Here's an example where we will use the CLI to simulate data from the FORBILD Head phantom, and reconstruct a tomogram from that data.

Start by creating a clean directory to work in:

mkdir headtest
cd headtest

Now, the first step is to create a .json file representing the experiment geometry. This can be done like so:

aprict-cli make-geom cone-cyl \
    --num 2000 \
    --R 250 --L-minus-R 250 --h 700 \
    --detwid 600 --dethei 600 \
    --pixwid-isoc 0.25 --pixhei-isoc 0.25 \
    --output projgeom.json

You might like to open this file in a text editor, to get a feel for what's inside. It describes an experiment geometry with a flat-panel trajectory.

Now, let's generate accurate, noiseless projection data from this geometry. We can specify any FORBILD file, assumed to have a file suffix of .pha, using the --phantom file.pha option. However, there are also a few built-in presets for convenience. Let's use --phantom-preset HEAD, which refers to the FORBILD Head phantom, with distances specified in units of millimeters.

aprict-cli project cbfp \
    --geom projgeom.json \
    --phantom-preset HEAD \
    --ssu 3 \
    --ssv 3 \
    --output projs \
    --plot-mode PYQTGRAPH

(Time on my machine: ~2 minutes, 55 seconds) The option --plot-mode PYQTGRAPH will display a plot window of the projection data after it is created. (This will not be available if the module was not installed with the [gui] option.)

Now, let's reconstruct with three different algorithms:

1. Global Backprojection-Convolution (GBC)
2. Filtered backprojection (FBP)
3. Filtered backprojection with volumetric weightings (FBP-VW)

To perform a GBC reconstruction, we do:

aprict-cli recon gbc-cone-cyl \
    --geom projs/projgeom.json \
    --projs projs/projs.npy \
    --voxwid 0.5 \
    --volshape '(525,525,525)' \
    --alg GREWAR \
    --output recon_gbc \
    --plot-mode PYQTGRAPH

(Time on my machine: ~37 seconds)

To perform an FBP reconstruction, we do:

aprict-cli recon fbp-cone-cyl \
    --geom projs/projgeom.json \
    --projs projs/projs.npy \
    --voxwid 0.5 \
    --volshape '(525,525,525)' \
    --alg STANDARD \
    --output recon_fbp \
    --plot-mode PYQTGRAPH

(Time on my machine: ~12 seconds)

To perform an FBP-VM reconstruction, we change --alg to NOOGREWAR:

aprict-cli recon fbp-cone-cyl \
    --geom projs/projgeom.json \
    --projs projs/projs.npy \
    --voxwid 0.5 \
    --volshape '(525,525,525)' \
    --alg NOOGREWAR \
    --output recon_fbp-vw \
    --plot-mode PYQTGRAPH

(Time on my machine: ~22 seconds)

Here's an example where we will use the CLI to simulate data from a FORBILD phantom resembling a double-chambered aluminium rock canister phantom.

Start by creating a clean directory to work in:

mkdir rockcantest
cd rockcantest

The first step is to create a .json file representing the fan-beam experiment geometry. This can be done like so:

aprict-cli make-geom fan-circ \
    --num 300 \
    --R 4 --L-minus-R 4 \
    --detwid 17.5 --nv 1 \
    --pixwid-isoc 0.002 --pixhei-isoc 0.002 \
    --output projgeom.json

Note the option --nv 1, which specifies that the detector height should be 1 pixel, which is appropriate for a fan-beam experiment, where we only intend to reconstruct a single z slice of the phantom. This is an alternative to supplying the --dethei option. Some options, such as these, are mutually exclusive. Use aprict-cli make-geom fan-circ --help to see detailed information about this.

Now, let's generate projection data from the phantom preset called ROCK_CAN. The z=0 layer of the phantom is not all that interesting, so we'll also translate the input phantom down along the z axis by -2.7 units (millimeters, in this case). Additionally, since we intend on simulating a 2D phantom here, we only want to sample rays at the vertical centre of each pixel. So, we will supply --ssv 1 to ensure that only the middle of each pixel is used for ray end-points. The option --ssu 100 ensures that 100 fanning rays are averaged to compute the projected attenuation at each pixel. This ensures greater accuracy, particularly in the presence of small features which may otherwise be missed by the pixel size.

aprict-cli project cbfp \
    --geom projgeom.json \
    --phantom-preset ROCK_CAN \
    --translate-vol '(0,0,-2.7)' \
    --ssu 100 \
    --ssv 1 \
    --output projs \
    --plot-mode PYQTGRAPH

(Time on my machine: ~1 minute, 46 seconds) The option --plot-mode PYQTGRAPH will display a plot window of the projection data (a sinogram) after it is created. (This will not be available if the module was not installed with the [gui] option.)

You will find a copy of the ROCK_CAN phantom in the output directory projs, beside the projection data. The phantom is always copied to the output directory, to ensure a good record of the inputs to the simulation.

Finally, let's perform a reconstruction using a filtered backprojection (FBP) method for this geometry:

aprict-cli recon fbp-fan-circ \
    --geom projs/projgeom.json \
    --projs projs/projs.npy \
    --voxwid 0.002 \
    --volshape '(3000,3000,1)' \
    --alg STANDARD \
    --output recon_standard \
    --plotmin -0.25 \
    --plotmax 2.25 \
    --plot-mode PYQTGRAPH

(Time on my machine: ~2 seconds) Once again, the option --plot-mode PYQTGRAPH ensures that a plot window will pop up once the reconstruction is done. Note the --plotmin -0.25 and --plotmax 2.25 options here, which control the plotting range of the saved *.png files in the output directory. The whole volume is stored as a numpy array (*.npy), but it's useful to have some preview PNGs generated as well.

The previous reconstruction contains a lot of undersampling artefacts, due to the small number of projections (300). To a lesser extent, it also contains aliasing artefacts, due to the (de)magnified pixel size subceeding the voxel size. So, let's try an alternative algorithm that better handles this situation, at the expensive of reduced sharpness on the edges. This can be done by specifying --alg NOOGREWAR:

aprict-cli recon fbp-fan-circ \
    --geom projs/projgeom.json \
    --projs projs/projs.npy \
    --voxwid 0.002 \
    --volshape '(3000,3000,1)' \
    --alg NOOGREWAR \
    --output recon_noogrewar \
    --plotmin -0.25 \
    --plotmax 2.25 \
    --plot-mode PYQTGRAPH

(Time on my machine: ~2 seconds)