Development of a national conceptual framework and measuring tool for Organisational Social Responsibility and Accountability for Health (OSRAH).

Organisations can have a significant impact (positive or negative) on society through their actions and decisions. Given this reality, it is important that they are held responsible and accountable for the consequences of their actions. This concept is often referred to as 'social responsibility'. However, 'social responsibility', as currently conceived in the literature, neglects a specific focus on health as a social goal. Additionally, there are no practical tools to capture this concept in a holistic way to facilitate implementation and monitoring of organisational improvement. This paper reports on the process of developing a more holistic conceptual framework and tool for assessing organisational social responsibility and accountability for health (OSRAH). We conducted a review of the published and grey literature and engaged in expert consultation and focus group discussions. The initial OSRAH framework and the self-assessment tool were finalised for implementation and used by 95 organisations at a national event in Iran in February 2017. The results of the assessment data collected at the event showed organisations scored lowest in the domain of community health and highest in the domain of employee health. The OSRAH framework and assessment tool represents a new understanding of health and its determinants in organisations outside the health sector. It integrates health within the existing Corporate Social Responsibility (CSR) culture of organisations. The process of creating the tool and implementing it at the national festival of OSRAH in Iran created momentum for intersectoral action. This experience can inspire researchers and practitioners in other countries, especially in developing countries, to develop their own local definition and practical assessment framework for responsibility and accountability.

Sparse-view neutron-photon computed tomography: Object reconstruction and material discrimination.

Taking into account the advantages of both neutron- and photon-based systems, we propose combined neutron-photon computed tomography (CT) under a sparse-view setting and demonstrate its performance for 3D object visualization and material discrimination. We use a high-performance regularization method for CT reconstruction by combining regularization based on total variation (TV) and curvelet transform in cone beam geometry. It is coupled with proposed 2D material signatures which is pairs of photon to neutron transmission ratios and neutron transmission values per object space voxels. Classification of materials is performed by association of a voxel signature with library signatures; and per object - by majority of voxels in the object. Representation of object-material pairs, for the model in our experiment, a complex scene with group of high-Z and low-Z materials, attains the reconstruction accuracy of 92.1% and the overall high-Z discrimination accuracy of object representation is 85%, and by about 7.5% higher discrimination accuracy than that with 1D signatures which are ratios of photon to neutron transmissions. With a relative noise level of 10%, the method yields the reconstruction accuracies of 87.2%. The analyses are performed in cone beam configuration, with Monte Carlo modeling of neutron-photon transport for the model of object geometry and material contents.

Compressed sensing magnetic resonance imaging based on shearlet sparsity and nonlocal total variation.

Compressed sensing (CS) has been utilized for acceleration of data acquisition in magnetic resonance imaging (MRI). MR images can then be reconstructed with an undersampling rate significantly lower than that required by the Nyquist sampling criterion. However, the CS usually produces images with artifacts, especially at high reduction rates. We propose a CS MRI method called shearlet sparsity and nonlocal total variation (SS-NLTV) that exploits SS-NLTV regularization. The shearlet transform is an optimal sparsifying transform with excellent directional sensitivity compared with that by wavelet transform. The NLTV, on the other hand, extends the TV regularizer to a nonlocal variant that can preserve both textures and structures and produce sharper images. We have explored an approach of combining alternating direction method of multipliers (ADMM), splitting variables technique, and adaptive weighting to solve the formulated optimization problem. The proposed SS-NLTV method is evaluated experimentally and compared with the previously reported high-performance methods. Results demonstrate a significant improvement of compressed MR image reconstruction on four medical MRI datasets.