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Updated: Jun 17, 2026
Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
Published on: June 7, 2020
Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching
Robert D Ambrosini1, Peng Wang, Walter G O'Dell
1Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA.
Purpose:
To demonstrate the efficacy of an automated three-dimensional (3D) template matching-based algorithm 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 radiologists 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 calculated 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 datasets 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.
