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Adaptive Boosting and Multi-Layer Perceptron Neural Network (ADMLP-NN) for Predicting Real-Time 3D Deformation Field Maps (DFM) for 4D Target Tracking

J Pham1*, W Harris2 , W Sun3 , Z Yang4 , F Yin1 , L Ren1 , (1) Duke University Medical Center, Durham, NC, (2) University of Pennsylvania, Philadelphia, PA, (4) Shandong University, Qingdao, China, (5) UT Southwestern Medical Center, Dallas, TX


(Thursday, 7/18/2019) 7:30 AM - 9:30 AM

Room: 225BCD

Purpose: To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking.

Methods: One phase of a prior 4D-MRI is set as the prior phase, MRIprior. Principal component analysis (PCA) is used to extract 3 major respiratory deformation modes from the DFMs generated between the prior and remaining phases. VC-MRI at each time-step is considered a deformation of MRIprior, where the DFM is represented as a weighted linear combination of the PCA components. The PCA weightings are solved by minimizing the differences between on-board 2D cine MRI and its corresponding VC-MRI slice. PCA weightings solved during the first 90s are used to train an ADMLP-NN to predict PCA weightings 120 ms ahead over the next 30s. The ADMLP-NN uses several identical multi-layer perceptron neural networks (MLP-NN) with an adaptive boosting algorithm. The predicted PCA weighting are used to build predicted 3D DFM and ultimately, predicted VC-MRIs. The method was evaluated using a 4D computerized phantom (XCAT) with patient-specific RPM curves. Effects of breathing amplitude change and ADMLP-NN parameter variation were assessed. The accuracy of the PCA curve prediction in each direction was evaluated. The predicted real-time 3D tumor was evaluated against the ground-truth using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center-of-Mass-Shift(COMS).

Results: The average VPD/VDC/COMS for the predicted tumor were 17.53±3.81%/0.92±0.02/1.20±0.65mm, respectively, across all predicted time-steps. The correlation coefficients between predicted and actual PCA curves generated through VC-MRI estimation for the 1st/2nd principal components were 0.99/0.88, 0.99/0.63, and 0.79/0.28 in the SI, AP, and lateral directions, respectively. The prediction is relatively robust against number of neurons used in ADMLP-NN.

Conclusion: Preliminary studies showed the robustness to use artificial neural networks to predict 3D DFMs to generate predicted VC-MRI for 4D target tracking.


Image-guided Therapy, MRI, Targeted Radiotherapy


IM/TH- MRI in Radiation Therapy: MRI/Linear accelerator combined- IGRT and tracking

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