Abstract #0538

Improved across-scanner reproducibility using vendor-agnostic diffusion sequences

Qiang Liu^1,2, Lipeng Ning¹, Imam Ahmed Shaik¹, Borjan Gagoski³, Berkin Bilgic^4,5, William Grissom⁶, Jon-Fredrik Nielsen⁷, Maxim Zaitsev⁸, and Yogesh Rathi¹

¹Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, ²School of Biomedical Engineering, Southern Medical University, Guangzhou, China, ³Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁵Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁶Department of Biomedical Engineering, Case School of Engineering, Case Western Reserve University, Cleveland, OH, United States, ⁷fMRI Laboratory and Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ⁸Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany

Synopsis

Keywords: Diffusion Acquisition, Diffusion Tensor Imaging

Motivation: The reproducibility of diffusion MRI (dMRI) data collected at multiple sites can be affected by differences between MRI scanners, especially scanners from different manufacturers.

Goal(s): To develop vendor-neutral dMRI pulse sequences using our Pulseq development platform and reduce the inter-scanner variability between scanners from different vendors.

Approach: Using a diffusion phantom and with three human subjects, we tested inter-scanner variability using Pulseq and vendor-specific product sequences. We report inter-scanner variations using standard error for mean diffusivity and fractional anisotropy.

Results: Pulseq sequence yielded dramatically better results (>2x reduction in variability) enhancing the reliability of dMRI measurements across scanners.

Impact: The vendor-neutral Pulseq-diffusion sequence has the potential to harmonize data acquisition and improve the robustness of diffusion MRI, making it an invaluable tool for advancing multi-site studies.

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