AbstractIn this study, a functioning and ventilated anthropomorphic phantom was further enhanced for the purpose of CT and MR imaging of the lung and liver. A deformable lung, including respiratory tract was 3D printed. Within the lung's inner structures is a solid region shaped from a patient's lung tumour and six nitro-glycerine capsules as reference landmarks. The full internal mesh was coated, and the tumour filled, with polyorganosiloxane based gel. A moulded liver was created with an external casing of silicon filled with polyorganosiloxane gel and flexible plastic internal structures. The liver, fitted to the inferior portion of the right lung, moves along with the lung's ventilation. In the contralateral side, a cavity is designed to host a dosimeter, whose motion is correlated to the lung pressure. A 4DCT of the phantom was performed along with static and 4D T1 weighted MR images. The CT Hounsfield units (HU) for the flexible 3D printed material were −600–100 HU (lung and liver structures), for the polyorganosiloxane gel 30–120 HU (lung coating and liver filling) and for the silicon 650–800 HU (liver casing). The MR image intensity units were 0–40, 210–280 and 80–130, respectively. The maximum range of motion in the 4D imaging for the superior lung was 1–3.5 mm and 3.5–8 mm in the inferior portion. The liver motion was 5.5–8.0 mm at the tip and 5.7–10.0 mm at the dome. No measurable drift in motion was observed over a 2 h session and motion was reproducible over three different sessions for sin2(t), sin4(t) and a patient-like breathing curve with the interquartile range of amplitudes for all breathing cycles within 0.5 mm. The addition of features within the lung and of a deformable liver will allow the phantom to be used for imaging studies such as validation of 4DMRI and pseudo CT methods.
Publication ReferencePhys. Med. Biol. 65 (2020) 07NT02
SponsorsDr Colvill has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 701647. This research was also partially funded under Swiss National Science Foundation Grant No. 320030_163330/1.