Radiological properties of 3D printed materials in kilovoltage and megavoltage photon beams.

PURPOSE: This study evaluates the radiological properties of different 3D printing materials for a range of photon energies, including kV and MV CT imaging and MV radiotherapy beams. METHODS: The CT values of a number of materials were measured on an Aquilion One CT scanner at 80kVp, 120kVp and a Tomotherapy Hi Art MVCT imaging beam. Attenuation of the materials in a 6MV radiotherapy beam was investigated. RESULTS: Plastic filaments printed with various infill densities have CT values of -743±4, -580±1 and -113±3 in 120kVp CT images which approximate the CT values of low-density lung, high-density lung and soft tissue respectively. Metal-infused plastic filaments printed with a 90% infill density have CT values of 658±1 and 739±6 in MVCT images which approximate the attenuation of cortical bone. The effective relative electron density REDeff is used to describe the attenuation of a megavoltage treatment beam, taking into account effects relating to the atomic number and mass density of the material. Plastic filaments printed with a 90% infill density have REDeff values of 1.02±0.03 and 0.94±0.02 which approximate the relative electron density RED of soft tissue. Printed resins have REDeff values of 1.11±0.03 and 1.09±0.03 which approximate the RED of bone mineral. CONCLUSIONS: 3D printers can model a variety of body tissues which can be used to create phantoms useful for both imaging and dosimetric studies.

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.