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| Home > Publications database > Assessing the quality of deformable image registration in magnetic resonance image guided carbon ion liver radiotherapy with an anthropomorphic phantom |
| Home > Publications database > Assessing the quality of deformable image registration in magnetic resonance image guided carbon ion liver radiotherapy with an anthropomorphic phantom |
| Journal Article | DKFZ-2026-01182 |
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2026
Elsevier Science
Amsterdam [u. a.]
Abstract: Background and purpose: Carbon ion radiotherapy (CIRT) for liver patients is usually administrated in four high-dose fractions, requiring high geometrical precision. Work is underway to implement online magnetic resonance (MR)-guided adaptive workflows for this indication. For the generation of a daily computed tomography (dCT), deformable image registration (DIR) can be used. Here, a study on a deformable anthropomorphic abdomen phantom was conducted to assess the accuracy of MR-to-CT DIR.Materials and methods: We applied eight different static compressions on the phantom to induce deformations, acquired CT and MR images and performed DIR to generate dCT images for each MR. We assessed the geometric uncertainties of DIR for T1- and T2-weighted MRI sequences by comparing the deformed structures against a manually segmented ground-truth. Furthermore, we assessed the DIR impact on the dose distribution on a single-beam CIRT plan, comparing the beam's range (R80%) on the dCT against those of the CT images.Results: From the geometric analysis, we obtained mean Dice similarity coefficients (DSC) for the liver of 0.94 ± 0.02 for the T1- and 0.95 ± 0.10 for the T2-weighted MR sequences. These values were above the mean deformation induced on the liver (DSC of 0.87 ± 0.08). As for the range uncertainty induced by DIR, we observed mean R80% uncertainties up to 1.35 mm when comparing treatment doses on the dCT and CT images.Conclusions: Geometric and beam range uncertainties have been assessed systematically and were found to be small. These results support further implementation of an MR-guided DIR-based online adaptive workflow for liver CIRT.
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