Incorporation of noise and prior images in penalized-likelihood reconstruction of sparse data

Yifu Ding; Jeffrey H. Siewerdsen; J. Webster Stayman

doi:10.1117/12.911667

3 March 2012 Incorporation of noise and prior images in penalized-likelihood reconstruction of sparse data

Yifu Ding, Jeffrey H. Siewerdsen, J. Webster Stayman

Proceedings Volume 8313, Medical Imaging 2012: Physics of Medical Imaging; 831324 (2012) https://doi.org/10.1117/12.911667
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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 during intervention. Radiation dose imparted for these repeat acquisitions present a concern. Because such image sequences share a great deal of information between acquisitions, using prior image information from baseline scans in the reconstruction of subsequent scans can relax data fidelity requirements of follow-up acquisitions. For example, sparse data acquisitions, including angular undersampling and limited-angle tomography, limit exposure by reducing the number of acquired projections. Various approaches such as prior-image constrained compressed sensing (PICCS) have successfully incorporated prior images in the reconstruction of such sparse data. Another technique to limit radiation dose is to reduce the x-ray fluence per projection. However, many methods for reconstruction of sparse data do not include a noise model accounting for stochastic fluctuations in such low-dose measurements and cannot balance the differing information content of various measurements. In this paper, we present a prior-image, penalized-likelihood estimator (PI-PLE) that utilizes prior image information, compressed-sensing penalties, and a Poisson noise model for measurements. The approach is applied to a lung nodule surveillance scenario with sparse data acquired at low exposures to illustrate performance under cases of extremely limited data fidelity. The results show that PI-PLE is able to greatly reduce streak artifacts that otherwise arise from photon starvation, and maintain high-resolution anatomical features, whereas traditional approaches are subject to streak artifacts or lower-resolution images.

Citation Download Citation

Yifu Ding, Jeffrey H. Siewerdsen, and J. Webster Stayman "Incorporation of noise and prior images in penalized-likelihood reconstruction of sparse data", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831324 (3 March 2012); https://doi.org/10.1117/12.911667

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