Patrick Barnes

Publication Details

  • Diffusion tensor imaging (DTI) with retrospective motion correction for large-scale pediatric imaging JOURNAL OF MAGNETIC RESONANCE IMAGING Holdsworth, S. J., Aksoy, M., Newbould, R. D., Yeom, K., Van, A. T., Ooi, M. B., Barnes, P. D., Bammer, R., Skare, S. 2012; 36 (4): 961-971

    Abstract:

    To develop and implement a clinical DTI technique suitable for the pediatric setting that retrospectively corrects for large motion without the need for rescanning and/or reacquisition strategies, and to deliver high-quality DTI images (both in the presence and absence of large motion) using procedures that reduce image noise and artifacts.We implemented an in-house built generalized autocalibrating partially parallel acquisitions (GRAPPA)-accelerated diffusion tensor (DT) echo-planar imaging (EPI) sequence at 1.5T and 3T on 1600 patients between 1 month and 18 years old. To reconstruct the data, we developed a fully automated tailored reconstruction software that selects the best GRAPPA and ghost calibration weights; does 3D rigid-body realignment with importance weighting; and employs phase correction and complex averaging to lower Rician noise and reduce phase artifacts. For select cases we investigated the use of an additional volume rejection criterion and b-matrix correction for large motion.The DTI image reconstruction procedures developed here were extremely robust in correcting for motion, failing on only three subjects, while providing the radiologists high-quality data for routine evaluation.This work suggests that, apart from the rare instance of continuous motion throughout the scan, high-quality DTI brain data can be acquired using our proposed integrated sequence and reconstruction that uses a retrospective approach to motion correction. In addition, we demonstrate a substantial improvement in overall image quality by combining phase correction with complex averaging, which reduces the Rician noise that biases noisy data.

    View details for DOI 10.1002/jmri.23710

    View details for Web of Science ID 000308884300022

    View details for PubMedID 22689498

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