Quantification and visualization of cardiovascular 4D velocity mapping accelerated with parallel imaging or k-t BLAST: head to head comparison and validation at 1.5 T and 3 T

<p>Abstract</p> <p>Background</p> <p>Three-dimensional time-resolved (4D) phase-contrast (PC) CMR can visualize and quantify cardiovascular flow but is hampered by long acquisition times. Acceleration with SENSE or k-t BLAST are two possibilities but results on validation are lacking, especially at 3 T. The aim of this study was therefore to validate quantitative in vivo cardiac 4D-acquisitions accelerated with parallel imaging and k-t BLAST at 1.5 T and 3 T with 2D-flow as the reference and to investigate if field strengths and type of acceleration have major effects on intracardiac flow visualization.</p> <p>Methods</p> <p>The local ethical committee approved the study. 13 healthy volunteers were scanned at both 1.5 T and 3 T in random order with 2D-flow of the aorta and main pulmonary artery and two 4D-flow sequences of the heart accelerated with SENSE and k-t BLAST respectively. 2D-image planes were reconstructed at the aortic and pulmonary outflow. Flow curves were calculated and peak flows and stroke volumes (SV) compared to the results from 2D-flow acquisitions. Intra-cardiac flow was visualized using particle tracing and image quality based on the flow patterns of the particles was graded using a four-point scale.</p> <p>Results</p> <p>Good accuracy of SV quantification was found using 3 T 4D-SENSE (r²= 0.86, -0.7 ± 7.6%) and although a larger bias was found on 1.5 T (r²= 0.71, -3.6 ± 14.8%), the difference was not significant (p = 0.46). Accuracy of 4D k-t BLAST for SV was lower (p < 0.01) on 1.5 T (r²= 0.65, -15.6 ± 13.7%) compared to 3 T (r²= 0.64, -4.6 ± 10.0%). Peak flow was lower with 4D-SENSE at both 3 T and 1.5 T compared to 2D-flow (p < 0.01) and even lower with 4D k-t BLAST at both scanners (p < 0.01). Intracardiac flow visualization did not differ between 1.5 T and 3 T (p = 0.09) or between 4D-SENSE or 4D k-t BLAST (p = 0.85).</p> <p>Conclusions</p> <p>The present study showed that quantitative 4D flow accelerated with SENSE has good accuracy at 3 T and compares favourably to 1.5 T. 4D flow accelerated with k-t BLAST underestimate flow velocities and thereby yield too high bias for intra-cardiac quantitative in vivo use at the present time. For intra-cardiac 4D-flow visualization, however, 1.5 T and 3 T as well as SENSE or k-t BLAST can be used with similar quality.</p

Policy-driven monitoring and evaluation : Does it support adaptive management of socio-ecological systems?

Inadequate Monitoring and Evaluation (M&E) is often thought to hinder adaptive management of socio-ecological systems. A key influence on environmental management practices are environmental policies: however, their consequences for M&E practices have not been well-examined. We examine three policy areas - the Water Framework Directive, the Natura 2000 Directives, and the Agri-Environment Schemes of the Common Agricultural Policy - whose statutory requirements influence how the environment is managed and monitored across Europe. We use a comparative approach to examine what is monitored, how monitoring is carried out, and how results are used to update management, based on publicly available documentation across nine regional and national cases. The requirements and guidelines of these policies have provided significant impetus for monitoring: however, we find this policy-driven M&E usually does not match the ideals of what is needed to inform adaptive management. There is a tendency to focus on understanding state and trends rather than tracking the effect of interventions; a focus on specific biotic and abiotic indicators at the expense of understanding system functions and processes, especially social components; and limited attention to how context affects systems, though this is sometimes considered via secondary data. The resulting data are sometimes publicly-accessible, but it is rarely clear if and how these influence decisions at any level, whether this be in the original policy itself or at the level of measures such as site management plans. Adjustments to policy-driven M&E could better enable learning for adaptive management, by reconsidering what supports a balanced understanding of socio-ecological systems and decision-making. Useful strategies include making more use of secondary data, and more transparency in data-sharing and decision-making. Several countries and policy areas already offer useful examples. Such changes are essential given the influence of policy, and the urgency of enabling adaptive management to safeguard socio-ecological systems. Highlights • Policy strongly influences Monitoring & Evaluation (M&E) of socio-ecological systems. • We examine M&E of 3 major European policies in 9 regional and national cases. • Policy-driven M&E is imperfect versus ideals of M&E to support adaptive management. • Attention needed to systems, social issues, sharing data, and sharing intended uses. • Examples from across Europe and different policies offer ideas for improvement

Understanding (non-) adoption of conservation agriculture in Kenya using the reasoned action approach

In recent years, Conservation Agriculture has been promoted in sub-Saharan Africa as an alternativefarming system for smallholder farmers to address declining soil productivity and climate change. CAhas to be tailored to the agro-ecological and socio-economic context of smallholder farmers to achieveimpact. But even if there is a ‘perfect fit’, the farmer still has his or her own reasons to choose whether toswitch to CA or not. This paper explores the reasons why farmers choose for CA or conventional farming,using the Reasoned Action Approach. Based on findings from a recent study in Kenya among CA farmerfield school members and their neighbours, the farmer’s decision making is analysed by distinguishingthree elements in the decision-making process: the farmer’s attitude towards CA, the farmer’s perceptionof the social norms towards CA, and the farmer’s perceived behavioural control (PBC) over practicing CA.Strong evidence was found that attitude and PBC are contributing to intentions to adopt CA practices. It isconcluded that experimentation and learning are key to support intentions and adoption of CA, becausethey contribute both to realistic attitudes towards CA and an improved perceived behavioural control

Climate change, natural capital and adaptation in Scotland's marginal lands

The boundary between productive land and hill land in Scotland has moved over time, in response to climate and also to market demand. Scotland’s climate is changing, and this will mean changes for those areas of Scotland that sit on the margins of productive agriculture. In this context sustainable soil management is a specific challenge as Scotland adapts to a changing climate. This report examines the four dominant ways that farmers will adapt to climate change, and their impact on different services

Policy-driven monitoring and evaluation : Does it support adaptive management of socio-ecological systems?

Inadequate Monitoring and Evaluation (M&E) is often thought to hinder adaptive management of socio-ecological systems. A key influence on environmental management practices are environmental policies: however, their consequences for M&E practices have not been well-examined. We examine three policy areas - the Water Framework Directive, the Natura 2000 Directives, and the Agri-Environment Schemes of the Common Agricultural Policy - whose statutory requirements influence how the environment is managed and monitored across Europe. We use a comparative approach to examine what is monitored, how monitoring is carried out, and how results are used to update management, based on publicly available documentation across nine regional and national cases. The requirements and guidelines of these policies have provided significant impetus for monitoring: however, we find this policy-driven M&E usually does not match the ideals of what is needed to inform adaptive management. There is a tendency to focus on understanding state and trends rather than tracking the effect of interventions; a focus on specific biotic and abiotic indicators at the expense of understanding system functions and processes, especially social components; and limited attention to how context affects systems, though this is sometimes considered via secondary data. The resulting data are sometimes publicly-accessible, but it is rarely clear if and how these influence decisions at any level, whether this be in the original policy itself or at the level of measures such as site management plans. Adjustments to policy-driven M&E could better enable learning for adaptive management, by reconsidering what supports a balanced understanding of socio-ecological systems and decision-making. Useful strategies include making more use of secondary data, and more transparency in data-sharing and decision-making. Several countries and policy areas already offer useful examples. Such changes are essential given the influence of policy, and the urgency of enabling adaptive management to safeguard socio-ecological systems