator against an actual IEEE 802.11 wireless testbed and illustrate the additional value of this integration

In-depth Analysis and Evaluation of Self-Organizing TDMA

Recent studies suggest that Self-organizing Time- Division Multiple Access (STDMA) might be a better medium access strategy in inter-vehicle communication networks than Carrier Sense Multiple Access (CSMA), especially when con- sidering safety focused applications. Although it is necessary to completely understand a protocol and the effect of its ‘turning knobs’ on performance before adoption, STDMA has not yet been subjected to such rigorous treatment in the literature. In order to address this shortcoming we perform and present an in-depth analysis and evaluation of STDMA’s fundamental principles. In particular, we contribute a detailed and complete description of the STDMA protocol, followed by the analysis and evaluation of two key questions: How can packet collisions occur in STDMA and whether packet collisions are ‘contagious’. We further perform a fair comparison with CSMA on the basis of which we provide recommendations on the configuration of STDMA. Our results show that STDMA coordinates multiple access effectively – even in highly congested situations – as long as all transmitted packets are decoded successfully. When non-decodable (but still carrier-sensible) transmissions are present, STDMA effectiveness drops below that achieved by CSMA due to the lack of control information. To ensure reproducibility and encourage further inquiry we release the STDMA implementation used in this paper to the wireless networks research community

TPSDicyc: Improved deformation invariant cross-domain medical image synthesis

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant model based on the deformation-invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods

Composably secure data processing for Gaussian-modulated continuous variable quantum key distribution

Continuous-variable (CV) quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties, and this is usually achieved via a Gaussian modulation of coherent states. The resulting secret key rate depends not only on the loss and noise in the communication channel, but also on a series of data processing steps that are needed for transforming shared correlations into a final string of secret bits. Here we consider a Gaussian-modulated coherent-state protocol with homodyne detection in the general setting of composable finite-size security. After simulating the process of quantum communication, the output classical data is post-processed via procedures of parameter estimation, error correction, and privacy amplification. In particular, we analyze the high signal-to-noise regime which requires the use of high-rate (non-binary) low-density parity check codes. We implement all these steps in a Python-based library that allows one to investigate and optimize the protocol parameters to be used in practical experimental implementations of short-range CV-QKD

Electronic recording of lifetime locomotory activity patterns of adult medflies.

Age-specific and diurnal patterns of locomotory activity, can be considered as biomarkers of aging in model organisms and vary across the lifetime of individuals. Τhe Mediterranean fruit fly (medfly), Ceratitis capitata, is a commonly used model-species in studies regarding demography and aging. In the present study, we introduce a modification of the automated locomotory activity electronic device LAM25system (Locomotory Activity Monitor)-Trikinetics, commonly used in short time studies, to record the daily locomotory activity patterns of adult medflies throughout the life. Additionally, fecundity rates and survival of adult medflies were recorded. Male and female medflies were kept in the system tubes and had access to an agar-based gel diet, which provided water and nutrients. The locomotory activity was recorded at every minute by three monitors in the electronic device. The locomotory activity of females was higher than that of males across the different ages. For both sexes locomotory rates were high during the first 20 days of the adult life and decreased in older ages. The activity of males was high in the morning and late afternoon hours, while that of females was constantly high throughout the photophase. Negligible locomotory activity was recorded for both sexes during the nighttime. Males outlived females. Fecundity of females was higher in younger ages. Our results support the adoption of LAM25system in studies addressing aging of insects using medfly as a model organism