| Autor: |
Lars E. Olsson, Sacha af Wetterstedt, Jonas Scherman, Adalsteinn Gunnlaugsson, Emilia Persson, Christian Jamtheim Gustafsson |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Physics and Imaging in Radiation Oncology, Vol 29, Iss , Pp 100557- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-6316 |
| DOI: |
10.1016/j.phro.2024.100557 |
| Popis: |
Background and Purpose: In magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy. Materials and Methods: For a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated. Results: sCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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