Cone-beam t r a c tMost small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry. IntroductionMany small animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory [1][2][3][4]. This configuration presents advantages over other alternatives used in clinical and preclinical applications: reduction of acquisition time, large axial field of view (FOV) without geometrical distortions, and optimization of radiated dose [5]. proposed by Feldkamp et al. (FDK) [6] are still widely used for solving the 3D reconstruction task because of their straightforward implementation and computational efficiency [4]. Almost every aspect of the reconstruction process has been studied: there is literature on algorithm variations for different trajectories [7,8], optimizations using graphic processing units (GPUs) [9][10][11][12][13][14][15], strategies to reduce cone beam artifacts [16,17], study of consistency conditions [18], optimization of the back-projection step [19], etc. However, in a real practical system, the implementation of a reconstruction algorithm core such as FDK is just an initial step of the process, and there 1

Abstract-This work compares two different X-ray flat-panel detectors for its use in high-speed, cone-beam CT applied to small-animal imaging. The main differences between these two devices are the scintillators and the achievable frame rate. Both devices have been tested in terms of system linearity, sensitivity, resolution, stability and noise properties, taking into account the different timing schemes for each one of them and the mandatory corrections on the raw data. Tomographic scans have been carried out using both detectors to evaluate its final performance as well as the delivered dose needed to achieve similar quality scans. An experimental cone-beam CT test-bench has been designed and implemented to perform the different measurements. It uses a micro-focus X-ray source and a rotating stage where the samples are placed. A modified FDK algorithm has been used to reconstruct the acquired data. Both detectors show similar results for pixel linearity and stability measurements, and their noise levels are comparable. The resolution and sensitivity features are better for the direct grown scintillator detector (9 Ipmm vs. 6 Ipmm, and -4 times more sensitive for similar delivered dose). Since tomographic reconstructed images for the higher frame-rate detector show acceptable quality, it can be used to implement a faster system for high-speed acquisition techniques like, for example, dynamic imaging or gated protocols.

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