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Hi all,
I am processing a large longitudinal dataset of T1-weighted anatomical images through the recon-all pipeline (N=680). I know that the gold standard for quality control following recon-all is visual inspection of the output surfaces and parcellations. However, given the large sample size, I was writing to see if the community has any recommendations for automatic QC pipelines.
I read a few papers discussing the use of Euler numbers to detect outlier datasets for exclusion (see citations below). Does anyone have any other recommendations?
Thank you in advance!
Best, Jenna
References: 1. Rosen, A. F., Roalf, D. R., Ruparel, K., Blake, J., Seelaus, K., Villa, L. P., ... & Satterthwaite, T. D. (2018). Quantitative assessment of structural image quality. *Neuroimage*, *169*, 407-418. 2. de Lange, A. M. G., Kaufmann, T., Quintana, D. S., Winterton, A., Andreassen, O. A., Westlye, L. T., & Ebmeier, K. P. (2021). Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study. *Behavioural Brain Research*, *414*, 113510.