Machine and deep learning for improving photoacoustic imaging for CAR T-cell cancer therapy

This EPSRC iCase funded project builds on an existing collaboration between the University of Surrey (UoS), the Institute of Cancer Research (ICR) and the National Physical Laboratory (NPL), to develop photoacoustic imaging to assist the optimisation of CAR (chimeric antigen receptor) T-cell cancer therapy. This is an emerging treatment technique, approved for patients in 2018 and expected to enable major advancements in personalised cancer medicine. It involves removal of the patient's T cells from the blood, their genetic modification, and re-infusion of the cells back into the patient. The project will develop the use of photoacoustic imaging for determining the biodistribution of the CAR-T cells in vivo. Photoacoustic imaging is an important biomedical imaging method which employs an ultrasound scanner in combination with a pulsed laser to create high-resolution 3D images of optical absorption spectra of molecules in tissues and cells. The project objectives involve the development of methodologies, including the application of machine and deep learning, to use combinations of spectral and temporal photoacoustic signatures to recognise CAR T cells against background tissue and to quantify CAR-T cell numbers. Methods will be validated against photoacoustic microscopy of cells in tissue-mimicking phantoms and in vivo after tumour or tissue sectioning. Opportunities will be available, given time and interest, to explore dynamic quantification of CAR-T cell numbers as they penetrate tumours. The student will be based at the University of Surrey, and some time may be spent at the ICR for data acquisition using a commercial photoacoustic imager and at the NPL.