# Evaluating the Effect of Intensity Standardisation
## 4 Discussion
In this regard, we suggest using atrophy generator pipelines in the future to have a sense of ground truth [30; 31; 32]. Second, we only used one method for assessing brain volume change, and thus it is difficult to generalise our findings to other medical image analysis tools. Furthermore, even though FSL-SIENA is highly accessible and relatively easy and ready to use, we are aware that it is not in the state-of-the-art of longitudinal atrophy quantification in multiple sclerosis and Alzheimer's disease, but the Jacobian determinant integration method [33; 34]. Thus, future work should consider testing the pertinence of intensity standardisation on other automatic image analysis pipelines. Third, we did not analyse the effect of other confounding factors, such as intra-subject intensity standardisation methods (e.g., bias field correction methods). However, those are likely to have an effect on atrophy quantification. Thus, future work should analyse the effect of these types of algorithms on FSL-SIENA. In conclusion, intensity standardisation can improve longitudinal whole-brain atrophy quantification using FSL-SIENA, but not all methods do. Their applicability depends to a great extent on whether their theoretical assumptions are met or not. We recommend incorporating z-score into FSL-SIENA, right after registration, to reduce inter-scan intensity variations as it is computationally efficient and improves FSL-SIENA's performance consistently. The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/4/1773/s1. Conceptualisation, E.E.C.-C. and J.B.; methodology, E.E.C.-C. and J.B.; software, E.E.C.-C. and J.B.; validation, J.B.; formal analysis, E.E.C.-C. and J.B.; investigation, E.E.C.-C. and J.B.; resources, M.T. ; data curation, E.E.C.-C.; writing--original draft preparation, E.E.C.-C. and J.B.; writing--review and editing, E.E.C.-C., J.B., A.O., X.L., and M.T. ; visualisation, E.E.C.-C., J.B., A.O., and X.L. ; supervision, J.B. and M.T. ; project administration, J.B.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the MRC Doctoral Training Programme in Precision Medicine (JB--Award Reference No. 2096671). This work has been partially supported by DPI2017-86696-R from the Ministerio de Ciencia, Innovacion y Universidades. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** We have made our source code publicly available at [[https://github.com/emyesme/IntensityStandardisation](https://github.com/emyesme/IntensityStandardisation)]([https://github.com/emyesme/IntensityStandardisation](https://github.com/emyesme/IntensityStandardisation)) [accessed on 10 February 2021]. Also, we have attached the list of selected cases from ADNI and OASIS as supporting documents to enable reproducibility. **Acknowledgments:** Data used in the preparation of this article were [in part] obtained from the OASIS dataset: Longitudinal: Principal Investigators: [PERSON], [PERSON], [PERSON], [PERSON]; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. Data collection and sharing for the ADNI project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81 XWH-12-2-001--adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.