Artifact Reduction in Interventional Devices Using Virtual Monoenergetic Images and Iterative Metal Artifact Reduction on Photon-Counting Detector CT.

OBJECTIVES

The aim of this study was to assess the impact of an iterative metal artifact reduction (iMAR) algorithm combined with virtual monoenergetic images (VMIs) for artifact reduction in photon-counting detector computed tomography (PCDCT) during interventions.

MATERIALS AND METHODS

Using an abdominal phantom, we conducted evaluations on the efficacy of iMAR and VMIs for mitigating image artifacts during interventions on a PCDCT. Four different puncture devices were employed under 2 scan modes (QuantumSn at 100 kV, Quantumplus at 140 kV) to simulate various clinical scenarios. Image reconstructions were initially performed without iMAR and subsequently with iMAR settings. The latter was tested with 7 different metal presets for each case. Furthermore, iMAR-reconstructed images were paired with VMIs at energy levels of 70 keV, 110 keV, 150 keV, and 190 keV. Qualitative assessments were conducted to evaluate image quality, artifact expression, and the emergence of new artifacts using a Likert scale. Image quality was rated on a scale of 1 (nondiagnostic) to 5 (excellent), whereas artifact severity was rated from 0 (none) to 5 (massive). Preferences for specific iMAR presets were documented. Quantitative analysis involved calculating Hounsfield unit (HU) differences between artifact-rich and artifact-free tissues.

RESULTS

Overall, 96 different scanning series were evaluated. The optimal combination for artifact reduction was found to be iMAR neurocoils with VMIs at 150 keV and 190 keV, showcasing the most substantial reduction in artifacts with a median rating of 1 (standard: 4). VMIs at higher keV levels, such as 190 keV, resulted in reduced image quality, as indicated by a median rating of 3 (compared with 70 keV with a median of 5). Newly emerged artifact expression related to reconstructions varied among intervention devices, with iMAR thoracic coils exhibiting the least extent of artifacts (median: 2) and iMAR neurocoils displaying the most pronounced artifacts (median: 4). Qualitative analysis favored the combination of iMAR neurocoils with VMIs at 70 keV, showcasing the best results. Conversely, quantitative analysis revealed that the combination of iMAR neurocoils with VMIs at 190 keV yielded the best results, with an average artifact expression of 20.06 HU (standard: 167.98 HU; P < 0.0001).

CONCLUSIONS

The study underscores a substantial reduction in artifacts associated with intervention devices during PCDCT scans through the synergistic application of VMI and iMAR techniques. Specifically, the combination of VMIs at 70 keV with iMAR neurocoils was preferred, leading to enhanced diagnostic assessability of surrounding tissues and target lesions. The study demonstrates the potential of iMAR and VMIs for PCDCT-guided interventions. These advancements could improve accuracy, safety, efficiency, and patient outcomes in clinical practice.