Deep learning for photoacoustic imaging of CAR-T cells in cancer immunotherapy

CAR-T cell immunotherapy is a promising technique for cancer treatment. To better understand and improve its efficacy for solid tumours, methods for quantifying the CAR-T cell distribution are necessary. One approach involves inserting a reporter gene into the CAR-T cells, causing them to express photochromic proteins that provide strong near-infrared (NIR) optical contrast. NIR photoacoustic (PA) imaging is then used to image these proteins, and implicitly the CAR-T cells. In this study machine learning techniques are used to classify and predict the spatial concentration of the photochromic proteins by analysing time series PA images. To address the need for large training datasets, we developed and validated a novel 3D simulation framework, which generates accurately labelled PA images of CAR-T cells expressing the reporter gene. Neural networks, including a Multi-Layer Perceptron and U-Net, demonstrated superior performance over previous methods, achieving high accuracy in protein concentration prediction.