Deep learning-based image reconstruction has shown substantial promise for addressing the highly non-uniform and under-sampled projections encountered in nonstop gated CBCT (ngCBCT). However, due to the statistical nature of data-driven learning and inherent...
Author profile
Noah Silverberg
Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Uncertainty Quantification of a Deep Learning Reconstruction Framework for Nonstop Gated CBCT Using an Auxiliary Network
Proffered Program · Therapy Physics
Deep Learning Enabled Freewill Gated CBCT for Respiratory Gating Radiotherapy
In respiratory gating radiotherapy (RG-RT), pretreatment imaging—particularly gated cone-beam CT (gCBCT)—is essential but operationally inefficient. Current gCBCT on C-arm linear accelerator is time-consuming (2–8 minutes) and often requires re-scans when gat...
Proffered Program · Therapy Physics
Toward a Unified, Site-Agnostic Deep Learning Reconstruction Framework for Nonstop Gated CBCT In Respiratory Gating Radiotherapy
Nonstop gated CBCT (ngCBCT) was developed to overcome the limitations of current gated CBCT (gCBCT), enabling 2-8x faster acquisitions and 2.5-3.5x lower imaging dose. However, ngCBCT produces highly non-uniform and under-sampled projection data that challeng...
Proffered Program · Therapy Physics