Deep learning-based segmentation of Jaszczak ACR phantom images for optimized Radium-223 dosimetry
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Radiation Physics and Chemistry
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Precise and personalized absorbed dose estimation in radionuclide therapy is crucial for optimizing treatment
efficiency while minimizing harm to healthy tissues. Radium-223 dichloride (Ra-223), an alpha emitter used in
treating metastatic castration-resistant prostate cancer, has shown positive results in extending patient survival.
However, the current practice of uniform Ra-223 activity administration based solely on patient weight can lead
to suboptimal treatment outcomes. Treatment efficacy evaluation involves quantifying activity and absorbed
dose through image quality analysis, revealing potential areas for optimization. This work introduces an innovative
approach that integrates a deep learning-based model for automated segmentation of the Jaszczak ACR
phantom—a tool for image quality analysis in nuclear medicine—with Monte Carlo simulation for dosimetry.
The model exhibits efficient segmentation, surpassing 83.7 % in class-wise Dice coefficients, offering a timeefficient
alternative to manual segmentation. The study highlights the superior performance of the 89 keV energy
window in image quality parameters, emphasizing its role in lesion detection. Additionally, it addresses
challenges in achieving accurate quantitative outcomes in nuclear medicine applications, particularly in Ra-223
therapy. These insights contribute to refining dosimetry protocols for Ra-223, enhancing the precision of
quantitative outcomes in nuclear medicine. The practical implications extend to improving daily routines for
clinical professionals in nuclear medicine applications, showcasing the potential of advanced imaging techniques
and computational tools in optimizing Ra-223 therapy.
Como referenciar
GRIEBLER, CRISTIAN F.; CORDEIRO, LEANDERSON P.; LIMA, LUIS F.; BOLZAN, VAGNER; DUTRA, VITOR; SA, LIDIA V. de; BONIFACIO, DANIEL A.B. Deep learning-based segmentation of Jaszczak ACR phantom images for optimized Radium-223 dosimetry. Radiation Physics and Chemistry, v. 237, p. 1-7, 2025. DOI: 10.1016/j.radphyschem.2025.113028. Disponível em: https://repositorio.ipen.br/handle/123456789/49519. Acesso em: 24 Mar 2026.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.