Combination of Iterative Metal Artifact Reduction and Virtual Monoenergetic Reconstruction Using Split-Filter Dual-Energy CT in Patients With Dental Artifact on Head and Neck CT.

BACKGROUND. Head and neck CT can be limited by dental hardware artifact. Both postprocessing-based iterative metal artifact reduction (IMAR) and virtual monoenergetic imaging (VMI) reconstruction in dual-energy CT (DECT) can reduce metal artifact. Their combination is poorly described for single-source DECT systems. OBJECTIVE. The purpose of this study was to compare metal artifact reduction between VMI, IMAR, and their combination (VMIIMAR) in split-filter single-source DECT of patients with severe dental hardware artifact. METHODS. This retrospective study included 44 patients (nine woman, 35 men; mean age, 66.0 ± 10.4 years) who underwent head and neck CT and had severe dental hardware artifact. Standard, VMI, IMAR, and VMIIMAR images were generated; VMI and VMIIMAR were performed at 40, 70, 100, 120, 150, and 190 keV. ROIs were placed to measure corrected attenuation in pronounced hyperattenuating and hypoattenuating artifacts and artifact-impaired soft tissue and to measure corrected artifact-impaired soft-tissue noise. Two radiologists independently assessed soft-tissue interpretability (1-5 scale), and pooled ratings were analyzed. Readers selected the preferred reconstruction for each patient. RESULTS. Mean hyperattenuating artifact-corrected attenuation was 521.0 HU for standard imaging, 496.4-892.2 HU for VMI, 48.2 HU for IMAR, and 32.8-91.0 HU for VMIIMAR. Mean hypoattenuating artifact-corrected attenuation was -455.1 HU for standard imaging, -408.5 to -679.9 HU for VMI, -37.3 for IMAR, and -17.8 to -36.9 HU for VMIIMAR. Mean artifact-impaired soft tissue-corrected attenuation was 10.8 HU for standard imaging, -0.6 to 24.9 HU for VMI, 4.3 HU for IMAR, and -2.0 to 7.8 HU for VMIIMAR. Mean artifact-impaired soft tissue-corrected noise was 58.7 HU for standard imaging, 38.2 to 129.7 HU for VMI, 11.0 HU for IMAR, and 5.8 to 45.6 HU for VMIIMAR. Median soft-tissue interpretability was 1.2 for standard imaging, 1.1-1.2 for VMI, 3.7 for IMAR, and 2.0-3.8 for VMIIMAR. Artifact-impaired soft tissue-corrected attenuation and soft-tissue interpretability significantly improved (p < .05) for VMIIMAR versus IMAR only at 100 keV. The two readers preferred VMIIMAR at 100 keV in 56.8% and 59.1% of examinations. CONCLUSION. For reducing severe artifact due to dental material, IMAR has greater effect than VMI. Though the results for IMAR and VMIIMAR were similar overall, VMIIMAR had a small benefit at 100 keV. CLINICAL IMPACT. VMI and IMAR techniques in split-filter DECT may be combined for clinical head and neck imaging to reduce artifact from dental hardware and improve image quality.

Interobserver reliability of scapula fracture classifications in intra- and extra-articular injury patterns.

BACKGROUND: Morphology and glenoid involvement determine the necessity of surgical management in scapula fractures. While being present in only a small share of patients with shoulder trauma, numerous classification systems have been in use over the years for categorization of scapula fractures. The purpose of this study was to evaluate the established AO/OTA classification in comparison to the classification system of Euler and Rüedi (ER) with regard to interobserver reliability and confidence in clinical practice. METHODS: Based on CT imaging, 149 patients with scapula fractures were retrospectively categorized by two trauma surgeons and two radiologists using the classification systems of ER and AO/OTA. To measure the interrater reliability, Fleiss kappa (κ) was calculated independently for both fracture classifications. Rater confidence was stated subjectively on a five-point scale and compared with Wilcoxon signed rank tests. Additionally, we computed the intraclass correlation coefficient (ICC) based on absolute agreement in a two-way random effects model to assess the diagnostic confidence agreement between observers. RESULTS: In scapula fractures involving the glenoid fossa, interrater reliability was substantial (κ = 0.722; 95% confidence interval [CI] 0.676-0.769) for the AO/OTA classification in contrast to moderate agreement (κ = 0.579; 95% CI 0.525-0.634) for the ER classification system. Diagnostic confidence for intra-articular fracture patterns was superior using the AO/OTA classification compared to ER (p < 0.001) with higher confidence agreement (ICC: 0.882 versus 0.831). For extra-articular fractures, ER (κ = 0.817; 95% CI 0.771-0.863) provided better interrater reliability compared to AO/OTA (κ = 0.734; 95% CI 0.692-0.776) with higher diagnostic confidence (p < 0.001) and superior agreement between confidence ratings (ICC: 0.881 versus 0.912). CONCLUSIONS: The AO/OTA classification is most suitable to categorize intra-articular scapula fractures with glenoid involvement, whereas the classification system of Euler and Rüedi appears to be superior in extra-articular injury patterns with fractures involving only the scapula body, spine, acromion and coracoid process.