Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with x20 performance increase over the closest FP32, GPU-accelerated option, and almost x3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.This work is part of the project TED2021-129938B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water-Fat MRI

Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water–fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF. Methods: 30 healthy subjects (age = 30 ± 6 years; 15 females) underwent CSE-MRI of the lumbar spine at 3T, using a six-echo 3D spoiled gradient echo sequence. Quadriceps (EXT) and ischiocrural (FLEX) muscles were segmented to extract mean PDFF and texture features. Muscle flexion and extension strength were measured with an isokinetic dynamometer. Results: Of the eleven extracted texture features, Variance(global) showed the highest significant correlation with extension strength (p 2adj = 0.712), and Correlation showed the highest significant correlation with flexion strength (p = 0.016, R2adj = 0.658). Multivariate linear regression models identified Variance(global) and sex, but not PDFF, as significant predictors of extension strength (R2adj = 0.709; p 2adj = 0.674; p < 0.001). Conclusions: Prediction of quadriceps muscle strength can be improved beyond mean PDFF by means of TA, indicating the capability to quantify muscular fat infiltration patterns

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Hydrology modelling R packages: a unified analysis of models and practicalities from a user perspective

Following the rise of R as a scientific programming language, the increasing requirement for more transferable research, and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever 5 provided a comparison of R packages for conceptual rainfall-runoff modelling from a user perspective, contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated in these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps inform selection of what packages could/should be used depending on the problem at hand. In this regard, technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R

Edge-elements formulation of 3D CSEM in geophysics : a parallel approach

Electromagnetic methods (EM) are an invaluable research tool in geophysics whose relevance has increased rapidly in recent years due to its wide industrial adoption. In particular, the forward modelling of three-dimensional marine controlled-source electromagnetics (3D CSEM FM) has become an important technique for reducing ambiguities in the interpretation of geophysical datasets through mapping conductivity variations in the subsurface. As a consequence, the 3D CSEM FM has application in many areas such as hydrocarbon/mineral exploration, reservoir monitoring, CO2 storage characterization, geothermal reservoir imaging and many others due to there quantities often displaying conductivity contrasts with respect to their surrounding sediments. However, the 3D CSEM FM at real scale implies a numerical challenge that requires an important computational effort, often too high for modest multicore computing architectures, especially if it fuels an inversion process. On the other hand, although the HPC code development is dominated by compiled languages, the popularity of high-level languages for scientific computations has increased considerably. Among all of them, Python is probably the language that has shown more interest, mainly because of flexibility and its simple and clean syntax. However, its use for HPC geophysical applications is still limited, which suggests a path for research, development and improvement. Therefore, this thesis reports the attempts at designing and implementing a methodology that has not been systematically applied for solving 3D CSEM FM with an HPC application baked upon Python. The net contribution of this effort is the development and documentation of a new open-source modelling code for 3D CSEM FM in geophysics, namely, the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM). The importance of having this modelling tools lies in the fact that they provide synthetic results that can be compared with real data which has a practical use both in the industry and academia. Still, available 3D CSEM FM codes are usually written in low-level languages whose implemented methods are often innaccessible to the scientific community since they are commercial. PETGEM is written mostly in Python and relies on mpi4py and petsc4py packages for parallel computations. Other scientific Python packages used include Numpy andScipy. This code is designed to cope with the main challenges encountered within the numerical simulation of the problem under consideration: tackle realistic problems with accuracy, efficiency and flexibility. It uses the Nédélec Edge Finite Element Method (EFEM) as discretisation technique because its divergence-free basis is very well suited for solving Maxwell¿s equations. Furthermore, it supports completely unstructured tetrahedral meshes which allows the representation of complex geometries and local refinement, positively impacting the accuracy of the solution. The parallel implementation of the code using shared/distributed-memory architectures is investigated and described throughout this document. In addition, the thesis deals with the numerical and physical challenges of the 3D CSEM FM problem. Through this work, frequency-domain Maxwell's equations have been discretised using EFEM and validated by comparison with analytical solutions and published data, proving that modelling results are highly accurate. Moreover, this work discusses an automatic mesh adaptation strategy and the convergence rate of the iterative solvers that are widely used in the literature for solving the EM problem is presented. In summary, this thesis shows that it is possible to integrate Python and HPC for the solution of 3D CSEM FM at large scale in an effective way. The new modelling tool is easy to use and the adopted algorithms are not only accurate and efficient but also have the possibility to easily add or remove components without having to rewrite large sections of the code.Los métodos electromagnéticos (EM) son una herramienta de investigación inestimable en geofísica, cuya relevancia ha aumentado rápidamente en los últimos años debido a su amplia adopción industrial. En particular, el modelado electromagnético de fuente controlada (3D CSEM FM) se ha convertido en una técnica importante para reducir las ambigüedades en la interpretación de datos geofísicos a través del mapeo de las variaciones de conductividad en el subsuelo. Como resultado, el 3D CSEM FM tiene aplicación en muchas áreas como la exploración de hidrocarburos/minerales, monitoreo de yacimientos, caracterización de almacenamiento de CO2, imágenes de yacimientos geotérmicos, entre otros, debido a que éstos muestran contrastes de conductividad con respecto a sus sedimentos circundantes. Sin embargo, el 3D CSEM FM a escala real implica un desafío numérico que requiere un esfuerzo computacional importante, a menudo demasiado exigente para arquitecturas multicore modestas, especialmente si éste forma parte de un proceso de inversión. Por otra parte, aunque el desarrollo aplicaciones HPC está dominado por lenguajes compilados, la popularidad de los lenguajes de alto nivel para cómputo científico ha aumentado considerablemente. Entre todos ellos, Python es probablemente el idioma que ha mostrado más interés, principalmente a su flexibilidad y sintaxis simple. Sin embargo, su uso para geocómputo con HPC sigue siendo limitado, lo que sugiere un camino para la investigación, el desarrollo y la mejora. Por lo tanto, esta tesis describe el diseño e implementación de una metodología que hasta ña fecha no se ha aplicado sistemáticamente para resolver el 3D CSEM FM con una aplicación HPC basada en Python. La contribución neta de este esfuerzo es el desarrollo y documentación de un nuevo código open-source para el modelado 3D CSEM FM en geofísica, es decir, Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM). La importancia del desarrollo de estas herramientas radica en el hecho de que proporcionan resultados sintéticos que pueden ser comparados con datos reales, lo cual tiene un uso práctico en la industria y el mundo académico. A pesar de ello, los códigos disponibles para 3D CSEM FM suelen estar escritos en lenguajes de bajo nivel, y en muchos casos sus métodos no son accesibles a la comunidad científica ya que son comerciales. PETGEM ha sido principalmente escrito en Python y se basa en paquetes mpi4py y petsc4py para cálculos paralelos. El código está diseñado para hacer frente a los principales desafíos que se encuentran en la simulación numérica del problema en cuestión: abordar problemas realistas con precisión, eficiencia y flexibilidad. Además, utiliza el Método de Elementos Finitos de Borde (EFEM) como técnica de discretización ya que sus bases son muy adecuadas para resolver las ecuaciones de Maxwell. Además, soporta mallas tetraédricas no estructuradas que permiten la representación de geometrías complejas y refinamiento local, impactando positivamente la precisión de la solución. A lo largo del documento se investiga la implementación paralela en arquitecturas de memoria compartida/distribuida. Además, la tesis revisa los desafíos numéricos y físicos del problema 3D CSEM FM. A través de este trabajo, las ecuaciones de Maxwell en el dominio de la frecuencia se han discretizado utilizando EFEM y validado contra soluciones analíticas y datos previamente publicados, lo que demuestra que los resultados del modelado son precisos. Por otra parte, este trabajo discute una estrategia de adaptación automática de malla y la tasa de convergencia de los solvers iterativos que se utilizan ampliamente en la literatura. En resumen, esta tesis muestra que es posible integrar Python y HPC para la solución de 3D CSEM FM a gran escala de una manera efectiva. La nueva herramienta de modelado es fácil de usar y los algoritmos adoptados no sólo son precisos y eficientes, sino también flexibles

Inverse Modelling at Recovery Glacier, Antarctica

The future ice loss of Recovery Glacier will probably be the largest of the East Antarctic Ice Sheet over the next millennia. Its evolution can be predicted by models solving the equations of the momentum and mass balance. Ice dynamics are fundamentally driven by bedrock conditions underneath the ice, but these can not simply be measured yet. This thesis utilizes an inverse method implemented in the Ice Sheet System Model (ISSM) to acquire basal parameters. The technique minimizes the difference between horizontal surface velocities derived from remote sensing and computed by the model. False values in the observations can lead to uncertainties in basal parameters. In order to remove such false values, this thesis presents a new filtering method. Data gaps are filled comparing four different interpolation methods. A sensitivity analysis shows that the influence of filtering outliers and interpolation on basal parameters derived from inverse modelling is large in specific regions. The resulting basal parameters do not indicate the existence of the previously proposed subglacial lakes at the onset of Recovery Glacier

Investigation of Flow Disturbances and Multi-Directional Wall Shear Stress in the Stenosed Carotid Artery Bifurcation Using Particle Image Velocimetry

Hemodynamics and shear forces are associated with pathological changes in the vascular wall and its function, resulting in the focal development of atherosclerosis. Flow complexities that develop in the presence of established plaques create environments favourable to thrombosis formation and potentially plaque rupture leading to stroke. The carotid artery bifurcation is a common site of atherosclerosis development. Recently, the multi-directional nature of shear stress acting on the endothelial layer has been highlighted as a risk factor for atherogenesis, emphasizing the need for accurate measurements of shear stress magnitude as well direction. In the absence of comprehensive patient specific datasets numerical simulations of hemodynamics are limited by modeling assumptions. The objective of this thesis was to investigate the relative contributions of various factors - including geometry, rheology, pulsatility, and compliance – towards the development of disturbed flow and multi-directional wall shear stress (WSS) parameters related to the development of atherosclerosis An experimental stereoscopic particle image velocimetry (PIV) system was used to measure instantaneous full-field velocity in idealized asymmetrically stenosed carotid artery bifurcation models, enabling the extraction of bulk flow features and turbulence intensity (TI). The velocity data was combined with wall location information segmented from micro computed tomography (CT) to obtain phase-averaged maps of WSS magnitude and direction. A comparison between Newtonian and non-Newtonian blood-analogue fluids demonstrated that the conventional Newtonian viscosity assumption underestimates WSS magnitude while overestimating TI. Studies incorporating varying waveform pulsatility demonstrated that the levels of TI and oscillatory shear index (OSI) depend on the waveform amplitude in addition to the degree of vessel constriction. Local compliance resulted in a dampening of disturbed flow due to volumetric capacity of the upstream vessel, however wall tracking had a negligible effect on WSS prediction. While the degree of stenosis severity was found to have a dominant effect on local hemodynamics, comparable relative differences in metrics of flow and WSS disturbances were found due to viscosity model, waveform pulsatility and local vessel compliance

Deep learning networks for automatic brain tumour segmentation from MRI data

Early diagnosis and appropriate treatment planning are the keys to brain tumour patients’ survial rate. Radiotherapy (RT) is a common treatment for brain tumour. RT planning requires segmentation of a gross tumour volume (GTV). Manual segmentation of the brain tumour done by experts oncologists or clinicians is time-consuming and subject to intra- and inter-observer variability. This research presents novel image processing and deep learning methods for automatic brain tumour regions segmentation from MRI data. The MRI data of brain tumour patients from Brain Tumour Segmentation or BraTS dataset from 2018-2021 are used in this study. 2D deep neural networks for semantic segmentation of brain tumour regions from 2D axial multimodal (T1, T1Gd, T2, and FLAIR) MRI slices is presented. This proposed network is trained and tested on manual consensus labels by experts from BraTS 2018 dataset. The network has similar architecture to U-Net, which consists of a stream of down-sampling blocks for feature extraction and a reduction in the image resolution, then a stream of up-sampling blocks to recover image’s resolution, integrate features, and classify pixels. The proposed network improved feature extraction by introducing two-pathways feature extraction in the first block of the down-sampling to extract local and global features directly from the input images. Transposed convolution was employed in up-sampling path. The proposed network was evaluated for the segmentation of five tumour regions: whole tumour (WT), tumour core(TC), necrotic and nonenhancing tumour (NCR/NET), edema (ED), and enhancing tumour (ET). The results obtained from the modified U-Net achieved mean Dice Similarity Coefficient (DSC) of 0.83, 0.62, 0.45, 0.69, and 0.70 for WT, TC, NCR/NET, ED, and ET, respectively. These results show a 9% improvement compared to the original U-Net’s performance. 2D predicted segmentation obtained from the proposed network are stacked to visualise the tumour volume. A novel deep neural network called 2D TwoPath U-Net for multi-class segmentation of brain tumour region is described. The proposed network has improved two-pathways feature extraction to provided cascaded local and global features from 2D multimodal MRI input. The proposed networks was trained using MRI data from BraTS 2019 dataset and test using MRI data from BraTS 2020 dataset. Data augmentation and different training strategies including the use of full-size images and patches were employed to improve the predicted segmentation. The results obtained from the proposed network feature all intra-structure (NCR/NET, ED, ET) of tumour to form the segmentation of WT and TC regions, and achieved mean DSC of 0.72 and 0.66 for WT and TC, respectively. A novel 3D deep neural network for brain tumour regions segmentation from MRI data called 3D TwoPath U-Net is described. The network has a similar structure to the 2D TwoPath U-Net, and uses two-pathways feature extraction to capture local and global features from volumetric MRI data from BraTS 2021 dataset. The volumetric data were created using T1Gd and FLAIR modalities. To construct a 3D deep neural network with significantly high computational parameters, cropped voxels from volumetric MRI were used to reduce the input resolution. Furthermore, high-performance GPUs were employed to implement the network. The proposed network achieved the mean DSC of 0.87, 0.70, and 0.58 for WT, TC, and ET segmentation, respectively, which represents a 25% improvement compared to the previous segmentation results obtained using the 2D approach. Moreover, the 3D smooth tumour volume generated from the proposed network output provide a more visually representative depiction of the tumour.Early diagnosis and appropriate treatment planning are the keys to brain tumour patients’ survial rate. Radiotherapy (RT) is a common treatment for brain tumour. RT planning requires segmentation of a gross tumour volume (GTV). Manual segmentation of the brain tumour done by experts oncologists or clinicians is time-consuming and subject to intra- and inter-observer variability. This research presents novel image processing and deep learning methods for automatic brain tumour regions segmentation from MRI data. The MRI data of brain tumour patients from Brain Tumour Segmentation or BraTS dataset from 2018-2021 are used in this study. 2D deep neural networks for semantic segmentation of brain tumour regions from 2D axial multimodal (T1, T1Gd, T2, and FLAIR) MRI slices is presented. This proposed network is trained and tested on manual consensus labels by experts from BraTS 2018 dataset. The network has similar architecture to U-Net, which consists of a stream of down-sampling blocks for feature extraction and a reduction in the image resolution, then a stream of up-sampling blocks to recover image’s resolution, integrate features, and classify pixels. The proposed network improved feature extraction by introducing two-pathways feature extraction in the first block of the down-sampling to extract local and global features directly from the input images. Transposed convolution was employed in up-sampling path. The proposed network was evaluated for the segmentation of five tumour regions: whole tumour (WT), tumour core(TC), necrotic and nonenhancing tumour (NCR/NET), edema (ED), and enhancing tumour (ET). The results obtained from the modified U-Net achieved mean Dice Similarity Coefficient (DSC) of 0.83, 0.62, 0.45, 0.69, and 0.70 for WT, TC, NCR/NET, ED, and ET, respectively. These results show a 9% improvement compared to the original U-Net’s performance. 2D predicted segmentation obtained from the proposed network are stacked to visualise the tumour volume. A novel deep neural network called 2D TwoPath U-Net for multi-class segmentation of brain tumour region is described. The proposed network has improved two-pathways feature extraction to provided cascaded local and global features from 2D multimodal MRI input. The proposed networks was trained using MRI data from BraTS 2019 dataset and test using MRI data from BraTS 2020 dataset. Data augmentation and different training strategies including the use of full-size images and patches were employed to improve the predicted segmentation. The results obtained from the proposed network feature all intra-structure (NCR/NET, ED, ET) of tumour to form the segmentation of WT and TC regions, and achieved mean DSC of 0.72 and 0.66 for WT and TC, respectively. A novel 3D deep neural network for brain tumour regions segmentation from MRI data called 3D TwoPath U-Net is described. The network has a similar structure to the 2D TwoPath U-Net, and uses two-pathways feature extraction to capture local and global features from volumetric MRI data from BraTS 2021 dataset. The volumetric data were created using T1Gd and FLAIR modalities. To construct a 3D deep neural network with significantly high computational parameters, cropped voxels from volumetric MRI were used to reduce the input resolution. Furthermore, high-performance GPUs were employed to implement the network. The proposed network achieved the mean DSC of 0.87, 0.70, and 0.58 for WT, TC, and ET segmentation, respectively, which represents a 25% improvement compared to the previous segmentation results obtained using the 2D approach. Moreover, the 3D smooth tumour volume generated from the proposed network output provide a more visually representative depiction of the tumour