Background Prior studies focused on utilization of dual-energy computed tomography (DECT) to better detect intracranial pathology and to reduce artifacts. It is still unclear whether virtual non-contrast (VNC) images of DECT can replace true non-contrast (TNC) images. Purpose To compare attenuation values and image quality of VNC images to TNC images of the brain, obtained using spectral detector CT (SDCT). Material and Methods We retrospectively evaluated patients that underwent head CT with and without contrast material, on a SDCT scanner at our institution (n = 33). The attenuation values of different brain structures were obtained from TNC images, the conventional images of the post-contrast exams (n = 16) or the CT angiography (CTA) (n = 17), and the derived VNC images. In total, 591 regions of interest were obtained, including white and gray matter. Two neuroradiologists independently evaluated the image quality of the VNC and TNC images, using a 5-point Likert scale. Results The mean difference between the attenuation values on the VNC versus the TNC images was <4 HU for almost all the structures. The difference reached statistical significance ( P < 0.05) for the deep gray structures but not for the white matter. The image quality score of the TNC images was 5 in all the patients (excellent gray–white matter differentiation). The scores of the VNC images differed between post-contrast and CTA examinations, with means of 4.9 ± 0.3 (excellent) and 3.2 ± 0.4 (fair), respectively ( P < 0.001). Conclusion Our results show minor differences between attenuation values of different brain structures on VNC versus TNC images of SDCT.

Abstract. We describe a case of native vertebral osteomyelitis (NVO) secondary to Listeria monocytogenes in a patient with polymyalgia rheumatica receiving chronic steroids. Treatment required surgical debridement of the epidural phlegmon and combination therapy with intravenous ampicillin and gentamicin.

Increasing utilization of cross-sectional imaging has resulted in more clinically significant incidental findings being discovered. However, the current approach for handling these findings is commonly inconsistent, and relies greatly on the efforts of individual clinicians. Making sure every actionable incidental finding is handled in a consistent and reliable manner can be difficult, especially for a large health system. We propose an approach to handling incidental findings aimed at improving patient follow up rates, which involves implementing system level processes that standardize the reporting of incidental findings, notification of clinicians and the patient, and centralized monitoring of longitudinal patient follow up. We will lay out a general framework for standardized reporting of incidental findings by the radiologist using software integrated into the daily workflow. This should enable simultaneous notification of the ordering clinician, the patient’s primary care provider, and an incidental findings navigator. The navigator will “close the loop” by working with clinicians to notify the patient of the finding, coordinate patient follow up, and document the finding and long term follow up. We hope this can serve as a basic framework to help large health systems design an incidental findings workflow to improve follow up rates and reduce patient harm.