Traumatic bone marrow lesions (TBMLs) are considered to represent a range of concealed bone injuries, including haemorrhage, infarction, and localised oedema caused by trabecular microfracture occurring in the cancellous bone. If TBMLs are not managed timeously, they potentially cause a series of complications that can lead to irreversible morbidity and prolonged recovery time. This article reviews interesting image findings of bone marrow lesions in dual-energy computed tomography (DECT). In addition to combining the benefits of traditional CT imaging, DECT also reveals and identifies various structures using diverse attenuation characteristics of different radiographic spectra. Therefore, DECT has the capacity to detect TBMLs, which have traditionally been diagnosed using MRI. Through evaluating DECT virtual non-calcium maps, the detection of TBMLs is rendered easier and more efficient in some acute accidents.

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.