Abstract
PURPOSE: To investigate a method to classify tissues types for synthetic CT generation using MRI for treatment planning in abdominal radiotherapy. METHODS: An institutional review board approved volunteer study was performed on a 3T MRI scanner. In-phase, fat and water images were acquired for five volunteers with breath-hold using an mDixon pulse sequence. A method to classify different tissue types for synthetic CT generation in the abdomen was developed. Three tissue clusters (fat, high-density tissue, and spine/air/lungs) were generated using a fuzzy-c means clustering algorithm. The third cluster was further segmented into three sub-clusters that represented spine, air, and lungs. Therefore, five segments were automatically generated. To evaluate segmentation accuracy using the method, the five segments were manually contoured on MRI images as the ground truth, and the volume ratio, Dice coefficient, and Hausdorff distance metric were calculated. The dosimetric effect of segmentation accuracy was evaluated on simulated targets close to air, lungs, and spine using a two-arc volumetric modulated arc therapy (VMAT) technique. RESULTS: The volume ratio of auto-segmentation to manual segmentation was 0.88-2.1 for the air segment and 0.72-1.13 for the remaining segments. The range of the Dice coefficient was 0.24-0.83, 0.84-0.93, 0.94-0.98, 0.93-0.96, and 0.76-0.79 for air, fat, lungs, high-density tissue, and spine, respectively. The range of the mean Hausdorff distance was 3-29.1 mm, 0.5-1.3 mm, 0.4-1 mm, 0.7-1.6 mm, and 1.2-1.4 mm for air, fat, lungs, high-density tissue, and spine, respectively. Despite worse segmentation accuracy in air and spine, the dosimetric effect was 0.2% ± 0.2%, with a maximum difference of 0.8% for all target locations. CONCLUSION: A method to generate synthetic CT in the abdomen was developed, and segmentation accuracy and its dosimetric effect were evaluated. Our results demonstrate the potential of using MRI alone for treatment planning in the abdomen.