Evaluation of normalized metal artifact reduction (NMAR) in kVCT using MVCT prior images for radiotherapy treatment planning

Paudel, Moti; MacKenzie, M; Fallone, B. G.; Rathee, S

doi:10.1118/1.4812416

Cited by 23 publications

(21 citation statements)

References 21 publications

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“…[2][3][4][5][6][7][8][9] While these algorithms are mostly focused on retrieving missing anatomical information and improving image quality for visualization, 2,9,10 more recently they have been evaluated for their accuracy in retrieving correct CT numbers in the regions affected by artifacts, and for any improvements in dosimetric accuracy of radiation treatment planning on corrected images. 3,5,6,8,11 Following FDA clearance, a commercial MAR solution has been recently introduced on the market and evaluated for utilization in CT simulation in radiation therapy. 12,13 Furthermore, a public license for the metal deletion technique (MDT) algorithm is available for local installation for both commercial and noncommercial uses, although not FDA sanctioned.…”

Section: Introductionmentioning

confidence: 99%

Clinical evaluation of the iterative metal artifact reduction algorithm for CT simulation in radiotherapy

Paidi²,

et al. 2015

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IMAR correction algorithm could be readily implemented in an existing clinical workflow upon commercial release. While residual errors still exist in IMAR corrected images, these images present with better overall conspicuity of the patient/phantom geometry and offer more accurate CT numbers for improved local dosimetry. The variety of different scenarios included herein attest to the utility of the evaluated IMAR for a wide range of radiotherapy clinical scenarios.

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Section: Introductionmentioning

confidence: 99%

Clinical evaluation of the iterative metal artifact reduction algorithm for CT simulation in radiotherapy

Paidi²,

et al. 2015

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“…In this paper, a standard “phantom” (30 cm diameter) was reconstructed for MVCBCT and kVCT scanning by using a previously described method18. The CT image of the corresponding phantom was obtained, and several tissue-like cylindrical long rods with different densities were inserted in the phantom.…”

Section: Methodsmentioning

confidence: 99%

“…Both LIMAR and NMAR are based on the kVCT image only. Paudel18 proposed a method that uses MVCT images as prior images in NMAR. In the present work, MVCBCT was used as prior image in NMAR and the method is marked as NMAR-MV.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, MVCBCT and kVCT images can be combined to reduce metal artifacts and improve image quality151617. Paudel18 used MVCT images as prior images in the normalized reduction of metal artifacts from the kVCT image to correct artifacts and determine absorbed dose distribution. Jeon19 used MV sinogram to replace the metal-affected signals of the kV sonogram and employed FBP on the hybrid sinogram to obtain the corrected image.…”

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confidence: 99%

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Metal artifact reduction through MVCBCT and kVCT in radiotherapy

Gao

Sun

et al. 2016

Sci Rep

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This study proposes a new method for removal of metal artifacts from megavoltage cone beam computed tomography (MVCBCT) and kilovoltage CT (kVCT) images. Both images were combined to obtain prior image, which was forward projected to obtain surrogate data and replace metal trace in the uncorrected kVCT image. The corrected image was then reconstructed through filtered back projection. A similar radiotherapy plan was designed using the theoretical CT image, the uncorrected kVCT image, and the corrected image. The corrected images removed most metal artifacts, and the CT values were accurate. The corrected image also distinguished the hollow circular hole at the center of the metal. The uncorrected kVCT image did not display the internal structure of the metal, and the hole was misclassified as metal portion. Dose distribution calculated based on the corrected image was similar to that based on the theoretical CT image. The calculated dose distribution also evidently differed between the uncorrected kVCT image and the theoretical CT image. The use of the combined kVCT and MVCBCT to obtain the prior image can distinctly improve the quality of CT images containing large metal implants.

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“…Such approaches can use sophisticated techniques including wavelet decompositions (Zhao et al 2000), Laplacian diffusion (Zhang et al 2007), or inpainting techniques (Arias et al 2009, Xinhui Duan et al 2008, Meyer et al 2012). Other approaches attempt to use prior imaging studies to replace the missing data (Bal and Spies 2006, Paudel et al 2013, Heußer et al 2014, Koehler et al 2012, Lell et al 2012). Some techniques apply additional corrections in a second pass (Prell et al 2009, 2010) or in a iterative fashion with refined interpolations based on the initial reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra

Uneri

Khanna

et al. 2017

Phys. Med. Biol.

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Metal artifacts can cause substantial image quality issues in computed tomography. This is particularly true in interventional imaging where surgical tools or metal implants are in the field-of-view. Moreover, the region-of-interest is often near such devices which is exactly where image quality degradations are largest. Previous work on known-component reconstruction (KCR) has shown the incorporation of a physical model (e.g. shape, material composition, etc.) of the metal component into the reconstruction algorithm can significantly reduce artifacts even near the edge of a metal component. However, for such approaches to be effective, they must have an accurate model of the component that include energy-dependent properties of both the metal device and the CT scanner, placing a burden on system characterization and component material knowledge. In this work, we propose a modified KCR approach that adopts a mixed forward model with a polyenergetic model for the component and a monoenergetic model for the background anatomy. This new approach called Poly-KCR jointly estimates a spectral transfer function associated with known components in addition to the background attenuation values. Thus, this approach eliminates both the need to know component material composition a prior as well as the requirement for an energy-dependent characterization of the CT scanner. We demonstrate the efficacy of this novel approach and illustrate its improved performance over traditional and model-based iterative reconstruction methods in both simulation studies and in physical data including an implanted cadaver sample.

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Evaluation of normalized metal artifact reduction (NMAR) in kVCT using MVCT prior images for radiotherapy treatment planning

Cited by 23 publications

References 21 publications

Clinical evaluation of the iterative metal artifact reduction algorithm for CT simulation in radiotherapy

Clinical evaluation of the iterative metal artifact reduction algorithm for CT simulation in radiotherapy

Metal artifact reduction through MVCBCT and kVCT in radiotherapy

Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra

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