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Title: SU-E-I-13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images

Abstract

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, 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,more » 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

Authors:
;  [1]
  1. BC Cancer Agency, Surrey, BC (United Kingdom)
Publication Date:
OSTI Identifier:
22493974
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 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; ACCURACY; ALGORITHMS; BIOMEDICAL RADIOGRAPHY; COMPUTER CODES; COMPUTERIZED TOMOGRAPHY; DATASETS; IMAGE PROCESSING; IMAGES; PHANTOMS

Citation Formats

Huang, V, and Kohli, K. SU-E-I-13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images. United States: N. p., 2015. Web. doi:10.1118/1.4924010.
Huang, V, & Kohli, K. SU-E-I-13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images. United States. doi:10.1118/1.4924010.
Huang, V, and Kohli, K. Mon . "SU-E-I-13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images". United States. doi:10.1118/1.4924010.
@article{osti_22493974,
title = {SU-E-I-13: Evaluation of Metal Artifact Reduction (MAR) Software On Computed Tomography (CT) Images},
author = {Huang, V and Kohli, K},
abstractNote = {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, 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.},
doi = {10.1118/1.4924010},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: CT simulation for patients with metal implants can often be challenging due to artifacts that obscure tumor/target delineation and normal organ definition. Our objective was to evaluate the effectiveness of Orthopedic Metal Artifact Reduction (OMAR), a commercially available software, in reducing metal-induced artifacts and its effect on computed dose during treatment planning. Methods: CT images of water surrounding metallic cylindrical rods made of aluminum, copper and iron were studied in terms of Hounsfield Units (HU) spread. Metal-induced artifacts were characterized in terms of HU/Volume Histogram (HVH) using the Pinnacle treatment planning system. Effects of OMAR on enhancing our abilitymore » to delineate organs on CT and subsequent dose computation were examined in nine (9) patients with hip implants and two (2) patients with breast tissue expanders. Results: Our study characterized water at 1000 HU with a standard deviation (SD) of about 20 HU. The HVHs allowed us to evaluate how the presence of metal changed the HU spread. For example, introducing a 2.54 cm diameter copper rod in water increased the SD in HU of the surrounding water from 20 to 209, representing an increase in artifacts. Subsequent use of OMAR brought the SD down to 78. Aluminum produced least artifacts whereas Iron showed largest amount of artifacts. In general, an increase in kVp and mA during CT scanning showed better effectiveness of OMAR in reducing artifacts. Our dose analysis showed that some isodose contours shifted by several mm with OMAR but infrequently and were nonsignificant in planning process. Computed volumes of various dose levels showed <2% change. Conclusions: In our experience, OMAR software greatly reduced the metal-induced CT artifacts for the majority of patients with implants, thereby improving our ability to delineate tumor and surrounding organs. OMAR had a clinically negligible effect on computed dose within tissues. Partially funded by unrestricted educational grant from Philips.« less
  • Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU valuesmore » were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation treatment planning accuracy.« less
  • Purpose: To evaluate the impact of a commercial orthopedic metal artifact reduction (O-MAR) algorithm on CT image quality and dose calculation for patients with spinal prostheses near spinal tumors. Methods: A CT electron density phantom was scanned twice: with tissue-simulating inserts only, and with a titanium insert replacing solid water. A patient plan was mapped to the phantom images in two ways: with the titanium inside or outside of the spinal tumor. Pinnacle and Eclipse were used to evaluate the dosimetric effects of O-MAR on 12-bit and 16-bit CT data, respectively. CT images from five patients with spinal prostheses weremore » reconstructed with and without O-MAR. Two observers assessed the image quality improvement from O-MAR. Both pencil beam and Monte Carlo dose calculation in iPlan were used for the patient study. The percentage differences between non-OMAR and O-MAR datasets were calculated for PTV-min, PTV-max, PTV-mean, PTV-V100, PTV-D90, OAR-V10Gy, OAR-max, and OAR-D0.1cc. Results: O-MAR improved image quality but did not significantly affect the dose distributions and DVHs for both 12-bit and 16- bit CT phantom data. All five patient cases demonstrated some degree of image quality improvement from O-MAR, ranging from small to large metal artifact reduction. For pencil beam, the largest discrepancy was observed for OARV-10Gy at 5.4%, while the other seven parameters were ≤0.6%. For Monte Carlo, the differences between non-O-MAR and O-MAR datasets were ≤3.0%. Conclusion: Both phantom and patient studies indicated that O-MAR can substantially reduce metal artifacts on CT images, allowing better visualization of the anatomical structures and metal objects. The dosimetric impact of O-MAR was insignificant regardless of the metal location, image bit-depth, and dose calculation algorithm. O-MAR corrected images are recommended for radiation treatment planning on patients with spinal prostheses because of the improved image quality and no need to modify current dose constraints. This work was supported by a research grant from Philips Healthcare. Paul Klahr is an employee of Philips Healthcare.« 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
  • 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