Detection of Optic Neuritis on Routine Brain MRI without and with the Assistance of an Image Postprocessing Algorithm.

BACKGROUND AND PURPOSE: At times, there is a clinical need for using routine brain MR imaging performed close to the time of onset of patients' visual symptoms to firmly establish the diagnosis of optic neuritis. Our aim was to assess the diagnostic performance of radiologists in detecting optic neuritis on routine brain MR images and whether this performance could be enhanced using a postprocessing algorithm. MATERIALS AND METHODS: In this retrospective case-control study of 60 patients (37 women, 23 men; mean age, 47.2 [SD, 17.9] years), 2 blinded neuroradiologists evaluated T2-weighted FLAIR and contrast-enhanced T1WI from brain MR imaging for the presence of imaging evidence of optic neuritis. Images were processed using an image-processing algorithm that aimed to selectively accentuate the signal intensity of diseased optic nerves. We assessed the effect of image processing on the contrast-to-noise ratio between the optic nerves and normal-appearing white matter and on the diagnostic performance of the neuroradiologists, including the interobserver reliability. RESULTS: The average sensitivity of readers was 55%, 56.5%, and 30.0% on FLAIR, coronal contrast-enhanced T1WI, and axial contrast-enhanced T1WI, respectively. Sensitivities were lower in the absence of fat saturation on FLAIR (P = .001) and coronal contrast-enhanced T1WI (P = .04). Processing increased the contrast-to-noise ratio of diseased (P value range = .03 to <.001) but not of control optic nerves. Processing did not improve the sensitivity but improved the specificity and positive predictive value. Interobserver agreement improved from slight to good. CONCLUSIONS: Detection of optic neuritis on routine brain MR imaging is challenging. Specificity, positive predictive value, and interobserver agreement can be improved by postprocessing of MR images.

Evaluating the Utility of a Postprocessing Algorithm for MRI Evaluation of Optic Neuritis.

BACKGROUND AND PURPOSE: MR imaging is useful for the detection and/or confirmation of optic neuritis. The objective of this study was to determine whether a postprocessing algorithm selectively increases the contrast-to-noise ratio of abnormal optic nerves in optic neuritis, facilitating this diagnosis on MR imaging. MATERIALS AND METHODS: In this retrospective case-control study, coronal FLAIR images and coronal contrast-enhanced T1WI from 44 patients (31 eyes with clinically confirmed optic neuritis and 28 control eyes) underwent processing using a proprietary postprocessing algorithm designed to detect and visually highlight regions of contiguous increases in signal intensity by increasing the signal intensities of regions that exceed a predetermined threshold. For quantitative evaluation of the effect on image processing, the contrast-to-noise ratio of equivalent ROIs and the contrast-to-noise ratio between optic nerves and normal-appearing white matter were measured on baseline and processed images. The effect of image-processing on diagnostic performance was evaluated by masked reviews of baseline and processed images by 6 readers with varying experience levels. RESULTS: In abnormal nerves, processing resulted in an increase in the median contrast-to-noise ratio from 17.8 to 85.0 (P < .001) on FLAIR and from 19.4 to 93.7 (P < .001) on contrast-enhanced images. The contrast-to-noise ratio for control optic nerves was not affected by processing (P = 0.13). Image processing had a beneficial effect on radiologists' diagnostic performance, with an improvement in sensitivities for 5/6 readers and relatively unchanged specificities. Interobserver agreement improved following processing. CONCLUSIONS: Processing resulted in a selective increase in the contrast-to-noise ratio for diseased nerves and corresponding improvement in the detection of optic neuritis on MR imaging by radiologists.