The Optimal Combination of Monochromatic and Metal Artifact Reconstruction Dual-energy CT to Evaluate Total Knee Replacement Arthroplasty

Optimisation of monoenergetic images to reduce banding artifacts in the lower cervical spine using dual-layer spectral computed tomography: a retrospective study

AIM

To investigate the application value of dual-layer detector computed tomography (CT) single-energy spectral images for reducing artefacts in the lower cervical spine.

MATERIALS AND METHODS

Sixty-three patients who underwent neck examination using spectral CT between March 2022 and January 2023 were selected as the participants. Conventional mixed-energy images and spectral imaging data at 40-200 KeV were obtained from the spectral CT scans. The standard deviation (SD), signal-to-noise ratio (SNR), |CTdifference| (the difference in CT value between the C6-7 and C3-4 artefact regions), and contrast-to-noise ratio (CNR) of the C6-7 spinal canal were measured. The image quality of the artefact region in the conventional and single images were compared.

RESULTS

The SDintervertebral disc value was highest in the single-level 40 KeV images and lowest in the 120 KeV images (P<0.05). The SNR was lowest in the 40 KeV images (2.07 ± 2.74) and highest in the 120 KeV images (6.20 ± 5.26) (P<0.05). The CTdifference was highest in the 40 KeV images and lowest in the 120 KeV images (108.38 vs. 10.63 ± 8.58). The subjective image quality scores were lowest in the 40 KeV images and highest in the 120 KeV images.

CONCLUSION

Our findings suggest that single-energy 120 KeV images may offer benefits such as low SDintervertebral disc, elevated SNR, higher CNR, and improved image quality.

Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques

Metal artifacts degrade CT image quality, hampering clinical assessment. Numerous metal artifact reduction methods are available to improve the image quality of CT images with metal implants. In this review, an overview of traditional methods is provided including the modification of acquisition and reconstruction parameters, projection-based metal artifact reduction techniques (MAR), dual energy CT (DECT) and the combination of these techniques. Furthermore, the additional value and challenges of novel metal artifact reduction techniques that have been introduced over the past years are discussed such as photon counting CT (PCCT) and deep learning based metal artifact reduction techniques.

Dual-Energy Computed Tomography Applications to Reduce Metal Artifacts in Hip Prostheses: A Phantom Study

Metal components of hip prostheses cause severe artifacts in CT images, influencing diagnostic accuracy. Metal artifact reduction (MAR) software and virtual monoenergetic reconstructions on dual-energy CT (DECT) systems are possible solutions that should be considered. In this study, we created a customized adjustable phantom to quantify the severity of artifacts on periprosthetic tissues (cortical and spongious bone, soft tissues) for hip prostheses. The severity of artifacts was classified by different thresholds of deviation from the CT numbers for reference objects not affected by artifacts. The in vitro setup was applied on four unilateral and three bilateral configurations of hip prostheses (made of titanium, cobalt, and stainless steel alloys) with a DECT system, changing the energy of virtual monoenergetic reconstructions, with and without MAR. The impact of these tools on the severity of artifacts was scored, looking for the best scan conditions for the different configurations. For titanium prostheses, the reconstruction at 110 keV, without MAR, always minimized the artifacts. For cobalt and stainless-steel prostheses, MAR should always be applied, while monoenergetic reconstruction alone did not show clear advantages. The available tools for reducing metal artifacts must therefore be applied depending on the examined prosthetic configuration.

Metal Artifact Reduction Dual-Energy CT as an Accurate and Reliable Method for Measuring Total Knee Arthroplasty Femoral Component Rotation Compared to Conventional CT

This article determines the accuracy and reliability of dual-energy computed tomography (DECT) with metal artifact reduction (MAR) in the evaluation of femoral component rotation after total knee arthroplasty (TKA), in comparison with conventional CT images. A total of 49 patients (mean age, 69 years; 42 women) who underwent TKA between January 2019 and March 2020 were retrospectively enrolled. Femoral component rotation, including the anatomic and surgical transepicondylar axes, was evaluated with preoperative conventional CT and postoperative conventional CT and DECT with MAR. Surgical femoral component rotation was also assessed as a reference standard. Accuracy was assessed using paired t-test, and inter- and intraobserver reliability using intraclass correlation coefficients (ICCs) based on postoperative conventional CT and DECT with MAR. Clinical outcomes were evaluated using the Knee Society objective and functional scores. Accuracy of femoral component rotation was not significantly different from that of surgical rotation with both conventional CT and DECT with MAR. However, inter- and intraobserver reliability were better for DECT with MAR (ICC: 0.953-0.966) than for conventional CT (ICC: 0.641-0.749). The Knee Society objective and functional scores improved 1 year postoperatively. CONCLUSION:  DECT with MAR showed accurate and more reliable results than did conventional CT in the evaluation of femoral component rotation after TKA.

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Methods

The imaging data of 55 patients with chest CT plain scan in the Xuancheng People's Hospital were collected retrospectively. The data of each patient included lung window reconstruction, mediastinum reconstruction, and bone window reconstruction. The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm. The pathological results were the gold standard for benign and malignant pulmonary nodules. The classification and segmentation algorithms under three CT reconstruction algorithms were compared and analyzed by analysis of variance.

Results

Under the three CT reconstruction algorithms, the classification accuracy of pulmonary nodule density types was 98.2%, 96.4%, and 94.5%, respectively. The Dice coefficients of all nodule segmentation were 80.32% ± 5.91%, 79.83% ± 6.12%, and 80.17% ± 5.89%, respectively. The diagnostic accuracy between benign and malignant pulmonary nodules under different reconstruction algorithms was 98.2%, 96.4%, and 94.5%, respectively. There was no significant difference in the classification accuracy, Dice coefficients, and diagnostic accuracy of pulmonary nodules under three different reconstruction algorithms (all P > 0.05).

Conclusion

The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification and segmentation algorithms.