VALIDATION OF DIR Raj Varadhan, PhD, DABMP Minneapolis Radiation Oncology
Overview Basics: Registration Framework, Theory Discuss Validation techniques Using Synthetic CT data & Phantoms What metrics to use? Validation in uniform low contrast anatomy Commissioning Take Home Messages Conclusion
Registration Rigid registration Affine Registration Deformable Registration
Registration
DIR
Deformable Transformation y y Transform Fixed Image x Moving Image x
Deformable Transformation y y Transform Fixed Image x Moving Image x
DIR Every DIR-Deformation Model, Similarity measure and Optimization method Parameter or model based (TPS, Bspline etc..) Non parametetric method ( Physical properties) e.g. Demons Optical Flow Equation ( Thirions) Image Matching evaluated with a metric MI, SSD, CC etc..for intensity based algorithms Contour based measures for model based
Example:Similarity Metric-SSD
Example:Deformation Models- B-Splines
BSplines Order Three Y = ( - 3X 3-6X 2 + 4 )/6 Piece-Wise Y = ( 3X 3-6X 2 + 4 )/6 Y = (2+X) 3 / 6 Y = (2-X) 3 / 6-2 -1 0 1 2
Deformable Transformation y x
BSplines Grid & Image Grid Calculation are made in an Element by Element basis
BSplines Grid & Image Grid Elements are connected at Nodes at which the displacement is solved
BSplines Grid & Image Grid Efficiency is gained by elemental computation
BSplines Grid & Image Grid Domain subdivision (Mesh) can be tailored to the underlying geometry of the image.
DIR Validation: Purpose of this study is to describe a framework to test the. accuracy of DIR based on computational modeling and evaluating using inverse consistency and other methods
Platform built Two ITK algorithms B-Splines &Diffeomorphic Demons implemented 3D-Slicer was used as visualization platform DIR analysis tools integrated with 3D-Slicer. Anatomical Correspondence, Physical characteristics of DVF and Image characteristics investigated to validate DIR
Methods & Materials ImSimQA software was used to generate a clinically relevant organ deformation in prostate, head & neck and lung cases. DIR was performed using B-Splines and Diffeomorphic Demons algorithms in both forward and inverse directions where the roles of source and target images were switched. DIR analysis was done based on Inverse consistency error, anatomical correspondence and image characteristics using custom built modules on 3D Slicer.
Methods-Flow chart for evaluation of DIR
Prostate: Applied Deformation
Head & Neck: Applied deformation
Lung: Applied Deformation
What Metrics to use? Should we look at the Deformation Vector Field? (DVF) Inverse Consistency? RT structure comparison? Image Characteristics?
Prostate Example: Forward DVF, Inverse DVF & Inverse Consistency Error for Demons
Results: Inverse Consistency Error 20 Inverse Consistency Error (ICE) comparison 18 16 14 12 ICE (mm) 10 8 PROSTATE HEAD & NECK LUNG 6 4 2 0 Diffeomorphic Demons B Splines ImSimQA forward DVF & forward diffeomorphic demons ImSimQA forward DVF & forward B Splines ImSimQA Inverse DVF & inverse Diffeomorphic Demons ImSimQA Inverse DVF & Inverse B Splines
Results: MSE, Jacobian & Harmonic Energy of DVF
Results: Prostate Anatomical Correspondence
Results: Head & Neck
Results: Lung
Results: Prostate: Our results on prostate case indicate that the ICE was comparable to both algorithms. Also, the MSE values were very similar for both methods. However the B-Splines algorithm had significantly better anatomical correspondence for rectum and prostate than diffeomorphic demons algorithm. So considering the anatomical correspondence of the RT structures one can conclude that the B-Splines algorithm performed better. In this example the MSE and ICE evaluation parameters provide no criteria to determine which method performs better. Head and Neck: For the head and neck case, the ICE was much larger for the demons algorithm (6.5 mm) as compared to B-Splines (0.7 mm). The MSE was comparable for both algorithms. However, since the induced neck flexion was large, neither algorithm had a desired anatomical correspondence for PTV and organs at risk that could make the result clinically acceptable. Similar to the prostate case, this example also indicates that considering only the ICE and MSE methods could lead to false positive conclusions. Lung: In the lung case B-Splines algorithm accurately estimated the deformations between images with variable contrast and was clearly superior in all the metrics that were evaluated. The demons algorithm had gross errors in areas of contrast differences between images. This was the only example where all metrics used for the DIR evaluation were in full agreement on the decision making of the DIR algorithm performance.
Conclusion: We conclude that the proposed framework offers the application of known deformations on any patient or phantom image sets, that provide clinical medical physicist tools to test, understand and quantify limitations of each algorithm before implementing deformable image registration in the clinic. The evaluation based on anatomical correspondence, physical characteristics of deformation field and image characteristics can facilitate DIR verification with the ultimate goal of implementing adaptive radiotherapy. The suitability of application of a particular evaluation method is strongly dependent on the clinical deformation observed.
How does DIR perform in uniform low contrast anatomy? Liver Pancreas Stomach Bladder Kidney Prostate Diaphragm Small Bowel
How to evaluate DIR in Low Contrast Anatomy? Accepted for Publication in Medical Physics
Force vs Def Deformation States
Implanted Markers
DIR Validation
Force vs Deformation
Results
Markers Introduced
Results
Conclusion in low contrast anatomy DIR performance very poor. There is a threshold limit of only ~ 5mm for commercial DIR algorithms The sensitivity of the DIR performance to the number of fiducial markers present indicates that if the DIR performance is solely assessed with the various contrast rich features present in clinical anatomy, the results may not be reflective of the true DIR performance in uniform low contrast anatomy.
Commissioning Visual verification NOT enough for initial commissioning. Quantify based on landmarks, Contours (DSC) or digital phantom data In clinical image, verify if the landmarks from image X map to the correct position in image Y TRE- Manual Technique, can identify gross errors Average residual error between the identified points on Study B and the points identified on Study A, mapped onto Study A through DIR
Commissioning Use the DIR QA if vendor provides it. Visualize the DVF in terms of vector maps if vendor provides that. For contour based comparison, calculate DSC of deformed RT contours by using Ground Truth RT structure on moving image If using digital phantom, compare known applied deformation with registration results Establish some patient specific QA policy prior to DIR put into clinical practice
Virtual Phantoms: Use Virtual phantom for validation prior to Clinical use.
Take Home Messages Use Virtual phantom for validation prior to clinical use.
Take Home Messages: 1) Understand the basics of image registration techniques 2) Understand the nuts and bolts of your commercial DIR algorithm to ensure appropriate clinical use 3) Perform some validation to verify the basic components of the DIR algorithm and what the potential limitations may be. ( Use digital phantom data or multiple kvct scans of the same patient)
Take Home Messages 4) Establish a protocol to ensure CMDs are using registration appropriately. Do NOT blindly use the workflows set up by the vendor 5) Have a consistent validation practice of the DIR or fusion in general preferably with the RO 6) Be VERY cautious of using DIR in uniform low contrast anatomy and especially for dose warping purposes 7. Communicate the accuracy of DIR to RO with respect alignment uncertainty
Vendors Should provide more details of regularization and algorithm. Provide DVF export capability. Provide ability to calculate TRE after DIR after identifying landmarks on 2 images. Ability to easily calculate DSC, Hausdorff distance etc.. More transparency!
Conclusion DIR is here to stay. Can be a great clinical tool if used appropriately-understand motion, integrate multimodality images, estimate actual dose delivered over the course of RT etc.. ART is promising especially for hypo fractionation schemes with the use of DIR What is lacking is QA Physicists must adopt and implement QA Deformable dose verification is challenging Thanks!
Dose Accumulation for Adaptive Therapy using Deformable Registration Deformable dose accumulation for adaptive therapy assumes that the dose during the previous part of the therapy was delivered as planned to the structure of interest and that the volume changed at the time of the new plan. The previously delivered dose is deformed to the new structure volume based on deformation of the old CT to the new CT. Assume the organ of interest has lost half its volume since the original planning CT, that the deformation is such that half of every original voxel is deformed to a voxel in the new volume and that the dose voxels are equal in size to the CT voxels.
Dose Accumulation for Tumor Recurrence using Deformable Registration
Deformation from Tumor Response
Deformation from Tumor Response
Deformation from Tumor Response
Deformation from Tumor Response
Deformation from Tumor Response