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

作者：Holdsworth Samantha J^*; Aksoy Murat; Newbould Rexford D; Yeom Kristen; Van Anh T; Ooi Melvyn B; Barnes Patrick D; Bammer Roland; Skare Stefan

来源：Journal of Magnetic Resonance Imaging, 2012, 36(4): 961-971.

DOI：10.1002/jmri.23710

• MRI
• echo-planar imaging (EPI)
• diffusion-weighted imaging (DWI)
• diffusion tensor imaging (DTI)
• parallel imaging
• motion correction

Purpose: 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. Materials and Methods: 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. Results: 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. Conclusion: 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. J. Magn. Reson. Imaging 2012;36:961971.

• 出版日期2012-10