ESTRO 2024 - Abstract Book

S3830

Physics - Image acquisition and processing

ESTRO 2024

2. Rudat V, Vanselow B, Wollensack P, et al (2000) Repeatability and prognostic impact of the pretreatment pO(2) histography in patients with advanced head and neck cancer. Radiother Oncol 57:31–37

3. Overgaard J (2011) Hypoxic modification of radiotherapy in squamous cell carcinoma of the head and neck-- a systematic review and meta-analysis. Radiother Oncol 100:22–32

4. Busk M, Overgaard J, Horsman MR (2020) Imaging of Tumor Hypoxia for Radiotherapy: Current Status and Future Directions. Semin Nucl Med 50:562–583

5. Hughes VS, Wiggins JM, Siemann DW (2019) Tumor oxygenation and cancer therapy-then and now. Br J Radiol 92:20170955

6. O’Connor JPB, Boult JKR, Jamin Y, et al (2016) Oxygen-Enhanced MRI Accurately Identifies, Quantifies, and Maps Tumor Hypoxia in Preclinical Cancer Models. Cancer Res 76:787–795

1336

Digital Poster

Commercial synthetic CT algorithms for pelvis: which one results in the highest performance?

Emilie Alvarez Andres 1,2 , Rudi Apolle 3 , Aswin L. Hoffmann 2,4 , Esther G. C. Troost 1,2,4

1 OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden; Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany. 2 Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus of Technische Universität Dresden, Dresden, Germany. 3 National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany. 4 Institute of Radiooncology – OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany

Purpose/Objective:

Magnetic Resonance Imaging (MRI)-only Radiation Therapy (RT) is believed to, e.g., increase treatment accuracy in (moving) soft tissue tumors, to enable adaptive workflows, and to avoid Computed Tomography (CT) acquisition and its radiation dose. Since MRI lacks electron density information required for planning, the generation of synthetic CT (sCT) scans is thoroughly being investigated. Meanwhile, multiple commercial solutions for pelvic sCT are available, either based on Bulk Density Assignment (BDA), multi-atlas, machine learning, or a combination thereof. The goal of this study was to compare three commercially available algorithms in terms of image fidelity and dosimetric validity on a cohort of prostate cancer patients.

Material/Methods: