Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation...

Area of Science:

• Neurosurgery
• Biomedical Engineering
• Medical Physics

Background:

• Laser interstitial thermal therapy (LITT) is a key treatment for brain tumors and epilepsy.
• Precise control of thermal damage during LITT is critical for patient outcomes.
• Accurate prediction of in vivo brain tissue temperature is challenging due to variable tissue properties.

Purpose of the Study:

• To develop a patient-specific method for predicting temperature distribution in brain lesions during LITT.
• To enhance the accuracy of thermal damage extent prediction for LITT procedures.

Main Methods:

• A magnetic resonance temperature imaging (MRTI) data-driven model was combined with a modified Pennes bioheat transfer equation (PBHE).
• The model incorporated patient-specific and temperature-dependent tissue properties (thermal, optical, and perfusion).
• Only intraoperative MRTI data from a single laser irradiation session were needed.

Main Results:

• The method significantly improved temperature distribution and thermal damage region prediction accuracy.
• Average root mean square error decreased by 69.54%.
• Average Dice similarity coefficient for thermal damage region prediction increased by 43.14%.

Conclusions:

• The proposed method enables accurate, patient-specific temperature and thermal damage prediction during LITT.
• This approach offers a valuable tool for optimizing LITT treatment planning in the brain.
• Improved prediction accuracy supports safer and more effective LITT interventions.