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

Needle artifact reduction during interventional CT procedures using a silver filter

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.

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

OBJECTIVES

To compare metal artifacts and evaluation of metal artifact reduction algorithms during probe positioning in computed tomography (CT)-guided microwave ablation (MWA), cryoablation (CRYO), and radiofrequency ablation (RFA).

MATERIALS AND METHODS

Using CT guidance, individual MWA, CRYO, and RFA ablation probes were placed into the livers of 15 pigs. CT imaging was then performed to determine the probe's position within the test subject's liver. Filtered back projection (B30f) and iterative reconstructions (I30-1) were both used with and without dedicated iterative metal artifact reduction (iMAR) to generate images from the initial data sets. Semi-automatic segmentation-based quantitative evaluation was conducted to estimate artifact percentage within the liver, while qualitative evaluation of metal artifact extent and overall image quality was performed by two observers using a 5-point Likert scale: 1-none, 2-mild, 3-moderate, 4-severe, 5-non-diagnostic.

RESULTS

Among MWA, RFA, and CRYO, compared with non-iMAR in B30f reconstruction, the largest extent of artifact volume percentages were observed for CRYO (11.5-17.9%), followed by MWA (4.7-6.6%) and lastly in RFA (5.5-6.2%). iMAR significantly reduces metal artifacts for CRYO and MWA quantitatively (p = 0.0020; p = 0.0036, respectively) and qualitatively (p = 0.0001, p = 0.0005), but not for RFA. No significant reduction in metal artifact percentage was seen after applying iterative reconstructions (p > 0.05). Noise, contrast-to-noise-ratio, or overall image quality did not differ between probe types, irrespective of the application of iterative reconstruction and iMAR.

CONCLUSION

A dedicated metal artifact algorithm may decrease metal artifacts and improves image quality significantly for MWA and CRYO probes. Their application alongside with dedicated metal artifact algorithm should be considered during CT-guided positioning.

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

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.