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Mayo Clinic
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Clinical proton dose prescription uses a fixed relative biological effectiveness (RBE) factor of 1.1 despite preclinical and clinical evidence that RBE varies with dose, radiation quality and tissue radiosensitivity, particularly near the distal edge of treat...
Toxicities to organs remain a critical limitation to dose escalation in radiotherapy. Ion radiotherapy reduces the absorbed dose to normal tissue but is characterized by a higher relative biological effectiveness (RBE). Existing clinically relevant methodolog...
Accurate voxel-wise RBE modeling for carbon ion radiation therapy (CIRT), such as the Mayo Clinic Florida Microdosimetric Kinetic Model (MCF-MKM), is computationally prohibitive for time-constrained clinical workflows. Monte Carlo (MC) calculations can requir...
The Mayo Clinic Florida microdosimetric kinetic model (MCF MKM) provides highly accurate relative biological effectiveness (RBE) calculations for carbon ion radiation therapy (CIRT), but its clinical use is limited by extreme memory and computational demands...
Synchrotron-based proton spot scanning includes non-productive dead time from layer switching and spill change/reinjection. In dose-driven continuous scanning, spill changes are commonly triggered only after beam exhaustion, causing sequential overheads. We p...
To present preliminary validation results for the first carbon treatment planning system (TPS) in the United States and to benchmark its GPU Monte Carlo dose engine using depth-resolved dose and microdosimetric metrics.
While Denoising Diffusion Probabilistic Models (DDPMs) have set new benchmarks for synthetic CT (sCT) image quality, their prohibitive inference times hinder integration into online adaptive radiation therapy (ART) workflows. This study introduces HQ-PatchNet...
Accurate prediction of radiotherapy toxicity requires integrating heterogeneous data, including 3D dose distributions, patient anatomy, and unstructured clinical text. We developed a multimodal pipeline that couples deep learning–based dose prediction (3D Med...