An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.

The primal-dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1-26) is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in this paper, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity x-ray illumination is presented.

Pluripotent mesenchymal stem cells (MSCs) are bone marrow stromal progenitor cells that can differentiate into osteogenic, chondrogenic, adipogenic, and myogenic lineages. Several signaling pathways have been shown to regulate the lineage commitment and terminal differentiation of MSCs. Here, we conducted a comprehensive analysis of the 14 types of bone morphogenetic protein (BMPs) for their abilities to regulate multilineage specific differentiation of MSCs. We found that most BMPs exhibited distinct abilities to regulate the expression of Runx2, Sox9, MyoD, and PPARγ2. Further analysis indicated that BMP-2, BMP-4, BMP-6, BMP-7, and BMP-9 effectively induced both adipogenic and osteogenic differentiation in vitro and in vivo. BMP-induced commitment to osteogenic or adipogenic lineage was shown to be mutually exclusive. Overexpression of Runx2 enhanced BMP-induced osteogenic differentiation, whereas knockdown of Runx2 expression diminished BMP-induced bone formation with a decrease in adipocyte accumulation in vivo. Interestingly, overexpression of PPARγ2 not only promoted adipogenic differentiation, but also enhanced osteogenic differentiation upon BMP-2, BMP-6, and BMP-9 stimulation. Conversely, MSCs with PPARγ2 knockdown or mouse embryonic fibroblasts derived from PPARγ2^−/− mice exhibited a marked decrease in adipogenic differentiation, coupled with reduced osteogenic differentiation and diminished mineralization upon BMP-9 stimulation, suggesting that PPARγ2 may play a role in BMP-induced osteogenic and adipogenic differentiation. Thus, it is important to understand the molecular mechanism behind BMP-regulated lineage divergence during MSC differentiation, as this knowledge could help us to understand the pathogenesis of skeletal diseases and may lead to the development of strategies for regenerative medicine.

We develop a primal-dual algorithm that allows for one-step inversion of spectral CT transmission photon counts data to a basis map decomposition. The algorithm allows for image constraints to be enforced on the basis maps during the inversion. The derivation of the algorithm makes use of a local upper bounding quadratic approximation to generate descent steps for non-convex spectral CT data discrepancy terms, combined with a new convex-concave optimization algorithm. Convergence of the algorithm is demonstrated on simulated spectral CT data. Simulations with noise and anthropomorphic phantoms show examples of how to employ the constrained one-step algorithm for spectral CT data.

We present a method for obtaining accurate image reconstruction from highly sparse data in diffraction tomography (DT). A practical need exists for reconstruction from few-view and limited-angle data, as this can greatly reduce required scan times in DT. Our method does this by minimizing the total variation (TV) of the estimated image, subject to the constraint that the Fourier transform of the estimated image matches the measured Fourier data samples. Using simulation studies, we show that the TV-minimization algorithm allows accurate reconstruction in a variety of few-view and limited-angle situations in DT. Accurate image reconstruction is obtained from far fewer data samples than are required by common algorithms such as the filtered-backpropagation algorithm. Overall our results indicate that the TV-minimization algorithm can be successfully applied to DT image reconstruction under a variety of scan configurations and data conditions of practical significance.