Original Research Computer-Aided Detection of Metastatic Brain Tumors Using Automated Three-Dimensional Template Matching

Purpose: To demonstrate the efficacy of an automated three-dimensional (3D) template matching-based algo-rithm in detecting brain metastases on conventional MR scans and the potential of our algorithm to be developed into a computer-aided detection tool that will allow radiol-ogists to maintain a high level of detection sensitivity while reducing image reading time. Materials and Methods: Spherical tumor appearance models were created to match the expected geometry of brain metastases while accounting for partial volume effects and offsets due to the cut of MRI sampling planes. A 3D normalized cross-correlation coefficient was calcu-lated between the brain volume and spherical templates of varying radii using a fast frequency domain algorithm to identify likely positions of brain metastases. Results: Algorithm parameters were optimized on training datasets, and then data were collected on 22 patient data-sets containing 79 total brain metastases producing a sensitivity of 89.9 % with a false positive rate of 0.22 per image slice when restricted to the brain mass. Conclusion: Study results demonstrate that the 3D template matching-based method can be an effective, fast, and accurate approach that could serve as a useful tool for assisting radiologists in providing earlier and more definitive diagnoses of metastases within the brain