Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique

Abstract

An issue in computerized X-ray tomography is the limited size of available detectors relative to objects of interest. A solution was provided in the past two decades by positioning the detector in a lateral offset position, increasing the effective field of view (FOV) and thus the diameter of the reconstructed volume. However, this introduced artifacts in the obtained reconstructions, caused by projection truncation and data redundancy. These issues can be addressed by incorporating an additional data weighting step in the reconstruction algorithms, known as redundancy weighting. In this work, we present an implementation of redundancy weighting in the widely-used Simultaneous Iterative Reconstruction Technique (SIRT), yielding the W-SIRT method. The new technique is validated using geometric phantoms and a rabbit specimen, by performing both simulation studies as well as physical experiments. The experiments are carried out in a highly flexible stereoscopic X-ray system equipped with X-ray image intensifiers (XRIIs). The simulations showed that higher values of CNR could be obtained using the W-SIRT approach as compared to a weighted implementation of SART. The convergence rate of the W-SIRT was accelerated by including a relaxation parameter in the W-SIRT algorithm, creating the aW-SIRT algorithm. This allowed to obtain the same results as the W-SIRT algorithm, but at half the number of iterations, yielding a much shorter computation time. The aW-SIRT algorithm has proven to perform well for both large as well as small regions of overlap, outperforming the pre-convolutional Feldkamp-David-Kress (FDK) algorithm for small overlap regions (or large detector offsets). The experiments confirmed the results of the simulations. Using the aW-SIRT algorithm, the effective FOV was increased by >75%, only limited by experimental constraints. Although an XRII is used in this work, the method readily applies to flat-panel detectors as well.

Evaluation of the feasibility of a multisource CBCT for maxillofacial imaging

“…To extend the FOV, the detector was shifted laterally by 70 mm to provide an effective FOV of 187 mm (width) ´100 mm (height), as illustrated in figure 2(b). The detector offset CBCT geometry has been investigated in the past (Cho et al 1996, Wang 2002, Li et al 2007, Sanctorum et al 2021 and implemented in several commercial dental CBCT systems (Scarfe and Farman 2008) and a prototype breast CBCT (Tseng et al 2021). The geometry of the ms-CBCT scanner was calibrated by measuring a phantom with two stainless steel beads embedded in an acrylic slab, following the method described by Yang et al (2006).…”

Sports Medical Image Modeling of Injury Prevention in Dance Learning and Sports Training

“…The setting of the thinnest layer thickness is not random. It is closely related to several main performance parameters, namely, cone-shaped X-ray harness reconstruction mathematics, scanning speed, X-ray dose, computer performance, and voxel isotropy, which is highly valued in clinical applications [10][11][12].…”

A carbon nanotube x-ray source array designed for a new multisource cone beam computed tomography scanner

“…The expanded FOV covers the entire maxillofacial region of a patient without data truncation when scanning with a full gantry rotation. In this detector offset geometry which is also used in other imaging systems (Santaella et al 2020, Sanctorum et al 2021, each x-ray exposure covers slightly over half of the FOV. For volumetric CT imaging, the N source number of collimated sources were electronically scanned across the object N view times, as the gantry rotated around the object around the object by 360 degrees.…”