Methods for the analysis of ordinal response data in medical image quality assessment.

The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimisation, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data, and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart, for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilisation of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care

Cross Adaptation - Heat and Cold Adaptation to Improve Physiological and Cellular Responses to Hypoxia

To prepare for extremes of heat, cold or low partial pressures of O2, humans can undertake a period of acclimation or acclimatization to induce environment specific adaptations e.g. heat acclimation (HA), cold acclimation (CA), or altitude training. Whilst these strategies are effective, they are not always feasible, due to logistical impracticalities. Cross adaptation is a term used to describe the phenomenon whereby alternative environmental interventions e.g. HA, or CA, may be a beneficial alternative to altitude interventions, providing physiological stress and inducing adaptations observable at altitude. HA can attenuate physiological strain at rest and during moderate intensity exercise at altitude via adaptations allied to improved oxygen delivery to metabolically active tissue, likely following increases in plasma volume and reductions in body temperature. CA appears to improve physiological responses to altitude by attenuating the autonomic response to altitude. While no cross acclimation-derived exercise performance/capacity data have been measured following CA, post-HA improvements in performance underpinned by aerobic metabolism, and therefore dependent on oxygen delivery at altitude, are likely. At a cellular level, heat shock protein responses to altitude are attenuated by prior HA suggesting that an attenuation of the cellular stress response and therefore a reduced disruption to homeostasis at altitude has occurred. This process is known as cross tolerance. The effects of CA on markers of cross tolerance is an area requiring further investigation. Because much of the evidence relating to cross adaptation to altitude has examined the benefits at moderate to high altitudes, future research examining responses at lower altitudes should be conducted given that these environments are more frequently visited by athletes and workers. Mechanistic work to identify the specific physiological and cellular pathways responsible for cross adaptation between heat and altitude, and between cold and altitude, is warranted, as is exploration of benefits across different populations and physical activity profiles