Dose Calculation and Verification for Tomotherapy

Transcription

1 Dose Calculation and Verification for Tomotherapy John P. Gibbons, PhD Chief of Clinical Physics Mary Bird Perkins Cancer Center Baton Rouge, LA Associate Professor Department of Physics and Astronomy Louisiana State University

2 2004 ACMP Meeting Scottsdale, AZ Tennis Anyone?

3 Tennis Anyone?

4 Outline Introduction TomoTherapy Experience at Mary Bird Perkins Cancer Center Dose Calculation with TomoTherapy Helical TomoTherapy delivery system Planning system algorithm and implementation Independent Check Algorithm Conclusions

5 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Facilities and Equipment Facilities: 2000 patients/year Baton Rouge Hammond Covington Gonzalez (2008) Equipment 4 Varian 21EX 1 BrainLab Novalis 1 TomoTherapy unit

6 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Staffing Levels 13 Medical Physicists 7 PhD s, 6 MS s 9 Clinical FTEs 9 Radiation Oncologists 8 Dosimetrists

7 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center October 2004: System Installed November 2004: Unit Accepted January 2005: First patient treated March 2006: TomoTherapy Timeline Research cluster added February 2007: 1 cm jaw commissioned

8 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Patient Load TomoTherapy Patient Load Budgeted (130) total Actual (74) total New Patient Starts Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

9 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Patient Load TomoTherapy Patient Load Budgeted (168) total Actual (122) total New Patient Starts Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

10 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Patient Load TomoTherapy Patient Load Budgeted (168) total Actual (173) total New Patient Starts Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

11 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Treatment Sites: First Year TomoTherapy Treatments by Site Prostate Head&Neck Pelvis Mediastinum CNS Abdomen Bladder Breast Skin Mantle Pancreas Urethra Met(s)

12 TomoTherapy Clinical Experience Mary Bird Perkins Cancer Center Treatment Sites: Through Jan 2008 Total: 399 Patients Treated TomoTherapy Patients by Site Thorax (Lung, Chest, Mantle) (72) Prostate (58) Head and Neck (64) Superficial (Chest Wall, Scalp) (71) Pelvis (Pelvis, Bladder, Rectum) (37) CNS (Spine, Brain) (27) Abdomen (Abdomen, Liver, Pancreas) (24) Other (14)

13 Helical TomoTherapy Delivery Mechanical Design

14 Helical TomoTherapy Delivery Helical Delivery

15 Helical TomoTherapy Delivery Beam Modulation Binary MLC system 64 Leaves, width 6.25mm at axis Thickness ~10 cm (<1% leakage) Transition ~20 ms

16 TomoTherapy Dose Calculation Projections and Beamlets Projection defined by beam from fixed gantry angle Beamlet defined by radiation through single leaf Beamlets computed only for rays which pass through a tumor ROI Calculation uses 51 projections per rotation (approximately every 7 o )

17 TomoTherapy Dose Calculation Sinograms Sinograms are 2D histograms which define the machine state versus time (e.g., projection, beam pulse) TomoTherapy sinograms are usually of two categories: Leaf Open Time Sinograms Exit Detector Signal Sinograms

18 TomoTherapy Dose Calculation Example Planning Sinograms Leaf open time versus projection number On-axis cylinder Off-axis cylinder Head & Neck Patient

19 TomoTherapy Dose Calculation General Tomotherapy uses a convolution/superposition (C/S) algorithm to compute dose D( r ) = µ ( r ) ( ) K( ) dv ρ Ψ r r r V TERMA is calculated first, followed by convolution with polyenergetic point kernels Heterogeneities handled by density scaling D( r ) = µ ( r ) ( ρ ' ) K( ρ ρ ' ) dv ρ Ψ r r r V

20 TomoTherapy Dose Calculation Tomo is Pinnacle, except Differences in C/S implementation Resolution of fluence calculation Resolution of convolution integrations Kernels computed at 15 o increments Less mass-energy absorption No electron contamination ROIs of the same type may not overlap Optimization procedure different

21 TomoTherapy Dose Calculation TERMA / Fluence Attenuation Table TomoTherapy uses Fluence Attenuation Table to calculate TERMA: TERMA µ r = ( ) ( ) ρ r Ψ 0 10 Fluence Attenuation Table µ = ( r ) Ψ0 e ρ µ ( ρ l ) ρ µ r = ( ) 0 FAT ( ρ, ρl) ρ Ψ Depth (cm) Density

22 TomoTherapy Dose Calculation Mass Attenuation Coefficients Mass attenuation coefficients interpolated using values for water and bone: µ, ρ ρwater ρ water µ µ µ = ww + wb, ρw < ρ < ρb ρ ρ ρ water bone µ, ρ ρbone ρ bone M ass A tten uatio n (cm ^2/g ) 0.10 Mass Attenuation Coeff. (from NIST website) Water Lung Tissue (Soft) Bone (Cortical) Energy (MeV)

23 TomoTherapy Dose Calculation Poly-energetic Kernel W i hν i Energy (MeV) Σ W i (hν i ) K i (hν i ) hν i K i K(MV) Monoenergetic kernel database

24 TomoTherapy Dose Calculation Dose calculation parameters

25 TomoTherapy Dose Calculation Optimization Modes During optimization, dose may be calculated using three modes: TERMA: No convolutions performed Full Scatter: At each iteration, 24 convolutions performed using TERMA calculated in 15 o arc segments Beamlet: Convolution calculations performed for each beamlet. Full Scatter calculation performed after optimization complete

26 TomoTherapy Dose Calculation Beamlet optimization Number of beamlets can be large. Example: beamlets projections rotations projection rotation fraction = [ beamlets] Dose from each beamlet calculated, but dose matrix is compressed if dose < threshold (0.025% for used ROIs; 1% for normal tissue). Compression is ~5x in version 2.2. Much larger beamlet compression (~600x) is performed in version 3.

27 TomoTherapy Dose Calculation Dose Calculation Grid Calculation dose grid is fixed in size and covers entire planning CT volume Dose grid resolution may be set to three values: Fine: Normal: Coarse: Resolution matches CT voxel resolution Resolution is ½ the CT resolution in the axial plane and matches in the longitudinal direction Resolution is ¼ the CT resolution in the axial plane and matches in the longitudinal direction

28 TomoTherapy Dose Calculation Dose Grid Effects HU s from CT (Grid: Normal) Fine)

29 TomoTherapy Dose Calculation Dose Grid Effects 46 Gy 54 Gy Calculated Doses in Gy (Grid: Normal) 53 Gy 61 Gy 60 Gy 64 Gy

30 TomoTherapy Dose Calculation Dose Grid Effects Calculated Doses in Gy (Grid: Normal) Upsampling of dose matrix creates artificial boxy isodoses

31 TomoTherapy Dose Calculation Dose Grid Effects Dose Calc Grid: Fine

32 TomoTherapy Dose Calculation Dose Grid Effects Dose Calc Grid: Normal

33 TomoTherapy Dose Calculation Dose Grid Effects Dose Calc Grid: Coarse

34 TomoTherapy Dose Calculation CT Resolution Effects 512 x x x 128

35 TomoTherapy Dose Calculation Convolution Origin (v2.2) r = voxel size d eff = r /2 r /2 Convolution originates from voxel center TomoTherapy implementation: Convolution originates from voxel proximal end

36 TomoTherapy Dose Calculation Surface Dose Calculation Depth Determined using Voxel Center Koren Smith, LSU MS Thesis, 2007

37 TomoTherapy Dose Calculation Surface Dose Calculation Depth Determined using Voxel Distal End Koren Smith, LSU MS Thesis, 2007

38 TomoTherapy Dose Calculation CT to Density Table (IVDT) Issues involved in IVDT Construction 1. Use physical density, not electron density The fluence attenuation table used in the dose calculator contains mass-attenuation coefficients. The massdensity is thus needed to calculate attenuation. The IVDT should therefore map to mass-density.

39 TomoTherapy Dose Calculation CT to Density Table (IVDT) Issues involved in IVDT Construction 2. Avoid non-physical heterogeneity plugs near water TomoTherapy Procedure: Do not use any plugs between +100 HU. Water should be measured to obtain an IVDT point near 0 HU and 1 g/cm 3. Air should be measured to obtain an IVDT point near HU and g/cm 3 Density (g/cm^3) Typical IVDT (close-up of water-like materials) Image Value (HU)

40 TomoTherapy Dose Calculation CT to Density Table (IVDT) Issues involved in IVDT Construction 3. IVDT should produce a density of ~1.014 for the Virtual Water TomoPhantom McEwen, M; Niven, D; Characterization of the phantom material Virtual Water in high-energy photon and electron beams, Med. Phys. 33 (2006).

41 TomoTherapy Dose Calculation CT to Density Table (IVDT) 4. Avoid using IVDT to correct for heterogeneities

42 In an open field, a bull mistakenly eats an explosive device. What word best describes this situation? 12% 8% 38% 15% 27% 1. Ridiculous 2. Frightening 3. Horrific 4. Abominable 5. Hungry Abominable (A-bomb-in-a-bull)

43 Tomotherapy dose calculation time for tens of thousands of beamlets is reduced by 42% 1. Down-sampling the planning kvct dataset 6% 0% 0% 52% 2. Reducing the modulation factor. 3. Reducing the penalties for all regions at risk 4. Reducing min dose objective for all tumors 5. All of the above

44 Helical TomoTherapy Dose Check Algorithm Objective: Verify patient treatment times within 5% produced by a TomoTherapy Planning System.

45 Helical TomoTherapy Dose Check Algorithm Algorithm designed to compute dose to a point in a high dose, low gradient region Total dose = sum of doses from each projection D P D = D & t P P, i i = Total dose to point P D P,i = Dose rate to point P from projection i t i = Time for projection i

46 Helical TomoTherapy Dose Check Algorithm SAD SPD O θ X d P P

47 Helical TomoTherapy Dose Check Algorithm 2 SAD DP, i = D& 0 OARX ( X i ) Scp TPR ( di ) OARY ( Yi, di ) SPDi { } D P = Total dose to point P D 0 = Dose rate under normalization conditions SAD = Source-axis distance (85 cm) SPD = Source-calculation point P distance S cp = Output factor TPR = Tissue phantom ratio OAR X = Transverse off-axis ratio OAR Y = Longitudinal off-axis ratio

48 Beam Modulation Sinogram approximated by a sum of symmetric, unmodulated segments: a) Example projection b) Symmetric (about leaf m) approximation to (a) c) Decomposition of (b) into 4 unmodulated segments. Leaf Open Time [secs] Leaf Open Time [secs] Leaf Open Time [secs] a) Sinogram projection m-4 m-3 m-2 m-1 m m+1 m+2 m+3 m+4 b) Symmetrized sinogram projection m-4 m-3 m-2 m-1 m m+1 m+2 m+3 m+4 c) Decomposition into segments m-4 m-3 m-2 m-1 m m+1 m+2 m+3 m+4 Leaf Number

49 Helical TomoTherapy Dose Check Algorithm Dosimetric Input Data: Data were obtained by simulating static fields on TomoTherapy planning system and extracting dose Measurements were made of a subset of these data to confirm agreement.

50 Dosimetric Input Data TPR TPR cm jaw 0.6 x 5.0 cm x 5.0 cm x 5.0 cm 2 40 x 5 cm 2 40 x 5 cm 2 (Measured) Depth [cm]

51 Dosimetric Input Data S cp S cp cm jaw - Measured 2.5-cm jaw - Simulated 5.0-cm jaw - Measured 5.0-cm jaw - Simulated Side of Equivalent Square [cm]

52 Dosimetric Input Data Off-Axis Ratios OAR x OAR y Lateral Profile b) 5 cm Jaw d=1.5 d=10 d=20 d=30 OAR y c) d=30 cm 1 open leaf 5 open leaves 15 open leaves All open Off-Axis Distance [cm] Off-Axis Distance [cm]

53 Clinical Evaluation of Algorithm I. Phantom Plan Studies Designed to test the accuracy of the dose calculation under different conditions Treatment Field Length Depth Off-Axis

54 Phantom Studies Accuracy vs. Field Length Treatment plans of varying field lengths performed on cylindrical phantom Dose in center of cylinder compared to algorithm 1 Rotation 20 Rotations

55 Phantom Studies Accuracy vs. Off Axis Distance Treatment plans performed on phantom positioned on CAX and 10 cm off-axis Dose in center of cylinder compared to algorithm Phantom/PTV Center TomoTherapy Axis

56 Phantom Study Results Phantom Treatment Plan Pitch MF TomoPlan Dose [Gy] Calculated Dose [Gy] Difference 20cm cyl 10 cm width; 1 rotation % 10 cm width; 3 rotations % 10 cm width; 20 rotations % 50cm cyl 10 cm width; 4 rotations % 10 cm width; 29 rotations % 20cm On-axis 20cm Off-axis 50 Gy to cylindrical PTV (7 cm diameter, 5 cm length) <0.1% <0.1%

57 Clinical Evaluation of Algorithm II. Patient Plan Studies 97 Patient Treatment plans were evaluated. Plans represented all treatment plans for which sinograms were available. Comparisons were made between doses calculated by treatment planning system and point dose algorithm.

58 Clinical Evaluation of Algorithm Choice of Calculation Point Calculation point automatically placed in center of PTV. If auto placement failed, point manually moved to high dose, low gradient region Calculation point kept at least 1 cm from lung

59 Clinical Evaluation of Algorithm Choice of Calculation Point

60 Patient Plan Results Number (Algorithm Dose TomoTherapy Dose)/TomoTherapy Dose Other CNS Superficial Abdomen Pelvis Head and Neck Prostate Thorax -16% -12% -8% -4% 0% 4% 8% 12% 16% Difference [%]

61 Patient Plan Results All treatment plans excluding lung and superficial sites Number Other % -6% -4% -2% 0% 2% 4% 6% 8% Difference [%]

62 Patient Plan Results Lung and Superficial Sites Only Thorax 30 Superficial Number Number % -6% -4% -2% 0% 2% 4% 6% 8% 0-8% -6% -4% -2% 0% 2% 4% 6% 8% Difference [%] Difference [%]

63 Patient Plan Results: Lung Sites Heterogeneity Correction Errors d eff

64 Patient Plan Results: Lung Sites Heterogeneity Correction Errors CORK POLY POLY a CORK POLY Mackie et al., Med Phys 12: 327 (1985)

65 Patient Plan Results: Superficial Sites Missing Phantom Scatter

66 Patient Plan Results 97 Patient Plans Evaluated 68 Treatment Plans excluding Lung/Thorax: 94% (64/68) Agreed within 2% Average difference 0.4% 38 Treatment Plans in Lung/Thorax Algorithm systematically overestimates dose Average difference =3.1%

67 Conclusions Independent dose algorithm accurately predicts dose to simple phantom geometries Calculations to patient sites excluding lung and superficial targets agree well with TomoTherapy calculated doses. Calculations to lung and superficial sites demonstrate systematic differences of ~3%.

68 The bomb exploded. What word best describes this situation? 4% 1. Sad 4% 8% 4% 81% 2. Disgusting 3. Horrific 4. Silly 5. Noble

69 For beams traversing lung, radiological path length correction algorithms 0% 0% 0% 100% 0% 1. Underestimate the dose within lung, but overestimate the soft tissue dose on the distal end of the lung 2. Underestimate the dose within lung and on the distal end of the lung 3. Overestimate the dose on the proximal and distal end of the lung 4. Overestimate the dose within lung and on the distal end of the lung 5. Correctly predicts the dose within the lung, but underestimates the soft tissue dose on the distal end of the lung

70 Acknowledgements TomoTherapy Eric Schnarr Gustavo Olivera Ken Ruchala Mary Bird Perkins/LSU Koren Smith Dennis Cheek Ricky Hesston

71 Nikos Papanikolau Acknowledgements