A model-based iterative learning approach for diffuse optical tomography

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a ‘model-based’ learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration

Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors

Protein kinase C-activating isophthalate derivatives mitigate Alzheimer's disease-related cellular alterations

Abnormal protein kinase C (PKC) function contributes to many pathophysiological processes relevant for Alzheimer's disease (AD), such as amyloid precursor protein (APP) processing. Phorbol esters and other PKC activators have been demonstrated to enhance the secretion of soluble APP alpha (sAPP alpha), reduce the levels of beta-amyloid (A beta), induce synaptogenesis, and promote neuroprotection. We have previously described isophthalate derivatives as a structurally simple family of PKC activators. Here, we characterised the effects of isophthalate derivatives HMI-1a3 and HMI-1b11 on neuronal viability, neuroinflammatory response, processing of APP and dendritic spine density and morphology in in vitro. HMI-1a3 increased the viability of embryonic primary cortical neurons and decreased the production of the pro-inflammatory mediator TNF alpha, but not that of nitric oxide, in mouse neuron-BV2 microglia co-cultures upon LPS- and IFN-gamma-induced neuroinflammation. Furthermore, both HMI-1a3 and HMI-1b11 increased the levels of sAPPa relative to total sAPP and the ratio of A beta 42/A beta 40 in human SH-Sv5v neuroblastoma cells. Finally, bryostatin-1, but not HMI-1a3, increased the number of mushroom spines in proportion to total spine density in mature mouse hippocampal neuron cultures. These results suggest that the PKC activator HMI-1a3 exerts neuroprotective functions in the in vitro models relevant for AD by reducing the production of TNF alpha and increasing the secretion of neuroprotective sAPPa.Peer reviewe

Moderate and heavy metabolic stress interval training improve arterial stiffness and heart rate dynamics in humans

Traditional continuous aerobic exercise training attenuates age-related increases of arterial stiffness, however, training studies have not determined whether metabolic stress impacts these favourable effects. Twenty untrained healthy participants (n = 11 heavy metabolic stress interval training, n = 9 moderate metabolic stress interval training) completed 6 weeks of moderate or heavy intensity interval training matched for total work and exercise duration. Carotid artery stiffness, blood pressure contour analysis, and linear and non-linear heart rate variability were assessed before and following training. Overall, carotid arterial stiffness was reduced (p  0.05). This study demonstrates the effectiveness of interval training at improving arterial stiffness and autonomic function, however, the metabolic stress was not a mediator of this effect. In addition, these changes were also independent of improvements in aerobic capacity, which were only induced by training that involved a high metabolic stress