Dual-energy CT virtual non-calcium: an accurate method for detection of knee osteoarthritis-related edema-like marrow signal intensity

• Published in: Insights into imaging Vol. 14; no. 1; p. 74
• Main Authors: Zhao, Heng, Li, Hui, Xie, Xia, Tang, Hai-yan, Liu, Xiao-xin, Wen, Yi, Xiao, Xin, Ye, Lu, Tang, You-wei, Dai, Gao-yue, He, Jia-ni, Chen, Li, Wang, Qian, Tang, De-qiu, Pan, Shi-nong
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 30.04.2023; Springer Nature B.V; SpringerOpen
• Subjects: Arthritis; Calcium; Computed tomography; Diagnosis; Diagnostic Radiology; Edema; Edema like marrow signal intensity; Imaging; Informed consent; Internal Medicine; Interventional Radiology; Joints (anatomy); Knee; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; MRI; Neuroradiology; Original; Original Article; Osteoarthritis; Performance evaluation; Qualitative analysis; Radiology; Ultrasound; Virtual non-calcium; Virtual non-calcium; Edema like marrow signal intensity; MRI; Osteoarthritis
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-023-01407-8

Objectives To evaluate the performance of a dual-energy computed tomography (DECT) virtual non-calcium (VNCa) technique in the detection of edema-like marrow signal intensity (ELMSI) in patients with knee joint osteoarthritis (OA) compared to magnetic resonance imaging (MRI). Methods The study received local ethics board approval, and written informed consent was obtained. DECT and MRI were used to examine 28 knees in 24 patients with OA. VNCa images were generated by dual-energy subtraction of calcium. The knee joint was divided into 15 regions for ELMSI grading, performed independently by two musculoskeletal radiologists, with MRI as the reference standard. We also analyzed CT numbers through receiver operating characteristics and calculated cut-off values. Results For the qualitative analysis, we obtained CT sensitivity (Readers 1, 2 = 83.7%, 89.8%), specificity (Readers 1, 2 = 99.5%, 99.5%), positive predictive value (Readers 1, 2 = 95.3%, 95.7%), and negative predictive value (Readers 1, 2 = 97.9%, 98.7%) for ELMSI. The interobserver agreement was excellent (κ = 0.92). The area under the curve for Reader 1 and Reader 2 was 0.961 (95% CI 0.93, 0.99) and 0.992 (95% CI 0.98, 1.00), respectively. CT numbers obtained from the VNCa images were significantly different between regions with and without ELMSI ( p  < .001). Conclusions VNCa images have good diagnostic performance for the qualitative and quantitative analysis of knee osteoarthritis-related ELMSI. Key points DECT-VNCa images had important clinical significance in diagnosing ELMSI. DECT-VNCa images can improve the diagnosis of ELMSI by cut-off value. The research of DECT-related post-processing technology in the diagnosis of OA-related pain needs further study.