Caution

This document might be outdated. We definitely should include NIfTI Phantoms here

Generating Phantoms

Phantoms are built from BrainWeb data. This data is not included directly. Instead, a BrainWeb downloader is part of mr0, that can be run once to download all segmentation data provided by BrainWeb, which is then filled to produce files that can be loaded as mentioned here.

Phantoms can be generated with different configurations, depending on the use-case, like 3T or 7T data, high-res, or whether to include fat are options. A fixed set of configuartions facilitates reproducibility. To execute generation, just use mr0.generate_brainweb_phantoms:

import MRzeroCore as mr0

mr0.generate_brainweb_phantoms("output/brainweb", "3T")

Loading predefined 2D or 3D phantoms

We have some phantoms predefined.

A simple 2D brain phantom as .mat file, 3T (PD,T1,T2, dB0, rB1, no T2’, no Diffusion)

!wget https://github.com/MRsources/MRzero-Core/raw/main/documentation/playground_mr0/numerical_brain_cropped.mat &> /dev/null

This can be loaded and modified via mr0.VoxelGridPhantom.load_mat():

obj_p = mr0.VoxelGridPhantom.load_mat('numerical_brain_cropped.mat')
brain_phantom_res = 64 #@param {type:"slider", min:16, max:128, step:16}
obj_p = obj_p.interpolate(brain_phantom_res, brain_phantom_res, 1)
obj_p.B0[:] = 0
# obj_p.D[:] = 0
obj_p = obj_p.build()

A filled brainweb phantom 3T (PD,T1,T2, dB0, rB1, T2’, Diffusion)

!wget https://github.com/MRsources/MRzero-Core/raw/main/documentation/playground_mr0/subject05.npz &> /dev/null

This can be loaded and modified via mr0.VoxelGridPhantom.brainweb():

sz = [64, 64]
# (i) load a phantom object from file
obj_p = mr0.VoxelGridPhantom.brainweb('subject05.npz')
obj_p = obj_p.interpolate(sz[0], sz[1], 32).slices([15])
obj_p.size[2]=0.08

if 1: # insert rectangular "Tumor" in the diffusion map
# typical brain tumor ADC values are around ~1.5 * 10^-3 mm^2/s,
# which lies between GM/WM and CSF (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3000221)
# mr0 uses D in units of 10^-3 * mm ^2/s  this is the same as µm^2/ms
    # construct tumor border region
    for ii in range(15, 25):
        for jj in range(15, 25):
            obj_p.D[ii, jj] = torch.tensor(0.75)

    # construct tumor filling
    for ii in range(16, 24):
        for jj in range(16, 24):
            obj_p.D[ii, jj] = torch.tensor(1.5)

# Store PD and B0 for comparison
D = obj_p.D
B0 = obj_p.B0
D=obj_p.D
obj_p.plot()
# Convert Phantom into simulation data, this is sparse, and cannot be plotted directly, see obj_p.recover()
obj_p = obj_p.build()

A simple voxel phantom via mr0.CustomVoxelPhantom():

For a single voxel:

obj_p = mr0.CustomVoxelPhantom(
    pos=[[-0.25, -0.25, 0]],
    PD=[1.0],
    T1=[3.0],
    T2=[0.5],
    T2dash=[30e-3],
    D=[0.0],
    B0=0,
    voxel_size=0.1,
    voxel_shape="box"
)

For a more complex structure, her an L-shape:

obj_p = mr0.CustomVoxelPhantom(
    pos=[[-0.4, -0.4, 0], [-0.4, -0.2, 0],
         [-0.3, -0.2, 0], [-0.2, -0.2, 0], [-0.1, -0.2, 0]],
    PD=[1.0, 1.0, 0.5, 0.5, 0.5],
    T1=[1.0, 0.5, 0.5, 0.5, 2],
    T2=0.1,
    T2dash=0.1,
    D=0.0,
    B0=0,
    voxel_size=0.1,
    voxel_shape="box"
)

.. autofunction:: MRzeroCore.phantom.brainweb.generate_brainweb_phantoms