This page describes fan-beam tomographic image reconstruction
using the Julia package
Sinograms.jl
.
This page focuses on fan-beam with a "flat" detector, i.e., one row of a flat-panel detector as used in many cone-beam CT (CBCT) systems.
This page was generated from a single Julia file: 05-fan-flat.jl.
Packages needed here.
using Plots: plot, gui # these 2 must precede Sinograms for Requires to work!
using Unitful: cm
using Sinograms: SinoFanFlat, rays, plan_fbp, Window, Hamming, fbp, sino_geom_plot!
using ImageGeoms: ImageGeom, fovs, MaskCircle
using ImagePhantoms: SheppLogan, shepp_logan, radon, phantom
using MIRTjim: jim, prompt
The following line is helpful when running this example.jl file as a script; this way it will prompt user to hit a key after each figure is displayed.
isinteractive() ? jim(:prompt, true) : prompt(:draw);
For illustration, we start by synthesizing a fan-beam sinogram of the Shepp-Logan phantom.
For completeness, we use units (from Unitful), but units are optional.
Use ImageGeom
to define the image geometry.
ig = ImageGeom(MaskCircle(); dims=(128,126), deltas = (0.2cm,0.2cm) )
Use SinoFanFlat
to define the sinogram geometry.
sg = SinoFanFlat( ; nb = 130, d = 0.3cm, na = 100, dsd = 50cm, dod = 14cm)
Examine the geometry to verify the FOV:
jim(axes(ig), ig.mask; prompt=false)
sino_geom_plot!(sg, ig)
prompt()
Ellipse parameters for Shepp-Logan phantom:
μ = 0.1 / cm # typical linear attenuation coefficient
ob = shepp_logan(SheppLogan(); fovs = fovs(ig), u = (1, 1, μ))
Arc fan-beam sinogram for Shepp-Logan phantom:
sino = radon(rays(sg), ob)
jim(sg.r, sg.ad, sino; title="Shepp-Logan sinogram", xlabel="r", ylabel="ϕ")
Here we start with a "plan",
which would save work if we were reconstructing many images.
For illustration we include Hamming
window.
plan = plan_fbp(sg, ig; window = Window(Hamming(), 1.0))
fbp_image, sino_filt = fbp(plan, sino)
A narrow color window is needed to see the soft tissue structures:
clim = (0.9, 1.1) .* μ
jim(axes(ig), fbp_image, "FBP image for flat case"; clim)
For comparison, here is the ideal phantom image
true_image = phantom(axes(ig)..., ob, 2)
jim(axes(ig)..., true_image, "True phantom image"; clim)
This notebook was generated using Literate.jl.