Study of Alternative Imaging Methods for In Vivo Boron Neutron Capture Therapy

Simple Summary Accurate in vivo boron dosimetry is crucial for successfully implementing Boron Neutron Capture Therapy in clinical settings. This investigation uses a Compton camera detector and Monte Carlo algorithms to evaluate different imaging methods for dosimetry and tumor monitoring. The study employed the Maximum Likelihood Expectation Maximization method for dosimetry tomography, while morphological filtering and Deep Learning techniques with Convolutional Neural Networks were explored for tumor monitoring. The research is significant as it addresses the critical need for precise in vivo boron dosimetry in Boron Neutron Capture Therapy. The findings highlight the potential of the Maximum Likelihood Expectation Maximization method for accurately assessing the boron dose and demonstrate the promising results of the Convolutional-Neural-Network-based approach for tumor monitoring. The research emphasizes the importance of optimizing imaging methods and clinical parameters in this treatment, paving the way for improved treatment outcomes and enhanced patient care. Boron Neutron Capture Therapy (BNCT) is an innovative and highly selective treatment against cancer. Nowadays, in vivo boron dosimetry is an important method to carry out such therapy in clinical environments. In this work, different imaging methods were tested for dosimetry and tumor monitoring in BNCT based on a Compton camera detector. A dedicated dataset was generated through Monte Carlo tools to study the imaging capabilities. We first applied the Maximum Likelihood Expectation Maximization (MLEM) iterative method to study dosimetry tomography. As well, two methods based on morphological filtering and deep learning techniques with Convolutional Neural Networks (CNN), respectively, were studied for tumor monitoring. Furthermore, clinical aspects such as the dependence on the boron concentration ratio in image reconstruction and the stretching effect along the detector position axis were analyzed. A simulated spherical gamma source was studied in several conditions (different detector distances and boron concentration ratios) using MLEM. This approach proved the possibility of monitoring the boron dose. Tumor monitoring using the CNN method shows promising results that could be enhanced by increasing the training dataset.

Automated summary

This study evaluates different imaging methods for boron dosimetry and tumor monitoring using a Compton camera detector and Monte Carlo algorithms. The findings demonstrate the accuracy of the Maximum Likelihood Expectation Maximization method for assessing the boron dose and the promising results of the Convolutional Neural Networks approach for tumor monitoring. The research highlights the importance of optimizing imaging methods and clinical parameters in Boron Neutron Capture Therapy for improved treatment outcomes and enhanced patient care.