Developing environmentally sustainable best practices for human brain imaging

磁共振成像(MRI)、正电子发射断层扫描(PET)和计算机断层扫描(CT)等人脑成像技术是无价的医疗研究工具。然而，它们的获取、存储和分析越来越多地产生了巨大的环境成本。目前，脑成像研究界对这些成本知之甚少，也几乎不了解如何降低这些成本。以能源需求为例，我们不知道在一个数据集的生命周期中到底使用了多少能源。这意味着很难确定大脑成像研究过程中的哪些确切步骤是为了减少能量。然而，我们可以从平行领域中借鉴一些新兴工具，这些工具在计算研究程序的碳足迹方面更先进--特别是来自机器学习(ML)领域的工具。该项目将重点评估与脑成像研究相关的碳影响，使用现有的碳跟踪ML模型来创建神经成像碳跟踪工具。该工具将嵌入常用的神经成像软件和研究工具中，使该领域能够测量大脑成像研究程序的碳足迹。然后，我们将使用神经成像碳跟踪器来测量各种脑成像研究程序和示例的碳足迹，包括使用公开可用的软件和工具来实现更广泛的采用。在确定了推动神经成像研究足迹的准确程序步骤后，我们将制定优化策略，以减少能源消耗，从而减少碳排放。例如，我们将研究如何删除不必要的分析步骤，以及如何以最佳方式存储数据。最终，这将使我们能够为神经科学的研究人员提供‘最佳实践’指导，使他们能够采用更可持续的研究程序。我们将通过开放源码软件工具、开放获取的“10个简单步骤”期刊出版物、会议，以及与医疗保健科学同行举办的跨部门研讨会，与研究社区分享这一指南。通过提高人们对人脑成像碳成本的认识，并就如何减少这一成本提供具体建议，我们将促进人脑成像人员和相关领域的同事将基于成像的生命科学研究的环境足迹降至最低。

Human brain imaging techniques such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Computerised Tomography (CT) are invaluable healthcare research tools. However, increasingly, their acquisition, storage, and analysis are generating substantial environmental costs.At present, the brain imaging research community has little awareness of these costs, and little to no understanding of how to reduce them. Taking energy demands as an example, we do not know exactly how much energy is used over a dataset lifecycle. This means that it is difficult to identify what precise steps in the brain imaging research process to target for energy reduction. There are, however, emerging tools we can draw upon from parallel fields that are more advanced in calculating carbon footprints of research procedures - in particular, from the field of machine learning (ML). This project will focus on assessing the carbon impacts associated with brain imaging research, using existing approaches to carbon tracking ML models to create a neuroimaging carbon tracker tool. This tool will be embedded within commonly used neuroimaging software and research tools, enabling the field to measure the carbon footprint of brain imaging research procedures. We will then use the neuroimaging carbon tracker to measure the carbon footprint of a variety of brain imaging research procedures and examples, including using publicly available software and tools to enable wider adoption. Having identified the precise procedural steps that drive neuroimaging research footprints, we will then develop optimisation strategies to reduce energy consumption, and thereby, carbon emissions. For example, we will investigate how to remove unnecessary analysis steps, and how to store data optimally. Ultimately, this will enable us to generate 'best practice' guidance for researchers in neuroscience, to enable them to adopt more sustainable research procedures. We will share this guidance with the research community via open source software tools, an open access '10 simple steps' journal publication, conferences, and a cross-sectoral workshop with colleagues across healthcare sciences. By raising awareness of the carbon costs of human brain imaging, and providing specific recommendations on how to reduce this, we will facilitate human brain imagers, and colleagues in related fields, to minimise the environmental footprint of imaging-based life sciences research.