Mañana seguimos con el seminario y arrancamos con las charlas sobre artículos publicados.
Vamos con Francisco de Izaguirre con el artículo:
M. Zarei, S. S. Paima, C. McCabe, E. Abadi and E. Samei, "A Physics-informed Deep Neural Network for Harmonization of CT Images," in IEEE Transactions on Biomedical Engineering, doi: 10.1109/TBME.2024.3428399. https://doi.org/10.1109/TBME.2024.3428399
Copio el abstract del artículo.
Abstract: Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs). Methods: An adversarial generative network was trained on virtual CT images acquired under various imaging conditions using a virtual imaging platform with 40 computational patient models. These models featured anthropomorphic lungs with different levels of pulmonary diseases, including nodules and emphysema. Imaging was conducted using a validated CT simulator at two dose levels and varying reconstruction kernels. The trained model was tested on an independent virtual test dataset and two clinical datasets. Conclusion: The proposed harmonizer significantly enhances image quality and quantification accuracy in CT imaging. Significance: The study demonstrated the potential utility of image harmonization for consistent CT image quality and reliable quantification, which is crucial for clinical applications and patient management.