Manual Quality Control with Freesurfer 7.1: Key Problem Areas and Importance of Corrections V. Vahermaa¹ ², B. Aydogan³ , T. Raij³ ⁴, R-L. Armio⁵, H. Laurikainen⁵, J. Saramäki ¹, J. Suvisaari² ¹ Department of Computer Science, School of Science, Aalto University, Finland ² Department of National Health and Welfare (THL), Helsinki, Finland ³ Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Finland ⁴ University of Helsinki and Helsinki University Hospital, Psychiatry, Helsinki, Finland ⁵ Department of Psychiatry, University of Turku, Finland Contact Information: Vesa Vahermaa vesa.vahermaa@aalto.fi Results Methods Introduction Conclusions Dataset FreeSurfer (FS) is a free software suite that provides a comprehensive processing stream for structural magnetic resonance imaging (MRI) images. ⁶ This stream includes brain extraction and grey-white matter segmentation, which outlines the brain. 51 FEP patients and 78 images were included in the dataset. All subjects were scanned with the same Siemens brand 3T scanner. The impact of pial surface errors related to overextension of gray matter boundary on automatically calculated brain volumes is very small. Unless the study requires highly precise volume measurements or focuses on the areas that were shown to have a significant number of errors, manual quality control may not be necessary. References Figure 1. Three regions of the brain with error clusters: lateral orbitofrontal cortex (A), paracentral lobule (B), and cerebellar tentorium (C). Table 1. Error frequency among inspected MRI images. A total of 233 errors related to overextension of gray matter boundary were recorded from the images. Here, we determine the number, location, and volume distorting effect of such errors in the brains of 51 first-episode psychosis (FEP) patients and 78 MRI images. 28 of the subjects had both baseline and 1-year follow-up images. The subjects had FEP in 2010–2016, and were recruited by the Finnish Institute for health and welfare (THL). The changes in local brain volumes as listed in aparc.stats between non-controlled and controlled images were on average very small (-0.09% +- 0.33). The average change in cortical thickness was -0.07% +- 0.14. 127 errors were in the left hemisphere, and 106 in the right hemisphere. The images went through manual quality control by a single researcher (V.V.), with error locations recorded. All MRI images underwent the full FS processing stream (recon-all) including automatic volume calculation. Mistakes in this automated process lead to the miscalculation of brain volume in the areas with incorrectly determined edges. 6 http://surfer.nmr.mgh.harvard.edu/ 7 Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig. User- guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006 Jul 1;31(3):1116-28. The pial surfaces of each brain were systematically scanned for gray matter boundary errors present in at least 3 consecutive slices, and any non-brain tissue was removed. The error coordinates were recorded, and location labels determined from the aparc+aseg —file of each subject. The errors were not uniformly distributed in the brain: the most affected areas were the middle cerebral artery (59 errors) and the immediate vicinity of the cerebellar tentorium (50 errors). Watch a video about Figure 1: The effect is calculated by having a researcher manually examine the MRI images and manually correct the errors, and comparing the pre- and post intervention datasets and their volume measurements. Each set of error coordinates was converted to MNI 305 space as a single voxel. A heatmap was generated by applying gaussian kernel smoothing to the voxels in MNI305 space, and overlaying the heatmap in ITK-Snap. ⁷ A B C Subjects with visible motion artefacts were excluded from the dataset. The errors were also