Deep learning-powered temperature prediction for optimizing transcranial MR-guided focused ultrasound treatment.

Abstract

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.