Exploration of a Co-Production Approach to Developing a Walking Group with People with Huntington's Disease.

BACKGROUND: People with Huntington's disease (HD) struggle to maintain regular physical activity despite evidence of the benefits of exercise. This study aimed to evaluate the experiences of people who co-produced a walking group for people with HD. METHODS: Three people with HD, a specialist HD advisor (sHDA), two project officers from Let's Walk Cymru (LWC) and the research team co-produced and participated in a walking group for people with HD. A walking group for people with HD was supported weekly by LWC for eight weeks and fortnightly for a further 12 weeks. Semi-structured interviews were undertaken with three people with HD, a sHDA and two project LWC project officers. Interviews were transcribed verbatim and analysed using thematic analysis. FINDINGS: Interviews identified six themes across participants: "organisation and planning"; "purpose of the walks"; "benefits"; "barriers", "the group" and "the future". People with HD enjoyed participating in the walks and reported increased confidence to be more active outside the home. All participants noted challenges including apathy, diminished planning skills, social stigma and motor problems specific to HD; people with HD perceived a lack of influence in relation to co-planning and co-execution of the walking group. CONCLUSIONS: The walking group was perceived as enjoyable, beneficial, and motivational. This is the first study to report co-production of a walking group with people with HD and the findings suggest that further research is needed to adapt models of co-production for people with a long-term complex condition.

High-performance iterative electron tomography reconstruction with long-object compensation using graphics processing units (GPUs).

Iterative reconstruction algorithms pose tremendous computational challenges for 3D Electron Tomography (ET). Similar to X-ray Computed Tomography (CT), graphics processing units (GPUs) offer an affordable platform to meet these demands. In this paper, we outline a CT reconstruction approach for ET that is optimized for the special demands and application setting of ET. It exploits the fact that ET is typically cast as a parallel-beam configuration, which allows the design of an efficient data management scheme, using a holistic sinogram-based representation. Our method produces speedups of about an order of magnitude over a previously proposed GPU-based ET implementation, on similar hardware, and completes an iterative 3D reconstruction of practical problem size within minutes. We also describe a novel GPU-amenable approach that effectively compensates for reconstruction errors resulting from the TEM data acquisition on (long) samples which extend the width of the parallel TEM beam. We show that the vignetting artifacts typically arising at the periphery of non-compensated ET reconstructions are completely eliminated when our method is employed.

On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs.

Expectation Maximization (EM) and the Simultaneous Iterative Reconstruction Technique (SIRT) are two iterative computed tomography reconstruction algorithms often used when the data contain a high amount of statistical noise, have been acquired from a limited angular range, or have a limited number of views. A popular mechanism to increase the rate of convergence of these types of algorithms has been to perform the correctional updates within subsets of the projection data. This has given rise to the method of Ordered Subsets EM (OS-EM) and the Simultaneous Algebraic Reconstruction Technique (SART). Commodity graphics hardware (GPUs) has shown great promise to combat the high computational demands incurred by iterative reconstruction algorithms. However, we find that the special architecture and programming model of GPUs add extra constraints on the real-time performance of ordered subsets algorithms, counteracting the speedup benefits of smaller subsets observed on CPUs. This gives rise to new relationships governing the optimal number of subsets as well as relaxation factor settings for obtaining the smallest wall-clock time for reconstruction-a factor that is likely application-dependent. In this paper we study the generalization of SIRT into Ordered Subsets SIRT and show that this allows one to optimize the computational performance of GPU-accelerated iterative algebraic reconstruction methods.