brainweb 0.3.0

Multimodal models of 20 normal brains

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BrainWeb: Multimodal models of 20 normal brains

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)

• Prerequisites

• Python: requirements.txt (e.g. pip install -r ../../requirements.txt)

---

• Author: Casper da Costa-Luis <[e-mail]>

• Date: 2017-19

• Licence: MPLv2.0

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

Transform

Convert raw image data:

• Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)

• PET/T1/T2/uMap intensities

• 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

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)

# 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"]);

~/.brainweb/subject_54.bin.gz

# add some lesions
brainweb.seed(1337)
im3d = brainweb.add_lesions(vol['PET'])
volshow(im3d[:, 100:-100, 100:-100], cmaps=['hot']);