Theoretical framework for a dynamic cone-beam reconstruction algorithm based on a dynamic particle model

Abstract

Dynamic cone-beam reconstruction algorithms are required to reconstruct three-dimensional (3D) image sequences on dynamic 3D CT combining multi-row two-dimensional (2D) detectors and sub-second scanners. The speed-up of the rotating gantry allows one to improve the temporal resolution of the image sequence, but at the same time, it implies increase in the dose delivered during a given time period to keep constant the signal-to-noise ratio associated with each frame. The alternative solution proposed in this paper is to process data acquisition on several half-turns in order to reduce the dose delivered per rotation with the same signal-to-noise ratio. In order to compensate for time evolution and motion artefacts, we propose to use a dynamic particle model to describe the object evolution during the scan. In this article, we first introduce the dynamic particle model and the dynamic CT acquisition model. Then, we explain the principle of the proposed dynamic cone-beam reconstruction algorithm. Lastly, we present preliminary results on simulated data.

Compensation of Some Time Dependent Deformations in Tomography

“…More work should be dedicated to the very dicult problem of the deformation estimation, see [1,3,13] for methods and references on this subject. Cardiac or respiration dynamic models could probably help to reach this aim [14].…”

“…These last years, several studies have been dedicated to this subject, mainly for cardiac imaging but also for patient motion compensation [2,3]. Indeed, reconstructions of moving organs suer from artifacts.…”

Dynamic cone beam CT angiography of carotid and cerebral arteries using canine model

“…Grangeat et al 23 developed a dynamic particle model and presented a framework to reduce motion artifact in the reconstruction of a dynamic multislice CT scan. Similarly, Montes et al 24 adopted an interpolation method on projection data to reduce the motion artifact in dynamic reconstruction in perfusion CT. Nett et al 25 developed a compressed sensing-based iterative algorithm to accurately reconstruct the enhancement of tissues using the nonenhanced anatomy as prior knowledge.…”

Respiratory Signal Extraction for 4D CT Imaging of the Thorax from Cone-Beam CT Projections

“…the motion of points in the 2D projective space over the time of acquisition. We used a Block Matching Algorithm (BMA) [8].…”

“…Grangeat et al [8] also proposed a dynamic reconstruction algorithm with CB projections obtained from a CBCT system with a fast rotative gantry, without such synchronization signal. However, the gantry rotation of modern CBCT systems coupled with a linear accelerator is too slow to apply Grangeat's dynamic reconstruction.…”