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Designing and Improving Medical Imaging Systems by Monte Carlo Studies using GATE

Designing and Improving Medical Imaging Systems by Monte Carlo Studies using GATE. Institute of Neurosciences and Medicine (INM) Research Center Juelich & Department of Mathematics and Natural Sciences University of Wuppertal Germany Uwe Pietrzyk. Outline:

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Designing and Improving Medical Imaging Systems by Monte Carlo Studies using GATE

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  1. Designing and Improving Medical Imaging Systemsby Monte Carlo Studies using GATE Institute of Neurosciences and Medicine (INM) Research Center Juelich & Department of Mathematics and Natural Sciences University of Wuppertal Germany Uwe Pietrzyk

  2. Outline: • Intro: Neuroscience in the Research Center Jülich • Motivation: Why using Monte-Carlo Techniquesfor Simulation? • Some Basics on Medical Imaging • The Concept of GATE / GEANT4 / ROOT • Examples of GATE Applications • Summary & Demo

  3. Outline: • Intro: Neuroscience in the Research Center Jülich • Motivation: Why using Monte-Carlo Techniquesfor Simulation? • Some Basics on Medical Imaging • The Concept of GATE / GEANT4 / ROOT • Examples of GATE Applications • Summary & Demo

  4. Areafor 9.4T MR/PET Institute of Neurosciences and Medicine

  5. Moving the Magnet for the 9.4T MR/PET Hybrid Scanner

  6. 9.4T MR/PET Hybrid Scanner / Magnet in place / PET-Module on site

  7. Nuclear Chemistry Prof. Dr. H.H. Coenen Molecular Organisation of the Brain Prof. Dr. K. Zilles Radionuclei Development Dr. B. Scholten Transmitters- receptors Prof. Dr. K. Zilles Radio- pharmacology Dr. D. Bier Molecular Neuroimaging Prof. Dr. A. Bauer Radiotracer Development Dr. D. Holschbach Structure of Synapses Prof. Dr. J. Lübke Radiotracer Production Dr. J. Ermert Dr. K. Hamacher Functional neuronal circuits Prof. Dr. D. Feldmeyer Institute of Neurosciences and Medicine (INM) Structural und Functional Organisation of the Brain Prof. Dr. K. Amunts Cognitive Neurology Prof. Dr. G.R. Fink Physics of Medical Imaging Prof. Dr. N.J. Shah Systems Biology and Neuroinformatics N.N. Neuromodulation Prof. Dr. Dr. P. A. Tass Ethics in the Neurosciences Prof. Dr. D. Surma Architectonics and brain function Prof. Dr. K. Amunts MR Physics Prof. Dr. N.J. Shah System Medicine Prof. Dr. Dr. P. A. Tass PET Prof. Dr. H. Herzog Multimodal Image Processing & Morphometry Prof. Dr. U. Pietrzyk Neurotechnology PD Dr. C. Hauptmann Brain tumours Prof. Dr. K.-J. Langen Mathematical Neuroscience PD Dr. O.V. Popovych

  8. Analysis of the structure and the functional processes of the brain at the organ level and at the cellular level. • To understand the organisational principles of the brain. • To explore the mechanisms of the normal and pathological nervous system. • Development of new techniques for diagnosis and therapy for neurological and psychiatric diseases, e.g. demand-driven, deep brain stimulation, electrical or chemical neuromodulation. • Development of new methodologies for imaging the in vivo brain.

  9. Brain Pacemaker Highlights from the Research Programme 3D Map of the Human Brain New MR Methods Molecular PET Imaging Neurodegenerative Diseases Mechanisms of Cognitive Processes in Normals and Patients

  10. Highlights from the Research Programme • Ligands for the depiction of cerebral receptors [18F]CPFPX Adenosin-A1 Rezeptors ligand • Amino acids for the diagnosis of brain tumours 2-[18F]Fluorethyl-L-tyrosin LN-Amino acid transporter ligand

  11. Outline: • Intro: Neuroscience in the Research Center Jülich • Motivation: Why using Monte-Carlo Techniquesfor Simulation? • Some Basics on Medical Imaging • The Concept of GATE / GEANT4 / ROOT • Examples of GATE Applications • Summary & Demo

  12. Motivation (1): Why using MonteCarlo techniques to simulate Medical Imaging Devices? Interesting for studying the features of detection systems, which cannot be described analytically: • How to estimate the acceptance of a multichannel detector for high energy photons? • How to determine the contribution from scatteredphotons prior to calculate the tracer uptake in a certain Region of Interest (ROI)  image analysis?

  13. Motivation (2): MonteCarlo techniques – Advantages and Disadvantages! Advantage: Can describe very complex systems!! Disadvantage: Often requires considerable computation times!! It is a statistically founded method, hence, bears intrinsical errors!!  Samples have to be sufficiently large!!

  14. Motivation (3): MonteCarlo technique –an interesting option! Processing Pipeline: From basic detector design  to reconstructed images and quantitative evaluation Note: Such images are reliable only, if we can handle all corrections.  Simulation is an essential support!

  15. Outline: • Intro: Neuroscience in the Research Center Jülich • Motivation: Why using Monte-Carlo Techniquesfor Simulation? • Some Basics on Medical Imaging • The Concept of GATE / GEANT4 / ROOT • Examples of GATE Applications • Summary & Demo

  16. Some Basics on Medical Imaging • The fundamental experimental setup •  the main components • Difference of functional and structural imaging: PET and SPECT vs. CT and MRI • Nuclear Medicine:Radiology: • PET = Positron Emission Tomography CT= X-Ray Computed Tomography • SPECT= Single Photon Emission MRI= Magnetic Resonance Imaging • Computed Tomography • Note: PET and SPECT are “counting experiments”!!

  17. The Basic Principle of Imaging Source (external)  Object (+ Source)  Selection / Definition using:(a) Diaphragm; (b) Grid; (c) Collimator;(d) Coincidence Circuit)  Detector X ✓

  18. Current Scene of Functional and Structural / Morphological Imaging PET PET/CT CT Image Fusion SPECT/CT MR/PET MRT SPECT 18

  19. Complementary Nature: example: MRI & PET Note: Already today, PET is mostly available as a combined modality, namely PET/CT 19

  20. Basics in Positron-Emission-Tomography (I) Detector Detector MRI NoteTwo co-linear photons  No collimation!Need correction for scatter and attenuation!Unknown tracer-distribution in an environment of unknown denstity 20

  21. photomultipliers scintillator A B B D g (511 keV) Basics in Positron-Emission-Tomography (II) • + Imaging System: Detector with high resolution and high sensitivity • Scintillators (LSO / GSO, ...) coupled to PMT or APD  fast electronics • + highly specifictracers, „smart probes“; nano molar concentrations • + suitable isotope: 18F (T1/2 109.8 min, avg. Ekin0.242 MeV, range: FHWM 0.22 mm) • + precise image reconstruction incl. corrections (a) 1×1×10mm LSO crystals, (b) polyurethane grid and (c) completed 12 × 12 scintillator array. MRI 21 21 21

  22. Outline: • Intro: Neuroscience in the Research Center Jülich • Motivation: Why using Monte-Carlo Techniquesfor Simulation? • Some Basics on Medical Imaging • The Concept of GATE / GEANT4 / ROOT • Examples of GATE Applications • Summary & Demo

  23. The Goals of Simulation in Nuclear Medicine: Scanner Design Protocol Optimization Image Reconstruction Data Analysis Scatter Correction Geant4 Application for Tomographic Emission Quantification / Recovery Testing new algorithms

  24. Two different Approaches: • General purpose simulation codes (GEANT4, EGS4, MCNP…)  wide range of physics  wide community of developers and users  documentation, maintenance and support  complexity  speed • Dedicated simulation codes (PETsim, SimSET, Eidolon, SIMIND…)  optimized for nuclear medical imaging applications (geometry, physics...)  ease of use and fast development  maintenance, upgrades

  25. PET/SPECT dedicated developments GEANT4 core features A Combined Approach (I): (by OpenGATE Collaboration) GATE (by GEANT4 Collaboration) • Realistic modeling of PET/SPECT experiments • modeling of detectors, sources, patient • movement (detector, patient) • time-dependent processes (radioactive decay, movement management, biological kinetics) • Ease-of-use • Fast • Long-term availability, support and training 25

  26. A Combined Approach (2): • Based on GEANT4 • object Oriented Analysis & Design • wide range of physics models • long term availability • upgrades, documentation & support • Specific developments regarding to Nuclear medical imaging needs • material database, sources, readout • time and movement management • Ease-of-use for non C++ programmers • scripting commands to define all paramaters of the simulation (construction of the geometry, specification of the physical processes involved, of the sources...) 26

  27. User interface Application classes Framework Geant4 GATE structure (1): General Scope • Three different levels: • GEANT4 core • Developer level • framework and application classes • C++ programming • User level • sequence of scripting commands • geometry construction • physical processes involved • sources (geometry, activity) • movement (type, speed…) • duration of the acquisition 27

  28. GATE structure (2): Geometry Construction • A specific mechanism has been developed to help the user construct easily a geometry • scripting commands • geometry = combination of geometric volumes, like ‘russian dolls’

  29. Scanner Source Body Head Rsector Crystal LSO BGO GATE structure (2): Geometry Construction world

  30. Scanner Source Body Head Rsector Crystal LSO BGO GATE structure (2): Geometry Construction world

  31. Scanner Source Body Head Rsector Crystal LSO BGO GATE structure (2): Geometry Construction world

  32. Scanner Source Body Head Rsector Crystal LSO BGO GATE structure (2): Geometry Construction world

  33. Scanner Source Body Head Rsector Crystal LSO BGO GATE structure (2): Geometry Construction world

  34. Scanner Source Body Head Rsector Crystal LSO BGO GATE structure (2): Geometry Construction world

  35. Multi-ring PET Triple-head gamma camera D. Strul IPHE Lausanne S. Staelens Uni Ghent GATE structure (3): Two Complete Examples D. Strul IPHE Lausanne

  36. 15O 11C GATE structure (4): Source Management • Multiple sources • controlled by source manager • inserted via scripting • complex geometries: customized GPS (General Particle Source) • Also: voxelized Sources, i.e. brain phantoms • Optimized decay • customized G4 Radioactive Decay Module (RDM) • PET-specific sources 36

  37. GATE structure (5): Timing and Motion • Simulation time • a clock models the passing of time during experiments • the user defines the experiment timing • Time-dependant objects • updated when time changes • allows programming of movement, tracer kinetics...

  38. PHOTONS ELECTRONS GATE structure (6): Physical Processes • Choices of processes via scripting commands: • low energy, standard or inactive • Cut settings LE photoelectric effect Standard photoelectric effect LE Compton scattering Standard Compton scattering LE Rayleigh scattering Standard Gamma conversion LE Gamma conversion Standard Ionisation LE Ionisation Standard Bremsstrahlung LE Bremsstrahlung

  39. GATE structure (7): Sensitive Detectors / Digitizer Hits Digis Energyresponse Spatialresponse Centroidreadout ThresholdElectronics • Pre-programmed components • Sensitive detectors • Trajectory analyser • Digitizer • Linear signal processing chain • Modular: set-up via scripting

  40. GATE structure (8): Data Output Formats • Multiple parallel output channels: •  ROOT (real-time display, storage in ROOT files for further analysis) • ASCII files • Binary files, incl. voxelized formats • Specific scanner formats (e.g Crystal Clear LMF…) • (Find ROOT at http://root.cern.ch/drupal/) GATE simulation Sinogram Reconstructed image

  41. Background of GATE: • OpenGATE Collaboration founded in 2002 • http://opengatecollaboration.healthgrid.org • Spokespersons: • Christian Morel (Lausanne / Marseille; till 2003) • Irene Buvat (INMC, Orsay / Paris; from 2003) GATE: a simulation toolkit for PET and SPECT Phys. Med. Biol. 49 (2004) 4543–4561 S Jan1, G Santin2,24, D Strul2,25, S Staelens3, K Assie4, D Autret5, S Avner6, R Barbier7, M Bardies5, P M Bloomfield8, D Brasse6, V Breton9, P Bruyndonckx10, I Buvat4, A F Chatziioannou11, Y Choi12, Y H Chung12, C Comtat1, D Donnarieix9,13, L Ferrer5, S J Glick14, C J Groiselle14, D Guez15, P-F Honore15, S Kerhoas-Cavata15, A S Kirov16, V Kohli11, M Koole3, M Krieguer10, D J van der Laan17, F Lamare18, G Largeron7, C Lartizien19, D Lazaro9, M C Maas17, L Maigne9, F Mayet20, F Melot20, C Merheb15, E Pennacchio7, J Perez21, U Pietrzyk21, F R Rannou11,22, M Rey2, D R Schaart17, C R Schmidtlein16, L Simon2,26, T Y Song12, J-M Vieira2, D Visvikis18, R Van deWalle3, EWieers10,23 and C Morel2 41

  42. Background of GATE: 42

  43. Outline: • Intro: Neuroscience in the Research Center Jülich • Motivation: Why using MonteCarlo Techniquesfor Simulation? • Some Basics on Medical Imaging • The Concept of GATE / GEANT4 / ROOT • Examples of GATE Applications • Summary & Demo

  44. Clinical Example (I) Estimating Scatter Contribution in Planar Gamma Camera Studies with Iodine 131 GE Infinia 3/8’’ / Hawkeye Helios Clinic Wuppertal • Zakhnini • (Diploma Thesis, University of Wuppertal) 44

  45. Clinical Example (II) Estimating Scatter Contribution in Planar Gamma Camera Studies with Iodine 131 Lateral View • Zakhnini • (Diploma Thesis, • University of Wuppertal) 45

  46. Clinical Example (II) Estimating Scatter Contribution in Planar Gamma Camera Studies with Iodine 131 Lateral View • Zakhnini • (Diploma Thesis, • University of Wuppertal) 46

  47. Clinical Example (II) Estimating Scatter Contribution in Planar Gamma Camera Studies with Iodine 131 Lateral View • Zakhnini • (Diploma Thesis, • University of Wuppertal) 47

  48. Simulating new Developments for PET in GATE (I) LSO PMT: Size:10-50 mm Gain: up to 10**6 Risetime: 1 ns QE: 20 % Classic solution: Scintillator + PMT LSO Modern solution: Scintillator + APD More Compact! APD: Size:5x5 mm**2 Gain: up to 200 Risetime: 5 ns QE: 70 % 48

  49. Simulating new Developments for PET in GATE (II) APD Hamamatsu 4x8 elements 10.5x20.7 mm2 pixelized scintillator block monolithic scintillator block • more compact PET • much less “dead space” • higher sensitivity 49

  50. Simulating new Developments for PET in GATE (III) Simulation of Optical Photons in a monolithic detector N. Kobert, PhD-study, FZ-Juelich / Uni Wuppertal Incident Gamma (511 keV) Crystal (wrapped in Teflon) full of optical photons APD (4 x 4 Elements) 50 50

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