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- W2003447293 abstract "Many imaging scenarios involve a sequence of tomographic data acquisitions to monitor change over time - e.g.,longitudinal studies of disease progression (tumor surveillance) and intraoperative imaging of tissue changes duringintervention. Radiation dose imparted for these repeat acquisitions present a concern. Because such image sequencesshare a great deal of information between acquisitions, using prior image information from baseline scans in thereconstruction of subsequent scans can relax data fidelity requirements of follow-up acquisitions. For example, sparsedata acquisitions, including angular undersampling and limited-angle tomography, limit exposure by reducing thenumber of acquired projections. Various approaches such as prior-image constrained compressed sensing (PICCS) havesuccessfully incorporated prior images in the reconstruction of such sparse data. Another technique to limit radiationdose is to reduce the x-ray fluence per projection. However, many methods for reconstruction of sparse data do notinclude a noise model accounting for stochastic fluctuations in such low-dose measurements and cannot balance thediffering information content of various measurements. In this paper, we present a prior-image, penalized-likelihoodestimator (PI-PLE) that utilizes prior image information, compressed-sensing penalties, and a Poisson noise model formeasurements. The approach is applied to a lung nodule surveillance scenario with sparse data acquired at lowexposures to illustrate performance under cases of extremely limited data fidelity. The results show that PI-PLE is ableto greatly reduce streak artifacts that otherwise arise from photon starvation, and maintain high-resolution anatomicalfeatures, whereas traditional approaches are subject to streak artifacts or lower-resolution images." @default.
- W2003447293 created "2016-06-24" @default.
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- W2003447293 date "2012-02-23" @default.
- W2003447293 modified "2023-09-23" @default.
- W2003447293 title "Incorporation of noise and prior images in penalized-likelihood reconstruction of sparse data" @default.
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- W2003447293 doi "https://doi.org/10.1117/12.911667" @default.
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