BioEM Simulations with CST STUDIO SUITE

Transcription

1 BioEM Simulations with CST STUDIO SUITE Tilmann Wittig, CST AG

2 Motivation for Simulation MRI System

3 Safety Aspects SAR distribution inside a multi-channel head coil Homogeneous phantom Homogeneous head Heterogeneous head Critical hotspot is only visible in detailed heterogeneous body model

4 Phantom versus Detailed Model Homogeneous head, no bioheat Heterogeneous head, with bioheat SAR and temperature distributions in phantoms and in vivo differ significantly Measurements with homogeneous phantoms are insufficient

5 Mobile Communication

6 Body Centric Communication realised gain at 2.4 GHz all antennas excited simultaneously

7 Pacemaker inside Human Body Model

8 Bio-EM Simulations

9 Biological Models The right choice of the biological model is essential for the reliability of an MRI simulation. homogeneous models CST Voxel Family other voxel data Visible Human voxel data

10 Homogeneous Body Model Can easily be posed in different positions Good to compare to measurements Can be simulated in time and frequency domain Simulate faster then voxel models

11 CST Voxel Family CHILD BABY EMMA KATJA (pregnant) LAURA DONNA GUSTAV

12 CST Voxel Family Material properties for all frequencies can be defined via macro Properties can be corrected for age

13 HUGO (Visible Human Project) Available in different resultions Materials of interest can be chosen Visible Human Project produced by the National Library of Medicine (NLM), Maryland

14 Posable Voxel Model Courtesy of TEMF, TU Darmstadt

15 Complete Technology, Example MRI Static fields: Gradient fields: HF fields: M-Static Solver Magneto-quasistatic Solver Transient Solver Frequency Domain Solver Circuit Simulation: Bioheat: CST Design Studio Stationary Thermal Solver Transient Thermal Solver

16 Improved Connectivity of Edge Elements Wires Lumped Elements Discrete Ports Voltage Monitors

17 Voxel Model with new Plot Engine

18 MRI Toolbox

19 Visualizing SAR Results Vizualized as 2D or 3D plot Get information about position of the maximum Visualize the max SAR cube

20 SAR Logfile General information Statistics for total volume, (extra statistic For subvolume possible)

21 SAR for Periodic Brodband Signals Averages Power Loss over a periodic brodband signal

22 SAR on TET Mesh Prototype implementation No field vizualization, just Log-File Sampling can be freely chosen

23 Conversion of simulation data into VOPs 1. Determined from local SAR matrices S i,j,k C K K per mesh cell (K = # Tx channels) S i,j,k contain normalized information on field interaction of individual Tx channels 2. Calculate averaged SAR matrices S n C K K for any 10 g tissue mass 3. Apply compression algorithm with specified maximum SAR overestimation Similar SAR matrices are clustered VOPs (Eichfelder and Gebhardt, MRM 66: (2011)) Compression algorithm provided by Siemens Details on Conversion of Simulation Data to VOP Models: Siemens IDEA UHF Community Wiki & TX Array Step 2 Operator Manual

24 RF Simulation: Studio Suite, CST, Germany. VOP Evaluation for 8-ch Head Coil male / 70 kg female / 59 kg male / 95 kg # SAR matrices up to 4 million (a) Head centered (b) Head close to lower coil element (c) Head centered (d) Head close to lower coil element (e) Head centered Number of VOPs Number of VOPs Body model (sex / weight) Overestimation (a) (b) (c) (d) (e) m / 70 kg f / 59 kg m / 95 kg Max. SAR overestimation Number of VOPs 1% % % Combination (a)+(b)+(c)+(d)+(e) The higher Over- the interaction 5% between channel field patterns, 376 the higher the estimation 10% number of VOPs (cf. config c) 70 Number of VOPs depends on efficiency of compression algorithm Algorithm always finds safe set of VOPs, but not necessarily global optimum

25 BioHeat Solver Converts RF losses into temperature distribution Considers biological effects (bloodflow, metabolic heating) Pennes bioheat equation: T C t k T ( SAR) A B( T Tb ) A=Basal metabolic rate B=Term associated with blood perfusion

26 Thermal Simulations of Biological Tissues Simulate either stationary or transient biological problems

27 RF Thermal Ablation Tumor treatment with a 40 Watt (375 khz) electrode applied to tumor in the liver MWS Co-simulation with thermodynamic solver Model courtesy of E. Della Loggia, A Orland (University of L Aquila, UAq EMC Laboratory, L Aquila, Italy, ) and B.Zobel (University Campus-Biomedico, Rome, Italy)

28 Temperature Dependent Materials Temperature distribution inside liver due to 40W heating Liver conductivity variation due to localized heating The maximum temperature in liver is reduced by 6K, due to increased liver conductivity in region around electrode DT = 10F / 6 C Temperature dependent liver properties obtained from Determination of the temperature-dependent electric conductivity of liver tissue ex vivo and in vivo: Importance for therapy planning for the radiofrequency ablation of liver tumours, International Journal of Hyperthermia, U Zurbuchen et al, Vol 26, No.1, Pages 26-33, 2010

29 Verifying simulated results 100 C / 212 F 103 C/ F

30 Human Thermoregulation Defined by: exponential factor, saturation factor basal temperature and basal bloodflow

31 Temperature Dependent Bloodflow Constant Bloodflow Temp.-Dep.Bloodflow

32 EM Thermal Co-Simulation Wizard

33 Summary Right choice of human models essential for efficient BioEM simulation Complete Technology for different application types SAR As FD quantity available on HEX and TET grid Averaged over time for broadband pulses Virtual Observation Points (VOPs) Bioheat Solver Stationary and transient Human thermo-regulation