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Accelerated preprocessing of large numbers of brain images by parallel computing on supercomputers

“Preprocessing” is the first step required in brain image analysis that improves the overall quality and reliability of the results. However, it is computationally demanding and time-consuming, particularly to handle and parcellate complicatedly folded cortical ribbons of the human brain. In this st...

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Detalles Bibliográficos
Autores principales: Jimbo, Takehiro, Matsuo, Hidetoshi, Imoto, Yuya, Sodemura, Takumi, Nishimori, Makoto, Fukui, Yoshinari, Hayashi, Takuya, Furuyashiki, Tomoyuki, Yokoyama, Ryoichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646110/
https://www.ncbi.nlm.nih.gov/pubmed/37963952
http://dx.doi.org/10.1038/s41598-023-46073-4
Descripción
Sumario:“Preprocessing” is the first step required in brain image analysis that improves the overall quality and reliability of the results. However, it is computationally demanding and time-consuming, particularly to handle and parcellate complicatedly folded cortical ribbons of the human brain. In this study, we aimed to shorten the analysis time for data preprocessing of 1410 brain images simultaneously on one of the world's highest-performing supercomputers, “Fugaku.” The FreeSurfer was used as a benchmark preprocessing software for cortical surface reconstruction. All the brain images were processed simultaneously and successfully analyzed in a calculation time of 17.33 h. This result indicates that using a supercomputer for brain image preprocessing allows big data analysis to be completed shortly and flexibly, thus suggesting the possibility of supercomputers being used for expanding large data analysis and parameter optimization of preprocessing in the future.