Background and purpose: The detection of new subtle brain pathology on MR imaging is a time-consuming and error-prone task for the radiologist. This article introduces and evaluates an image-registration and subtraction method for highlighting small changes in the brain with a view to minimizing the risk of missed pathology and reducing fatigue.
Materials and methods: We present a fully automated algorithm for highlighting subtle changes between multiple serially acquired brain MR images with a novel approach to registration and MR imaging bias field correction. The method was evaluated for the detection of new lesions in 77 patients undergoing cardiac surgery, by using pairs of fluid-attenuated inversion recovery MR images acquired 1-2 weeks before the operation and 6-8 weeks postoperatively. Three radiologists reviewed the images.
Results: On the basis of qualitative comparison of pre- and postsurgery FLAIR images, radiologists identified 37 new ischemic lesions in 22 patients. When these images were accompanied by a subtraction image, 46 new ischemic lesions were identified in 26 patients. After we accounted for interpatient and interradiologist variability using a multilevel statistical model, the likelihood of detecting a lesion was 2.59 (95% CI, 1.18-5.67) times greater when aided by the subtraction algorithm (P = .017). Radiologists also reviewed the images significantly faster (P < .001) by using the subtraction image (mean, 42 seconds; 95% CI, 29-60 seconds) than through qualitative assessment alone (mean, 66 seconds; 95% CI, 46-96 seconds).
Conclusions: Use of this new subtraction algorithm would result in considerable savings in the time required to review images and in improved sensitivity to subtle focal pathology.
© 2017 by American Journal of Neuroradiology.