There is renewed interest in film dosimetry for the verification of dose delivery of complex treatments, particularly small fields, compared to treatment planning system calculations. A new radiochromic film, Gafchromic EBT-XD, is available for high-dose treatment verification and we present the first published evaluation of its use. We evaluate the new film for MV photon dosimetry, including calibration curves, performance with single- and triple-channel dosimetry, and comparison to existing EBT3 film. In the verification of a typical 25 Gy stereotactic radiotherapy (SRS) treatment, compared to TPS planned dose distribution, excellent agreement was seen with EBT-XD using triple-channel dosimetry, in isodose overlay, maximum 1.0 mm difference over 200-2400 cGy, and gamma evaluation, mean passing rate 97% at 3% locally-normalised, 1.5 mm criteria. In comparison to EBT3, EBT-XD gave improved evaluation results for the SRS-plan, had improved calibration curve gradients at high doses, and had reduced lateral scanner effect. The dimensions of the two films are identical. The optical density of EBT-XD is lower than EBT3 for the same dose. The effective atomic number for both may be considered water-equivalent in MV radiotherapy. We have validated the use of EBT-XD for high-dose, small-field radiotherapy, for routine QC and a forthcoming multi-centre SRS dosimetry intercomparison.
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient management and improve clinical workflow. The accuracy and robustness is challenged by low contrast, small target region and low through-plane resolution. We introduce a 2.5D convolutional neural network (CNN) able to exploit the different in-plane and through-plane resolutions encountered in standard of care imaging protocols. We use an attention module to enable the CNN to focus on the small target and propose a supervision on the learning of attention maps for more accurate segmentation. Additionally, we propose a hardness-weighted Dice loss function that gives higher weights to harder voxels to boost the training of CNNs. Experiments with ablation studies on the VS tumor segmentation task show that: 1) the proposed 2.5D CNN outperforms its 2D and 3D counterparts, 2) our supervised attention mechanism outperforms unsupervised attention, 3) the voxel-level hardness-weighted Dice loss can improve the performance of CNNs. Our method achieved an average Dice score and ASSD of 0.87 and 0.43 mm respectively. This will facilitate patient management decisions in clinical practice.
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