• J Magn Reson Imaging · Oct 2012

    Diffusion tensor imaging (DTI) with retrospective motion correction for large-scale pediatric imaging.

    • Samantha J Holdsworth, Murat Aksoy, Rexford D Newbould, Kristen Yeom, Anh T Van, Melvyn B Ooi, Patrick D Barnes, Roland Bammer, and Stefan Skare.
    • Department of Radiology, Stanford University, Stanford, California, USA. sholdsworth@stanford.edu
    • J Magn Reson Imaging. 2012 Oct 1; 36 (4): 961-71.

    PurposeTo 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.Materials And MethodsWe 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.ResultsThe 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.ConclusionThis 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.Copyright © 2012 Wiley Periodicals, Inc.

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