Image reconstruction in reduced circular sinusoidal cone-beam CT

Abstract

Cone-beam computed tomography (CBCT) plays an important role in industrial, nondestructive testing applications not to mention in medical applications. Circular scanning configuration is widely used for its mechanical simplicity and for readily available and efficient reconstruction algorithms based on the Feldkamp algorithm. However, due to the lack of data sufficiency, circular CBCT does not guarantee image accuracy, and is not free from image artifacts related to the cone-angle and axial variation of the object anatomy. Therefore, non-circular scanning geometries such as helix or circular sinusoid, which provide sufficient data for exact image reconstruction within the scanned volume, are favorable when accurate images free of cone-angle artifacts are desired. Analytic algorithms have been developed for image reconstruction from cone-beam data acquired with a full-scan (or, equivalently, a closed) circular sinusoidal trajectory. In many applications such as industrial inspection of a product where a high throughput is important, it is desirable to have a short scan time. In this work, we propose an innovative imaging approach in which a reduced-scan (or, equivalently, an open) circular sinusoidal trajectory is used for acquiring data sufficient for exact 3D image reconstruction. A filtered backprojection (FBP) algorithm based on Pack-Noo's reconstruction formula is applied for image reconstruction in reduced-scan circular sinusoidal scans. We have conducted numerical studies to demonstrate the reduced-scan approach and to validate the FBP reconstruction algorithm in the proposed approach. The proposed scanning method can contribute to increasing the throughput of a scanner, while improving the image quality compared to a conventional circular scan.

A line fiducial method for geometric calibration of cone-beam CT systems with diverse scan trajectories

“…some of which are also investigated specifically in this paper. The sinusoid-on-sphere orbit in figure 1(a) is akin to the reduced circular sinusoid of Xia et al (2009), except that sinusoidal out-of-plane motion is implemented via gantry rotations rather than translations. Recent work has also presented methods for optimizing orbit geometries to the anatomy of a specific patient and visualization task (Stayman et al 2015, Ouadah et al 2017, leading to improved detectability of implants and interventional instruments for a given radiation dose.…”

Emerging technologies for image guidance and device navigation in interventional radiology

“…Methods for performing noncircular scan geometries have been proposed and in the future a combination of trajectories with nonisocentric orbits may be available for organ-specific CBCT imaging. 85,86 Incorporation of novel high-sensitivity microangiographic detectors, or alternatively solid-state image intensifiers, might further increase resolution, lower noise, and improve dynamic imaging of FPD x-ray units. [87][88][89] Image fusion software applications that superimpose cross-sectional CBCT datasets or three-dimensional rotational angiography onto real-time fluoroscopy are already commercially available from several vendors.…”

A BPF‐FBP tandem algorithm for image reconstruction in reverse helical cone‐beam CT

“…The proposed algorithm allows accurate image reconstruction of a long object from reverse helical cone-beam data, and it may be applicable to other scanning configurations such as reduced circular sinusoidal trajectories. 18 There may be several challenges in the proposed approach for practical applications in IGRT. Imaging radiation dose, although it should be discussed in conjunction with the image quality, can be higher than that from conventional circular scans.…”

“…We have applied this approach to image reconstruction in CBCT with general, open source trajectories. 7,18 Because the Pack-Nooformula-based algorithm is not dependent on the existence of chords, it can reconstruct an image within the chordless region where chord-based algorithms cannot. However, this algorithm can accurately reconstruct images only from nontruncated data of a short object that is completely enclosed by the polygons for a reverse helical trajectory.…”