Towards online patient imaging during helical radiotherapy

Exit-detector data from helical radiation therapy have been studied extensively for delivery verification and dose reconstruction. Since the same radiation source is used for both imaging and treatment, this work investigates the possibility of utilising exit-detector raw data for imaging purposes. This gives rise to potential clinical applications such as retrospective daily setup verification and inter-fractional setup error detection. The exit-detector raw data were acquired and independently analysed using Python programming language. The raw data were extracted from the treatment machine's onboard computer, and converted into 2D array files. The contours of objects (phantom or patient) were acquired by applying a logarithmic function to the ratio of two sinograms, one with the object in the beam and one without. The setup variation between any two treatment deliveries can be detected by applying the same function to their corresponding exit-detector sinograms. The contour of the object was well defined by the secondary radiation from the treatment beam and validated with the imaging beam, although no internal structures were discernible due to the interference from the primary radiation. The sensitivity of the setup variation detection was down to 2 mm, which was mainly limited by the resolution of the exit-detector itself. The exit-detector data from treatment procedures contain valuable photon exit fluence maps which can be utilised for contour definition and verification of patient alignment without reconstruction.