Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

This study addresses a fundamental limitation of In-beam Positron Emission Tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data.

Approach. We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a Visual Transformer (ViT) Neural Network (NN), we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model's performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images - serving as the benchmark.

Main Results. The Structural Similarity Index (SSIM) was computed at a mean value across all patients of 0.91, while the Mean Absolute Error (MAE) measured 22 Hounsfield Units (HU). Root Mean Squared Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) values were 56 HU and 30 dB, respectively. The Dice Similarity Coefficient (DSC) exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT...
Source: Physics in Medicine and Biology - Category: Physics Authors: Source Type: research