# Metal Artifact Reduction in X-ray CT via Ray Profile Correction
## 1 Introduction
More recently proposed iterative algorithms attempt to reduce beam hardening effects without the need of any prior knowledge by decomposing the image to be reconstructed into low and high density components [16,17]. Nevertheless, iterative reconstruction techniques have the downside that they are computationally demanding and so using them in clinical practice remains a challenge. On the other hand, the sinogram correction methods aim to directly correct the _metal shadow_ regions in the projection data in which the corresponding rays have interacted with metal objects. One early approach replaces the corrupted data with their neighbors using linear [18] or high-order interpolation schemes [19, 20, 21]. However, the interpolation-based MARs often suffer from loss of detail around the metal objects, and they also have high propensity to introduce new streak artifacts [22]. To address the lack of structural information, [PERSON] et al. [23] and [PERSON] et al. [24] attempted to build prior CT images by roughly segmenting the uncorrected or pre-corrected CT image into soft-tissue, air, and bone equivalent materials. This has been a promising idea and further efforts have emerged that seek to produce a better prior image with the help of advanced computer vision techniques [25,26]. In this paper, we present a new MAR method that also uses the general approach of correcting a contaminated sinogram by substituting corrupted data by cleaner data available in prior images. The synthesis process we propose is not unlike the one often used in image-guided surgery (IGS) [27]. These methods perform a real-time correlation of the operative field with a preoperative imaging dataset to show the precise location of a selected surgical instrument in the surrounding anatomic structures. To realize this, before the surgery, the patient undergoes a series of CT scans that reveal the soft tissues and bony structures. In our scenario, these preoperative CT images serve as prior images to help remove the metal artifacts that appear in intra- or post-surgery CT scans due to the implanted metal objects. Since such prior images have been acquired from the same patient, they will likely contain very similar internal structures, especially around metal implants. Furthermore, as these regions are often at least partially surrounded by bone, it is unlikely that they are markedly deformed during the surgery. Thus, to find surrogate values to replace unreliable data in the sinogram, we first search ray paths in the prior images that have very similar density profiles along the ray passing through the metal objects. Then, the best matched prior ray profiles are used to correct the ray paths profiles that are corrupted by metal artifacts. Finally, the unreliable data are replaced with the re-projections of corrected ray profiles. We explored this general idea in [28] but this preliminary work was limited to 2D fan-beam CT geometry. In this paper, we generalize our method to 3D cone-beam CT geometry and also present a significantly refined and mature framework. In the following, Section 2 describes the proposed method and its technical details. Then, in Section 3, we show metal artifact reduction results. Section 4 concludes the paper with a discussion on future research directions for the proposed method.