The development of accurate and efficient algorithms for image reconstruction from helical cone-beam projections remains a subject of active research. In the last few years, a number of quasi-exact and exact algorithms have been developed. Among them, the Katsevich algorithms are of filtered backprojection type and thus possess computational advantages over other existing exact algorithms. In this work, we propose an alternative approach to reconstructing exactly an image from helical cone-beam projections. Based on this approach, we develop an algorithm that requires less data than do the existing quasi-exact and exact algorithms, including the Katsevich algorithms. Our proposed algorithm is also of filtered backprojection type with one-dimensional filtering performed along a PI-line in image space. Therefore, it is (at least) computationally as efficient as the Katsevich algorithms. We have performed a preliminary numerical study to demonstrate and validate the proposed algorithm using computer-simulation data. The implication of the proposed approach and algorithm appears to be significant in that they can naturally address the long object problem as well as the super-short scan problem and, most importantly, in that they provide the opportunity to reconstruct images within any selected region of interest from minimum data, allowing the use of detector with a reduced size, the selection of a minimum number of rotation angles and thus the reduction of radiation dose delivered to the imaged subject.

US NIH ; China National Scientific Foundation of China [20805038, 20620130427]; National Basic Research Program of China [2007CB935603, 2010CB732402, 2009ZX10004-312]; ACS Division of Analytical Chemistr

Portable devices with the advantages of rapid, on-site, user-friendly, and cost-effective assessment are widely applied in daily life. However, only a limited number of quantitative portable devices are commercially available, among which the personal glucose meter (PGM) is the most successful example and has been the most widely used. However, PGMs can detect only blood glucose as the unique target. Here we describe a novel design that combines a glucoamylase-trapped aptamer-cross-linked hydrogel with a PGM for portable and quantitative detection of non-glucose targets. Upon target introduction, the hydrogel collapses to release glucoamylase, which catalyzes the hydrolysis of amylose to produce a large amount of glucose for quantitative readout by the PGM. With the advantages of low cost, rapidity, portability, and ease of use, the method reported here has the potential to be used by the public for portable and quantitative detection of a wide range of non-glucose targets.

Knowledge of the x-ray spectrum in diagnostic imaging is important for dose calculations, correction for beam-hardening artifacts, and dual-energy computed tomography. One way to determine the x-ray source spectrum is to estimate it from transmission data of a known phantom. Although such an approach is experimentally simple, spectrum estimation from transmission data is known to be an ill-conditioned problem. The contribution of this work is twofold. First, we present a systematic method of designing the transmission measurements to reduce the mathematical instability of spectrum estimation. Second, we apply the expectation-maximization (EM) method, which is known to yield robust solutions to positive, linear integral equations such as the one that describes spectrum estimation from transmission data. The proposed EM method is compared to other algorithms in the literature on simulated data from x-ray spectra typical to mammography and computed tomography. The EM method appears to outperform existing algorithms for spectrum estimation.

This review aims to provide in-depth insights into CTC analysis, including various techniques for isolation of CTCs and single-cell analysis of CTCs, as well as current developmental trends and promising research directions.