Efficient Julia Optimization through an MRI Case Study

By: Steven Whitaker

Re-posted from: https://blog.glcs.io/optimization-mri

Welcome to our firstJulia for Devsblog post!This will be a continuously running series of postswhere our team will discusshow we have used Juliato solve real-life problems.So, let’s get started!

In this Julia for Devs post,we will discuss using Juliato optimize scan parametersin magnetic resonance imaging (MRI).

If you are interestedjust in seeing example codeshowing how to use Juliato optimize your own cost function,feel free to skip ahead.

While we will discuss MRI specifically,the concepts(and even some of the code)we will seewill be applicablein many situations where optimization is useful,particularly when there is a modelfor an observable signal.The model could be, e.g.,a mathematical equationor a trained neural network,and the observable signal(aka the output of the model)could be, e.g.,the image intensity of a medical imaging scanor the energy needed to heat a building.

Problem Setup

In this post,the signal model we will useis a mathematical equationthat gives the image intensityof a balanced steady-state free precession (bSSFP) MRI scan.This model has two sets of inputs:those that are user-definedand those that are not.In MRI,user-defined inputs are scan parameters,such as how long to run the scan foror how much power to use to generate MRI signal.The other input parameters are called tissue parametersbecause they are properties that are intrinsicto the imaged tissue (e.g., muscle, brain, etc.).

Tissue parameters often are assumedto take on a pre-defined valuefor each specific tissue type.However,because they are tissue-specificand can vary with health and age,sometimes tissue parameters are considered to be unknown valuesthat need to be estimated from data.

The problem we will discuss in this postis how to estimate tissue parametersfrom a set of bSSFP scans.Then we will discusshow to optimize the scan parametersof the set of bSSFP scansto improve the tissue parameter estimates.

To estimate tissue parameters,we need the following:

  1. a signal model, and
  2. an estimation algorithm.

Signal Model

Here’s the signal model:

# Reference: https://www.mriquestions.com/true-fispfiesta.htmlfunction bssfp(TR, flip, T1, T2)    E1 = exp(-TR / T1)    E2 = exp(-TR / T2)    (sin, cos) = sincosd(flip)    num = sin * (1 - E1)    den = 1 - cos * (E1 - E2) - E1 * E2    signal = num / den    return signalend

This function returns the bSSFP signalas a function of two scan parameters(TR, a value in units of seconds,and flip, a value in units of degrees)and two tissue parameters(T1 and T2, each a value in units of seconds).

Estimation Algorithm

There are various estimation algorithms out there,but, for simplicity in this post,we will stick with a grid search.We will compute the bSSFP signalover many pairs of T1 and T2 values.We will compare these computed signalsto the actual observed signal,and the pair of T1 and T2 valuesthat results in the closest matchwill be chosen as the estimates of the tissue parameters.Here’s the algorithm in code:

# `signal` is the observed signal.# `TR` and `flip` are the scan parameters that were used,# and we want to estimate `T1` and `T2`.# `signal`, `TR` and `flip` should be vectors of the same length,# representing running multiple scans# and recording the observed signal for each pair of `TR` and `flip` values.function gridsearch(signal, TR, flip)    # Specify the grid of values to search over.    # `T1_grid` and `T2_grid` could optionally be inputs to this function.    T1_grid = exp10.(range(log10(0.5), log10(3), 40))    T2_grid = exp10.(range(log10(0.005), log10(0.7), 40))    T1_est = T1_grid[1]    T2_est = T2_grid[1]    best = Inf    # Pre-allocate memory for some computations to speed up the following loop.    residual = similar(signal)    # Iterate over the Cartesian product of `T1_grid` and `T2_grid`.    for (T1, T2) in Iterators.product(T1_grid, T2_grid)        # Physical constraint: T1 is greater than T2, and both are positive.        T1 > T2 > 0 || continue        # For the given T1 and T2 pairs, compute the (noiseless) bSSFP signal        # for each bSSFP scan and subtract them from the given signals.        # Tip: In Julia, one can apply a scalar function elementwise on vector        #      inputs using the "dot" notation (see the `.` after `bssfp` below).        residual .= signal .- bssfp.(TR, flip, T1, T2)        # Compute the norm squared of the above difference.        err = residual' * residual        # If the candidate T1 and T2 pair fit the given signals better,        # keep them as the current estimate of T1 and T2.        if err < best            best = err            T1_est = T1            T2_est = T2        end    end    isinf(best) && error("no valid grid points; consider changing `T1_grid` and/or `T2_grid`")    return (T1_est, T2_est)end

Cost Function

Now,to optimize scan parameterswe need a function to optimize,also called a cost function.In this case,we want to minimize the errorbetween what our estimator tells usand the true tissue parameter values.But because we need to optimize scan parametersbefore running any scans,we will simulate bSSFP MRI scansusing the given sets of scan parametersand average the estimator errorover several sets of tissue parameters.Here’s the code for the cost function:

# Load the Statistics standard library package to get access to the `mean` function.using Statistics# `TR` and `flip` are vectors of the same length# specifying the pairs of scan parameters to use.function estimator_error(TR, flip)    # Specify the grid of values to average over and the noise level to simulate.    # For a given pair of `T1_true` and `T2_true` values,    # bSSFP signals will be simulated, and then the estimator    # will attempt to estimate what `T1_true` and `T2_true` values were used    # based on the simulated bSSFP signals and the given scan parameters.    T1_true = [0.8, 1.0, 1.5]    T2_true = [0.05, 0.08, 0.1]    noise_level = 0.01    T1_avg = mean(T1_true)    T2_avg = mean(T2_true)    # Pre-allocate memory for some computations to speed up the following loop.    signal = float.(TR)    # The following computes the mean estimator error over all pairs    # of true T1 and T2 values.    return mean(Iterators.product(T1_true, T2_true)) do (T1, T2)        # Ignore pairs for which `T2 > T1`.        T2 > T1 && return 0        # Simulate noisy signal using the true T1 and T2 values.        signal .= bssfp.(TR, flip, T1, T2) .+ noise_level .* randn.()        # Estimate T1 and T2 from the noisy signal.        (T1_est, T2_est) = gridsearch(signal, TR, flip)        # Compare estimates to truth.        T1_err = (T1_est - T1)^2        T2_err = (T2_est - T2)^2        # Combine the T1 and T2 errors into one single error metric        # by averaging the respective errors        # after normalizing by the mean true value of each parameter.        err = (T1_err / T1_avg + T2_err / T2_avg) / 2    endend

Optimization

Now we are finally ready to optimize!We will use Optimization.jl.Many optimizers are available,but because we have a non-convex optimization problem,we will use the adaptive particle swarm global optimization algorithm.Here’s a function that does the optimization:

# Load needed packages.# Optimization.jl provides the optimization infrastructure,# while OptimizationOptimJL.jl wraps the Optim.jl package# that provides the optimization algorithm we will use.using Optimization, OptimizationOptimJL# Load the Random standard library package to enable setting the random seed.using Random# `TR_init` and `flip_init` are vectors of the same length# that provide a starting point for the optimization algorithm.# The length of these vectors also determines the number of bSSFP scans to simulate.function scan_optimization(TR_init, flip_init)    # Ensure randomly generated noise is the same for each evaluation.    Random.seed!(0)    N_scans = length(TR_init)    length(flip_init) == N_scans || error("`TR_init` and `flip_init` have different lengths")    # The following converts the cost function we created (`estimator_error`)    # into a form that Optimization.jl can use.    # Specifically, Optimization.jl needs a cost function that takes two inputs    # (the first of which contains the parameters to optimize)    # and returns a real number.    # The input `x` is a concatenation of `TR` and `flip`,    # i.e., `TR == x[1:N_scans]` and `flip == x[N_scans+1:end]`.    # The input `p` is unused, but is needed by Optimization.jl.    cost_fun = (x, p) -> estimator_error(x[1:N_scans], x[N_scans+1:end])    # Specify constraints.    # The lower and upper bounds are chosen to ensure reasonable bSSFP scans.    (min_TR, max_TR) = (0.001, 0.02)    (min_flip, max_flip) = (1, 90)    constraints = (;        lb = [fill(min_TR, N_scans); fill(min_flip, N_scans)],        ub = [fill(max_TR, N_scans); fill(max_flip, N_scans)],    )    # Set up and solve the problem.    f = OptimizationFunction(cost_fun)    prob = OptimizationProblem(f, [TR_init; flip_init]; constraints...)    sol = solve(prob, ParticleSwarm(lower = prob.lb, upper = prob.ub, n_particles = 3))    # Extract the optimized TRs and flip angles, remembering that `sol.u == [TR; flip]`.    TR_opt = sol.u[1:N_scans]    flip_opt = sol.u[N_scans+1:end]    return (TR_opt, flip_opt)end

Results

Let’s see how scan parameter optimizationcan improve tissue parameter estimates.We will use the following functionto take scan parameters,simulate bSSFP scans,and then estimate the tissue parametersfrom those scans.We will compare the results of this functionwith manually chosen scan parametersto those with optimized scan parameters.

# Load the LinearAlgebra standard library package for access to `norm`.using LinearAlgebra# Load Plots.jl for displaying the results.using Plots# Helper function for plotting.function plot_true_vs_est(T_true, T_est, title, rmse, clim)    return heatmap([T_true T_est];        title,        clim,        xlabel = "RMSE = $(round(rmse; digits = 4)) s",        xticks = [],        yticks = [],        showaxis = false,        aspect_ratio = 1,    )endfunction run(TR, flip)    # Create a synthetic object to scan.    # The background of the object will be indicated with values of `0.0`    # for the tissue parameters.    nx = ny = 128    object = map(Iterators.product(range(-1, 1, nx), range(-1, 1, ny))) do (x, y)        r = hypot(x, y)        if r < 0.5            return (0.8, 0.08)        elseif r < 0.8            return (1.3, 0.09)        else            return (0.0, 0.0)        end    end    # Simulate the bSSFP scans.    T1_true = first.(object)    T2_true = last.(object)    noise_level = 0.001    signal = map(T1_true, T2_true) do T1, T2        # Ignore the background of the object.        (T1, T2) == (0.0, 0.0) && return 0.0        bssfp.(TR, flip, T1, T2) .+ noise_level .* randn.()    end    # Estimate the tissue parameters.    T1_est = zeros(nx, ny)    T2_est = zeros(nx, ny)    for i in eachindex(signal, T1_est, T2_est)        # Don't try to estimate tissue parameters for the background.        signal[i] == 0.0 && continue        (T1_est[i], T2_est[i]) = gridsearch(signal[i], TR, flip)    end    # Compute the root mean squared error.    m = T1_true .!= 0.0    T1_rmse = sqrt(norm(T1_true[m] - T1_est[m]) / count(m))    T2_rmse = sqrt(norm(T2_true[m] - T2_est[m]) / count(m))    # Plot the results.    p_T1 = plot_true_vs_est(T1_true, T1_est, "True vs Estimated T1", T1_rmse, (0, 2.5))    p_T2 = plot_true_vs_est(T2_true, T2_est, "True vs Estimated T2", T2_rmse, (0, 0.25))    return (p_T1, p_T2)end

First, let’s see the results with no optimization:

TR_init = [0.005, 0.005, 0.005]flip_init = [30, 60, 80](p_T1_init, p_T2_init) = run(TR_init, flip_init)
display(p_T1_init)

T1 estimates using initial scan parameters

display(p_T2_init)

T2 estimates using initial scan parameters

Now let’s optimize the scan parametersand then see how the tissue parameter estimates look:

(TR_opt, flip_opt) = scan_optimization(TR_init, flip_init)(p_T1_opt, p_T2_opt) = run(TR_opt, flip_opt)
display(p_T1_opt)

T1 estimates using optimized scan parameters

display(p_T2_opt)

T2 estimates using optimized scan parameters

We can see that the optimized scansresult in much better tissue parameter estimates!

Summary

In this post,we saw how Julia can be usedto optimize MRI scan parametersto improve tissue parameter estimates.Even though we discussed MRI specifically,the concepts presented hereeasily extend to other applicationswhere signal models are knownand optimization is required.

Note that most of the code for this postwas taken from a Dash appI helped create for JuliaHub.Feel free to check it outif you want to see this code in action!

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