# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 1 Introduction  
The presence of high attenuation objects in the scanning field leads to artifacts in computed tomography (CT) imaging, which substantially decrease the image quality. The generic term for these kinds of artifacts is metal artifacts, which are a combination of beam hardening, scattering, photon starvation, and edge effects [1]. Metal artifacts are common in CT-guided interventions due to the presence of metallic instruments such as biopsy needles [2; 3; 4] or catheters [5]. In many interventions, iodine contrast agent is used, leading to additional beam hardening [6]. These artifacts often obscure clinically relevant structures, which can complicate the intervention. For example, the visibility of liver lesions is significantly reduced during liver biopsy [2] or during transarterial chemoembolization (TACE) [7; 8], where catheters are used in combination with contrast agents. Several CT reconstruction methods have been developed to improve image quality in the presence of metal objects. Statistical iterative reconstruction techniques can be used to correct beam hardening and thus mitigate metal artifacts [9]. Furthermore, dual-energy CT allows one to reconstruct virtual monoenergetic images at high kiloelectron volt levels, which substantially reduces metal artifacts [5; 10]. The most common type of metal artifact reduction (MAR) method is based on inpainting projection data that has beenaffected by metal. In these approaches, the metal objects are first automatically detected (e.g., via thresholding) in the uncorrected CT image. The metal objects are then forward-projected into the sinogram domain to obtain a metal trace. The projection data in this metal trace are treated as missing data and are interpolated, e.g., via linear interpolation (LIMAR) [11]. [PERSON] et al. proposed a modification of the LIMAR approach called normalized MAR (NMAR) [12]. NMAR uses a forward projection of an image prior to flatten the uncorrected sinogram before interpolation. This additional step smoothes the sinogram, which reduces the streak artifacts caused by interpolation. In NMAR, the image prior is obtained by identifying air, soft tissue, and bone, in either the uncorrected CT or pre-corrected LIMAR image. With the rapidly increasing popularity of deep learning in medical imaging in recent years [13], a plethora of novel MAR methods have emerged. Deep learning networks are mostly trained in a supervised manner and thus require a metal-free and a corresponding metal-affected dataset. These metal-affected data are commonly synthesized by inserting metallic objects into the metal-free data. [PERSON] et al. presented a convolutional neural network (CNN) called CNN-MAR, which outputs an improved image prior [14]. This image prior is forward-projected, and the resulting sinogram data are used to fill in the metal trace in the original sinogram. Several CNN approaches that operate in the sinogram domain have been introduced [15; 16; 17]. [PERSON] et al. developed a sophisticated sinogram inpainting approach that works in the presence of motion. A segmentation network identifies the metal trace in the projection domain; a second network fills in the missing sinogram data; and, after reconstruction, a third network reinserts the metal objects in the corrected image [18]. A popular class of deep learning MAR techniques are image-based CNNs. They take the uncorrected images as input and either learn a direct mapping to the artifact-free images [14; 19; 20] or to the artifact residuals [21]. These image-based methods often rely on input data that has already been pre-corrected to produce reasonable results [14; 19]. Another option for MAR in the image domain is unsupervised image-to-image translation, which has the advantage that no synthesized metal artifacts are necessary and thus training can be conducted with unaltered clinical data [22; 23; 24]. Compared to supervised models, unsupervised models can achieve similar performance on synthetic data [22]. [PERSON] et al.