Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach.

PURPOSE: Anti-scatter grids suppress the scatter substantially thus improving image contrast in radiography. However, its active use in cone-beam CT for the purpose of improving contrast-to-noise ratio (CNR) has not been successful mainly due to the increased noise related to Poisson statistics of photons. This paper proposes a sparse-view scanning approach to address the above issue. METHOD: Compared to the conventional cone-beam CT imaging framework, the proposed method reduces the number of projections and increases exposure in each projection to enhance image quality without an additional cost of radiation dose to patients. For image reconstruction from sparse-view data, an adaptive-steepest-descent projection-onto-convex-sets (ASD POCS) algorithm regularized by total-variation (TV) minimization was adopted. Contrast and CNR with various scattering conditions were evaluated in projection domain by a simulation study using GATE. Then we evaluated contrast, resolution, and image uniformity in CT image domain with Catphan phantom. A head phantom with soft-tissue structures was also employed for demonstrating a realistic application. A virtual grid-based estimation and reduction of scatter has also been implemented for comparison with the real anti-scatter grid. RESULTS: In the projection domain evaluation, contrast and CNR enhancement was observed when using an anti-scatter grid compared to the virtual grid. In the CT image domain, the proposed method produced substantially higher contrast and CNR of the low-contrast structures with much improved image uniformity. CONCLUSION: We have shown that the proposed method can provide high-quality CBCT images particularly with an increased contrast of soft-tissue at a neutral dose for image-guidance.

An Image-Based Reduction of Metal Artifacts in Computed Tomography.

OBJECTIVE: Various strategies have been developed in the past to reduce the excessive effects of metal artifacts in computed tomography images. From straightforward sinogram inpainting-based methods to computationally expensive iterative methods, all have been successful in improving the image quality up to a certain degree. We propose a novel image-based metal artifact subtraction method that achieves a superior image quality and at the same time provides a quantitatively more accurate image. METHODS: Our proposed method consists of prior image-based sinogram inpainting, metal sinogram extraction, and metal artifact image subtraction. Reconstructing the metal images from the extracted metal-contaminated portions in the sinogram yields a streaky image that eventually can be subtracted from the uncorrected image. The prior image is reconstructed from the sinogram that is free from the metal-contaminated portions by use of a total variation (TV) minimization algorithm, and the reconstructed prior image is fed into the forward projector so that the missing portions in the sinogram can be recovered. Image quality of the metal artifact-reduced images on selected areas was assessed by the structure similarity index for the simulated data and SD for the real dental data. RESULTS: Simulation phantom studies showed higher structure similarity index values for the proposed metal artifact reduction (MAR) images than the standard MAR images. Thus, more artifact suppression was observed in proposed MAR images. In real dental phantom data study, lower SD values were calculated from the proposed MAR images. The findings in real human arm study were also consistent with the results in all phantom studies. Thus, compared with standard MAR images, lesser artifact intensity was exhibited by the proposed MAR images. CONCLUSIONS: From the quantitative calculations, our proposed method has shown to be effective and superior to the conventional approach in both simulation and real dental phantom cases.