1. Image-guided management of uncertainties in scanned particle therapy 13 March 2014 Giovanni Fattori Department of Electronics, Information and Bioengineering Politecnico di Milano -- PhD Thesis Defense --

2. Presentation outline Image-guided particle therapy @ CNAO Optical tracking and X-ray imaging Development and clinical implementation Geometrical accuracy Dosimetrical aspects Treatment of moving targets Optical tracking for real time motion monitoring 4D dosimetry (experimental studies) 2 / 35 3. Particle radiotherapy 1. PHYSICS Favorable tissue depth-dose distribution 2. RELATIVE BIOLOGICAL EFFECTIVENESS Microscopic spatial energy distribution DOSE DELIVERY Active scanning Kramer et al. [2012 J. Phys] Kramer et al. [2010 Eur. Phys. J. D ] Durante et al. [2009 Nat Rev Clin Oncol] 3 / 35 RBE = Dphoton Dion iso 4. Uncertainties in therapy Challenge Development of technologies for therapy to manage treatment uncertainties Setup errors Moving targets Critical issues Particle sensitivity to tissue density variation Organ motion Inter-fractional Intra-fractional Treatment of moving targets Questionable cost-benefit ratio 4 / 35 5. IGRT: the case of CNAO In-room imaging 6 DOF treatment couch (PPS) Solutions for real time monitoring OTS OPTICAL TRACKING SYSTEM Non-invasive Real time patient monitoring PVS PATIENT VERIFICATION SYSTEM Schaer-engineering: isocentric double projection Custom system for robotic imaging: Radiograph and Cone Beam CT TREATMENT ROOM 1-3 TREATMENT ROOM 2 TREATMENT SETUP PPS OTS PVS NOMINAL POSITION 5 / 35 6. Optical tracking Point-based patient registration and motion monitoring SMART, BTS Bioengineering 3 free-standing infrared TVC cameras 15 min calibration procedure Accuracy = 0.3 mm in 1 m3 volume Frequency: 70/100 Hz PATIENT MODEL OPTICAL DATA 6 / 35 What is needed: nominal point-based geometry from planning CT images Sub millimeter scale accuracy (1-3 mm slice thickness in CT) Manual segmentation Low 3D accuracy Inter-operator variability Automatic fiducials localization in CT images 7. Automatic fiducials localization in CT images SURFACE EXTRACTION SURFACE PROCESSING MARKER RECOGNITION Geometric filters 1. 10 mm < diag < 22 mm 2. Hausdorff < 20 mm2 3. n triangles < 650 4. Side difference < 5 mm CANDIDATE SURFACE Geometrical prior knowledge: aluminum spheres. (1cm diameter, 1800 HU) Fattori et al. [2012 IEEE TBME] 7 / 35 8. Automatic fiducials localization in CT images LOCALIZATION ACCURACY 3 mm 1 mm I 0.1640 0.2113 II 0.2374 0.0322 II I 0.1414 0.2054 LOCALIZATION ROBUSTNESS Accuracy: 20 m N patients CT resolution Fiducials per patient N of fiducials Fiducials found 10 head 25 thorax 1.27x1.27x3 mm 6/8 233 215 3 head 0 0 0 92.3% LEICA LTD 500 ** CLINICAL USE SINCE 2011 ** Fattori et al. [2012 IEEE TBME] High true positive ratio No false positive High accuracy 3D error [mm] 8 / 35 9. In-room imaging system for CNAO central room Specifications: Limited operating space (Horizontal & Vertical beam lines) X-ray radiographs and Cone Beam CT Registration performance comparable with lateral rooms Geometric residual error < 1mm / 1 Patient setup procedure < 2 min Integration with existing technologies (PPS, PACS) Project leader: Image registration: Robot & Safety: Software: G. Baroni M. Riboldi G. Fattori M. Peroni P. Cerveri A. Pella G. Fattori G. Fattori M. Riboldi Treatment Isocenter Imaging Isocenter 9 / 35 2 YEARS PROJECT 1st year: 2D-3D 2nd year: 3D-3D 10. Hardware components 10 / 35 Custom C-arm SID=1772.2 mm; SAD=1272.2mm Robotic arm Kawasaki ZX300S Flat panel detector Varian 4030D (30 Hz, 2048x1536 pixels 193.8x194 um pitch) X-ray source Varian A277 Generator Sedecal HF series Exposure controlGantry pendant Intrusion detection 11. X-ray Patient Positioning Verification software Gantry control Exposure settings Automatic registration PPS communication PACS integration ROI Interactive checkerboard Images overlay Visualization settings Automatic registration Manual registration ROI Load/Save 11 / 35 12. Geometry calibration Calibration phantom (Brandis Medizintechnik Vertriebs GmbH, Weinheim, Germany) 36 metal bearings Optimization of on-plane projection error Free parameters: Image center Panel orientation System geometric daily QA SOURCE (0,0) Center of rotation (-cx,-cy) Panel rotations Imaging Isocenter 12 / 35 13. System installation in CNAO Room 2 CLINICAL WORKFLOW PPS MOTION TO TREATMENT ISOCENTER OTS Point-based registration at treatment isocenter (1min) PPS MOTION TO IMAGING ISOCENTER (3min) Image-based registration at imaging isocenter PPS MOTION TO TREATMENT ISOCENTER (3min) Treatment start 1. Images acquisition 2. Automatic registration (1min) 3. PPS correction 4. Images acquisition (verification) 5. Automatic registration Image-based registration 13 / 35 14. XPPV Double projection patient registration Projection angels RL (right-left) AP (anterior-posterior) Flat panel Resolution: 2048x1536 with 193.8x194 um pitch 2D-3D registration (< 1 min) Adapted from plastimatch reg23 Gradient difference metric Evolutionary 1+1 optimizer 14 / 35 15. XPPV CBCT patient registration Gantry rotation 220 in 40 sec (5.4 /sec) Post acquisition recover 20 sec Flat panel Frame rate = 15Hz (0.36 per image) Resolution: 1024x768 with 387.7x388 um pitch CBCT (adapted from plastimatch FDK) Field-of-view: 30x20x15 cm Resolution: 1.1x1.1x1.5 mm (70 sec) 3D-3D registration (ITK) (< 1 min) Normalized Mutual Information metric Nelder Meade optimizer 16. CBCT Image Quality SENSITOMETRY (CTP 404)SPATIAL RESOLUTION (CTP 528) CATPHAN-600 PHANTOM Air PMP LDPE Delrin Teflon 1.25 mm 1 mm 0.83 mm 1.1x1.5 mm 1.67 mm 2.5 mm FUTURE DEVELOPMENTS Artifacts analysis/mitigation Shading artifact Streaks artifact Large deviation within inserts 16 / 35 17. System geometric accuracy PVS / OTS agreement 1. Manual alignment at treatment Iso (lasers) 2. OTS setup verification 3. Reg23 setup verification 4. Reg33 setup verification Recovery of 3 transformation (range: 1 cm, 5) 1. Reg33 setup verification 2. Reg23 setup verification 3. Setup perturbation 4. Reg33 setup verification 5. Reg23 setup verification Rx 1 Setup error (3D/3D) Setup error (2D/3D) Delta wrt imposed error (3D/3D) Delta wrt imposed error (2D/3D) X 1mm Y 1mmZ 1mm Rx 1 Ry 1 Rz 1 Setup error (3D/3D) Setup error (2D/3D) Delta wrt imposed error (3D/3D) Delta wrt imposed error (2D/3D) Z 1mm Rx 1 Ry 1 Setup error (3D/3D) Setup error (2D/3D) Delta wrt imposed error (3D/3D) Delta wrt imposed error (2D/3D) PTW P43029 Good agreement between OTS and PVS Good agreement between 2D-3D and 3D-3D 17 / 35 18. Registration performance (2D-3D) ** ROOM 2 IN OPERATION SINCE APRIL 2013 WITH DOUBLE PROJECTION ** Simulation of 10 setup errors (range: 1 cm, 5) 1. 2D-3D Correction vector calculation 2. Implementation of PPS correction 3. 2D-3D Correction vector calculation Sub-millimeter / sub-gradual residual error Translational error Rotational error Alderson RANDO man Antropomorphic phantom 18 / 35 19. Clinical implementation (2D-3D) HEAD 21 pat. 633 fract. PELVIS 8 pat. 236 fract. PROSTATE 2 pat. 32 fract. ROOM 2: Custom robotic imaging + XPPV ROOM 1, 3: Shaer Engineering imaging + MedCom Verisuite PVS HEAD 5 pat. 105 fract. PELVIS 5 pat. 46 fract. 0.94 mm 0.9 1.32 mm 1.34 2.82 mm 1.28 2.58 mm 1.04 1.56 mm 0.58 19 / 35 Residual error Correction vector 20. Dosimetric consequences of setup errors after IGRT Nominal Setup errors D95 D105D05 HU-WE Setup CI = Vol95% VolCTV IC = (MaxDose - MinDose) MinDose Purpose To provide clinicians with dosimetric information about treatment setup besides the residual geometric error Range Interpretation of results Indexes clearly readable by clinicians Envelope DVH (D95CTV, D105CTV, D05OAR) Conformity Index for CTV Inhomogeneity Coefficient for CTV Materials and Methods Image processing & TRiP98 (M. Krmer) Comparison of treatment delivery in nominal situation and in presence of uncertainty (Optimum=1) (Optimum =0) 20 / 35 21. Simulation of setup errors ERROR SPACE SAMPLING Orthogonal sampling (64 simulations) 6 Dimensions (translations, rotations) Implementation of isocentric 6DOF Correction vector on patient CT 1. Image resampling T 2. Dose calculation 3. Dose cube resampling Tinv Figure 2. Plan no 299. (Upper part) Convergence of the various algorithms as function of iteration steps (left hand side) and computation time (right hand side). (Lower part) DVH (left hand side) and dose distribution in a CT-slice (right hand side). Only results of CGFR optimization are shown. The indicated isodoses are in percent of the prescribed dose. For the sake of completeness we additionally present the LevenbergMarquardt minimization (LMM) which we also investigated. As far as the number of iterations are concerned (see gure 1, upper left part) LMM looks quite promising but the computation times are extremely large (see gure 1, upper right part). The disadvantage of LMM is that in every iteration step a large system of linear equations has to be solved. Solving the system of linear equations with the Cholesky decomposition requires about 60 times more computation time compared with CGFR (gure 1). We investigated alternative equation solvers, for example the iterative Krylov subspace methods. With the Krylov subspace methods the computation times could be decreased by a factor of approximately 3 (Buschbacher 2009), which is by far not enough to allow the usage of LMM in our context. We further investigated the distribution of the resultant particle numbers on the raster grid. This is important because large uctuations of particle numbers between rasterspots might require changing of the particle intensities by the ion accelerator system. This is time consuming and could potentially decrease the number of patients treated per day. We examined some treatment plans and independently from the chosen algorithm we did not observe large uctuations of particle numbers between neighbouring rasterspots. 20% 40% 60% 80% 95% 105% > 105%Figure 2. Plan no 299. (Upper part) Convergence of the various algorithms as function of iteration steps (left hand side) and computation time (right hand side). (Lower part) DVH (left hand side) and dose distribution in a CT-slice (right hand side). Only results of CGFR optimization are shown. The indicated isodoses are in percent of the prescribed dose. For the sake of completeness we additionally present the LevenbergMarquardt minimization (LMM) which we also investigated. As far as the number of iterations are concerned (see gure 1, upper left part) LMM looks quite promising but the computation times are extremely large (see gure 1, upper right part). The disadvantage of LMM is that in every iteration step a large system of linear equations has to be solved. Solving the system of linear equations with the Cholesky decomposition requires about 60 times more computation time compared with CGFR (gure 1). We investigated alternative equation solvers, for example the iterative Krylov subspace methods. With the Krylov subspace methods the computation times could be decreased by a factor of approximately 3 (Buschbacher 2009), which is by far not enough to allow the usage of LMM in our context. We further investigated the distribution of the resultant particle numbers on the raster grid. This is important because large uctuations of particle numbers between rasterspots might require changing of the particle intensities by the ion accelerator system. This is time consuming and could potentially decrease the number of patients treated per day. We examined some treatment plans and independently from the chosen algorithm we did not observe large uctuations of particle numbers between neighbouring rasterspots. 6. Summary and conclusion The task for the optimization of RBE-weighted dose is depending nonlinearly on the particle numbers 20% 40% 60% 80% 95% 105% > 105% -30 Dos +30 CT 1 mm 1 setup error 21 / 35 22. 25% 20% 15% 10% 5% 0% 25% 20% 15% 10% 5% 0% Simulation study: 5 head chordoma patients 4.4 Gy (RBE) 2mm CTV-to-PTV margin OAR: Brainstem 2-3 treatment fields 22 / 35 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 Simulation study design: 1. Setup error simulation 2. On worst case envelope, simulation of range uncertainty IC CI D95CTV D105CTV D05OAR Worst cases for D95CTV and D05OAR 25% 20% 15% 10% 5% 0% 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 1 2 3 4 5 0 5 10 15 20 25 23. Patient 1: worst case simulation for D95CTV ce of the various algorithms as function of iteration ght hand side). (Lower part) DVH (left hand side) de). Only results of CGFR optimization are shown. scribed dose. 20% 40% 60% 80% 95% 105% > 105% rgence of the various algorithms as function of iteration me (right hand side). (Lower part) DVH (left hand side) nd side). Only results of CGFR optimization are shown. e prescribed dose. ionally present the LevenbergMarquardt ed. As far as the number of iterations are looks quite promising but the computation ht part). The disadvantage of LMM is that in 20% 40% 60% 80% 95% 105% > 105% SETUP ERROR SETUP AND RANGE ERROR (Expected) Results: Dose coverage remains acceptable Conformity is reduced Inhomogeneity is increased Quantification of dosimetric deviations wrt nominal condition 23 / 35 LL: AP: SI: Pitch: Rotate: Roll: -0.34mm -0.87mm -0.97mm -0.19 -0.97 0.78 Setup error + Rel WEL +2.6% 24. IGRT: Conclusion and Limitations Tools for automated fiducials localization in treatment planning CT images Development of a custom robotic in-room imaging system Implementation at CNAO Double projection: Clinical use since April 2013 CBCT: foreseen for April 2014 Overall residual setup error following CNAO IGRT strategy (OTS + in- room imaging): Millimeter and degree scale. Tool to provide valuable dosimetric information to clinicians Not far from pre-treatment setup verification: about 10 mins (single simulation) Treatment plan robustness test: 2 hours 24 / 35 25. From static to moving target with active scanning X-RAY SOFT-TISSUE IMAGING US MRI PHASE1 4D IMAGING (4D CT) PHASEN OPTICAL TRACKING + CORRELATION MODELS BASIC ASSUMPTION: TARGET MOTION REPEATIBILTY 4D TREATMENT PLAN (TP) TIME RESOLVED TREATMENT DELIVERY ENERGY ADAPTATIONLATERAL DEFLECTION MOTION MONITORING DOSE DELIVERY (beam tracking) MOTION MONITORING SYSTEMS Real time feedback to TCS to drive the treatment delivery Verify consistency wrt TP Trigger image acquisition MOTION MITIGATION STRATEGIES BEAM TRACKING GATING RESCANNING Direct observation Surrogate signal 25 / 35 26. Optical tracking for time resolved treatment PURPOSE To interface a commercial solution for optical tracking with a Therapy Control System for particles: beam tracking and gating WHAT IS REQUIRED Real time monitoring of multiple surrogates Compatibility with 4DCT acquisition protocols Real time communication with TCS: delay compensation Static Residual Interplay 26 / 35 27. The tracking code package Optical Tracking System Therapy Control System Correction vector 3D OTS DATA BTU Wedge range shifter Steering magnets Depth compensation Lateral compensation100 Hz frame rate LABELLER TARGE T FRAMES INTERPOLATION POLYNOMIAL COEFFICIENTS TIMECRITICALTHREADOTSDRIVENTHREAD BREATHING SIGNAL MOTION PHASE DETECTION CORRELATION MODELS [ x y z ] [ MP ] PATIENT MODEL MOTION PHASE TABLE RCS TRANSFORM MATRIX SHARED RESOURCES DIGITALCOMMUNICATION(UDPSOCKET) Treatment plan Fattori et al. [2012 AAPM] KEY FEATURES: Phase/Amplitude 4DCT Ethernet link (UDP) Signal time prediction Ready for Gating and Beam tracking experiments 27 / 35 28. Procedure for system latencies quantification DEPTH WE compensation Mean Std.Dev 1 mm 27.43 7.51 9 mm 34.1 6.29 5measurements(0, DEPTH Calculatedbydiffere TOTALLATERAL Laser distantiometer OTS marker Fattori et al. [2013 TCRT Express] LAT ERAL OTSbenchmark with Laser distantiometer (1KHz frm.rate) 5 measurements(0,5,10,15,20 msec.advance prediction) DEPT H Calculated by difference TOTAL LATERAL Motion OTS LATERAL OTSbenchmark with Laser distantiometer (1KHz frm.rate) 5 measurements(0,5,10,15,20 msec.advance prediction) DEPTH Calculated by difference TOTAL LATERAL Motion OTS TCS 34 (mea 1 MAGNETS WEDGEFILTER LATERAL 14.6 msec DEPT H Calculated by difference TOTAL LATERAL Motion OTS 28 / 35 29. Signal time prediction accuracy Polynomial fitting: Ist order 5 samples history, 100 Hz data Time compensated Vs. Reference Fattori et al. [2013 TCRT Express] REFERENCE NON COMPENSATED TIME COMPENSATED Reference = Non-compensated + (=14.6ms) 10 mins acquisition: RMS = 0.05 mm RMS = 0.1 mm 30. Beam tracking @ GSI: Setup Purpose: To evaluate the feasibility of OTS driven 4D treatment Steidl et al. [2012 PMB] Breathing phantom Correlated target/thorax motion 10x5x10 cm (x,y,depth) Treatment plan 1 Gy homogeneous, 12C 35 mm side 4DCT: 8 MPh, phase binned Motion monitoring: SMART-DX100, 2 TVC Dose measurement 16 PTW Pinpoint ionization 30 / 35 31. Beam tracking @ GSI: Results Fattori et al. [2013 TCRT Express] Note: Pure translational target motion No soft tissue material inside the thorax Excellent target and thorax motion repeatibility Median(IQR) 2.0 (25.9) % -0.3 (2.3) % -1.2 (9.3) % Measured delta wrt static irradiation 31 / 35 32. Lateral beam tracking @ CNAO Phantom - Planar target motion (2D) - 25 mm (lat) 18 mm (vert) peak-to-peak - Planarity: median 0.038 mm (IQR:0.09) - Repeatibility: mean std 0.18 0.3 mm Treatment plan - Squared PTV - 6 cm side Purpose Proof the OTS/TCS integration STATIC TRACKING INTERPLAY GATING Average flatness 4 % 5.7 % 24 % 9.5 % Average penumbra 9.2 mm 9 mm 19 mm 9.1 mm 32 / 35 Pella, Fattori et al. [PTCOG52] 33. Moving targets: Conclusion and Limitations General solution for OTS/TCS integration was described Ethernet link, UDP protocol Procedure for delays quantification The tracking code package available for research (CNAO, GSI, ) Development and benchmark of int-ext correlation model (M. Seregni) Functional gating and beam tracking modules GSI: 3D optical driven beam tracking CNAO: 2D optical driven beam tracking and gating BEAM TRACKING: how to deal with deviations from treatment plan? 1. Real time dose compensation with beam tracking [Lchtenborg 2012, Med Phys] 2. Dose changes outside the VOIs (inverse interplay effect) 33 / 35 34. Final remarks IGRT 1. Development and implementation of state-of-the art methods for IGRT Point based Anatomical information (bone anatomy + soft tissue imaging) 2. CNAO Room 2: Custom system for robotic imaging: (!) 2D-3D available for clinical use (!) CBCT almost available for clinical use (!) Double projection & CBCT dataset: 2D-3D / 3D-3D Comparison Treatment of moving targets 1. GSI: beam tracking, lateral and depth compensation 2. CNAO: lateral compensation ready for gated treatment: (!) strategy to compensate for residual motion in the gating window 34 / 35 35. Future directions IGRT @ CNAO 1. CT-of-the-day software module Pre-treatment dose simulation on the updated CT Anatomical information in perspective of PET in-room 2. 4D CBCT Treatment of moving targets Tailored treatment on patient specific basis: Motion reduction: gating + rescanning/overlapped pencil beams Motion compensation: multiple points + tumor tracking Adequate strategy for margin definition 35 / 35 36. Thank you May 2012 Beamtime, GSI