Abstract
Spectral CT utilizes spectral information of X-ray sources to reconstruct energy-resolved X-ray images and has wide medical applications. Compared with conventional energy-integrated CT scanners, however, spectral CT faces serious technical difficulties in hardware, and hence its clinical use has been expensive and limited. The goal of this paper is to present a software solution and an implementation of a framelet-based spectral reconstruction algorithm for multi-slice spiral scanning based on a conventional energy-integrated CT hardware platform. In the present work, we implement the framelet-based spectral reconstruction algorithm using compute unified device architecture (CUDA) with bowtie filtration. The platform CUDA enables fast execution of the program, while the bowtie filter reduces radiation exposure. We also adopt an order-subset technique to accelerate the convergence. The multi-slice spiral scanning geometry with these additional features will make the framelet-based spectral reconstruction algorithm more powerful for clinical applications. The method provides spectral information from just one scan with a standard energy-integrating detector and produces color CT images, spectral curves of the attenuation coefficient at every point inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. The proposed algorithm is tested with a Catphan phantom and real patient data sets for its performance. In experiments with the Catphan 504 phantom, the synthesized color image reveals changes in the level of colors and details and the yellow color in Teflon indicates a special spectral property which is invisible in regular CT reconstruction. In experiments with clinical images, the synthesized color images provide some extra details which are helpful for clinical diagnosis, for example, details about the renal pelvis and lumbar join. The numerical studies indicate that the proposed method provides spectral image information which can reveal fine structures in clinical images and that the algorithm is efficient regarding to the computational time. Thus, the proposed algorithm has a great potential in practical application.
Highlights
In conventional computed tomography (CT) reconstruction algorithms, X-rays are assumed to be monochromatic, the attenuation coefficients of objects are independent of the X-ray energy and determined by the material only
We extend the framelet-based spectral reconstruction algorithm from fan-beam geometry to multi-slice spiral scanning and implement the algorithm using compute unified device architecture (CUDA) with bowtie filtration
The proposed algorithm is evaluated with a Catphan phantom and real patient data sets
Summary
In conventional computed tomography (CT) reconstruction algorithms, X-rays are assumed to be monochromatic, the attenuation coefficients of objects are independent of the X-ray energy and determined by the material only. On the other hand, using energy information in polychromatic X-rays could provide properties of the material being scanned, such as the density and atomic number. Spectral CT, which uses multiple energies of X-rays, has attracted much attention from both clinical physicists and researchers since last decade when the technology became possible. The basis of spectral CT is that a CT scan can be decomposed into a set of multiple basis materials if the projection data is measured at different energies [4]. This leads to the development of dual-energy CT and multi-spectral CT [5]. Dual-energy refers to the use of two X-ray energies in scanning. Dual-energy CT is the most common form of spectral CT used in clinical applications due to the limit of technology [6]
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