Virtual monoenergetic reconstruction of contrast-enhanced CT scans of the abdomen and pelvis at 40 keV improves the detection of peritoneal metastatic deposits

Purpose To evaluate the role of virtual monoenergetic imaging (VMI) in the detection of peritoneal metastatic disease in contrast-enhanced computed tomography (CT) of the abdomen and pelvis and to compare this technique to the conventional 120 kV mixed dataset. Materials and methods Institutional review board approval was obtained with no informed consent required for this retrospective analysis. 43 consecutive patients with histopathologically confirmed peritoneal disease were scanned using a standard protocol on a 128-section dual-source, dual-energy CT system (100/140 keV). Scans were retrospectively reconstructed at VMI energy levels from 40–110 keV in 10 keV increments and were analyzed both quantitatively and qualitatively. CNR values for peritoneal metastatic deposits were recorded using region of interest (ROI) analysis at each energy level for all VMI datasets. Subjective analysis was performed by two independent fellowship-trained readers with combined experience of greater than 15 years. Qualitative parameters included diagnostic acceptability, subjective noise, and contrast resolution and confidence. Results The contrast-to-noise ratios (CNRs) for peritoneal metastatic deposits at the different VMI energy levels were compared using a one-way ANOVA with Tukey Post Test, and the optimal CNR was observed at 40 keV ( p < 0.0001). Qualitative parameters were compared using a Paired T Test. Subjective noise, diagnostic acceptability, and contrast resolution was significantly better on the conventional images, but readers reported increased confidence on VMI at 40 keV ( p < 0.001). Conclusion VMI reconstruction of contrast-enhanced dual-energy CT scans of the abdomen and pelvis at 40 keV maximizes the conspicuity of metastatic peritoneal deposits and improves radiologists’ diagnostic confidence compared with conventional CT images. We recommend using virtual monoenergetic datasets at 40 keV as a tool for improving the detection of these lesions in routine clinical practice.