Detection of Focal Longitudinal Changes in the Brain by Subtraction of MR Images.

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.

Modelling of impaired cerebral blood flow due to gaseous emboli.

Bubbles introduced to the arterial circulation during invasive medical procedures can have devastating consequences for brain function but their effects are currently difficult to quantify. Here we present a Monte Carlo simulation investigating the impact of gas bubbles on cerebral blood flow. For the first time, this model includes realistic adhesion forces, bubble deformation, fluid dynamical considerations, and bubble dissolution. This allows investigation of the effects of buoyancy, solubility, and blood pressure on embolus clearance. Our results illustrate that blockages depend on several factors, including the number and size distribution of incident emboli, dissolution time and blood pressure. We found it essential to model the deformation of bubbles to avoid overestimation of arterial obstruction. Incorporation of buoyancy effects within our model slightly reduced the overall level of obstruction but did not decrease embolus clearance times. We found that higher blood pressures generate lower levels of obstruction and improve embolus clearance. Finally, we demonstrate the effects of gas solubility and discuss potential clinical applications of the model.