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DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
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Department of Radiation Oncology, Stanford University
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Cherenkov imaging (CI) provides direct, real-time visualization of dose on a patient's surface and has been used for treatment verification and error detection in radiation therapy. However, clinical adoption of CI remains in its infancy; existing image revie...
To develop a WEb-Accessible comprehensiVE (WEAVE) platform, that combines AI-driven segmentation with a user-friendly interface to automatically segment Vestibular Schwannomas (VS) and track longitudinal volumes for disease assessment, planning, and monitorin...
To address fragmentation and variability in longitudinal brain lesion assessment, we developed Brain-Dynamics, a vendor-neutral platform integrating auto‑segmentation, multimodal co‑registration, lesion labeling/tracking, and quantitative analytics for resear...
While MR-guided radiotherapy enables real-time, soft-tissue–based motion management, clinical 2D cine MRI often sacrifices spatial resolution to maintain frame rate, which can contrast oncological contrast. This study aims to optimize a 2D cine MRI protocol o...
To develop and evaluate a simulation-free, single-visit workflow for MRI-guided stereotactic ablative radiotherapy (SABR) for liver tumors using on-table imaging and online adaptive replanning, and to validate an MR-only dose calculation approach based on bul...