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SU-E-J-177: Characterization of the Effect of 'Lung Detail' CT Reconstruction Algorithm on Radiation Therapy Dose
Purpose:
Precise tumor delineation is important in thoracic radiation therapy planning, and using a 'lung detail' computed tomography (CT) reconstruction algorithm can assist in visualizing the tumor. We seek to determine the dosimetric impact of utilizing a lung detail algorithm versus a standard algorithm on calculated dose in radiation treatment planning.
Methods:
Ten patients, with 12 tumors, were analyzed in this study. Two CT scans, one reconstructed using a standard algorithm and one using a lung detail algorithm, were generated for each of 12 lung tumors. Treatment plans were calculated for each CT scan, with 7 tumors receiving stereotactic ablative radiotherapy (SABR) and 5 receiving intensity-modulated radiation therapy (IMRT). The Hounsfield unit (HU) and dose values for each voxel of the planning tumor volume (PTV), esophagus, spinal cord, and contralateral lung in both the CT and dose images were exported to MATLAB. For each contour, the voxel-by-voxel differences in the HU and dose distributions between the two scans were analyzed along with dose-volume histogram (DVH) data.
Results:
Despite changes in HU values, the voxel-by-voxel analysis showed a negligible shift in dose values. The mean differences in dose for PTV, esophagus, spinal cord, and contralateral lung ranged from -12.12 to 22.57, -2.21 to 7.40, -0.50 to 5.93, and -1.12 to 7.41 cGy, respectively. DVH comparisons demonstrated no meaningful difference between plans. The mean PTV, esophagus, spinal cord, and contralateral lung doses measured from the DVH shifted between plans an average of 3.5, 2.93, -0.6 and -0.35 cGy, respectively. These dose differences are all less than 1% of the dose prescribed to the tumor and are not measurable by current technology.
Conclusions:
The lung detail reconstruction algorithm, when applied to thoracic radiation treatment planning CT scans, can help precisely delineate tumor with negligible dosimetric impact.
