Journal of neurosurgery · 2025
Deep learning-powered temperature prediction for optimizing transcranial MR-guided focused ultrasound treatment.
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Xiong Yongqin, Yang Mingliang, Arkin Mukadas, Li Yan, Duan Caohui, Bian Xiangbing, Lu Haoxuan, Zhang Luhua, Wang Song, Ren Xiaojing, Li Xuemei, Zhang Ming, Zhou Xin, Pan Longsheng, Lou Xin
Аннотация
OBJECTIVE: Precise temperature control is challenging during transcranial MR-guided focused ultrasound (MRgFUS) treatment. The aim of this study was to develop a deep learning model integrating the treatment parameters for each sonication, along with patient-specific clinical information and skull metrics, for prediction of the MRgFUS therapeutic temperature.
METHODS: This is a retrospective analysis of sonications from patients with essential tremor or Parkinson's disease who underwent unilateral MRgFUS thalamotomy or pallidothalamic tractotomy at a single hospital from January 2019 to June 2023. For model training, a dataset of 600 sonications (72 patients) was used, while a validation dataset comprising 199 sonications (18 patients) was used to assess model performance. Additionally, an external dataset of 146 sonications (20 patients) was used for external validation.
RESULTS: The developed deep learning model, called Fust-Net, achieved high predictive accuracy, with normalized mean absolute errors of 1.655°C for the internal dataset and 2.432°C for the external dataset, which closely matched the actual temperature. The graded evaluation showed that Fust-Net achieved an effective temperature prediction rate of 82.6%.
CONCLUSIONS: These results showcase the exciting potential of Fust-Net for achieving precise temperature control during MRgFUS treatment, opening new doors for enhanced precision and safety in clinical applications.
METHODS: This is a retrospective analysis of sonications from patients with essential tremor or Parkinson's disease who underwent unilateral MRgFUS thalamotomy or pallidothalamic tractotomy at a single hospital from January 2019 to June 2023. For model training, a dataset of 600 sonications (72 patients) was used, while a validation dataset comprising 199 sonications (18 patients) was used to assess model performance. Additionally, an external dataset of 146 sonications (20 patients) was used for external validation.
RESULTS: The developed deep learning model, called Fust-Net, achieved high predictive accuracy, with normalized mean absolute errors of 1.655°C for the internal dataset and 2.432°C for the external dataset, which closely matched the actual temperature. The graded evaluation showed that Fust-Net achieved an effective temperature prediction rate of 82.6%.
CONCLUSIONS: These results showcase the exciting potential of Fust-Net for achieving precise temperature control during MRgFUS treatment, opening new doors for enhanced precision and safety in clinical applications.