Dose warping performance in deformable image registration in lung

作者：Moriya Shunsuke; Tachibana Hidenobu^*; Kitamura Nozomi; Sawant Amit; Sato Masanori

来源：Physica Medica, 2017, 37: 16-23.

DOI：10.1016/j.ejmp.2017.03.016

• Deformable image registration
• Generalized equivalent uniform dose
• Four-dimensional computed tomography
• Lung cancer

Purpose: It is unclear that spatial accuracy can reflect the impact of deformed dose distribution. In this study, we used dosimetric parameters to compare an in-house deformable image registration (DIR) system using NiftyReg, with two commercially available systems, MIM Maestro (MIM) and Velocity AI (Velocity).
Methods: For 19 non-small-cell lung cancer patients, the peak inspiration (0%)-4DCT images were deformed to the peak expiration (50%)-4DCT images using each of the three DIR systems, which included computation of the deformation vector fields (DVF). The 0%-gross tumor volume (GTV) and the 0%-dose distribution were also then deformed using the DVFs. The agreement in the dose distributions for the GTVs was evaluated using generalized equivalent uniform dose (gEUD), mean dose (D-mean), and three-dimensional (3D) gamma index (criteria: 3 mm/3%). Additionally, a Dice similarity coefficient (DSC) was used to measure the similarity of the GTV volumes.
Results: D-mean and gEUD demonstrated good agreement between the original and deformed dose distributions (differences were generally less than 3%) in 17 of the patients. In two other patients, the Velocity system resulted in differences in gEUD of 50.1% and 29.7% and in D-mean of 11.8% and 4.78%. The gamma index comparison showed statistically significant differences for the in-house DIR vs. MIM, and MIM vs. Velocity.
Conclusions: The finely tuned in-house DIR system could achieve similar spatial and dose accuracy to the commercial systems. Care must be taken, as we found errors of more than 5% for D-mean and 30% for gEUD, even with a commercially available DIR tool.

• 出版日期2017-5
• 单位中国医学科学院肿瘤医院