Abstract Book

S1185

ESTRO 37

Material and Methods The accuracy of 4 different contouring approaches were investigated. A ground truth was generated by manually contouring the spinal cord, brainstem, left parotid, right parotid, and mandible for 5 head and neck cancer patients 10 times. The intra-physician variability was calculated by taking the average of all dice coefficients from 10 combinations of each patient and structure separately. Two atlas-based systems, MIM Maestro (MIM), and RayStation (RS), and one deep learning-based system, Nimble ContourTM (NC) (Nimble Therapy, San Francisco, CA), were evaluated. In total 102 patients were considered in this study. 82 patients were used to train the MIM and NC algorithms, while due to limitations of the system, 10 patients were used to train RS. In the latter, 5 patients used as the ground truths were selected from the remaining 20 validation patients. The accuracy of each algorithm was evaluated by calculating the average dice coefficients from all 10 contour variations relative to the ground truth (Figure 1). Results For all contours, the average dice coefficients were the highest for the intra-physician variability, ranging from 0.848 for right parotid to 0.956 for mandible (Table 1). However, NC was able to achieve very similar results and outperformed both of the other algorithms. The average difference between the intra-physician variability and NC for the spinal cord, brainstem, left parotid, right parotid, and mandible was 0.002, 0.009, 0.012, 0.006, and 0.027 respectively, while the average difference between NC and the next best algorithm was 0.032, 0.032, 0.074, 0.093, and 0.034 respectively.

The 1.5T Philips Ingenia system was used to acquire B0 maps in the Brain and Pelvis during clinical patient scanning using a duel echo spoilt gradient echo, with an echo spacing of 4.6 ms. The B0 maps were unwrapped using the FSL Prelude algorithm to eliminate phase wraps. As distortion due to B0 inhomogenities are dependent on acquisition parameters, displacement maps were calculated using the readout gradient value of a 3D T1-weighted TFE scan. Patient-induced distortions, that occur along the readout direction, were added to the corresponding dimension of the system-distortion maps, and the total distortion vector was calculated the RSS of the displacements along all three orthogonal maps. Results Figure 1 shows the system distortions, and the total distortions in brain and pelvis scans. The system distortions appear better for the MR-Linac, with only the edges of the FOV displaying greater distortion than the MR-Sim. These findings are confirmed by Table 1, where the mean and maximum values in the brain are lower for the MR-Linac. While in the larger pelvis scan, the maximum value is slighter larger for the MR-Linac, the mean and P90 values are also lower.

Conclusion Although there are spatial differences, total distortion is comparable between the MR-Sim and MR-Linac. Total distortion was found to be <1.3mm for the Philips Ingenia and <1mm for the Elekta Unity MR-Linac in the two anatomies investigated. Future work will implement this methodology into the online MR-Linac workflow to directly assess geometric accuracy. In a second stage, this method can be used to automatically correct for the combined effect of B0 and gradient induced distortions. EP-2148 Approaching Intra-Physician Contouring Variability: Head and Neck Auto-Contouring with Deep Learning J. Chan 1 , V. Kearney 1 , S. Wu 1 , M. Bogdanov 1 , S. Haaf 2 , N. Dixit 1 , A. Sudhyadhom 1 , S. Yom 1 , T. Solberg 1 1 University of California UCSF, Radiation Oncology, San Francisco CA, USA 2 Nimble Therapy- LLC, Department of Artificial Intelligence, San Francisco CA, USA Purpose or Objective To investigate the feasibility of approaching intra- physician contouring accuracy using a deep learning- based automatic contouring system.

Conclusion By moving towards deep learning-based algorithms, auto- contouring accuracy can approach intra-physician variability. EP-2149 A priori scatter correction of clinical cone- beam CTs to enable on-line proton dose calculations A.G. Andersen 1 , Y. Park 2 , B. Winey 3 , G. Sharp 3 , U. Elstrøm 1 , J. Petersen 1 , L. Bentzen 4 , L. Muren 1 1 Aarhus University Hospital, Department of Medical Physics, Aarhus V, Denmark 2 UT Southwestern, Department of Radiation Oncology, Dallas- Texas, USA 3 Massachusetts General Hospital, Department of Medical Physics, Boston- Massachusetts, USA 4 Aarhus University Hospital, Department of Radiation Oncology, Aarhus V, Denmark Purpose or Objective The favorable dose characteristics of proton therapy can best be exploited if dose degradations caused by density variations and anatomy changes in the patient are accounted for and minimised. Imaging devices such as on-