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Volume change determination of metastatic lung tumors in CT images using 3-D template matching

The ability of a clinician to properly detect changes in the size of lung nodules over time is a vital element to both the diagnosis of malignant growths and the monitoring of the response of cancerous lesions to therapy. We have developed a novel metastasis sizing algorithm based on 3-D template matching with spherical tumor appearance models that were created to match the expected geometry of the tumors of interest while accounting for potential spatial offsets of nodules in the slice thickness direction. The spherical template that best-fits the overall volume of each lung metastasis was determined through the optimization of the 3-D normalized cross-correlation coefficients (NCCC) calculated between the templates and the nodules. A total of 17 different lung metastases were extracted manually from real patient CT datasets and reconstructed in 3-D using spherical harmonics equations to generate simulated nodules for testing our algorithm. Each metastasis 3-D shape was then subjected to 10%, 25%, 50%, 75% and 90% scaling of its volume to allow for 5 possible volume change combinations relative to the original size per each reconstructed nodule and inserted back into CT datasets with appropriate blurring and noise addition. When plotted against the true volume change, the nodule volume changes calculated by our algorithm for these 85 data points exhibited a high degree of accuracy (slope = 0.9817, R2 = 0.9957). Our results demonstrate that the 3-D template matching method can be an effective, fast, and accurate tool for automated sizing of metastatic tumors.