Single-energy versus dual-energy imaging during CT-guided biopsy using dedicated metal artifact reduction algorithm in an in vivo pig model

Duong, Thuy; Heim, J; Skornitzke, Stephan; Melzig, Claudius; Vollherbst, Dominik F; Faerber, Michael; Pereira, Philippe L.; Kauczor, Hans‐Ulrich; Sommer, Christof M.

doi:10.1371/journal.pone.0249921

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Iterative metal artifact reduction (iMAR) algorithms have shown important metal artifact reduction from the biopsy needles and antennas for microwave ablation, especially regarding photon starvation artifacts. However, the generation of new artifacts with additional blooming artifacts around the trocars and peripherical dark streaks has also been observed [ 3 , 19 ]. Overall, based on beam hardening and using an Ag additional filter, the method presented in this work significantly decreased the severity of metal artifacts, improved structures’ visibility, and improved target structures’ correct location without changing the image quality.…”

Section: Discussionmentioning

confidence: 99%

“…FWHM and FWTM values, measured on the needle artifact, represent the degree of the blurring effect in the needle, which should be low enough not to change the actual size of the needle on the CT image. The CT often overestimates the needle width due to blooming artifacts [ 3 , 19 – 21 ]. In this work, the smallest FWHM and FWTM values were obtained for the beam of 120 kV-Ag, which means a value closer to the actual diameter of the needle and less blooming artifact in the CT image.…”

Section: Discussionmentioning

confidence: 99%

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Needle artifact reduction during interventional CT procedures using a silver filter

Reynoso-Mejia,

Troville,

Wagner

et al. 2024

BMC biomed eng

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Background MAR algorithms have not been productized in interventional imaging because they are too time-consuming. Application of a beam hardening filter can mitigate metal artifacts and doesn’t increase computational burden. We evaluate the ability to reduce metal artifacts of a 0.5 mm silver (Ag) additional filter in a Multidetector Computed Tomography (MDCT) scanner during CT-guided biopsy procedures. Methods A biopsy needle was positioned inside the lung field of an anthropomorphic phantom (Lungman, Kyoto Kagaku, Kyoto, Japan). CT acquisitions were performed with beam energies of 100 kV, 120 kV, 135 kV, and 120 kV with the Ag filter and reconstructed using a filtered back projection algorithm. For each measurement, the CTDIvol was kept constant at 1 mGy. Quantitative profiles placed in three regions of the artifact (needle, needle tip, and trajectory artifacts) were used to obtain metrics (FWHM, FWTM, width at − 100 HU, and absolute error in HU) to evaluate the blooming artifact, artifact width, change in CT number, and artifact range. An image quality analysis was carried out through image noise measurement. A one-way analysis of variance (ANOVA) test was used to find significant differences between the conventional CT beam energies and the Ag filtered 120 kV beam. Results The 120 kV-Ag is shown to have the shortest range of artifacts compared to the other beam energies. For needle tip and trajectory artifacts, a significant reduction of − 53.6% (p < 0.001) and − 48.7% (p < 0.001) in the drop of the CT number was found, respectively, in comparison with the reference beam of 120 kV as well as a significant decrease of up to − 34.7% in the artifact width (width at − 100 HU, p < 0.001). Also, a significant reduction in the blooming artifact of − 14.2% (FWHM, p < 0.001) and − 53.3% (FWTM, p < 0.001) was found in the needle artifact. No significant changes (p > 0.05) in image noise between the conventional energies and the 120 kV-Ag were found. Conclusions A 0.5 mm Ag additional MDCT filter demonstrated consistent metal artifact reduction generated by the biopsy needle. This reduction may lead to a better depiction of the target and surrounding structures while maintaining image quality.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Needle artifact reduction during interventional CT procedures using a silver filter

Reynoso-Mejia,

Troville,

Wagner

et al. 2024

BMC biomed eng

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Semi-automatic artifact quantification in thermal ablation probe and algorithms for the evaluation of metal artifact reduction

Haas

Vollherbst

et al. 2023

International Journal of Hyperthermia

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End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction

et al. 2021

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Metal artifacts are common in CT-guided interventions due to the presence of metallic instruments. These artifacts often obscure clinically relevant structures, which can complicate the intervention. In this work, we present a deep learning CT reconstruction called iCTU-Net for the reduction of metal artifacts. The network emulates the filtering and back projection steps of the classical filtered back projection (FBP). A U-Net is used as post-processing to refine the back projected image. The reconstruction is trained end-to-end, i.e., the inputs of the iCTU-Net are sinograms and the outputs are reconstructed images. The network does not require a predefined back projection operator or the exact X-ray beam geometry. Supervised training is performed on simulated interventional data of the abdomen. For projection data exhibiting severe artifacts, the iCTU-Net achieved reconstructions with SSIM = 0.970±0.009 and PSNR = 40.7±1.6. The best reference method, an image based post-processing network, only achieved SSIM = 0.944±0.024 and PSNR = 39.8±1.9. Since the whole reconstruction process is learned, the network was able to fully utilize the raw data, which benefited from the removal of metal artifacts. The proposed method was the only studied method that could eliminate the metal streak artifacts.

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Single-energy versus dual-energy imaging during CT-guided biopsy using dedicated metal artifact reduction algorithm in an in vivo pig model

Cited by 3 publications

References 27 publications

Needle artifact reduction during interventional CT procedures using a silver filter

Needle artifact reduction during interventional CT procedures using a silver filter

Semi-automatic artifact quantification in thermal ablation probe and algorithms for the evaluation of metal artifact reduction

End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction

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