Publications by authors named "S Capostagno"

Hypothesis: Virtual fixtures can be enforced in cooperative-control robotic mastoidectomies with submillimeter accuracy.

Background: Otologic procedures are well-suited for robotic assistance due to consistent osseous landmarks. We have previously demonstrated the feasibility of cooperative-control robots (CCRs) for mastoidectomy.

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Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5-30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties.

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Model-based iterative reconstruction (MBIR) for cone-beam CT (CBCT) offers better noise-resolution tradeoff and image quality than analytical methods for acquisition protocols with low x-ray dose or limited data, but with increased computational burden that poses a drawback to routine application in clinical scenarios. This work develops a comprehensive framework for acceleration of MBIR in the form of penalized weighted least squares optimized with ordered subsets separable quadratic surrogates. The optimization was scheduled on a set of stages forming a morphological pyramid varying in voxel size.

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Pulmonary neuroendocrine (NE) cells are neurosensory cells sparsely distributed throughout the bronchial epithelium, many in innervated clusters of 20-30 cells. Following lung injury, NE cells proliferate and generate other cell types to promote epithelial repair. Here, we show that only rare NE cells, typically 2-4 per cluster, function as stem cells.

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Model-based iterative reconstruction (MBIR) offers improved noise-resolution tradeoffs and artifact reduction in cone-beam CT compared to analytical reconstruction, but carries increased computational burden. An important consideration in minimizing computation time is reliable selection of the stopping criterion to perform the minimum number of iterations required to obtain the desired image quality. Most MBIR methods rely on a fixed number of iterations or relative metrics on image or cost-function evolution, and it would be desirable to use metrics that are more representative of the underlying image properties.

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