Lung tumor motion prediction using data learned offline and during treatment

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1 GA1 Lung tumor motion prediction using data learned offline and during treatment Troy Teo, K. Guo, B. Ahmed, P. Kawalec, N. Alayoubi & S. Pistorius Medical Physics, CancerCare Manitoba, Winnipeg. Physics and Astronomy, University of Manitoba, Winnipeg. 1

2 Slide 1 GA1 Ganiyu Asuni, 2/24/2014

3 Introduction Intra fraction image guided adaptive RT Clinical implementation of concurrent (intra fraction) tumor irradiation & tracking: i. Cyberknife system (Accuray Inc) ii. Vero gimbaled linac system (Mitsubishi, Japan & BrainLAB) iii. DMLC system with EM guided transponder (Varian) * * P. Keall, et al., First clinical implementation of electromagnetic transponder guided MLC tracking, Med. Phys. 41(2) (5pp.) (2014). 2

4 Introduction Intra fraction image guided adaptive RT Image-guidance: Electronic portal image (EPI) tracking Adaptation of treatment: Modify treatment aperture 4. Motion correction 3. Automatic real time motion detection 5. Dynamic treatment delivery 1. High energy x-ray treatment beam 2. Image formed from residual treatment beam 3

$Introduction Intra fraction image guided adaptive RT Image-guidance: Electronic portal image (EPI) tracking Adaptation of treatment: Modify$ High energy x-ray treatment beam 2. Image formed from residual treatment beam Tracking of tumor with global motion Accuracy : ~0.5 mm * * P.T. Teo et.

High energy x-ray treatment beam 2. Image formed from residual treatment beam Tracking of tumor with global motion Accuracy : ~0.5 mm * * P.T. Teo et.

5 Introduction Intra fraction image guided adaptive RT Image-guidance: Electronic portal image (EPI) tracking Adaptation of treatment: Modify treatment aperture 4. Motion correction 3. Automatic real time motion detection 5. Dynamic treatment delivery 1. High energy x-ray treatment beam 2. Image formed from residual treatment beam Tracking of tumor with global motion Accuracy : ~0.5 mm * * P.T. Teo et. al., Tracking lung tumour motion using a dynamically weighted optical flow algorithm and electronic 4 portal imaging device", Measurement Science Tech. (Inst. of Physics), 2013.

6 Introduction The need for prediction in adaptive RT No tracking system responds instantaneously (system latency) - system lag times can be ms 1 1. S. Pollock, D. Lee, P. Keall and T. Kima, Audiovisual biofeedback improves motion prediction accuracy, Med. Phys. 40 (4), , (2013). 5

7 Introduction The need for prediction in adaptive RT No tracking system responds instantaneously (system latency) - system lag times can be ms 1 - future tumor position should be predicted 5. Motion correction 4. Motion Prediction to compensate system latency 3. Automatic real time motion detection 6. Dynamic treatment delivery 1. High energy x-ray treatment beam 2. Image formed from residual treatment beam 1. S. Pollock, D. Lee, P. Keall and T. Kima, Audiovisual biofeedback improves motion prediction accuracy, Med. Phys. 40 (4), , (2013). 6

8 Introduction How to predict tumor motion? Build a prediction model : learning of tumor motion pattern offline learning with pre treatment data, or online learning (adaptive) during treatment delivery 7

9 Introduction Offline vs online learning Offline learning (Pros): prediction model optimized prior to treatment prediction can potentially commence when treatment begins Offline learning (cons) : i. motions learned motions during treatment delivery 2 ii. model obsolete over time Model optimized offline Position (mm) Prediction (during treatment delivery) Time (s) Actual Predicted 2. J. Schuster, et al., Variations of the tumor position in frameless lung sbrt: assessment of predictive factors including tumor volume changes, J Nucl Med Radiat Ther 4 (147) (2013). 8

Introduction Offline vs online learning Online learning (Pros): model updated with current trend errors do not increase over time Online learning (Cons): longer time to learn &

10 Introduction Offline vs online learning Online learning (Pros): model updated with current trend errors do not increase over time Online learning (Cons): longer time to learn & optimize longer time before making 1 st prediction adaption of treatment can only occur after initial waiting period 9 takes longer time to optimize model during initial treatment delivery 9

11 Introduction Objectives of current study To predict tumor position over a prediction horizon of 650ms (latency) with aim of reducing initial waiting period prior to making 1 st prediction. 10

12 Introduction Objectives of current study To predict tumor position over a prediction horizon of 650ms (latency) with aim of reducing initial waiting period prior to making 1 st prediction. Methods: i. optimize model with tumor traces from patient database (offline learning) ii. use 1 st breathing cycle detected during treatment delivery to synchronize & adapt model for online learning 11

13 Methods 1. Neural Network (NN) Design Parameters to optimize for NN: Weights: memory of an NN (updated by a learning algorithm) Input nodes : past histories needed for pattern recognition hidden nodes : storage capacity 12

14 Methods 1. Neural network design with offline learning (Nos. of hidden neurons & input data length) Parametric study with : input data size & nos. of hidden neuron 10 to 100 (steps of 10) obtain 2D array of MSE for each patient

15 Methods 2. Generalized Neural Network (GNN) with offline learning To determine parameters for GNN: i. average prediction response from 7 patients Patient 1 Patient 6 Patient 7 Average

parameters for GNN: i. average prediction response from 7 patients ii.

16 Methods 2. Generalized Neural Network (GNN) with offline learning To determine parameters for GNN: i. average prediction response from 7 patients ii. select 1 pair of input data size & # hidden neurons ( i.e.35, 20) for offline optimized GNN Patient 1 Patient 6 Patient 7 35 samples ~ 4.66 s Average

17 Methods 3. Sliding window online learning (treatment delivery) S I position (mm) Time (s) Sliding window width = 1 st period of tumor trajectory detected during treatment delivery Prediction horizon = 650ms (~5 EPID image sample) Predicted position 16

18 Methods 3. Sliding window online learning (treatment delivery) S I position (mm) Time (s) Sliding window width = 1 st period of tumor trajectory detected during treatment delivery Prediction horizon = 650ms (~5 EPID image sample) Predicted position 17

19 Methods 3. Sliding window online learning (treatment delivery) S I position (mm) Time (s) Sliding window width = 1 st period of tumor trajectory detected during treatment delivery Prediction horizon = 650ms (~5 EPID image sample) Predicted position 18

20 Methods 3. Sliding window online learning (treatment delivery) S I position (mm) Time (s) Sliding window width = 1 st period of tumor trajectory detected during treatment delivery Prediction horizon = 650ms (~5 EPID image sample) Predicted position 19

21 Methods 3. Sliding window online learning (treatment delivery) S I position (mm) Time (s) Sliding window width = 1 st period of tumor trajectory detected during treatment delivery Prediction horizon = 650ms (~5 EPID image sample) Predicted position 20

22 Methods 3. Sliding window online learning (treatment delivery) S I position (mm) Time (s) Sliding window width = 1 st period of tumor trajectory detected during treatment delivery Prediction horizon = 650ms (~5 EPID image sample) Predicted position

Results Online prediction of tumor motion (during treatment) Max abs error (mm) RMSE (mm) P 1 3.86 1.

23 Results Online prediction of tumor motion (during treatment) Max abs error (mm) RMSE (mm) P P P P P P P Average

24 Results Online prediction of tumor motion (during treatment) Max abs error (mm) RMSE (mm) P P P P P P P Average

25 Results A tumor motion-compensating prototype with prediction Actuator mounted on mechanical slider Aim: maintain a stationary tumor w.r.t treatment beam Actuator : tumor motion Slider : counter steer tumor motion (with prediction to overcome latency) 24

26 Discussion Comparison with other studies a) Choice of Input data length (35 samples = 4.66s) for GNN within same range (3 7.7 s) 5, 7 11 b) Results within same range of accuracies (or better) References Avg. RMSE (mm) Prediction horizon (ms) Current study 0.83 ± Ref ± c) Shorter initial learning period (on average) References Current study Initial learning period (s) 8.8 (avg.) Refs

27 Discussion Significance This work shows that it is feasible to account for system latencies by using an efficient, generalized NN optimized with offline & online learning 26

28 Conclusions Predicted tumor position is within ±1mm (average) for a system latency of 650ms with: 1. shorter initial waiting period so that prediction & treatment adaptation can be made as soon as treatment begins 1 st attempt to use hybrid of offline (generalized model) & online data (use 1 st breathing cycle as sliding window) 2. minimized effort to implement prediction models for different patients generalized model 27

29 Thank you! Financial support from NSERC PGS D2 Scholarship, NSERC DG and the CancerCare Manitoba Foundation is gratefully acknowledged. The authors would like to thank Dr YeLin Suh for making the Cyberknife dataset available to our project. 28

30 References 1. S. Pollock, D. Lee, P. Keall and T. Kima, Audiovisual biofeedback improves motion prediction accuracy, Med. Phys. 40 (4), , (2013). 2. J. Schuster, et al., Variations of the tumor position in frameless lung sbrt: assessment of predictive factors including tumor volume changes, J Nucl Med Radiat Ther 4 (147) (2013). 3. Y. Suh, et al., An analysis of thoracic and abdominal tumor motion for stereotactic body radiotherapy patients, Phys. Med. Biol. 53, (2008). 4. J. Rottmann, et al., Markerless EPID image guided dynamic multi leaf collimator tracking for lung tumors, Phys. Med. Biol. 58, (2013). 5. J. Rottmann et al., Using an external surrogate for predictor model training in real time motion management of lung tumors, Med Phys 41, (2014). 6. M. J. Menten, et. al., Comparison of a multileaf collimator tracking system and a robotic treatment couch tracking system for organ motion compensation during radiotherapy Med. Phys. 39, 7032 (2012). 7. A. Krauss, S. Nill and U. Oelfke, The comparative performance of four respiratory motion predictors for real time tumor tracking, Phys. Med. Biol., 56, (2011). 8. J. H. Goodband et al., A comparison of neural network approaches for online prediction in IGRT, Med. Phys., 35(3), (2008). 9. J. Yun et al., An artificial neural network (ANN) based lung tumor motion predictor for intrafractional MR tumor tracking, Med. Phys. 39 (7) (2012). 10. G. C. Sharp et al. Prediction of respiratory tumor motion for real time image guided radiotherapy, Phys. Med. Biol., 49(3), (2004). 11. R. Ernst, Evaluating and comparing algorithms for respiratory motion prediction, Phys. Med. Biol. 58(11), (2013). 29

31 Appendix: relation between traces Correlation Matrix

32 Methods 5. Characteristics of patient tumor traces i. Obtained from CyberKnife Synchrony dataset [3] ii. Data re sampled with 7.5Hz (0.133s ) iii. 1 minute tumor motion (S I) of 7 patients selected to represent Lung tumor trajectories Breaths per min, Groupings Range of Amplitudes (mm) Max. Acceleration (mms 2 ) P1 24, P2 25, General Description Irregular peak & trough positions Regular shape & period P3 24, One larger breath 3. Y. Suh, et al., An analysis of thoracic and abdominal tumor motion for stereotactic body radiotherapy patients, Phys. Med. Biol. 53, (2008).

33 Methods 5. Characteristics of patient tumor traces Lung tumor trajectories Breaths per min, Groupings Range of Amplitudes (mm) Max. Acceleration (mms 2 ) P4 17, P5 15.5, General Description High acceleration, one deep inhale followed by an irregular period Irregular peaks & troughs positions P6 12, Irregular period P7 11, Non symmetry between inhale & exhale.

34 Methods 5. Characteristics of patient tumor traces Lung tumor trajectories P1 P2 P3 P4 P5 P6 P7 Average 1 st period of tumor trajectory (s) Sliding window size* (data samples)

35 Summary Generalized NN parameters & performance Parameters Sliding window Values Avg. = 29 samples (3.86 s) # Input data 35 samples (4.66 s) # Hidden nodes 25 Pred. horizon Weights initialization Training Epochs (Iterations) Training Algo. 5 samples (650 ms) Inherited 800 Backpropagation with grad descent

36 Summary Generalized NN parameters & performance Performance Avg. RMSE Avg. prediction time (for a new tumor position) Avg. initial waiting time prior to making 1 st prediction Values 0.83 mm 0.20 s 8.8 s

37 Results 3. Generalized vs personalized NN Model (Input data size, Nos. of hidden neurons) Mean error (mm) MAE (mm) Max of MAE (mm) RMSE (mm) Generalized (35, 20) Personalized (5, 5)

38 Results 3. Testing with leave one out data Mean error (mm) MAE (mm) Max abs error (mm) RMSE (mm) P

39 Results Online prediction of tumor motion (during treatment) Max abs error (mm) RMSE (mm) P P P P P P P Average

40 Discussion Limitation prediction accuracy is significantly impacted by the change in periodicity, esp. with a prolonged exhale breathing. Possible solution make parallel predictions using an ensemble of NNs each NN has different parameters that allows them to handle different motion pattern Use prediction from the most suitable network based on their recent performance & pattern of trajectory detected 39

Tumor Tracking Current & Future Developments. Josh Evans, Ph.D. Virginia Commonwealth University Richmond, VA

Tumor Tracking Current & Future Developments Josh Evans, Ph.D. Virginia Commonwealth University Richmond, VA None. Disclosures To understand: Learning Objectives the physiological characteristics of tumor