Sarah Capostagno
Papers
2
Total Citations
29
H-Index
2
About
Sarah Capostagno is a leading researcher in advanced medical imaging, with a primary focus on cone-beam computed tomography (CBCT) and its application to neuroradiology. Her major contributions lie at the intersection of image reconstruction algorithms and task-driven imaging optimization. In her highly cited 2019 work, she pioneered the application of task-driven source–detector trajectories for CBCT, developing customized imaging orbits that optimize image quality for specific clinical tasks in interventional neuroradiology. This work, which has garnered 23 citations, represents a significant advance in personalized imaging protocols. Capostagno has also made important contributions to computational efficiency in 3D image reconstruction. Her 2021 paper introduced a novel morphological pyramid approach combined with a noise-power convergence criterion, achieving accelerated model-based iterative reconstruction (MBIR) while maintaining superior noise-resolution tradeoffs. This work addresses a critical barrier to clinical adoption of MBIR—its computational burden—and has already influenced the field with 6 citations. Her research demonstrates a rare ability to bridge theoretical optimization with practical clinical needs, positioning her as an emerging leader in the evolution of CBCT technology for precision medicine.
Research Focus
Key Achievements
Top Papers
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