using SPECTrecon: SPECTplan, psf_gauss, project!, backproject!, mlem, mlem! using MIRTjim: jim, prompt using Plots: scatter, plot!, default; default(markerstrokecolor=:auto) isinteractive() ? jim(:prompt, true) : prompt(:draw); nx,ny,nz = 64,64,50 T = Float32 xtrue = zeros(T, nx,ny,nz) xtrue[(1nx÷4):(2nx÷3), 1ny÷5:(3ny÷5), 2nz÷6:(3nz÷6)] .= 1 xtrue[(2nx÷5):(3nx÷5), 1ny÷5:(2ny÷5), 4nz÷6:(5nz÷6)] .= 2 average(x) = sum(x) / length(x) function mid3(x::AbstractArray{T,3}) where {T} (nx,ny,nz) = size(x) xy = x[:,:,ceil(Int, nz÷2)] xz = x[:,ceil(Int,end/2),:] zy = x[ceil(Int, nx÷2),:,:]' return [xy xz; zy fill(average(xy), nz, nz)] end jim(mid3(xtrue), "Middle slices of xtrue") px = 11 psf1 = psf_gauss( ; ny, px) jim(psf1, "PSF for each of $ny planes") nview = 60 psfs = repeat(psf1, 1, 1, 1, nview) size(psfs) dy = 8 # transaxial pixel size in mm mumap = zeros(T, size(xtrue)) # zero μ-map just for illustration here plan = SPECTplan(mumap, psfs, dy; T) using LinearMapsAA: LinearMapAA using LinearAlgebra: mul! forw! = (y,x) -> project!(y, x, plan) back! = (x,y) -> backproject!(x, y, plan) idim = (nx,ny,nz) odim = (nx,nz,nview) A = LinearMapAA(forw!, back!, (prod(odim),prod(idim)); T, odim, idim) using Distributions: Poisson if !@isdefined(ynoisy) # generate (scaled) Poisson data ytrue = A * xtrue target_mean = 20 # aim for mean of 20 counts per ray scale = target_mean / average(ytrue) scatter_fraction = 0.1 # 10% uniform scatter for illustration scatter_mean = scatter_fraction * average(ytrue) # uniform for simplicity background = scatter_mean * ones(T,nx,nz,nview) ynoisy = rand.(Poisson.(scale * (ytrue + background))) / scale end jim(ynoisy, "$nview noisy projection views") x0 = ones(T, nx, ny, nz) # initial uniform image niter = 30 if !@isdefined(xhat1) xhat1 = mlem(x0, ynoisy, background, A; niter) end size(xhat1) if !@isdefined(xhat2) xhat2 = copy(x0) mlem!(xhat2, x0, ynoisy, background, A; niter) end @assert xhat1 ≈ xhat2 jim(mid3(xhat2), "ML-EM at $niter iterations")