GOAL: Solve inverse problem using Standard Diffusion and Monte Carlo Models.
I. Impact of Optical Properties on Spatially-Resolved Reflectance
- On the "Inverse" Panel select: (a) Forward Model Engine: Scaled Monte Carlo (g=0.8), Inverse Model Engine: Standard Diffusion (Analytic - Point Source) and (c) Solution Domain: Steady State(R(ρ)).
- Select start and stop locations to 0.5 and 9.5 mm, respectively with 10 points (every 1 mm).
- Set Optimization Parameters to: μa and μ's.
- Simulate measured data: set Forward Simulation Optical Properties to: μa = 1 mm-1, μ's = 1 mm-1, g = 0.8 and n = 1.4 and 2% noise.
- Plot Measured Data and make sure "Hold On" is checked.
- Set Initial Guess Optical Properties to: μa = 0.05 mm-1, μ's = 1.5 mm-1, g = 0.8 and n = 1.4.
- Plot Initial Guess.
- Press "Run Inverse Solver".
- To what optical property values did the inverse solver converge? (Scroll to the bottom of the page to see the output).
- Why are the converged values not exactly the forward simulation optical properties?
- Perform the same analysis with 0% noise added to the simulated measured data. How accurate are the converged properties now?
II. Change Inverse Model Engine
- Perform the same analysis changing the Inverse Model Engine to Monte Carlo.
- Which Model Engine produced the more accurate converged values?
III. Change number of detectors
- Repeat II using only 2 detectors. Can you strategically place the two detectors to obtain the same accuracy in the converged values as you obtained with 10 detectors?
- Use the analysis plots of ∂R/∂μa and ∂R/∂μ's to help guide their placement.