Program Information
A Framework of Physical-Law-Based Respiratory-Perturbation Modeling for Motion Prediction
G Li1*, C Gaebler2 , H Huang3 , A Yuan4 , J Wei5 , (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Memorial Sloan Kettering Cancer Center, New York, NY, (4) Memorial Sloan Kettering Cancer Center, New York, NY, (5) City College of New York, New York, NY
Presentations
SU-E-J-54 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:
A physics-law-based respiratory motion model should be more resilient to breathing irregularities. We assess the accuracy of a physical respiratory motion-perturbation (RMP) model, which is derived with physical relationships, patient-specific anatomy, and measureable breathing parameters.
Methods:
The analytical RMP model was developed under the assumption that chest raise (ΔVthorax) contributes primarily to the anterior-posterior motion of the lung while belly motion (ΔVabdomen) caused by diaphragmatic motion contributes to superior-inferior motion. All model parameters were determined from patient-specific anatomy. This RMP model aims to use the base motion trajectory from simulation 4DCT and motion perturbations due to breathing irregularities at treatment from optical surface imaging (OSI) to calculate the tidal volume (TV=ΔVtorso) and breathing pattern (BPv=ΔVthorax/ΔVtorso). To test the prediction accuracy, two sets of 4DCT from eleven patients were used to predict lung motion from one set to the other. An in-house ITK-based C/C++ program was developed and used to automatically identify 20 bifurcation points per patient, calculate their motion trajectories, and sets their correspondence between the two 4DCT with visual verification. The base motion from one 4DCT and perturbations (ΔTV and ΔBP) from the other 4DCT (mimicking OSI) were used for prediction and the accuracy was assessed by comparing the predicted with the actual motion trajectory: A comparison was made using an established 5D model that was trained with one 4DCT to predict the other. The motion difference between the two 4DCT was used as a control.
Results:
The RMP model prediction provides the prediction accuracy of -0.1±1.9mm, improved from the control of 0±3mm. The RMP produces similar results to the 5D model, but does not require model training. The motion variations range is 0-15mm.
Conclusion:
The physical RMP model, which does not require training, predicts motion with reduced uncertainties from breathing irregularities. Further prospective patient studies are needed.
Funding Support, Disclosures, and Conflict of Interest: This work is in part supported by NIH (U54CA137788 and U54CA132378).
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