Rare Pulmonary Neuroendocrine Cells Are Stem Cells Regulated by Rb, p53, and Notch.

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. These fully differentiated cells display features of classical stem cells. Most proliferate (self-renew) following injury, and some migrate into the injured area. A week later, individual cells, often just one per cluster, lose NE identity (deprogram), transit amplify, and reprogram to other fates, creating large clonal repair patches. Small cell lung cancer (SCLC) tumor suppressors regulate the stem cells: Rb and p53 suppress self-renewal, whereas Notch marks the stem cells and initiates deprogramming and transit amplification. We propose that NE stem cells give rise to SCLC, and transformation results from constitutive activation of stem cell renewal and inhibition of deprogramming.

Volumetric Accuracy Analysis of Virtual Safety Barriers for Cooperative-Control Robotic Mastoidectomy.

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. CCRs manipulate instruments simultaneously with the surgeon, allowing the surgeon to control instruments with robotic augmentation of motion. CCRs can also enforce virtual fixtures, which are safety barriers that prevent motion into undesired locations. Previous studies have validated the ability of CCRs to allow a novice surgeon to safely complete a cortical mastoidectomy. This study provides objective accuracy data for CCR-imposed safety barriers in cortical mastoidectomies. METHODS: Temporal bone phantoms were registered to a CCR using preoperative computed tomography (CT) imaging. Virtual fixtures were created using 3D Slicer, with 2D planes placed along the external auditory canal, tegmen, and sigmoid, converging on the antrum. Five mastoidectomies were performed by a novice surgeon, moving the drill to the limit of the barriers. Postoperative CT scans were obtained, and Dice coefficients and Hausdorff distances were calculated. RESULTS: The average modified Hausdorff distance between drilled bone and the preplanned volume was 0.351 ±â€Š0.093 mm. Compared with the preplanned volume of 0.947 cm3, the mean volume of bone removed was 1.045 cm3 (difference of 0.0982 cm3 or 10.36%), with an average Dice coefficient of 0.741 (range, 0.665-0.802). CONCLUSIONS: CCR virtual fixtures can be enforced with a high degree of accuracy. Future studies will focus on improving accuracy and developing 3D fixtures around relevant surgical anatomy.

Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods.

We develop a mathematical framework for the design of orbital trajectories that are optimal to a particular imaging task (or tasks) in advanced cone-beam computed tomography systems that have the capability of general source-detector positioning. The framework allows various parameterizations of the orbit as well as constraints based on imaging system capabilities. To accommodate nonstandard system geometries, a model-based iterative reconstruction method is applied. Such algorithms generally complicate the assessment and prediction of reconstructed image properties; however, we leverage efficient implementations of analytical predictors of local noise and spatial resolution that incorporate dependencies of the reconstruction algorithm on patient anatomy, x-ray technique, and geometry. These image property predictors serve as inputs to a task-based performance metric defined by detectability index, which is optimized with respect to the orbital parameters of data acquisition. We investigate the framework of the task-driven trajectory design in several examples to examine the dependence of optimal source-detector trajectories on the imaging task (or tasks), including location and spatial-frequency dependence. A variety of multitask objectives are also investigated, and the advantages to imaging performance are quantified in simulation studies.

Task-driven source-detector trajectories in cone-beam computed tomography: II. Application to neuroradiology.

We apply the methodology detailed in "Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods" by Stayman et al. for task-driven optimization of source-detector orbits in cone-beam computed tomography (CBCT) to scenarios emulating imaging tasks in interventional neuroradiology. The task-driven imaging framework is used to optimize the CBCT source-detector trajectory by maximizing the detectability index, d ' . The approach was applied to simulated cases of endovascular embolization of an aneurysm and arteriovenous malformation and was translated to real data first using a CBCT test bench followed by implementation on an interventional robotic C-arm. Task-driven trajectories were found to generally favor higher fidelity (i.e., less noisy) views, with an average increase in d ' ranging from 7% to 28%. Visually, this resulted in improved conspicuity of particular stimuli by reducing the noise and altering the noise correlation to a form distinct from the spatial frequencies associated with the imaging task. The improvements in detectability and the demonstration of the task-driven workflow using a real interventional imaging system show the potential of the task-driven imaging framework to improve imaging performance on motorized, multiaxis C-arms in neuroradiology.