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A Mass-Conserving 4D XCAT Phantom Enabling Normal Tissue Deformable Dose Accumulation


C Williams

C Williams1,2*, P Mishra1,2, J Seco2,3, S St. James1,2, M Wagar1, R Mak1,2, R Berbeco1,2, J Lewis1,2, (1) Brigham and Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, (2) Harvard Medical School, Boston, MA (3) Massachusetts General Hospital, Boston, MA

SU-C-141-7 Sunday 1:00PM - 1:55PM Room: 141

Purpose: The XCAT phantom is a realistic 4D digital torso phantom that is widely used in imaging and therapy research. However, lung mass is not conserved between respiratory phases of the phantom, making detailed dosimetric simulations unphysical. A framework is presented for enforcing local mass conservation in the XCAT lung and is combined with a dose calculation algorithm to create a tool for dose accumulation studies. As an application of this technique, the impact of irregular breathing on 4DCT-based predictions of accumulated lung dose is assessed.

Methods: A displacement vector field (DVF) between a respiratory state and a reference image is directly determined from the XCAT motion model and its divergence is used to correct the lung density. A series of phantoms with regular and irregular breathing (based on patient data) are generated and corrected. Monte Carlo simulations of conventional and SBRT treatments are performed, and the dose is deformed and accumulated. A 4DCT is simulated for the irregular breathing patient, and the 4DCT dose calculation is compared with the full accumulated delivered dose.

Results: The presented framework successfully conserves mass in the XCAT lung. The spatial distribution of the lung dose was qualitatively changed by the use of mass conservation; however the DVH does not change significantly. The comparison of the delivered dose with the 4DCT-based prediction shows similar lung metric results, however dose differences of 10% can be seen in different regions of the lung.

Conclusion: The XCAT phantom has been successfully modified so that it conserves lung mass during respiration, enabling it to be used as a tool to perform dose accumulation studies in the lung without relying on deformable image registration. These studies can reveal potentially significant differences between the predicted dose and the actual delivered dose. The software is freely available from the authors.

Funding Support, Disclosures, and Conflict of Interest: The project described was supported, in part, by an RSNA Research Scholar Grant and Award Number R21CA156068 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA, National Cancer Institute or the National Institutes of Health.

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