A5065666823- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Medical Imaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Atomic and Subatomic Physics Research
- AI in cancer detection
- Lung Cancer Diagnosis and Treatment
- Computer Graphics and Visualization Techniques
- Image and Signal Denoising Methods
- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Seismic Imaging and Inversion Techniques
- Ultrasound in Clinical Applications
Institut Polytechnique de Bordeaux
2023-2024
Laboratoire Bordelais de Recherche en Informatique
2023-2024
Université de Bordeaux
2023-2024
Centre National de la Recherche Scientifique
2023-2024
Institut de Mathématiques de Bordeaux
2023
Background Lung MRI with ultrashort echo times (UTEs) enables high-resolution and radiation-free morphologic imaging; however, its image quality is still lower than that of CT. Purpose To assess the clinical applicability synthetic CT images generated from UTE by a generative adversarial network (GAN). Materials Methods This retrospective study included patients cystic fibrosis (CF) who underwent both on same day at one six institutions between January 2018 December 2022. The two-dimensional...
In clinical practice, the modality of choice for lung diagnosis is usually computed tomography (CT), which exposes patients to ionizing radiations and could potentially affect patients' health. Conversely, MR scan considered safe non-invasive but seems challenging due low proton density lungs respiratory artifacts. Recently, ultrashort echo-time (UTE) MRI has been developed assessment shows promising results. this work, we propose generating 2D synthetic CT slices from UTE slices, improve...
In medical image synthesis, the precision of localized structural details is crucial, particularly when addressing specific clinical requirements such as identification and measurement fine structures. Traditional methods for translation synthesis are generally optimized global reconstruction but often fall short in providing finesse required detailed local analysis. This study represents a step toward this challenge by introducing novel anatomical feature-prioritized (AFP) loss function...