Author profile
Department of Radiation Oncology, Stanford University
To develop and validate a 3D deep learning autosegmentation model for prostate and OAR contouring on post-brachytherapy-catheter CT images for HDR prostate brachytherapy planning.
Clinical translation of FLASH requires treatment planning systems (TPS) capable of accurately modeling ultra-high dose rate delivery. For FLASH configurations on clinical machines, vendor-provided phase space files are not applicable, thus necessitating indiv...
Configuring clinical linear accelerators (LINACs) for ultra-high dose rate (UHDR) electron experiments typically requires invasive hardware modification or manufacturer intervention, limiting accessibility. We developed an independent, non-invasive, software-...
To evaluate a MR-CT deformable registration workflow using a novel 3D-printed anthropomorphic phantom that enables controlled, known lumbar spine deformation, with assessment of geometric accuracy and quantitative integrity of MR fat-fraction (FF).
Leadership
Catheter position uncertainty produces dosimetric error in HDR prostate brachytherapy, with reported displacements between planning and delivery of up to 5-7mm. Current inverse planning methods optimize dwell times for fixed catheter positions without account...
Focal dose escalation to the dominant intraprostatic lesion (GTV) improves biochemical control but is limited by urethral toxicity. 177Lu-PSMA radiopharmaceutical therapy (RPT) offers a targeted boost, yet its spatial heterogeneity in uptake leaves GTV sub-re...
To design and fabricate a 3D-printed anthropomorphic phantom of the lumbar spine and pelvis to evaluate and improve the quantitative accuracy of SPECT/CT imaging and dosimetry for bone marrow and vertebral body lesions.
Traditional reliability analyses of linear accelerators (LINACs) have typically focused on individual components using limited datasets, failing to exploit the comprehensive operational insights contained within service logs. This study presents a novel, auto...