An unsupervised stochastic model-based approach to image segmentation is described, and some of its properties investigated. In this approach, the problem of model parameter estimation is formulated as a problem of parameter estimation from incomplete data, and the expectation-maximization (EM) algorithm is used to determine a maximum-likelihood (ML) estimate. Previously, the use of the EM algorithm in this application has encountered difficulties since an analytical expression for the conditional expectations required in the EM procedure is generally unavailable, except for the simplest models. In this paper, two solutions are proposed to solve this problem: a Monte Carlo scheme and a scheme related to Besag's (1986) iterated conditional mode (ICM) method. Both schemes make use of Markov random-field modeling assumptions. Examples are provided to illustrate the implementation of the EM algorithm for several general classes of image models. Experimental results on both synthetic and real images are provided.

Recently there has been significant interest in dual energy CT imaging with several acquisition methods being actively pursued. Here we investigate fast kVp switching where the kVp alternates between low and high kVp every view. Fast kVp switching enables fine temporal registration, helical and axial acquisitions, and full field of view. It also presents several processing challenges. The rise and fall of the kVp, which occurs during the view integration period, is not instantaneous and complicates the measurement of the effective spectrum for low and high kVp views. Further, if the detector digital acquisition system (DAS) and generator clocks are not fully synchronous, jitter is introduced in the kVp waveform relative to the view period.In this paper we develop a method for estimation of the resulting spectrum for low and high kVp views. The method utilizes static kVp acquisitions of air with a small bowtie filter as a basis set. A fast kVp acquisition of air with a small bowtie filter is performed and the effective kVp is estimated as a linear combination of the basis vectors. The effectiveness of this method is demonstrated through the reconstruction of a water phantom acquired with a fast kVp acquisition. The impact of jitter due to the generator and detector DAS clocks is explored via simulation. The error is measured relative to spectrum variation and material decomposition accuracy.

In a conventional X-ray CT system, where an object is scanned with a selected incident x-ray spectrum, or kVp, the reconstructed images only approximate the linear X-ray attenuation coefficients of the imaged object at an effective energy of the incident X-ray beam. The errors are primarily the result of beam hardening due to the polychromatic nature of the X-ray spectrum. Modem clinical CT scanners can reduce this error by a process commonly referred to as spectral calibration. Spectral calibration linearizes the measured projection value to the thickness of water. However, beam hardening from bone and contrast agents can still induce shading and streaking artifacts and cause CT number inaccuracies in the image.In this paper, we present a dual kVp scanning method, where during the scan, the kVp is alternately switching between target low and high preset values, typically 80kVp and 140 kVp, with a period less than 1ms. The measured projection pairs are decomposed into the density integrals of two basis materials in projection space. The reconstructed density images are further processed to obtain monochromatic attenuation coefficients of the object at any desired energy. Energy levels yielding optimized monochromatic images are explored, and their analytical representations are derived.

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

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