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- W4293322914 abstract "Purpose: There is growing interest in the use of modern 3D printing technology to implement intensity-modulated radiation therapy (IMRT) on the preclinical scale which is analogous to clinical IMRT. However, current 3D-printed IMRT methods suffer from complex modulation patterns leading to long delivery times, excess filament usage, and inaccurate compensator fabrication. In this work, we have developed a total variation regularization (TVR) approach to address these issues. Methods: TVR-IMRT, a technique designed to minimize the intensity difference between neighboring beamlets, was used to optimize the beamlet intensity map, which was then converted to corresponding compensator thicknesses in copper-doped PLA filament. IMRT and TVR-IMRT plans using five beams were generated to treat a mouse heart while sparing lung tissue. The individual field doses and composite dose were delivered to film and compared to the corresponding planned doses using gamma analysis. Results: TVR-IMRT reduced the total variation of both the beamlet intensities and compensator thicknesses by around 50% when compared to standard 3D printed compensator IMRT. The total mass of compensator material consumed and radiation beam-on time were reduced by 20-30%, while DVHs remained comparable. Gamma analysis passing rate with 3%/0.3mm criterion was 89.07% for IMRT and 95.37% for TVR-IMRT. Conclusion: TVR can be applied to small animal IMRT beamlet intensities in order to produce fluence maps and subsequent 3D-printed compensator patterns with less total variation, simplifying 3D printing and reducing the amount of filament required. The TVR-IMRT plan required less beam-on time while maintaining the dose conformity when compared to a traditional IMRT plan." @default.
- W4293322914 created "2022-08-27" @default.
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- W4293322914 date "2021-07-01" @default.
- W4293322914 modified "2023-10-16" @default.
- W4293322914 title "Improving the efficiency of small animal 3D printed compensator IMRT with beamlet intensity total variation regularization" @default.
- W4293322914 doi "https://doi.org/10.48550/arxiv.2107.00699" @default.
- W4293322914 hasPublicationYear "2021" @default.
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