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Title: SU-C-206-03: Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity

Abstract

Purpose: Metal implants such as orthopedic hardware and dental fillings cause severe bright and dark streaking in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. Additionally, such artifacts negatively impact patient set-up in image guided radiation therapy (IGRT). In this work, we propose a novel method for metal artifact reduction which utilizes the anatomical similarity between neighboring CT slices. Methods: Neighboring CT slices show similar anatomy. Based on this anatomical similarity, the proposed method replaces corrupted CT pixels with pixels from adjacent, artifact-free slices. A gamma map, which is the weighted summation of relative HU error and distance error, is calculated for each pixel in the artifact-corrupted CT image. The minimum value in each pixel’s gamma map is used to identify a pixel from the adjacent CT slice to replace the corresponding artifact-corrupted pixel. This replacement only occurs if the minimum value in a particular pixel’s gamma map is larger than a threshold. The proposed method was evaluated with clinical images. Results: Highly attenuating dental fillings and hip implants cause severe streaking artifacts on CT images. The proposed method eliminates the dark and bright streaking and improvesmore » the implant delineation and visibility. In particular, the image non-uniformity in the central region of interest was reduced from 1.88 and 1.01 to 0.28 and 0.35, respectively. Further, the mean CT HU error was reduced from 328 HU and 460 HU to 60 HU and 36 HU, respectively. Conclusions: The proposed metal artifact reduction method replaces corrupted image pixels with pixels from neighboring slices that are free of metal artifacts. This method proved capable of suppressing streaking artifacts, improving HU accuracy and image detectability.« less

Authors:
; ; ; ;  [1]
  1. Emory University, Winship Cancer Institute, Atlanta, GA (United States)
Publication Date:
OSTI Identifier:
22624344
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ACCURACY; ANATOMY; BIOMEDICAL RADIOGRAPHY; COMPUTERIZED TOMOGRAPHY; ERRORS; IMAGE PROCESSING; IMAGES; IMPLANTS; PATIENTS; RADIATION DOSES; RADIOTHERAPY

Citation Formats

Dong, X, Yang, X, Rosenfield, J, Elder, E, and Dhabaan, A. SU-C-206-03: Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity. United States: N. p., 2016. Web. doi:10.1118/1.4955585.
Dong, X, Yang, X, Rosenfield, J, Elder, E, & Dhabaan, A. SU-C-206-03: Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity. United States. doi:10.1118/1.4955585.
Dong, X, Yang, X, Rosenfield, J, Elder, E, and Dhabaan, A. 2016. "SU-C-206-03: Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity". United States. doi:10.1118/1.4955585.
@article{osti_22624344,
title = {SU-C-206-03: Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity},
author = {Dong, X and Yang, X and Rosenfield, J and Elder, E and Dhabaan, A},
abstractNote = {Purpose: Metal implants such as orthopedic hardware and dental fillings cause severe bright and dark streaking in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. Additionally, such artifacts negatively impact patient set-up in image guided radiation therapy (IGRT). In this work, we propose a novel method for metal artifact reduction which utilizes the anatomical similarity between neighboring CT slices. Methods: Neighboring CT slices show similar anatomy. Based on this anatomical similarity, the proposed method replaces corrupted CT pixels with pixels from adjacent, artifact-free slices. A gamma map, which is the weighted summation of relative HU error and distance error, is calculated for each pixel in the artifact-corrupted CT image. The minimum value in each pixel’s gamma map is used to identify a pixel from the adjacent CT slice to replace the corresponding artifact-corrupted pixel. This replacement only occurs if the minimum value in a particular pixel’s gamma map is larger than a threshold. The proposed method was evaluated with clinical images. Results: Highly attenuating dental fillings and hip implants cause severe streaking artifacts on CT images. The proposed method eliminates the dark and bright streaking and improves the implant delineation and visibility. In particular, the image non-uniformity in the central region of interest was reduced from 1.88 and 1.01 to 0.28 and 0.35, respectively. Further, the mean CT HU error was reduced from 328 HU and 460 HU to 60 HU and 36 HU, respectively. Conclusions: The proposed metal artifact reduction method replaces corrupted image pixels with pixels from neighboring slices that are free of metal artifacts. This method proved capable of suppressing streaking artifacts, improving HU accuracy and image detectability.},
doi = {10.1118/1.4955585},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: In this article, an approach to metal artifact reduction is proposed that is practical for clinical use in radiation therapy. It is based on a new interpolation scheme of the projections associated with metal implants in helical computed tomography (CT) scanners. Methods and Materials: A three-step approach was developed consisting of an automatic algorithm for metal implant detection, a correction algorithm for helical projections, and a new, efficient algorithm for projection interpolation. The modified raw projection data are transferred back to the CT scanner device where CT slices are regenerated using the built-in reconstruction operator. The algorithm was testedmore » on a CT calibration phantom in which the density of inserted objects are known and on clinical prostate cases with two hip prostheses. The results are evaluated using the CT number and shape of the objects. Results: The validations on a CT calibration phantom with various inserts of known densities show that the algorithm improved the overall image quality by restoring the shape and the representative CT number of the objects in the image. For the clinical hip replacement cases, a large fraction of the bladder, rectum, and prostate that were not visible on the original CT slices were recovered using the algorithm. Precise contouring of the target volume was thus feasible. Without this enhancement, physicians would have drawn bigger margins to be sure to include the target and, at the same time, could have prescribed a lower dose to keep the same level of normal tissue toxicity. Conclusions: In both phantom experiment and patient studies, the algorithm resulted in significant artifact reduction with increases in the reliability of planning procedure for the case of metallic hip prostheses. This algorithm is now clinically used as a preprocessing before treatment planning for metal artifact reduction.« less
  • Purpose: The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. Methods: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of ''metal shadows'' in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection datamore » are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. Results: The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. Conclusions: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general ''missing data'' image reconstruction problems.« less
  • Purpose: In computed tomography imaging metal objects in the region of interest introduce inconsistencies during data acquisition. Reconstructing these data leads to an image in spatial domain including star-shaped or stripe-like artifacts. In order to enhance the quality of the resulting image the influence of the metal objects can be reduced. Here, a metal artifact reduction (MAR) approach is proposed that is based on a recomputation of the inconsistent projection data using a fully three-dimensional Fourier-based interpolation. The success of the projection space restoration depends sensitively on a sensible continuation of neighboring structures into the recomputed area. Fortunately, structural informationmore » of the entire data is inherently included in the Fourier space of the data. This can be used for a reasonable recomputation of the inconsistent projection data. Methods: The key step of the proposed MAR strategy is the recomputation of the inconsistent projection data based on an interpolation using nonequispaced fast Fourier transforms (NFFT). The NFFT interpolation can be applied in arbitrary dimension. The approach overcomes the problem of adequate neighborhood definitions on irregular grids, since this is inherently given through the usage of higher dimensional Fourier transforms. Here, applications up to the third interpolation dimension are presented and validated. Furthermore, prior knowledge may be included by an appropriate damping of the transform during the interpolation step. This MAR method is applicable on each angular view of a detector row, on two-dimensional projection data as well as on three-dimensional projection data, e.g., a set of sequential acquisitions at different spatial positions, projection data of a spiral acquisition, or cone-beam projection data. Results: Results of the novel MAR scheme based on one-, two-, and three-dimensional NFFT interpolations are presented. All results are compared in projection data space and spatial domain with the well-known one-dimensional linear interpolation strategy. Conclusions: In conclusion, it is recommended to include as much spatial information into the recomputation step as possible. This is realized by increasing the dimension of the NFFT. The resulting image quality can be enhanced considerably.« less
  • Purpose: A new commercially available metal artifact reduction (MAR) software in computed tomography (CT) imaging was evaluated with phantoms in the presence of metals. The goal was to assess the ability of the software to restore the CT number in the vicinity of the metals without impacting the image quality. Methods: A Catphan 504 was scanned with a GE Optima RT 580 CT scanner (GE Healthcare, Milwaukee, WI) and the images were reconstructed with and without the MAR software. Both datasets were analyzed with Image Owl QA software (Image Owl Inc, Greenwich, NY). CT number sensitometry, MTF, low contrast, uniformity,more » noise and spatial accuracy were compared for scans with and without MAR software. In addition, an in-house made phantom was scanned with and without a stainless steel insert at three different locations. The accuracy of the CT number and metal insert dimension were investigated as well. Results: Comparisons between scans with and without MAR algorithm on the Catphan phantom demonstrate similar results for image quality. However, noise was slightly higher for the MAR algorithm. Evaluation of the CT number at various locations of the in-house made phantom was also performed. The baseline HU, obtained from the scan without metal insert, was compared to scans with the stainless steel insert at 3 different locations. The HU difference between the baseline scan versus metal scan was improved when the MAR algorithm was applied. In addition, the physical diameter of the stainless steel rod was over-estimated by the MAR algorithm by 0.9 mm. Conclusion: This work indicates with the presence of metal in CT scans, the MAR algorithm is capable of providing a more accurate CT number without compromising the overall image quality. Future work will include the dosimetric impact on the MAR algorithm.« less
  • Purpose: High-Z (metal) implants in CT scans cause significant streak-like artifacts in the reconstructed dataset. This results in both inaccurate CT Hounsfield units for the tissue as well as obscuration of the target and organs at risk (OARs) for radiation therapy planning. Herein we analyze two metal artifact reduction algorithms: GE’s Smart MAR and a Metal Deletion Technique (MDT) for geometric and Hounsfield Unit (HU) accuracy. Methods: A CT-to-electron density phantom, with multiple inserts of various densities and a custom Cerrobend insert (Zeff=76.8), is utilized in this continuing study. The phantom is scanned without metal (baseline) and again with themore » metal insert. Using one set of projection data, reconstructed CT volumes are created with filtered-back-projection (FBP) and the MAR and the MDT algorithms. Regions-of-Interest (ROIs) are evaluated for each insert for HU accuracy; the metal insert’s Full-Width-Half-Maximum (FWHM) is used to evaluate the geometric accuracy. Streak severity is quantified with an HU error metric over the phantom volume. Results: The original FBP reconstruction has a Root-Mean-Square-Error (RMSE) of 57.55 HU (STD=29.19, range=−145.8 to +79.2) compared to baseline. The MAR reconstruction has a RMSE of 20.98 HU (STD=13.92, range=−18.3 to +61.7). The MDT reconstruction has a RMSE of 10.05 HU (STD=10.5, range=−14.8 to +18.6). FWHM for baseline=162.05; FBP=161.84 (−0.13%); MAR=162.36 (+0.19%); MDT=162.99 (+0.58%). Streak severity metric for FBP=19.73 (22.659% bad pixels); MAR=8.743 (9.538% bad); MDT=4.899 (5.303% bad). Conclusion: Image quality, in terms of HU accuracy, in the presence of high-Z metal objects in CT scans is improved by metal artifact reduction reconstruction algorithms. The MDT algorithm had the highest HU value accuracy (RMSE=10.05 HU) and best streak severity metric, but scored the worst in terms of geometric accuracy. Qualitatively, the MAR and MDT algorithms increased detectability of inserts, although there is a loss of in-plane resolution near the metallic insert.« less