Deep Convolutional Framelets for Dose Reconstruction in Boron Neutron Capture Therapy with Compton Camera Detector.

backgroundBoron neutron capture therapy (BNCT) is an innovative binary form of radiation therapy with high selectivity towards cancer tissue based on the neutron capture reaction

methodsThis study aims to develop deep neural network models to estimate the dose distribution by using a simulated dataset of BNCT Compton camera images. The models pursue the avoidance of the iteration time associated with the maximum-likelihood expectation-maximization algorithm (MLEM), enabling a prompt dose reconstruction during the treatment. The U-Net architecture and two variants based on the deep convolutional framelets framework have been used for noise and artifact reduction in few-iteration reconstructed images.

resultsThis approach has led to promising results in terms of reconstruction accuracy and processing time, with a reduction by a factor of about 6 with respect to classical iterative algorithms.

conclusionsThis can be considered a good reconstruction time performance, considering typical BNCT treatment times. Further enhancements may be achieved by optimizing the reconstruction of input images with different deep learning techniques.