SU‐C‐218‐06: Evaluation of a CT‐Based, Semi‐Automated Lung Mass Estimation Method Under Varying Acquisition Conditions

Purpose: To evaluate a simple, semi‐automated lung mass estimation method on CT scans from a variety of acquisition techniques for mass correction of MIRD dose estimates. Methods: CT scans from ten patients undergoing stereotactic body radiation therapy treatment planning on a GE DST PET/CT scanner were analyzed retrospectively. For each patient, free‐ breathing (FB) and respiratory gated 4D‐CT scans were acquired. 4D‐CT scans were reconstructed and sorted into ten phases representing one complete respiratory cycle. An average CT (ACT) was derived from the ten phase reconstructions. Half the patients also had breath‐hold (BH) scans. Scans were analyzed using Osirix MD's V 1.3 (Pixmeo, Geneva Switzerland) Grow Region segmentation tool. Lung volume; average lung, tissue (∼lcm ROI in the descending aorta), and air (∼1 cm ROI in the trachea) HU's were recorded for each acquisition type. Lung mass was calculated by assuming each voxel is a linear combination of only air and tissue. The fraction of total lung volume consisting of lung tissue is assumed to be (lung HU ‐ air HU) / (tissue HU ‐ air HU), and mass = total volume X fraction X 1 g/cm3. Results: 4D‐CT mass estimates showed variability, with a minimum at end‐expiration and a maximum just after end‐inspiration. ACT generally produced the highest estimate. FB and BH estimates were near the median. Conclusions: Although calculated mass increased with increasing total lung volume, the difference between minimum and maximum population means in the 4D‐CT data was 5.5%. FB and BH estimates fell within that 5.5%, while the ACT mean was 9.8% above the global mean. The range of estimated masses (524 g to 977 g) suggests the value of lung mass estimates for more patient‐specific MIRD dose assessment. Excluding ACT, the results indicate that any of the acquisition techniques can provide a reasonable lung mass estimate. This work was supported in part by a residency grant from the AAPM.