Examinando por Autor "Mazal, Alejandro"

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Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers
(Wiley, 2024-04-03) Viar-Hernandez, David; Molina-Maza, Juan Manuel; Vera-Sánchez, Juan Antonio; Perez-Moreno, Juan Maria; Mazal, Alejandro; Rodriguez-Vila, Borja; Malpica, Norberto
Background Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCT images. Purpose To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinical applications. Methods The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testing purposes. Results The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results of CBCT. Conclusions We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications
Enhancing Proton Therapy Planning Through Uncertainty Quantification Maps
(Sociedad Española de Ingeniería Biomédica (SEIB), 2024-12-02) Hernandez-Rubia, Javier; Rodriguez-Gonzalez, Blanca; Vera-Sanchez, Juan Antonio; Mazal, Alejandro; Malpica, Norberto; Torrado-Carvajal, Ángel; Rodriguez-Vila, Borja
This project discusses the evaluation of a state-of-the-art deep learning-based model for improving the efficacy of the preparation of a proton therapy treatment. This technique is known due to its precision in targeting tumoral areas, which requires a thorough planning to minimize damage of healthy surrounding tissues. The importance of Artificial Intelligence in medical imaging led to the development of the nnUNet model, considered the gold standard for image segmentation. Thus, this work focuses on the study of this model to better understand the process of automatic segmentation of critical organs in CT examinations compared to a ground truth of segmentation by radiation oncologists, by performing a full study on the results. Initial evaluations were conducted, ending up with a Dice Coefficient of 0.810 and Hausdorff Distance of 3.381mm, which led to an uncertainty quantification study to assess the robustness of the model with the Test Time Augmentation method. This study concluded that adaptive architectures are clinically more effective than complex deep learning models. Due to the robustness nature of the nnUNet model, its usability for proton therapy planning is proven.