Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware

Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan

PURPOSE

To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ResUNet) and traditional linear interpolation MAR method (LI) in oral and maxillofacial CT.

METHODS

Supervised models, including Sup-SFTrans and a state-of-the-art model termed ResUNet, were trained with paired simulated CT images, while semi-supervised model, Semi-SFTrans, was trained with both paired simulated and unpaired clinical CT images. For evaluation on the simulated data, we calculated Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on the images corrected by four methods: LI, ResUNet, Sup-SFTrans, and Semi-SFTrans. For evaluation on the clinical data, we collected twenty-two clinical cases with real metal artifacts, and the corresponding intraoral scan data. Three radiologists visually assessed the severity of artifacts using Likert scales on the original, Sup-SFTrans-corrected, and Semi-SFTrans-corrected images. Quantitative MAR evaluation was conducted by measuring Mean Hounsfield Unit (HU) values, standard deviations, and Signal-to-Noise Ratios (SNRs) across Regions of Interest (ROIs) such as the tongue, bilateral buccal, lips, and bilateral masseter muscles, using paired t-tests and Wilcoxon signed-rank tests. Further, teeth integrity in the corrected images was assessed by comparing teeth segmentation results from the corrected images against the ground-truth segmentation derived from registered intraoral scan data, using Dice Score and Hausdorff Distance.

RESULTS

Sup-SFTrans outperformed LI, ResUNet and Semi-SFTrans on the simulated dataset. Visual assessments from the radiologists showed that average scores were (2.02 ± 0.91) for original CT, (4.46 ± 0.51) for Semi-SFTrans CT, and (3.64 ± 0.90) for Sup-SFTrans CT, with intra correlation coefficients (ICCs)＞0.8 of all groups and p < 0.001 between groups. On soft tissue, both Semi-SFTrans and Sup-SFTrans significantly reduced metal artifacts in tongue (p < 0.001), lips, bilateral buccal regions, and masseter muscle areas (p < 0.05). Semi-SFTrans achieved superior metal artifact reduction than Sup-SFTrans in all ROIs (p < 0.001). SNR results indicated significant differences between Semi-SFTrans and Sup-SFTrans in tongue (p = 0.0391), bilateral buccal (p = 0.0067), lips (p = 0.0208), and bilateral masseter muscle areas (p = 0.0031). Notably, Semi-SFTrans demonstrated better teeth integrity preservation than Sup-SFTrans (Dice Score: p < 0.001; Hausdorff Distance: p = 0.0022).

CONCLUSION

The semi-supervised MAR model, Semi-SFTrans, demonstrated superior metal artifact reduction performance over supervised counterparts in real dental CT images.

Investigating the effectiveness of MAR algorithm on magnitude of artifacts in CBCT images: a systematic review

BACKGROUND

There has been an increasing interest in the use of implants to treat edentulous patients. In this regard, the use of cone beam computed tomography (CBCT) offers a variety of advantages compared with other imaging methods. However, the creation of beam-hardening artifacts adversely affects the quality of images. To our knowledge, little is known about the actual effectiveness of the Metal Artifact Reduction (MAR) algorithm on image quality improvement.

OBJECTIVES

The objective of this study is to conduct a systematic review to investigate the effectiveness of the MAR algorithm on the magnitude of artifacts generated in CBCT images.

MATERIALS AND METHODS

An electronic search was performed in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. For each database, the search strategy was defined specifically. Studies that had the inclusion criteria for this review were imported into Endnote version 20. The risk of bias in the studies included in this systematic review was assessed by two independent reviewers based on the Joanna Briggs Institute (JBI)'s Critical Appraisal checklist. The selected final articles were scored based on the specified checklist. After reviewing selected articles, it was not possible to perform a meta-analysis due to the heterogeneity and multiplicity of the variables, and the studies were included in the systematic review.

RESULTS

A total of 4738 studies were identified. After eliminating duplicate and unrelated articles, 10 articles met the inclusion criteria. Results showed that the use of the MAR algorithm in the preparation of CBCT scans reduces the standard deviation (SD) of gray values. However, no definite result was achieved in relation to the contrast-to-noise ratio (CNR). In fact, it cannot be definitively concluded whether the use of the MAR algorithm will increase the CNR.

CONCLUSION

The results of this systematic review demonstrated that we cannot provide a definite answer regarding the effect of the MAR algorithm on reducing the artifacts around dental implants. The explanation is that this factor is affected by many variables, whose change can have a significant effect on the magnitude of artifacts generated in the image.

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study

PURPOSE

To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT scans from a conventional single-energy protocol.

METHOD

A total of 294 head and neck CT scans (98 VMIs operated at 60 keV, 102 MD iodine images, and 94 images from a 120 kVp single-energy CT (SECT) protocol) were retrospectively evaluated. VMIs and MD iodine images were generated using the Gemstone Spectral Imaging (GSI) mode using DLIR and metal artifact reduction (MAR) algorithms. SECT images were generated using adaptive statistical iterative reconstruction (ASIR-V). Images were scored by two independent readers on a 6-point Likert-type scale for overall image quality, vessel contrast, soft tissue contrast, noise texture, noise intensity, artifact reduction, and sharpness.

RESULTS

Subjective overall image quality was rated as superior or excellent in 98 % of DLIR-based MD iodine images and VMIs, but only in 55 % of ASIR-V-based SECT images. For each individual quality criterion, image quality of VMIs and MD iodine images was rated as better than that of SECT images (p < 0.001 in each case). Noise texture and intensity were rated better in MD iodine images than in VMIs.

CONCLUSION

DECT using both DLIR and MAR for the generation of VMIs and MD iodine images resulted in higher subjective quality of oncologic head and neck images than ASIR-V-based SECT. Noise reduction and noise texture were best achieved with DLIR-based MD iodine images.

Chronic non-bacterial osteomyelitis of the mandible - orthodontic considerations and management: A case report

This case report describes the orthodontic management of a case of chronic non-bacterial osteomyelitis (CNO) of the mandible, a rare non-infective variant of osteomyelitis that exhibits a marked predilection in children and adolescents. The patient presented with a unilateral facial swelling associated with fluctuating pain. Radiographic examination, along with tissue biopsy and culture, as well as multispecialty input, led to confirmation of the diagnosis. There was no clear aetiological factor and pharmacological, symptomatic management was indicated. CNO requires multidisciplinary input, with good interspecialty communication and discussion, for an accurate diagnosis. Orthodontic management should be considered on a case-by-case basis, with tailored aims appropriate for each patient.

Improving the quality of computed tomography brain images in the presence of cochlear implant induced metal artefacts through the additional use of tissue mimicking materials alongside metal artefact reduction software

INTRODUCTION

Metal artefact reduction software (MAR) can be used to improve Computed Tomography (CT) image quality in the presence of implanted metalwork; however, this software is not effective for superficial metallic structures such as cochlear implants (CI). This study aimed to investigate whether the effectiveness of MAR software could be improved for brain scans with CI present through the use of tissue mimicking materials (TMM) placed exteriorly to the implant.

METHODS

In this two-part study, a CI was positioned on the surface of water and anthropomorphic phantoms and imaged using a helical CT brain protocol. Three TMM, Superflab, Sure Thermal heat packs, and Bart's Bolus, were utilised and images were acquired to assess the resulting artefact reduction in terms of CT numbers, noise and artefact index (Aind). Changes in CTDIvol were assessed for the anthropomorphic phantom scans.

RESULTS

In the water phantom, statistically significant reductions in CT number (p = 0.038) and noise (p = 0.033) were observed for Superflab, whilst the heat packs produced similar significant reductions in CT number (p < 0.001) and noise (p = 0.001) for the anthropomorphic phantom images. Aind values were significantly reduced through the use of Superflab (p = 0.009) and the heat packs (p < 0.001). No significant effects were observed for Bart's Bolus. CTDIvol increases of generally less than 5% were observed for scans with TMM in place.

CONCLUSION

The additional use of TMM alongside MAR software yielded statistically significant reductions in CI induced metal artefacts on both water and anthropomorphic phantom scans with minimal dose increases.

IMPLICATIONS FOR PRACTICE

The extent of metal artefacts in clinical head scans with CI in place could be significantly reduced through combined use of TMM and MAR software, consequently providing greater diagnostic confidence in the images.

Ultra-high resolution photon-counting CT with tin prefiltration for bone-metal interface visualization

PURPOSE

To investigate the metal artifact suppression potential of combining tin prefiltration and virtual monoenergetic imaging (VMI) for osseous microarchitecture depiction in ultra-high-resolution (UHR) photon-counting CT (PCCT) of the lower extremity.

METHOD

Derived from tin-filtered UHR scans at 140 kVp, polychromatic datasets (T3D) and VMI reconstructions at 70, 110, 150, and 190 keV were compared in 117 patients with lower extremity metal implants (53 female; 62.1 ± 18.0 years). Three implant groups were investigated (total arthroplasty [n = 48], osteosynthetic material [n = 43], and external fixation [n = 26]). Image quality was assessed with regions of interest placed in the most pronounced artifacts and adjacent soft tissue, measuring the respective attenuation. Additionally, artifact extent, bone-metal interface interpretability and overall image quality were independently evaluated by three radiologists.

RESULTS

Artifact reduction was superior with increasing keV level of VMI. While T3D was superior to VMI70keV (p ≥ 0.117), artifacts were more severe in T3D than in VMI ≥ 110 keV (all p ≤ 0.036). Image noise was highest for VMI70keV (all p < 0.001) and lowest for VMI110keV with comparable results for VMI110keV - VMI190keV. Subjective image quality regarding artifacts was superior for VMI ≥ 110 keV (all p ≤ 0.042) and comparable for VMI110keV - VMI190keV. Bone-metal interface interpretability was superior for VMI110keV (all p ≤ 0.001), while T3D, VMI150keV and VMI190keV were comparable. Overall image quality was deemed best for VMI110keV and VMI150keV. Interreader reliability was good in all cases (ICC ≥ 0.833).

CONCLUSIONS

Tin-filtered UHR-PCCT scans of the lower extremity combined with VMI reconstructions allow for efficient artifact reduction in the vicinity of bone-metal interfaces.

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.