Information Propagation in Prior-Image-Based Reconstruction.
Advanced reconstruction methods for computed tomography include sophisticated forward models of the imaging system that capture the pertinent physical processes affecting the signal and noise in projection measurements. However, most do little to integrate prior knowledge of the subject - often relying only on very general notions of local smoothness or edges. In many cases, as in longitudinal surveillance or interventional imaging, a patient has undergone a sequence of studies prior to the current image acquisition that hold a wealth of prior information on patient-specific anatomy. While traditional techniques tend to treat each data acquisition as an isolated event and disregard such valuable patient-specific prior information, some reconstruction methods, such as PICCS[1] and PIR-PLE[2], can incorporate prior images into a reconstruction objective function. Inclusion of such information allows for dramatic reduction in the data fidelity requirements and more robustly accommodate substantial undersampling and exposure reduction with consequent benefits to imaging speed and reduced radiation dose. While such prior-image-based methods offer tremendous promise, the introduction of prior information in the reconstruction raises significant concern regarding the accurate representation of features in the image and whether those features arise from the current data acquisition or from the prior images. In this work we propose a novel framework to analyze the propagation of information in prior-image-based reconstruction by decomposing the estimation into distinct components supported by the current data acquisition and by the prior image. This decomposition quantifies the contributions from prior and current data as a spatial map and can trace specific features in the image to their source. Such "information source maps" can potentially be used as a check on confidence that a given image feature arises from the current data or from the prior and to more quantitatively guide the selection of parameter values affecting the strength of prior information in the resulting image.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21212 USA, phone: 410-955-1314; fax: 410-955-1115.
J Webster
JW
Stayman
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD.
Jerry L
JL
Prince
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD.
Jeffrey H
JH
Siewerdsen
CT Reconstruction; Penalized-Likelihood Estimation; Prior Image