Imaging Characterization of Non-Rheumatoid Retro-Odontoid Pseudotumors: Comparison with Atlantoaxial Manifestation of Rheumatoid Arthritis.

Backgroundand Objectives: To date, imaging characterization of non-rheumatic retro-odontoid pseudotumors (NRROPs) has been lacking; therefore, NRROPs have been confused with atlantoaxial joint involvement of rheumatoid arthritis (RA). It is important to differentiate these two disease because the treatment strategies may differ. The purpose of this study is to characterize imaging findings of NRROPs and compare them with those of RA. Material and Methods: From January 2015 to December 2019, 27 patients (14 women and 13 men) with NRROPs and 19 patients (15 women and 4 men) with RA were enrolled in this study. We evaluated various imaging findings, including atlantoaxial instability (AAI), and measured the maximum diameter of preodontoid and retro-odontoid spaces with magnetic resonance imaging (MRI) and computed tomography (CT). Results: Statistical significance was considered for p < 0.05. AAI was detected in eight patients with NRROPs and in all patients with RA (p < 0.0001). Seventeen patients with NRROPs and six patients with RA showed spinal cord compression (p = 0.047). Compressive myelopathy was observed in 14 patients with NRROPs and in 4 patients with RA (p = 0.048). Subaxial degeneration was observed in 25 patients with NRROPs and in 9 patients with RA (p = 0.001). Moreover, C2-3 disc abnormalities were observed in 11 patients with NRROPs and in 2 patients with RA (p = 0.02). Axial and longitudinal diameter of retro-odontoid soft tissue and preodontoid and retro-odontoid spaces showed significant differences between NRROP and RA patients (p < 0.0001). Furthermore, CT AAI measurements were differed significantly between NRROP and RA patients (p < 0.05). Conclusions: NRROPs showed prominent retro-odontoid soft tissue thickening, causing compressive myelopathy and a high frequency of subaxial and C2-3 degeneration without AAI.

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction.

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.

Symptomatic Pneumocephalus After Spinal Intradural-Extramedullary Tumor Surgery.

OBJECTIVE: Durotomy is a necessary procedure for resection of spinal intradural-extramedullary (IDEM) tumor. The purpose of this study was to investigate the prevalence and clinical implications of pneumocephalus after spinal IDEM tumor surgery and also the association between pneumocephalus and postoperative headache. METHODS: This prospective study was conducted in a cohort of 20 patients who were diagnosed with a spinal IDEM tumor and underwent surgical resection. The mean patient age was 53.6 years (range, 29-75 years). Histological findings revealed schwannoma in 16 cases (80.0%) and meningioma in 4 cases (20.0%). Brain computed tomography scan was performed immediately after surgery. The prevalence and severity were analyzed based on the classification into 4 groups according to the extension of pneumocephalus: absence, mild, moderate, and severe. A visual analog scale (VAS; range 0-10) for headache was reported daily for the first postoperative week. Headache-related pneumocephalus was defined as a VAS score of ≥5 points at least once in that 1-week period. RESULTS: The prevalence of pneumocephalus was 90.0% (18 patients). Five patients (25.0%) had severe pneumocephalus extending to the extra-axial space. Seven of the 20 patients (38.9%) complained of significant headache. Five of these patients had severe pneumocephalus, 1 patient had moderate pneumocephalus, and 1 patient had moderate pneumocephalus. A statistically significant association was observed between the severity of pneumocephalus and headache (P = 0.001). CONCLUSIONS: Pneumocephalus is a common complication after spinal IDEM tumor surgery. This report is the first study to investigate the prevalence of pneumocephalus and analyze its association with headache after spine surgery. The severity of pneumocephalus was significantly related to postoperative headache.