Abstract #0700

Diffusion MRI harmonization by Linear scaling of Rotational Invariants of the Cumulant Expansion (LinearRICE): a multi-vendor multi-site study

Kouhei Kamiya¹, Ryo Kurokawa², Yuichi Suzuki³, Shinsuke Koike⁴, Naohiro Okada⁵, Yoshiyuki Hirano⁶, Jinichi Hirano⁷, Masaaki Hori¹, Takuya Hayashi⁸, and Osamu Abe²

¹Department of Radiology, Faculty of Medicine, Toho University, Tokyo, Japan, ²Department of Radiology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan, ³Radiology Center, the University of Tokyo Hospital, Tokyo, Japan, ⁴Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, the University of Tokyo, Tokyo, Japan, ⁵Department of Neuropsychiatry, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan, ⁶Research Center for Child Mental Development, Chiba University, Chiba, Japan, ⁷Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan, ⁸Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan

Synopsis

Keywords: DWI/DTI/DKI, DWI/DTI/DKI, harmonization, multi-vendor, traveling subjects, reproducibility

Motivation: The state-of-the-art diffusion MRI harmonization technique, LinearRISH, is based on signal representation on spherical harmonics, where the sensitivity to higher harmonic order terms can vary across scanners and protocols.

Goal(s): To explore if we can achieve better harmonization by exploiting the fact that DKI well describes the signal at lower b-values.

Approach: We represent the diffusion and kurtosis tensors, instead of the MRI signal, on a spherical harmonics basis and do scaling akin to LinearRISH. We dubbed this method LinearRICE and compared it with LinearRISH in a multi-vendor traveling subject dataset.

Results: LinearRICE reduced the inter-site variabilities better than LinearRISH.

Impact: We proposed a new method for retrospective harmonization of DKI-type diffusion MRI data. The proposed method outperformed the current state-of-the-art technique in a multi-vendor traveling subject dataset and can be useful for multi-institutional studies.

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