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BrainWeb-based multimodal models of 20 normal brains

Project description

The following example may be launched interactively via any of the following:

BrainWeb-based multimodal models of 20 normal brains

This project was initially inspired by “BrainWeb: 20 Anatomical Models of 20 Normal Brains.”

However there are a number of generally useful tools, image processing & display functions included in this project. For example, this includes volshow() for interactive comparison of multiple 3D volumes, get_file() for caching data URLs, and register() for image coregistration.

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Download and Preprocessing for PET-MR Simulations

This notebook will not re-download/re-process files if they already exist.

  • Output data
    • ~/.brainweb/subject_*.npz: dtype(shape): float32(127, 344, 344)
  • Raw data source
    • ~/.brainweb/subject_*.bin.gz: dtype(shape): uint16(362, 434, 362)
  • Install
    • pip install brainweb

from __future__ import print_function, division
%matplotlib notebook
import brainweb
from brainweb import volshow
import numpy as np
from os import path
from tqdm.auto import tqdm
import logging
logging.basicConfig(level=logging.INFO)

Raw Data

# download
files = brainweb.get_files()

# read last file
data = brainweb.load_file(files[-1])

# show last subject
print(files[-1])
volshow(data, cmaps=['gist_ncar']);
~/.brainweb/subject_54.bin.gz
https://raw.githubusercontent.com/casperdcl/brainweb/master/raw.png

Transform

Convert raw image data:

  • Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)
  • PET/T1/T2/uMap intensities
    • PET defaults to FDG intensity ratios; could use e.g. Amyloid instead
  • randomised structure for PET/T1/T2
    • t (1 + g [2 G_sigma(r) - 1]), where
      • r = rand(127, 344, 344) in [0, 1),
      • Gaussian smoothing sigma = 1,
      • g = 1 for PET; 0.75 for MR, and
      • t = the PET or MR piecewise constant phantom
# show region probability masks
PetClass = brainweb.FDG
label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels)
volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False);
https://raw.githubusercontent.com/casperdcl/brainweb/master/pmasks.png
brainweb.seed(1337)

for f in tqdm(files, desc="mMR ground truths", unit="subject"):
    vol = brainweb.get_mmr_fromfile(
        f,
        petNoise=1, t1Noise=0.75, t2Noise=0.75,
        petSigma=1, t1Sigma=1, t2Sigma=1,
        PetClass=PetClass)
# show last subject
print(f)
volshow([vol['PET' ][:, 100:-100, 100:-100],
         vol['uMap'][:, 100:-100, 100:-100],
         vol['T1'  ][:, 100:-100, 100:-100],
         vol['T2'  ][:, 100:-100, 100:-100]],
        cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],
        titles=["PET", "uMap", "T1", "T2"],
        frameon=False);
~/.brainweb/subject_54.bin.gz
https://raw.githubusercontent.com/casperdcl/brainweb/master/mMR.png
# add some lesions
brainweb.seed(1337)
im3d = brainweb.add_lesions(vol['PET'])
volshow(im3d[:, 100:-100, 100:-100], cmaps=['hot']);
https://raw.githubusercontent.com/casperdcl/brainweb/master/lesions.png
# bonus: use brute-force registration to transform
#!pip install -U 'brainweb[register]'
reg = brainweb.register(
    data[:, ::-1], target=vol['PET'],
    src_resolution=brainweb.Res.brainweb,
    target_resolution=brainweb.Res.mMR)

volshow({
    "PET":    vol['PET'][:, 100:-100, 100:-100],
    "RawReg": reg[       :, 100:-100, 100:-100],
    "T1":     vol['T1' ][:, 100:-100, 100:-100],
}, cmaps=['hot', 'gist_ncar', 'Greys_r'], ncols=3, tight_layout=5, figsize=(9.5, 3.5), frameon=False);
https://raw.githubusercontent.com/casperdcl/brainweb/master/reg.png

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