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A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis
Ziad Saleh1, Maria Thor, Aditya P Apte
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave., New York, NY 10065, USA.
Physics in Medicine and Biology
|July 30, 2016
Summary
The distance discordance metric (DDM) quantifies deformable image registration (DIR) uncertainties in adaptive radiotherapy (RT). DDM shows significant variability and correlates well with volume changes, improving outlier identification for better treatment adaptation.
Area of Science:
- Medical Physics
- Radiotherapy
- Image Analysis
Background:
- Deformable image registration (DIR) is crucial for adaptive radiotherapy (RT) to account for anatomical changes in patients undergoing treatment.
- Existing methods for evaluating DIR uncertainties are not widely adopted, necessitating new quantitative approaches.
Purpose of the Study:
- To evaluate intra-patient DIR uncertainties for the bladder and rectum in prostate cancer RT.
- To assess the utility of the distance discordance metric (DDM) for quantifying DIR uncertainties.
Main Methods:
- DIR uncertainties were evaluated using DDM on weekly CT scans from 38 prostate cancer patients.
- Group-wise B-spline registration was employed to calculate DDM from repeat CT scans.
- DDM was compared with other metrics like inverse consistency error (ICE), transitivity error (TE), Dice similarity (DSC), and volume ratios.
Main Results:
- DDM revealed significant DIR variability across subjects and structures, with mean DDM ranging from 1-13 mm for the bladder and rectum.
- DDM showed a strong correlation with volume ratios (R=0.51-0.68) and a negative correlation with DSC (R=-0.23 to -0.63).
- DDM correlated better with volume ratios and DSC than with TE or ICE.
Conclusions:
- The DDM is a valuable metric for quantifying DIR uncertainties in adaptive RT.
- DDM effectively identifies regions with substantial DIR variability and potential anatomical/scan outliers.
- The DDM can enhance the adaptive RT process by providing more accurate assessments of image registration accuracy.
