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TU-F-18A-03: Improving Tissue Segmentation for Monte Carlo Dose Calculation Using DECT Data.

PURPOSE
To develop a new segmentation technique using dual energy CT (DECT) to overcome limitations related to segmentation from a standard Hounsfield unit (HU) to electron density (ED) calibration curve. Both methods are compared with a Monte Carlo analysis of dose distribution.


METHODS
DECT allows a direct calculation of both ED and effective atomic number (EAN) within a given voxel. The EAN is here defined as a function of the total electron cross-section of a medium. These values can be effectively acquired using a calibrated method from scans at two different energies. A prior stoichiometric calibration on a Gammex RMI phantom allows us to find the parameters to calculate EAN and ED within a voxel. Scans from a Siemens SOMATOM Definition Flash dual source system provided the data for our study. A Monte Carlo analysis compares dose distribution simulated by dosxyz_nrc, considering a head phantom defined by both segmentation techniques.


RESULTS
Results from depth dose and dose profile calculations show that materials with different atomic compositions but similar EAN present differences of less than 1%. Therefore, it is possible to define a short list of basis materials from which density can be adapted to imitate interaction behavior of any tissue. Comparison of the dose distributions on both segmentations shows a difference of 50% in dose in areas surrounding bone at low energy.


CONCLUSION
The presented segmentation technique allows a more accurate medium definition in each voxel, especially in areas of tissue transition. Since the behavior of human tissues is highly sensitive at low energies, this reduces the errors on calculated dose distribution. This method could be further developed to optimize the tissue characterization based on anatomic site.