Marco Riboldi

Latest

• Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images
• Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects
• Reduction of cone-beam CT artifacts in a robotic CBCT device using saddle trajectories with integrated infrared tracking
• Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac
• Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors
• Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy
• Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac
• Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT Images
• Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy
• Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning
• Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients
• 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
• Assessment of intrafractional prostate motion and its dosimetric impact in MRI-guided online adaptive radiotherapy with gating
• Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis
• DeepClassPathway: Molecular pathway aware classification using explainable deep learning
• Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy
• Single-isocenter stereotactic radiosurgery for multiple brain metastases: Impact of patient misalignments on target coverage in non-coplanar treatments
• Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts
• Risk Stratification Using 18F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
• Porcine lung phantom-based validation of estimated 4D-MRI using orthogonal cine imaging for low-field MR-Linacs