Linear scan cone-beam computed tomography (LSCBCT) is a technically simple computed tomography (CT) configuration and is powerful enough to inspect long objects. As one kind of limited-angle problem, the image reconstruction of LSCBCT is ill posed. However, the total variation minimization (TVM)-projection on convex sets (POCS) reconstruction algorithm, which is based on the TVM and POCS, has been proven effective for solving the limited-angle problem. While applying the TVM-POCS algorithm to the LSCBCT reconstruction, the reconstructed image is distorted near the edges of the object. To solve this problem, an improved iterative reconstruction algorithm was developed. The improved method integrated simultaneous algebraic reconstruction technique, TVM, and C-V model. The C-V model can detect objects whose boundaries are not necessarily defined by gradient. The developed algorithm can reduce artifacts by piecewise constant modification and get a more accurate image. Numerical simulations are presented to illustrate the effectiveness of the algorithm. Moreover, the developed algorithm can be applied to other x-ray CT reconstruction problems.
Despite impressive advancements in computed tomography (CT) technology in recent years, there are still critical and
immediate needs in cardiac CT in terms of high spatial resolution, high temporal resolution and low radiation dose.
Because carbon nanotube (CNT) x-ray sources can be compactly integrated, this technology can be used for multisource or stationary system to improve temporal resolution. To avoid the rotation of x-ray source, a lot of source-detector pairs are needed in a stationary CNT-based x-ray system. Limited by the space and costs, the number of source-detector pairs could not be too large, which results in a few-view scan problem. The reconstruction can be modeled as a l1-norm minimization problem, which usually can be solved by compressive sensing (CS) based algorithms. To evaluate the data completeness of candidate next generation cardiac CT architectures with CNT x-ray source, based on the fact that smaller restricted isometry property (RIP) constants lead to a better l1-norm recovery, we construct a measurement related to the RIP constants. The results show that the proposed RIP-based evaluation method coincides with the known CT reconstruction theory. This method is simple and easy to be implemented for different CT scan architectures, and it provides a practical tool to evaluate the data completeness in the framework of l1-norm recovery theory without a specific CS-based reconstruction algorithm.
Current cardiac computed tomography (CT) is not fast enough for high or irregular heart rates, and the high radiation
dose from cardiac CT scans remains a public concern. The primary cause of those unsatisfactory performances is the
current CT architecture, in which one or two x-ray tubes need to be mechanically spun around an object to collect
projection images, and x-ray beams need to be wide enough to cover the entire transaxial extent of an object without
truncation. Here we present a new cardiac CT architecture. The new architecture features three distributed x-ray sources
and three x-ray detectors. The three sources are stationary, with each containing an array of about 100 x-ray beams. The
three detectors are rotating, and simultaneously acquire truncated projection data for the same interior region-of-interest.
In synchrony with the rotating detectors, the three source arrays are electronically activated to simulate the spinning of
three traditional single-beam x-ray sources. By estimate, the new architecture is expected to provide ≤50ms temporal
resolution and ≤1mSv radiation dose.