Metal artifact reduction in CT by identifying missing data hidden in metals

Abstract

There is increasing demand in the field of dental and medical radiography for effective metal artifact reduction (MAR) in computed tomography (CT) because artifact caused by metallic objects causes serious image degradation that obscures information regarding the teeth and/or other biological structures. This paper presents a new MAR method that uses the Laplacian operator to reveal background projection data hidden in regions containing data from metal. In the proposed method, we attempted to decompose the projection data into two parts: data from metal only (metal data), and background data in the absence of metal. Removing metal data from the projections enables us to perform sparsity-driven reconstruction of the metal component and subsequent removal of the metal artifact. The results of clinical experiments demonstrated that the proposed MAR algorithm improves image quality and increases the standard of 3D reconstruction images of the teeth and mandible.

Characterization of Metal Artifacts in X‐Ray Computed Tomography

“…Using a similar argument as in the proof of Theorem 3.3, we present characterizations of other effects that cause streaking artifacts, such as scattered radiations and noises (see Section 4). Finally, we provide numerical simulation results and clinical CT images to support these observations (see Various works have studied the wavefront set for Radon transforms [11, 18, 20, 24, 36, 37, 54-58, 60, 61] and metal artifacts [1,2,9,10,14,34,40,42,50,67,72], but surprisingly this paper reports as far as we know the first mathematical analysis to characterize the structure of metal streaking artifacts.…”

A Two-Stage Approach for Beam Hardening Artifact Reduction in Low-Dose Dental CBCT

“…Dual-energy CT [1], [30], [56] requires a higher dose of radiation compared with singleenergy CT [10]; therefore, this approach is not suitable for low-dose dental CBCT. In raw data correction methods, unreliable background data due to the presence of metallic objects can be recovered using various inpainting techniques such as interpolation [2], [6], [26], [31], [45], normalized interpolation (NMAR) [35], Poisson inpainting [39], wavelet [36], [57], [58], tissue-class model [4], total variation [13], and Euler's elastica [17]. These methods might introduce new artifacts that did not previously exist.…”

Tomographic Inverse Problem with Estimating Missing Projections

“…Here, * ∈ + was obtained by the following procedure. Let inaccurate projections be the outer neighborhood of the metal affected projections that are identified by thresholding [7,28,30]. The inaccurate measured projections, , in Eq.…”