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Reproducibility of automated calculation technique for diffusion tensor image analysis along the perivascular space

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Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Purpose

The method of diffusion tensor image analysis along the perivascular space (DTI-ALPS) was gathering attention to evaluate the brain’s glymphatic function or interstitial fluid dynamics. However, to the best knowledge, no study was conducted on the reproducibility of these automated methods for ALPS index values. Therefore, the current study evaluated the ALPS index reproducibility based on DTI-ALPS using two major automated calculation techniques in scan and rescan of the same subject on the same day.

Materials and methods

This study included 23 participants, including 2 with Alzheimer’s disease, 15 with mild cognitive impairment, and 6 with cognitive normals. Scan and rescan data of diffusion magnetic resonance images were obtained, as well as automatically index for ALPS (ALPS index) and ALPS index maintaining tensor vector orientation information (vALPS index) with region of interest on the template fractional anisotropy map calculated by FSL software.These ALPS indices were compared in terms of scan and rescan reproducibility.

Results

The absolute difference in ALPS-index values between scan and rescan was larger in the ALPS index than in the vALPS index by approximately 0.6% as the relative difference. Cohen’s d for the left and right ALPS indices between methods were 0.121 and 0.159, respectively.

Conclusion

The vALPS index based on DTI-ALPS maintaining tensor vector orientation information has higher reproducibility than the ALPS index. This result encourages a multisite study on the ALPS index with a large sample size and helps detect a subtle pathological change in the ALPS index.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP22H04926, 18K18164, and 21K12153. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding

Data collection and sharing for this project were funded by the ADNI (National Institutes of Health grant no. U01 AG024904) and Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). This study was partially supported by the Juntendo Research Branding Project, JSPS KAKENHI I (grant nos. 16H06280, 18K18164, 18H02772, 19K17244, 21K07690, 21K12153, 22H04926, 23H02865), a Grant-in-Aid for Special Research in Subsidies for ordinary expenses of private schools from The Promotion and Mutual Aid Corporation for Private Schools of Japan, the Brain/MINDS Beyond program (grant no. JP19dm0307101) of the Japan Agency for Medical Research and Development (AMED), and AMED under grant number JP21wm0425006.

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Authors and Affiliations

  1. Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Yuya Saito, Koji Kamagata, Christina Andica, Wataru Uchida, Kaito Takabayashi, Seina Yoshida, Moto Nakaya, Yuya Tanaka, Satoru Kamiyo, Kanako Sato, Mitsuo Nishizawa, Toshiaki Akashi, Keigo Shimoji, Akihiko Wada & Shigeki Aoki

  2. Faculty of Health Data Science, Juntendo University, Chiba, Japan

    Christina Andica

  3. Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan

    Seina Yoshida

  4. Department of Radiology, University of Tokyo, Tokyo, Japan

    Moto Nakaya, Yuya Tanaka & Satoru Kamiyo

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  1. Yuya Saito
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Correspondence to Koji Kamagata.

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This article used open-source data from the ADNI 3 brain project (https://adni.loni.usc.edu/adni-3/) and did not involve studies with human participants or animals performed by the authors at our institution.

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Saito, Y., Kamagata, K., Andica, C. et al. Reproducibility of automated calculation technique for diffusion tensor image analysis along the perivascular space. Jpn J Radiol 41, 947–954 (2023). https://doi.org/10.1007/s11604-023-01415-0

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