3rd International Conference on Chemo and BioInformatics, Kragujevac, September 25-26. 2025. (pp. 148-151)
AUTOR(I) / AUTHOR(S): Milena P. Živković, Abdulhady Abas Abdulla, Tarik A Rashid, Dragana Ž. Krstić
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DOI: 10.46793/ICCBIKG25.148Z
SAŽETAK / ABSTRACT:
Magnetic resonance imaging (MRI)-only radiotherapy planning seeks to replace computed tomography (CT) by generating synthetic CT (sCT) images directly from MRI, exploiting MRI’s superior soft-tissue contrast; however, MRI lacks the electron density information required for accurate dose calculation, necessitating a dual-modality CT–MRI workflow that increases scanning time and registration uncertainty. This CT–MRI paradigm subjects patients to additional radiation and prolonged imaging sessions, which can degrade planning accuracy, making a reliable MRI-only solution critical for safer, faster, and more precise radiotherapy. To address this, an end-to-end CycleGAN framework is presented to synthesize CT images from routine T1-weighted brain MRI using unpaired data, eliminating the need for exact MRI–CT pairs; the architecture employs U-Net-based generators and PatchGAN discriminators with cycle-consistency and identity losses for robust domain translation. On 100 held-out paired MR–CT slices, the generated sCT achieved a mean absolute error of 58 ± 10 HU and a structural similarity index of 0.92 ± 0.03 compared to ground-truth CT, preserving bone interfaces, air cavities, and soft-tissue boundaries, thus demonstrating suitability for dosimetric integration.
KLJUČNE REČI / KEYWORDS:
MRI, Synthetic CT, CycleGAN, Hounsfield units, deep learning
PROJEKAT / ACKNOWLEDGEMENT:
This work was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia through the Agreements No. 451-03-137/2025- 03/200122 and No. 451-03-136/2025-03/200122.
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