Substantia nigra analysis by tensor decomposition of T2-weighted images for Parkinson’s disease diagnosis

Hayato Itoh; Masahiro Oda; Shinji Saiki M.D.; Nobutaka Hattori M.D.; Koji Kamagata M.D.; Shigeki Aoki M.D.; Kensaku Mori

doi:10.1117/12.2612830

4 April 2022 Substantia nigra analysis by tensor decomposition of T2-weighted images for Parkinson’s disease diagnosis

Hayato Itoh, Masahiro Oda, Shinji Saiki M.D., Nobutaka Hattori M.D., Koji Kamagata M.D., Shigeki Aoki M.D., Kensaku Mori

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323T (2022) https://doi.org/10.1117/12.2612830
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

We propose a pattern-expression method based on rank-one tensor decomposition for analysis for substantia nigra in T2-weighted images. Capturing discriminative features in observed medical data is an important task in diagnosis. In diagnosing Parkinson’s disease, capturing the change of volumetric data of substantia nigra supports the clinical diagnosis. Furthermore, in drug discovery researches for Parkinson’s disease, statistical evaluations of changes of substantia nigra, which are caused by a developed medicine, also might be necessary. Therefore, we tackle the development of the pattern-expression method to analyse volumetric data of substantia nigra. Experimental results showed the different distributions of computed coefficients for rank-one tensors between Parkinson’s disease and healthy state. The results indicated the validity of the tensor-decomposition-based pattern-expression method for the analysis.

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Hayato Itoh, Masahiro Oda, Shinji Saiki M.D., Nobutaka Hattori M.D., Koji Kamagata M.D., Shigeki Aoki M.D., and Kensaku Mori "Substantia nigra analysis by tensor decomposition of T2-weighted images for Parkinson’s disease diagnosis", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323T (4 April 2022); https://doi.org/10.1117/12.2612830

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