William Vale
About
My research project
Deep Learning for Improving Photoacoustic Imaging of CAR-T CellsDuring CAR-T cell cancer therapy, a patients T cells are modified to recognise and attack cancer cells. My work aims to use deep learning paired with reporter genes and photoacoustic imaging to track CAR-T cells in vivo.
Supervisors
During CAR-T cell cancer therapy, a patients T cells are modified to recognise and attack cancer cells. My work aims to use deep learning paired with reporter genes and photoacoustic imaging to track CAR-T cells in vivo.
ResearchResearch interests
Medical Physics, Medical Imaging, Artificial Intelligence, Computed Tomography.
Research interests
Medical Physics, Medical Imaging, Artificial Intelligence, Computed Tomography.
Publications
Quantitative photoacoustic imaging aims to determine the spatial distribution of the tissue’s optical absorption coefficient from photoacoustic (PA) signals measured at its surface. We combine large scale optical and acoustic modelling to estimate the optical absorption coefficient from simulated PA signal measurements using a band-limited transducer array that provides limited angular coverage. We validated our approach using a digital mouse atlas, and a PA imaging forward model which is based on the MSOT in-Vision 256TM system (iThera GmbH, Munich). We were able to recover the absorption coefficient when it was assumed that the scattering coefficient was known exactly, and that the digital phantom was an extrusion out of the 2D imaging plane. We then investigated how the performance was affected when these two assumptions were relaxed, and when substantial negative pressure artifacts were present in the reconstructed images.