brainweb 0.2.1

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
from utils import volshow
import utils as brainweb
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
data = []
for f in tqdm(files, desc="Uncompressing into memory", unit="subject"):
    data.append(brainweb.load_file(f))

# show last subject
print(files[-1])
volshow(data[-1]);

~/.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)

with tqdm(total=len(files), desc="mMR ground truths", unit="subject") as progress:
    for (f, vol) in zip(files, data):
        cache = f.replace('.bin.gz', '.npz')
        vol = brainweb.get_mmr(cache, vol,
                               petNoise=1, t1Noise=0.75, t2Noise=0.75,
                               petSigma=1, t1Sigma=1, t2Sigma=1)
        progress.update()

# show last subject
#f = files[-1].replace('.bin.gz', '.npz')
#vol = np.load(f)
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