Abstract #0568

Rapid, open-source, cross-platform 3D multiparametric mapping for multisite neuroimaging

Shohei Fujita^1,2,3,4, Borjan Gagoski^2,5, Jon-Fredrik Nielsen⁶, Maxim Zaitsev⁷, Yohan Jun^1,2, Jaejin Cho^1,2, Xingwang Yong^1,2,8, Eugene Milshteyn⁹, Shaik Imam¹⁰, Qiang Liu¹¹, Qingping Chen⁷, Yogesh Rathi^11,12, and Berkin Bilgic^1,2,13

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Department of Radiology, Juntendo University, Tokyo, Japan, ⁴Department of Radiology, The University of Tokyo, Tokyo, Japan, ⁵Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ⁶Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ⁷Division of Medical Physics, Department of Radiology, Faculty of Medicine, Medical Center–University of Freiburg, Freiburg, Germany, ⁸Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Zhejiang, China, ⁹GE HealthCare, Boston, MA, United States, ¹⁰Department of Radiology, Vanderbilt University, Nashville, TN, United States, ¹¹Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, United States, ¹²Department of Radiology, Brigham and Women’s Hospital, Boston, MA, United States, ¹³Harvard/MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Synopsis

Keywords: Quantitative Imaging, Precision & Accuracy, Validation; Cross-vendor

Motivation: To address the unmet need for a cross-platform, multiparametric technique to facilitate data harmonization across different sites.

Goal(s): To implement and evaluate a fully transparent 3D multiparametric mapping for multisite neuroimaging.

Approach: A multiparametric mapping technique, 3D-QALAS, was implemented in Pulseq. The acquired T1 and T2 maps were compared within-scanner, cross scanners, software versions, sites, and vendors.

Results: The Pulseq implementation exhibited significantly higher reproducibility than vendor-native implementations, particularly for T2 values, in both phantom and in vivo studies. This approach enabled ADNI-compliant field-of-view sizes with 1mm isotropic resolution within 5 minutes, while maintaining a cross-platform coefficient of variation below 4%.

Impact: An open-source implementation across different vendors and scanners, along with a consistent reconstruction and fitting pipeline, improved measurement reproducibility. This approach facilitates data harmonization, version control and error-propagation assessment, making it also suitable for extracting quantitative information for downstream analysis.

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