Retrospective Automatic Motion Detection in fMRI using k-space information

Motivation: Handling subject motion in fMRI is an important issue. Thus, it's ideal to correct motion as thoroughly as possible during data preprocessing. Goal(s): We aim to develop an algorithm that accurately detects precise motion corrupted measurements to efficiently correct motion. Approach: Motion signal was obtained from the k-space data by calculating average squared difference between first measurement and successive measurements . Equation of threshold value to verify presence of motion was formulated. Lastly, the outliers in motion signal were detected using WLS(weighted least square) optimization framework. Results: The proposed method successfully detects precise motion corrupted measurements automatically. Impact: The proposed method can facilitate precise fMRI analysis by detecting motion corrupted measurements in advance to data preprocessing procedure and applying suitable corrections, which enables us to input less motion contaminated data from the early stages of fMRI data preprocessing.