Mitigating the limited view problem in photoacoustic tomography for a planar detection geometry by regularised iterative reconstruction

The use of a planar detection geometry in photoacoustic tomography results in the so-called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularisation were evaluated using in-silico , experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularisation parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries

Artificial intelligence in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation

Numerical approximation of a control problem for advection-diffusion processes

The Legacy of Camillo Possio to Unsteady Aerodynamics -- The Possio Integral Equation of Aeroelasticity: A Modern View -- Beyond Possio Equation: The Legacy of Camillo Possio to Flight Dynamics and Hydrodynamics -- One Hundred Years Since the Introduction of the Set Distance by Dimitrie Pompeiu -- Analysis of a PDE Model for Sandpile Growth -- On Warm Starts for Interior Methods -- Recent Advances in Bound Constrained Optimization -- P-Factor-Approach to Degenerate Optimization Problems -- Non Monotone Algorithms for Unconstrained Minimization: Upper Bounds on Function Values -- On the Efficiency of the?-Subgradient Methods Over Nonlinearly Constrained Networks -- Preconditioned Conjugate Gradient Algorithms for Nonconvex Problems with Box Constraints -- Multiobjective Optimization for Risk-Based Maintenance and Life-Cycle Cost of Civil Infrastructure Systems -- Application of Multi-Objective Genetic Algorithm to Bridge Maintenance -- A Method for the Mixed Discrete Non-Linear Problems by Particle Swarm Optimization -- Optimization of Cooling Pipe System of Plastic Molding -- Optimum Design of Cooling Pipe Systems by Branching Tree Model in Nature -- Implementation of Multiobjective Optimization Procedures at the Product Design Planning Stage -- On the Numerical Solution of Stochastic Optimization Problems -- Parameter Estimation of Parabolic Type Factor Model and Empirical Study of US Treasury Bonds -- Multi-Stage Stochastic Electricity Portfolio Optimization in Liberalized Energy Markets -- SSD Consistent Criteria and Coherent Risk Measures -- Optimal Policies Under Different Pricing Strategies in a Production System with Markov-Modulated Demand -- An Adaptation of Bicgstab for Nonlinear Biological Systems -- Numerical Approximation of a Control Problem for Advection-Diffusion Processes -- A New Low Rank Quasi-Newton Update Scheme for Nonlinear Programming -- Reliability in Computer Networks