Prospective Motion Correction in Functional MRI
By Maxim Zaitsev, B. Akin, Pierre LeVan, Benjamin R. Knowles. 2016
115 citations recorded by the source corpus.
&NA; Due to the intrinsic low sensitivity of BOLD‐fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra‐volume correction, nor can these techniques correct for the spin‐history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no‐motion conditions, which motivates further research in the area of adaptive dynamic imaging.
Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “In prospective motion correction, real-time MR physics is also an important part of the picture [223,224].”
- Methods for cleaning the BOLD fMRI signal (2016, unknown): “…al., 2014; Speck et al., 2006; Todd et al., 2015) or the use of dedicated sequences with navigators echoes or active markers (Hu and Kim, 1994; Lee et al., 1996; Muraskin et al., 2013; Pfeuffer et al., 2002; White et al., 2010) can be useful for prospective motion correction (Zaitsev et al., 2016).”