# Evaluating the Effect of Intensity Standardisation
## 3 Results
### Scan-Rescan Repeatability  
The scan-rescan repeatability experiment consisted of examining whether intensity standardisation could result in increase scan-rescan error compared to when omitted. We ran FSL-SIENA on pairs of scans of subjects from OASIS, that were acquired in a single session (as explained in Section 2.1). We used the resulting percentage of brain volume change yielded by FSL-SIENA as measure of error as this variation should be close to zero. The results are presented in Figure 4. While most intensity standardisation methods did not compromise the original robustness of FSL-SIENA against subtle imaging artefacts, WhiteStripe did (\(p<0.001\)). Furthermore, the application of intensity standardisation before registration led to less dispersion of scan-rescan errors overall. Figure 3: Similarity between histograms of intensities with and without standardisation. We equipped FSL-SIENA with intensity standardisation before or after registration. We measured the degree of similarity between the histogram of intensities of baseline and follow-up using the Kullback–Leibler (KL) divergence. We used the Wilcoxon signed-rank test to examine whether the median KL divergence obtained after standardisation was lower than that obtained when omitted (\(p\)-values on top). FCM: Fuzzy c-means. GMM: Gaussian mixture model. KDE: Kernel density estimation. HM: Histogram matching. WS: WhiteStripe. Effect of Intensity Standardisation on Atrophy Differences between Alzheimer's Disease and Normal Control Subjects  
We evaluated the effect of intensity standardisation on atrophy differences between the pathological and control groups in ADNI and OASIS. The procedure was as follows. First, we ran FSL-SIENA, with and without intensity standardisation, on pairs of baseline and follow-up scans. We tested the effect of standardising either before or after registration, as illustrated in Figure 2. Second, we tested for differences in atrophy between the two groups, after adjusting for age, biological sex, baseline normalised brain volume (total brain volume divided by intracranial volume) and time between visits. We report the estimated mean annual atrophy for both groups and whether differences between estimates were significant or not in Table 2.  
The estimated annual atrophy for Alzheimer's disease patients was higher than that of the control group regardless of the method and dataset. Nevertheless, the values themselves varied depending on the dataset under examination. We observed that atrophy gauged in OASIS disagreed with that in ADNI. Brain tissue loss measured in subjects in the former dataset was consistently lower than that gauged in ADNI, regardless of whether standardisation was considered or not. For example, in the absence of standardisation, the mean annual brain volume change for Alzheimer's disease and control subjects in OASIS was \(-0.61\) (95% CI \(-0.85\), \(-0.37\)) % and \(-0.10\) (95% \(-0.35\), 0.17) %, while estimates for the same groups were equal to \(-0.89\) (95% CI \(-1.10\), \(-0.69\)) and \(-0.27\) (95% \(-0.45\), \(-0.08\)) in ADNI. Further inspection of other clinical parameters revealed that the mean normalised baseline brain volume differed between datasets: mean values were approximately 0.58 (95% CI 0.57, 0.59) % and 0.55 (95% CI 0.55, 0.56) % for the pathological and control group in ADNI and 0.72 (95% CI 0.71, 0.73) % and 0.75 (95% CI 0.74, 0.76) in OASIS. Additionally, image acquisition parameters (Table 1) could have contributed to these inter-dataset atrophy variations as these parameters determine tissue contrast and partial volume effects, which could have led to a distinct characterisation of brain boundaries in each dataset. Estimated annual brain volume changes also differed depending on whether intensity standardisation was carried out or not. We noticed two clear trends in this regard.