# Compressed Sensing 2D

This example illustrates how to perform 2D compressed sensing image reconstruction from Cartesian sampled MRI data with 1-norm regularization of orthogonal wavelet coefficients, using the Julia language.

This entire page was generated using a single Julia file: 2-cs-wl-l1-2d.jl. In any such Julia documentation, you can access the source code using the "Edit on GitHub" link in the top right.

The corresponding notebook can be viewed in nbviewer here: 2-cs-wl-l1-2d.ipynb, and opened in binder here: 2-cs-wl-l1-2d.ipynb.

This demo is somewhat similar to Fig. 3 in the survey paper "Optimization methods for MR image reconstruction," in Jan 2020 IEEE Signal Processing Magazine, except that the sampling is 1D phase encoding instead of 2D.

Packages used in this demo (run Pkg.add as needed):

using ImagePhantoms: shepp_logan, SheppLoganEmis, spectrum, phantom
using MIRT: embed, Afft, Aodwt
using MIRTjim: jim, prompt
using MIRT: pogm_restart
using LinearAlgebra: norm
using Plots; default(markerstrokecolor=:auto, label="")
using FFTW: fft
using Random: seed!
using InteractiveUtils: versioninfo

The following line is helpful when running this jl-file as a script; this way it will prompt user to hit a key after each image is displayed.

isinteractive() && jim(:prompt, true);

## Create (synthetic) data

Shepp-Logan phantom (unrealistic because real-valued):

nx,ny = 192,256
object = shepp_logan(SheppLoganEmis(); fovs=(ny,ny))
Xtrue = phantom(-(nx÷2):(nx÷2-1), -(ny÷2):(ny÷2-1), object, 2)
Xtrue = reverse(Xtrue, dims=2)
clim = (0,9)
jim(Xtrue, "true image"; clim)

Somewhat random 1D phase-encode sampling:

seed!(0); sampfrac = 0.2; samp = rand(ny) .< sampfrac; sig = 1
mod2 = (N) -> mod.((0:N-1) .+ Int(N/2), N) .- Int(N/2)
samp .|= (abs.(mod2(ny)) .< Int(ny/8)) # fully sampled center rows
samp = trues(nx) * samp'

## Wavelet sparsity in synthesis form

The image reconstruction optimization problem here is

$$$\arg \min_{x} \frac{1}{2} \| A x - y \|_2^2 + \beta \; \| W x \|_1$$$

where $y$ is the k-space data, $A$ is the system model (simply Fourier encoding F here), $W$ is an orthogonal discrete (Haar) wavelet transform, again implemented as a LinearMapAA object. Because $W$ is unitary, we make the change of variables $z = W x$ and solve for $z$ and then let $x = W' z$ at the end. In fact we use a weighted 1-norm where only the detail wavelet coefficients are regularized, not the approximation coefficients.

Orthogonal discrete wavelet transform operator (LinearMapAO):

W, scales, _ = Aodwt((nx,ny) ; T = eltype(F))
isdetail = scales .> 0
jim(
jim(scales, "wavelet scales"),
jim(real(W * Xtrue) .* isdetail, "wavelet detail coefficients"),
)

Inputs needed for proximal gradient methods:

Az = F * W' # another operator!
Fnullz = (z) -> 0 # cost function in z not needed
f_gradz = (z) -> Az' * (Az * z - y)
f_Lz = nx*ny # Lipschitz constant for single coil Cartesian DFT
regz = 0.03 * nx * ny # oracle from Xtrue wavelet coefficients!
costz = (z) -> 1/2 * norm(Az * z - y)^2 + regz * norm(z,1) # 1-norm regularizer
soft = (z,c) -> sign(z) * max(abs(z) - c, 0) # soft thresholding
g_prox = (z,c) -> soft.(z, isdetail .* (regz * c)) # proximal operator (shrink details only)
z0 = W * X0
jim(z0, "Initial wavelet coefficients")

## Iterate

Run ISTA=PGM and FISTA=FPGM and POGM, the latter two with adaptive restart See Kim & Fessler, 2018 for adaptive restart algorithm details.

Functions for tracking progress:

function fun_ista(iter, xk_z, yk, is_restart)
xh = W' * xk_z
return (costz(xk_z), nrmse(xh), is_restart) # , psnr(xh)) # time()
end

function fun_fista(iter, xk, yk_z, is_restart)
xh = W' * yk_z
return (costz(yk_z), nrmse(xh), is_restart) # , psnr(xh)) # time()
end;

Run and compare three proximal gradient methods:

niter = 50
z_ista, out_ista = pogm_restart(z0, Fnullz, f_gradz, f_Lz; mom=:pgm, niter=niter,
restart=:none, restart_cutoff=0., g_prox=g_prox, fun=fun_ista)
Xista = W'*z_ista
@show nrmse(Xista)

z_fista, out_fista = pogm_restart(z0, Fnullz, f_gradz, f_Lz; mom=:fpgm, niter=niter,
restart=:gr, restart_cutoff=0., g_prox=g_prox, fun=fun_fista)
Xfista = W'*z_fista
@show nrmse(Xfista)

z_pogm, out_pogm = pogm_restart(z0, Fnullz, f_gradz, f_Lz; mom=:pogm, niter=niter,
restart=:gr, restart_cutoff=0., g_prox=g_prox, fun=fun_fista)
Xpogm = W'*z_pogm
@show nrmse(Xpogm)

jim(
jim(Xfista, "FISTA/FPGM"),
jim(Xpogm, "POGM with ODWT"),
)

## POGM is fastest

Plot cost function vs iteration:

cost_ista = [out_ista[k][1] for k=1:niter+1]
cost_fista = [out_fista[k][1] for k=1:niter+1]
cost_pogm = [out_pogm[k][1] for k=1:niter+1]
cost_min = min(minimum(cost_ista), minimum(cost_pogm))
plot(xlabel="iteration k", ylabel="Relative cost")
scatter!(0:niter, cost_ista  .- cost_min, label="Cost ISTA")
scatter!(0:niter, cost_fista .- cost_min, markershape=:square, label="Cost FISTA")
scatter!(0:niter, cost_pogm  .- cost_min, markershape=:utriangle, label="Cost POGM")
isinteractive() && prompt();

Plot NRMSE vs iteration:

nrmse_ista = [out_ista[k][2] for k=1:niter+1]
nrmse_fista = [out_fista[k][2] for k=1:niter+1]
nrmse_pogm = [out_pogm[k][2] for k=1:niter+1]
plot(xlabel="iteration k", ylabel="NRMSE %", ylims=(3,6.5))
scatter!(0:niter, nrmse_ista, label="NRMSE ISTA")
scatter!(0:niter, nrmse_fista, markershape=:square, label="NRMSE FISTA")
scatter!(0:niter, nrmse_pogm, markershape=:utriangle, label="NRMSE POGM")

Show error images:

p1 = jim(Xtrue, "true")
p2 = jim(X0, "X0: initial")
p3 = jim(Xpogm, "POGM recon")
p5 = jim(X0 - Xtrue, "X0 error", clim=(0,2))
p6 = jim(Xpogm - Xtrue, "Xpogm error", clim=(0,2))
jim(p2, p3, p5, p6)