Metal artifact correction for x‐ray computed tomography using kV and selective MV imaging

Wu, Meng; Keil, Andreas; Constantin, Dragoş; Star‐Lack, Josh; Zhu, Lei; Fahrig, Rebecca

doi:10.1118/1.4901551

Cited by 34 publications

(33 citation statements)

References 85 publications

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“…In recent years, several model‐based methods have been investigated to reduce scatter, metal, cupping, and beam‐hardening artifacts in CBCT . In addition to these conventional model‐based artifact‐reduction methods, convolutional neural networks (CNNs)‐based methods have also been explored for image quality enhancement for CT .…”

Section: Introductionmentioning

confidence: 99%

Synthetic CT generation from CBCT images via deep learning

et al. 2020

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Purpose Cone‐beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U‐net‐based approach that synthesizes CT‐like images with accurate numbers from planning CT, while keeping the same anatomical structure as on‐treatment CBCT. Methods We formulated the CT synthesis problem under a deep learning framework, where a deep U‐net architecture was used to take advantage of the anatomical structure of on‐treatment CBCT and image intensity information of planning CT. U‐net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U‐net (sCTU‐net), we include on‐treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU‐net, we use another seven independent patient cases (560 slices) for testing. Results We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same‐day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU. Conclusions The proposed sCTU‐net can synthesize CT‐quality images with accurate CT numbers from on‐treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

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Section: Introductionmentioning

confidence: 99%

Synthetic CT generation from CBCT images via deep learning

et al. 2020

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“…So, for metal artifact reduction, in order to maintain as high an overall SNR as possible, the kV and MV datasets should be combined in a way that only uses the (lower contrast) MV rays when absolutely necessary. Wu et al presented a simulation study in which penalized weighted least‐square iterative image reconstruction was used to optimally combine the data and achieve high quality metal artifact reduction . More recently, Jeon et al studied another hybrid kV/MV CBCT imaging approach on phantoms and three patients with dental implants using a TomoTherapy (TomoTherapy, Inc., Madison, WI) system .…”

Section: Introductionmentioning

confidence: 99%

Investigation of combined kV/MV CBCT imaging with a high‐DQE MV detector

et al. 2018

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Purpose Combined kV–MV cone‐beam tomography (CBCT) imaging has been proposed for two potentially important image‐guided radiotherapy applications: (a) scan time reduction (STR) and (b) metal artifact reduction (MAR). However, the feasibility of these techniques has been in question due to the low detective quantum efficiencies (DQEs) of commercially available electronic portal imagers (EPIDs). The goal of the work was to test whether a prototype high DQE MV detector can be used to generate acceptable quality pretreatment CBCT images at acceptable dose levels. Methods 6MV and 100 kVp projection data were acquired on a Truebeam system (Varian, Palo Alto, CA). The MV data were acquired using a prototype EPID containing two scintillators (a) a standard copper‐gadolinium oxysulfide (Cu‐GOS) screen having a zero‐frequency DQE (DQE(0)) value of 1.4%, and (b) a prototype‐focused cadmium tungstate (CWO) pixelated “strip” with a DQE(0) = 22%. The kV data were acquired using the standard onboard imager (DQE(0) = 70%). The angular spacing of the MV projections was 0.81° and the source output was 0.03 MU/projection while the kV projections were acquired with an angular spacing of 0.4° at 0.3 mAs/projection. Image quality was evaluated using (a) an 18‐cm diameter electron density phantom (CIRS, Norfolk, VA) with nine contrast inserts and (b) the resolution section of the 20‐cm diameter Catphan phantom (The Phantom Laboratory, Greenwich, NY). For the MAR studies, two opposing CIRS phantom inserts were replaced by steel rods. The reconstruction methods were based on combining MV and kV data into one sinogram. The MAR reconstruction utilized mostly kV raw data with only those rays corrupted by metal requiring replacement with MV data (total absorbed dose = 0.7 cGy). For the STR study, projections from partially overlapping 105°kV and MV acquisitions were combined to create a complete dataset that could have been acquired in 18 sec (absorbed dose = 2.5 cGy). MV‐only (4.3 cGy) and kV‐only (0.3 cGy) images were also reconstructed. Results The average signal‐to‐noise ratio (SNR) of the inserts in the MV‐only CWO and GOS CIRS phantom images were 0.62× and 0.12× the SNR of the inserts in kV‐only image, respectively. The limiting spatial resolutions in the MV‐only GOS, MV‐only CWO, and kV‐only Catphan images were 3, 6, and 8 lp/cm, respectively. In the combined kV/CWO STR reconstruction, all contrast inserts were visible while only two were detectable in the kV/Cu‐GOS image due to high levels of noise (average SNRs of kV/CWO and kV/GOS inserts were 0.97× and 0.18× the SNR of the kV‐only inserts, respectively). In the kV–MV MAR reconstructions, streaking artifacts were substantially reduced with all inserts becoming clearly visible in the kV/CWO image while only two were visible in the kV/Cu‐GOS image (average SNRs of the kV/CWO and kV/Cu‐GOS CIRS with metal inserts were 0.94× and 0.35× the SNRs of the kV‐only CIRS without metal inserts). Conclusions We have demonstrated that a high‐DQE MV detector can be applied to generating high‐quali...

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“…Higher voltages (e.g. : 160kVp or megavoltage CT) could be used to improve penetration and reduce photon starvation effects, but they are generally not dose-efficient, especially in contrast exams, or only exist in radiotherapy equipment [29, 30]. …”

Section: Introductionmentioning

confidence: 99%

High-kVp Assisted Metal Artifact Reduction for X-Ray Computed Tomography

Jin

Man

et al. 2016

IEEE Access

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In X-ray computed tomography (CT), the presence of metallic parts in patients causes serious artifacts and degrades image quality. Many algorithms were published for metal artifact reduction (MAR) over the past decades with various degrees of success but without a perfect solution. Some MAR algorithms are based on the assumption that metal artifacts are due only to strong beam hardening and may fail in the case of serious photon starvation. Iterative methods handle photon starvation by discarding or underweighting corrupted data, but the results are not always stable and they come with high computational cost. In this paper, we propose a high-kVp-assisted CT scan mode combining a standard CT scan with a few projection views at a high-kVp value to obtain critical projection information near the metal parts. This method only requires minor hardware modifications on a modern CT scanner. Two MAR algorithms are proposed: dual-energy normalized MAR (DNMAR) and high-energy embedded MAR (HEMAR), aiming at situations without and with photon starvation respectively. Simulation results obtained with the CT simulator CatSim demonstrate that the proposed DNMAR and HEMAR methods can eliminate metal artifacts effectively.

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Metal artifact correction for x‐ray computed tomography using kV and selective MV imaging

Cited by 34 publications

References 85 publications

Synthetic CT generation from CBCT images via deep learning

Synthetic CT generation from CBCT images via deep learning

Investigation of combined kV/MV CBCT imaging with a high‐DQE MV detector

High-kVp Assisted Metal Artifact Reduction for X-Ray Computed Tomography

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