Introduction to Radiation Therapy : Basics, and Recent Technologies

Transcription

1 Introduction to Radiation Therapy : Basics, and Recent Technologies Pusan National University Undergraduate Student Seminar Justin. C. Park (Chun Joo Park) Dept. of Electrical and Computer Engineering Dept. of Radiation Oncology University of California, San Diego

2 San Diego, California

3 University of California San Diego (UCSD) In short history, 17 UCSD faculty members have been awarded Nobel Prizes (including 9 current faculty members) since Nobel Prizes were in Physiology & Medicine: George Palade (1974), Renata Dulbecco (1975), Sydney Brenner (2002), Roger Tsien (2008) Top Research Medical School: Ranked 15 th (Out of 120) in the Nation

4 Radiation Therapy (RT)

5 Goal of RT To deliver maximum radiation dose to the tumor volume, while minimizing dose to surrounding healthy tissues/organs 2/3 of all cancer are treated with radiation therapy

6 3-D conformal hypothesis ~ early 80 s Courtesy: Lichter et al. Multi-Leaf Collimator (MLC) Varian Medical Systems Inc.

7 Image Guided Radiation Therapy (IGRT)

8 Why IGRT? Prostate cancer radiation therapy: Plan During each treatment fraction

9 Computed Tomography (CT)

10 Ring Gantry Systems Tomotherapy Vero (Mitsubishi)

11 Linac Hybrid Systems NovalisTx-Varian hybrid Hokkaido RTRT CyberKnife MGH IRIS

12 IGRT Systems with Cone Beam CT Varian Elekta Siemens kv/mv Siemens GE Novalis

13 Varian TrueBeam System

14 Radiation Therapy Procedure

15 RT process Treatment preparation Treatment simulation Build a virtual patient model (reference geometry) Treatment planning Perform virtual treatment using virtual machine on virtual patient Treatment execution Patient positioning Reproduce the reference geometry acquired through treatment simulation and used for treatment planning Treatment delivery Maintain the reference geometry

16 RT process Treatment preparation Treatment simulation Build a virtual patient model (reference geometry) Treatment planning Perform virtual treatment using virtual machine on virtual patient Treatment execution Patient positioning Reproduce the reference geometry acquired through treatment simulation and used for treatment planning Treatment delivery Maintain the reference geometry

17 RT process Treatment preparation Treatment simulation Build a virtual patient model (reference geometry) Treatment planning Perform virtual treatment using virtual machine on virtual patient Treatment execution Patient positioning Reproduce the reference geometry acquired through treatment simulation and used for treatment planning Treatment delivery Maintain the reference geometry

18 RT process Treatment preparation Treatment simulation Build a virtual patient model (reference geometry) Treatment planning Perform virtual treatment using virtual machine on virtual patient Treatment execution Patient positioning Reproduce the reference geometry acquired through treatment simulation and used for treatment planning Treatment delivery Maintain the reference geometry

19 What do we need to do?

20 Current Strategies in RT Non-gating Gating Tumor tracking CTV CTV CTV ITV = MIP 100% Large margin Efficient delivery Small margin Delivery less efficient Small margin Efficient delivery Technically Challenging

21 Gated IGRT

22 Research Projects in UCSD 1. Tumor Motion Tracking

23 Tumor position tracking Feature extraction of internal surrogates surrounding the tumor from X-ray projection images Fully automated algorithm using a priori known marker shape and size, similar to other template matching techniques

24 Tumor position tracking

25 Applications : 4D-CBCT Imaging

26 Research Projects in UCSD 2. Nano Particle Inserts

27 Nano particle Particles are sized between 1 and 100 nanometers. Property Size/shape dependent property changes Surface plasmon resonance in some metal particles Superparamagnetism in magnetic materials Unexpected optical properties Gold nanoparticles appear deep red to black in solution

28 Nano particle Property High surface area to volume ratio Metal, dielectric, and semiconductor nanoparticles have formed, as well as hybrid structures Semiconduction material may also be labeled quantum dots Au colloid Au Nanoshells

29 Detection of tumor with nano particle Design of EGF-SERS nanoparticle for labeling and detection of CTCs. A, preparation and schematic structures of Raman-encoded, PEG-stabilized, and EGF-peptide functionalized SERS nanoparticle. B, TEM image and DLS measurement. C, assay principle of CTC detection from whole blood using EGF-SERS nanoparticles.

30 Detection of tumor with nano particle Cancer cell targeting and spectroscopic detection by using antibody-conjugated SERS nanoparticles. (a) Preparation of targeted SERS nanoparticles by using a mixture of SH-PEG and a hetero-functional PEG (SH-PEG-COOH). Covalent conjugation of an EGFR-antibody fragment occurs at the exposed terminal of the hetero-functional PEG. 30

31 Real time tumor tracking: using 4D-CBCT and nano particle Pharmacokinetics of gold nanoparticles (a d) and iodine contrast agent (e h, OmnipaqueH) in mice. (a,e) Before injection. (b,f) 2 min after injection; (c,g) 10 min after injection; (d,h) 60 min after injection. The gold nanoparticles show low liver and spleen uptake and clearance via kidneys and bladder (b d). At 60 min (d), the contrast in the gold-injected mouse is similar to the uninjected mouse (a), indicating efficient clearance. 31

32 Research Projects in UCSD 3. Low-dose CBCT

33 Low-Dose CBCT Reconstruction Daily imaging dose is a concern in IGRT! Solution: Statistical (or iterative) reconstruction techniques Time is an issue! The idea is to solve the TV-based constrained convex optimization problem of the form: min f(x) Ax b 2 2 λ TV(x) 1 1 s.t x 0

34 Popular Solutions vs Our Intuition x k=0 x k=0 x * Constant Step size x K=1 Optimal Step Size x * x K=2 x K=2 x * x K=3 x K=3 Only regularization term is solved with numerical optimization x optimum Fidelity + Regularization are considered on optimization process x optimum Popular Solutions Our Intuition

35 Result: Full-Fan CBCT Head & Neck patient with 120 projections (i.e., 67% dose reduction) (a) FDK = 364 (b) FDK = 120 (c) GP-BB = 120 Park et al, Med Phys 2011 (under review) 12 iterations seconds

36 Result: Half-Fan CBCT Lung patient with 130 projections (i.e., 80% dose reduction) (a) FDK = 657 (b) FDK = 130 (c) GP-BB = iterations seconds Park et al, Med Phys 2011 (under review)

37 Result: Half-Fan CBCT Prostate patient with 220 projections (i.e., 67% dose reduction) (a) FDK = 657 (b) FDK = 220 (c) GP-BB = iterations seconds Park et al, Med Phys 2011 (under review)

38 Research Projects in UCSD 4. Dynamic Modulated Brachytherapy

39 What is Brachytherapy? Podgorsak et al., IJROBP 1987;13(12):

40 What is Brachytherapy?

41 Current Technologies Sun Myint et al., Col Dis 2010;12(2):30-36Devic et al., JACMP 2005;6(2):44-49

42 Dynamic Modulated Brachytherapy No one talks about dynamic modulation of radiation That is, intensity is directed & moving during treatment

43 Dynamic Modulated Brachytherapy To achieve this, we need: 1) Optimal shielding design(s) 2) Monte Carlo simulation of Ir-192 radiation transport inside shielding 3) Treatment planning software that can incorporate translational and rotational movements of the shield 4) Mechanical engineering of the robotic applicator 5) Validation of the end-to-end treatment accuracy 6) Prove that DMBT produces better plans than the current solutions*

44 Current Tumor Delivery

45 DMBT Demo: Delivery

46 To become Successful Graduate Engineering Student!! 1) Passion 2) Challenge 3) Mathematics Lot and Lot More Mathematics (Sorry to Say but True )

47 Thank You! Collaborators : Supervisors : Colleagues :