A5018521137- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Radiomics and Machine Learning in Medical Imaging
- Advanced Radiotherapy Techniques
- Orthopedic Surgery and Rehabilitation
- Reconstructive Surgery and Microvascular Techniques
- Medical Imaging and Analysis
- Bone fractures and treatments
- AI in cancer detection
- Elbow and Forearm Trauma Treatment
- Shoulder and Clavicle Injuries
- Medical Image Segmentation Techniques
- Generative Adversarial Networks and Image Synthesis
- Topic Modeling
- Advanced Neural Network Applications
- Musculoskeletal synovial abnormalities and treatments
- Lung Cancer Diagnosis and Treatment
- Advanced Image Processing Techniques
- Shoulder Injury and Treatment
- Image Retrieval and Classification Techniques
- Food Security and Health in Diverse Populations
- Head and Neck Cancer Studies
- Image and Signal Denoising Methods
- Cannabis and Cannabinoid Research
- Seismic Imaging and Inversion Techniques
Laboratoire Traitement du Signal et de l'Image
2023-2025
Centre Eugène Marquis
2023-2025
Inserm
2023-2025
Université de Rennes
2023-2025
Centre Hospitalier Universitaire de Rennes
2023
Labex Corail
2018
Hôpital Saint-Antoine
2006
Sorbonne Université
2006
Hôpital Bichat-Claude-Bernard
2003
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data accurate dose calculations. However, accurately representing patient anatomy challenging, especially adaptive radiotherapy, where CT not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it...
Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results achieving high sCT accuracies. However, existing synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies proposed approaches, such as multicenter training . Material methods: The...
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Abstract Objective. Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger accelerate replanning but is still a challenge due to poor soft tissue contrast, artifacts, limited field-of-view these images, alongside lack large, annotated datasets train deep learning models. This study aims develop comprehensive framework segment 25 HN OARs...
Image registration is fundamental in medical imaging, enabling precise alignment of anatomical structures for diagnosis, treatment planning, image-guided interventions, and longitudinal monitoring. This work introduces IMPACT (Image Metric with Pretrained model-Agnostic Comparison Transmodality registration), a novel similarity metric designed robust multimodal image registration. Rather than relying on raw intensities, handcrafted descriptors, or task-specific training, defines semantic...
Abstract Purpose To evaluate deep learning (DL)‐based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients. Methods and Materials Data including 341 CBCTs (209 daily, 132 weekly) 23 planning CTs from patients was retrospectively analyzed. Anatomical deformation treatment estimated using free‐form (FFD) method Elastix DL‐based VoxelMorph approaches. The investigated anatomical scans (VMorph_Sc) or label images (VMorph_Msk), the combination...
Introduction For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT has shown encouraging results if the images used training network and generation come same device. objective this study was create evaluate a generic DL model capable sCTs various devices prostate Materials In total, 90 patients three centers (30 CT-MR...
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data accurate dose calculations. However, accurately representing patient anatomy challenging, especially adaptive radiotherapy, where CT not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it...