Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based...

The objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep-learning-based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)-only radiotherapy.

Polyurethane-based and silicone-based materials with various silicone oil concentrations were scanned using 0.35 T MR and CT scanner to determine the tissue surrogate. Five tissue surrogates were determined by comparing the organ intensity with patient CT and MR images. Patient-specific organ modeling for three-dimensional printing was performed by manually delineating the structures of interest. The phantom was finally fabricated by casting materials for each structure. For the quantitative evaluation, the mean and standard deviations were measured within the regions of interest on the MR, simulation CT (CT_sim ), and synthetic CT (CT_syn ) images. Intensity-modulated radiation therapy plans were generated to assess the impact of different electron density assignments on plan quality using CT_sim and CT_syn . The dose calculation accuracy was investigated in terms of gamma analysis and dose-volume histogram parameters.

For the prostate site, the mean MR intensities for the patient and phantom were 78.1 ± 13.8 and 86.5 ± 19.3, respectively. The mean intensity of the synthetic image was 30.9 Hounsfield unit (HU), which was comparable to that of the real CT phantom image. The original and synthetic CT intensities of the fat tissue in the phantom were -105.8 ± 4.9 HU and -107.8 ± 7.8 HU, respectively. For the target volume, the difference in D_95% was 0.32 Gy using CT_syn with respect to CT_sim values. The V_65Gy values for the bladder in the plans using CT_sim and CT_syn were 0.31% and 0.15%, respectively.

This work demonstrated that the anthropomorphic phantom was physiologically and geometrically similar to the patient organs and was employed to quantitatively evaluate the deep-learning-based synthetic CT algorithm.