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Dear Ramesh, the most important parameters for checking quality are: - Weighted overall image quality: This is a combined parameter based on noise, bias and spatial resolution and represents the quality before preprocessing the data and can be also given as rating in terms of notes - Mean correlation: This is a measure how similar your data are after preprocessing to find potential outliers The Mahalanobis distance tries to combine these two different measures. The ratings for the overall image quality for data 12 is about 2.27, which is good. Please check the section about quality assurance in the CAT12 online help for the rating and more information. Even if some data as your data12 are deviate more than 1 or 2 SDs this does not mean that you have to remove them because of the classical outlier definition. This just means that you have to carefully check these data that deviate most and to decide whether there are any issues with quality (e.g. motion artefacts). Best, Christian On Wed, 5 Jul 2017 17:04:26 +0530, Ramesh Babu <[log in to unmask]> wrote: >Dear Experts, >I have T1 images of 2 groups and 40 images in each group. I did >segmentation using CAT12 seg. and quality check. I need your suggestions on >quality check outputs. I am getting confusion on selecting poor quality >data. For example data 12 comes under very high poor ratings for image >quality whereas same data comes under very less poor ratings. Should I >exclude this data for further analysis or I can continue. Please see the >attached file and give your suggestions. > >Thanks in advance >RB >