ESTRO 2020 Abstract Book

S222 ESTRO 2020

and a neural network for MR-only external beam treatment planning of cervix cancer patients. Material and Methods A pretrained generative adversarial network was utilized for sCT generation. It was trained and tested on an independent patient dataset consisting of 41 patients with pelvic malignancies (prostate and cervix; 25 for training + 16 for evaluation) for MR to sCT conversion. Five patients with locally advanced cervical cancer were retrospectively selected. All patients received pre-treatment MR scans in a 0.35T MAGNETOM C! open-MR scanner (Siemens, Erlangen) in treatment position. Target volumes and organs at risk (bladder, rectum, bowel, femoral heads) were contoured on the MR images. For each patient a VMAT plan with two arcs was generated on the sCT image with the aim of delivering 45 Gy in 25 fractions to the PTV. In one patient two simultaneous integrated boosts with 55 Gy were planned for pathological lymph nodes. All treatments were planned for a 10 MV photon beam provided by an Elekta Synergy LINAC. sCT images were rigidly registered (without rotation) to the planning CT (pCT) scan and the VMAT plan was recalculated on the pCT images. The two dose distributions were compared using a gamma analysis (3 mm/3%) and dose volume metrics of target and organ structures (D mean , D 2% , D 98% ; for bowel: D 2cc , V 40Gy , V 30Gy ). Results The gamma agreement index between pCT and sCT images was 98%±1%, when a threshold of 10 Gy was used. Deviations of PTV dose parameters were below 2% for all patients. For bladder and rectum the agreement was in the range of 3%, whereas the femoral heads showed higher deviations up to 4.5% with no systematic difference. The volumes V 40Gy and V 30Gy of the bowel were slightly elevated on sCT images with deviations up to 10% in one patient compared to pCT dose distributions.

Conclusion This study demonstrated the feasibility of generating sCTs from low field MR images for MR-only VMAT planning of cervix cancer. The deviations in dose distributions from the ground truth pCT images were considered small and are rather caused by anatomical variations between MR and pCT scans than from inaccuracies in sCT grey values. The performance of the network was good although having been trained on a high proportion of prostate scans. Future studies will examine larger patient numbers and proton dose distributions. A limitation of this approach is the limited field of view in longitudinal direction of the low field MRs which may be a problem for patients with paraaortic irradiation. PH-0412 MRI-only in prostate radiotherapy planning using multiple individual atlases: a preliminary study S. Nici 1 , A.F. Monti 1 , D. Lizio 1 , R. Pellegrini 2 , M.G. Brambilla 1 , C. Carbonini 1 , M.J. Arias Garces 1 , M.M.J. Felisi 1 , B. Bortolato 3 , C. Frassica 4 , C. Coletti 3 , A. Vanzulli 4 , A. Torresin 1 1 ASST Grande Ospedale Metropolitano Niguarda, Medical Physics, Milan, Italy ; 2 Elekta, Clinical Science, Milan, Italy ; 3 ASST Grande Ospedale Metropolitano Niguarda, Radiotherapy, Milan, Italy ; 4 ASST Grande Ospedale Metropolitano Niguarda, Radiology, Milan, Italy Purpose or Objective In this work, we aim to implement a magnetic resonance imaging (MRI)-based workflow in prostate cancer radiation therapy for planning purposes by using a hybrid technology. In computed tomography (CT) the voxel value is related to the tissue electron density; on the other hands MRI voxel is related to tissue proton density, and not useful for dose calculation. Therefore, it is necessary to convert MRI into electron density or HU by creating a surrogate of CT, a synthetic CT (sCT), to allow the treatment planning system (TPS) to calculate the dose distribution. Material and Methods To create sCTs from MRI, we propose a hybrid method that is a combination of bulk and multi-atlas-based approach. MR images from 10 volunteers were acquired by using an optimized 3D T1 VIBE-Dixon sequence with a 1.5T Siemens Aera MRI scanner with a FOV enclosing the whole body contour, flat table couch and fixation devices for feet and knees. Contours of these volunteers were delineated by identifying bone structures and main organs-at-risk (OARs), such as bladder, femoral heads, femurs, pelvic bones, rectum and sacrum, to create atlases as reference database for an auto segmentation software (ADMIRE, Elekta). This software approximates the anatomy contours by comparing several individual atlases, applying elements of maximum likelihood forms to a new patient image-set, and creates a structure set to fit the actual patient’s anatomy.