A Low-dose, Accurate Medical A Low-dose, Accurate Medical Imaging - PowerPoint PPT Presentation

A Low-dose, Accurate Medical A Low-dose, Accurate Medical Imaging Method for Proton Therapy: Imaging Method for Proton Therapy: Proton Computed Tomography Proton Computed Tomography Bela Erdelyi Department of Physics, Northern I llinois University, and Physics Division, Argonne National Laboratory FFAG’09, Fermilab, Batavia, September 21-25, 2009

Acknowledgments • Northern I llinois University – Physics: G. Coutrakon, V. Rykalin, K. Wong – Computer Science: N. Karonis, K. Duffin, K. Naglich, J. Panici • Loma Linda University Medical Center – R. Schulte, V. Bashkirov, F. Hurley, S. Penfold • Santa Cruz I nstitute for Particle Physics – H. Sadrozinski, M. Petterson, N. Blumenkrantz, B. Colby, J. Feldt, J. Heimann, R. Johnson, D. Lucia, D. C. Williams September 21-25, 2009 Proton Computed Tomography 2

Outline • The main idea • A little history • The fundamentals • Current status • Summary September 21-25, 2009 Proton Computed Tomography 3

The Main Idea • For proton therapy, one positions the Bragg peak onto the tumor • For pCT, raise the initial energy so protons traverse the object to be imaged • Measure the phase space data of each proton individually • Use this data to construct the electron density map of the object traversed by protons • Use the resulting electron density map for diagnosis, proton therapy treatment plan, adaptive treatment, positioning verification, etc. September 21-25, 2009 Proton Computed Tomography 4

Motivation: Range Uncertainties September 21-25, 2009 Proton Computed Tomography 5

Overview September 21-25, 2009 Proton Computed Tomography 6

Advantages and Benefits • Practically eliminates range uncertainties , therefore allowing very accurate and precise proton treatment plans • Provides fast patient positioning verification and adaptive treatments , if necessary • Achieves a reduced dose necessary for imaging relative to XCT • Provides a quantification of the range uncertainties as a function of tumor site, type, etc. that will be useful to any proton therapy facility in operation that lacks a pCT system. September 21-25, 2009 Proton Computed Tomography 7

History of pCT (1) September 21-25, 2009 Proton Computed Tomography 8

History of pCT (2) • I n the late 1970s, Ken Hanson (LANL) and Kramer et al. (ANL) experimentally explored the advantages of pCT and proton radiography • They pointed out the dose reduction w.r.t. XCT and the problem of limited spatial resolution due to proton scattering • I n the 1990s Ron Martin (ANL) proposed building a proton CT system using a scanning beam proton gantry • During the 1990s Uwe Schneider (PSI ) further developed the idea of proton radiography as a tool for quality control in proton therapy • I n the late 1990s Piotr Zygmanski (MGH) Harvard Cyclotron tested a cone beam CT system with protons • pCT Collaboration (2003- ) September 21-25, 2009 Proton Computed Tomography 9

The Fundamentals (1) The Bethe-Bloch formula gives the mean energy loss rate of protons in a medium − d E ( ~ r ) = η e ( ~ r ) F ( I ( ~ r ) , E ( ~ r )) d l · µ 2 m e c 2 ¶ ¸ β 2 ( E ) 1 − β 2 ( E ) F ( I ( ~ r ) , E ( ~ r )) = K ln β 2 ( E ) 1 − β 2 ( E ) I ( ~ r ) Z L Z E ( L ) d E r ) d l = − η e ( ~ F ( I ( ~ r ) , E ( ~ r )) 0 E 0 I ( ~ r ) 7 → I water ⇒ rhs is a numerical integration ⇒ b i , i = 1 , m. Discretize η e ( ~ r ) over some basis functions (typically voxels) ⇒ x i , i = 1 , n. Each proton will determine a linear equation in the variables x i x = ~ A ~ b September 21-25, 2009 Proton Computed Tomography 10

The Fundamentals (2) x = ~ A ~ b Determined by Determined by average Electron densities proton paths proton energy loss over paths Not known exactly Not known exactly due to energy loss From measurements due to MCS straggling x ( ξ ) = ~ A( ξ ) ~ b ( ξ ) Random realization with ξ drawn from a probability distribution September 21-25, 2009 Proton Computed Tomography 11

The Most Likely Path Deterministic systems known computed Transfer map fixed September 21-25, 2009 Proton Computed Tomography 12

The Most Likely Path Stochastic systems known known Transfer map computed September 21-25, 2009 Proton Computed Tomography 13

Spatial Resolution (1) A measure of our ability to predict each individual proton’s trajectory A measure of our ability to predict each individual proton’s trajectory inside the object to be imaged is the spatial resolution inside the object to be imaged is the spatial resolution Multiple Coulomb Scattering (MCS) Example: 200 MeV protons in r 20cm of water have · µ l ¶¸ 13 . 6 MeV l σ =39mrad -> σ lat =3.5mm c σ ( l, E ) = 1 + 0 . 038 ln β ( E ) p ( E ) X X Use constrains to reduce uncertainty: position, direction, energy September 21-25, 2009 Proton Computed Tomography 14

Spatial Resolution (2) • Developed new formalism to include energy as a constraint • Equivalently, it fixes the trajectory length • Example: 2 protons, with exactly the same incoming energy, position, direction, and outgoing position and direction – but different outgoing energy • Previous MLP formalism gives the same MLP, new one is different due to the different path lengths • Also implies improved spatial resolution – difficult to compute analytically September 21-25, 2009 Proton Computed Tomography 15

Electron Density Resolution (1) x ( ξ ) = ~ A( ξ ) ~ b ( ξ ) A measure of our ability to predict from the random vector is A measure of our ability to predict from the random vector is x ~ ~ x ( ξ ) the electron density resolution the electron density resolution sP n i =0 σ 2 Definition: Definition: x i ( ξ ) σ x = n σ h E out i k A σ x = g vF ( h E out i ) √ m v September 21-25, 2009 Proton Computed Tomography 16

Electron Density Resolution (2) September 21-25, 2009 Proton Computed Tomography 17

Reconstruction Methods Projection Methods Basic property: To reach any goal that is related to the whole family of sets by performing projections onto the individual sets. Basic ability: To handle huge-size problems whose dimensions are beyond the capabilities of current, more sophisticated, methods. September 21-25, 2009 Proton Computed Tomography 18

Algebraic Reconstruction Technique k x H 5 H 4 H 3 H 2 H 1 x + k 1 September 21-25, 2009 Proton Computed Tomography 19

Block Iterative Projection B = (1,2,3) 1 H 6 B = k x (4,5,6) 2 H 5 H 4 x + k 1 H 3 H 2 H 1 September 21-25, 2009 Proton Computed Tomography 20

String Averaging I = (1,3,5,6) 1 I = k x H 6 (2) 2 I = H 5 (6,4) 3 H 4 x + k 1 H 3 H 2 H 1 September 21-25, 2009 Proton Computed Tomography 21

Hardware Implementation • Desktop/ laptop • Compute Clusters • GP-GPU • GP-GPU clusters • Hybrid: multi-core CPUs + GP-GPUs (clusters) Speedup of the rel. electron density calculation when performed with a NVIDIA GTX280 GPU relative to a Intel Q6600 quad core CPU (Scott McAllister, Master’s Thesis, Cal State SB, 2009) September 21-25, 2009 Proton Computed Tomography 22

GEANT4 Simulations September 21-25, 2009 Proton Computed Tomography 23

Reconstruction Results From Simulations 1 cycle 5 cycles 10 cycles DROP CARP BICAV OS-SART September 21-25, 2009 Proton Computed Tomography 24

Reconstruction Results From Real Data pCT prototype pCT prototype pCT phantom pCT phantom September 21-25, 2009 Proton Computed Tomography 25

The pCT System Prototype Schematic Ready by early 2010 September 21-25, 2009 Proton Computed Tomography 26

System Components September 21-25, 2009 Proton Computed Tomography 27

Next Phase September 21-25, 2009 Proton Computed Tomography 28

Summary • pCT is a new medical imaging method that will greatly benefit proton therapy in general and will offer a low-dose diagnostic imaging modality • The project is truly interdisciplinary involving physics, mathematics, computer science and engineering • The NI U-LLUMC-SCI PP Collaboration is well underway; the first pCT prototype system capable of imaging head-sized objects will be ready by early 2010 • Further work is necessary towards a fully clinical operation-ready pCT system September 21-25, 2009 Proton Computed Tomography 29